From 985653198f7e8d5865b6556a0a5f04315faf6547 Mon Sep 17 00:00:00 2001 From: zachrewolinski Date: Mon, 6 Jan 2025 23:28:28 -0800 Subject: [PATCH] changes to pipeline include scaling and enumerating glm variants --- .../current/openml-classification.ipynb | 189 + .../subgroup/current/openml-regression.ipynb | 1515 ++ .../subgroup/current/subgroup copy.ipynb | 1048 + .../subgroup/current/subgroup-class.ipynb | 1033 + .../current/subgroup-debug-pipeline2.ipynb | 16901 +--------------- .../subgroup/current/subgroup-debug.ipynb | 1046 +- .../subgroup/current/subgroup-runner.sh | 17 +- .../subgroup/current/subgroup.ipynb | 1091 +- .../subgroup/current/subgroup.py | 423 +- .../subgroup/current/subgroup.sh | 14 +- 10 files changed, 5056 insertions(+), 18221 deletions(-) create mode 100644 feature_importance/subgroup/current/openml-classification.ipynb create mode 100644 feature_importance/subgroup/current/openml-regression.ipynb create mode 100644 feature_importance/subgroup/current/subgroup copy.ipynb create mode 100644 feature_importance/subgroup/current/subgroup-class.ipynb diff --git a/feature_importance/subgroup/current/openml-classification.ipynb b/feature_importance/subgroup/current/openml-classification.ipynb new file mode 100644 index 0000000..a0b8cef --- /dev/null +++ b/feature_importance/subgroup/current/openml-classification.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import openml\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "import pandas as pd\n", + "import numpy as np\n", + "warnings.filterwarnings(action='ignore', category=FutureWarning,\n", + " module='openml')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "dataids = [31, 10101, 3913, 3, 3917, 9957, 9946, 3918, 3903, 37, 9971, 9952,\n", + " 3902, 49, 43, 9978, 10093, 219, 9976, 14965, 6, 9977, 53, 11, 15, 16,\n", + " 14, 32, 3549, 12, 9981, 18, 28, 2074, 29, 45, 125922, 9960, 9964, 22,\n", + " 2079, 14969, 3560, 14952, 125920, 23, 3904, 3022, 9985, 9910, 14970,\n", + " 3021, 3481, 7592, 3573, 146824, 146820, 146822, 146195, 146800, 146817,\n", + " 146819, 146821, 167119, 14954, 167141, 167140, 167120, 167125, 146825,\n", + " 167124, 167121]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "72" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dataids)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3917 0 False\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + " ... \n", + "2104 False\n", + "2105 False\n", + "2106 False\n", + "2107 False\n", + "2108 False\n", + "Name: defects, Length: 2109, dtype: bool\n", + "3918 0 False\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + " ... \n", + "1104 False\n", + "1105 False\n", + "1106 False\n", + "1107 False\n", + "1108 False\n", + "Name: defects, Length: 1109, dtype: bool\n", + "3903 0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "1558 False\n", + "1559 False\n", + "1560 False\n", + "1561 False\n", + "1562 False\n", + "Name: c, Length: 1563, dtype: bool\n", + "3902 0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "1453 False\n", + "1454 False\n", + "1455 False\n", + "1456 False\n", + "1457 False\n", + "Name: c, Length: 1458, dtype: bool\n", + "3904 0 False\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + " ... \n", + "10880 False\n", + "10881 False\n", + "10882 False\n", + "10883 False\n", + "10884 False\n", + "Name: defects, Length: 10885, dtype: bool\n" + ] + } + ], + "source": [ + "categorical_counter = 0\n", + "binary_ids = []\n", + "for id in dataids:\n", + " task = openml.tasks.get_task(id, download_splits=False, download_data=False,\n", + " download_features_meta_data=False,\n", + " download_qualities=False)\n", + " dataset = task.get_dataset()\n", + " X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute)\n", + " # get # of unique values in y\n", + " if len(y.unique()) > 2:\n", + " # print(id, 'multiclass')\n", + " pass\n", + " if isinstance(y.dtype, pd.CategoricalDtype):\n", + " categorical_counter += 1\n", + " # print(id, 'categorical')\n", + " # print(y)\n", + " y = pd.get_dummies(y, drop_first=True, dtype=float)\n", + " else:\n", + " print(id, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "binary_ids" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/current/openml-regression.ipynb b/feature_importance/subgroup/current/openml-regression.ipynb new file mode 100644 index 0000000..4c53417 --- /dev/null +++ b/feature_importance/subgroup/current/openml-regression.ipynb @@ -0,0 +1,1515 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import openml\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings(action='ignore', category=FutureWarning,\n", + " module='openml')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from subgroup import get_openml_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1628920/4046681467.py:1: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.\n", + " get_openml_data(361234)\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([['M', 0.455, 0.365, ..., 0.2245, 0.101, 0.15],\n", + " ['M', 0.35, 0.265, ..., 0.0995, 0.0485, 0.07],\n", + " ['F', 0.53, 0.42, ..., 0.2565, 0.1415, 0.21],\n", + " ...,\n", + " ['M', 0.6, 0.475, ..., 0.5255, 0.2875, 0.308],\n", + " ['F', 0.625, 0.485, ..., 0.531, 0.261, 0.296],\n", + " ['M', 0.71, 0.555, ..., 0.9455, 0.3765, 0.495]], dtype=object),\n", + " array([15., 7., 9., ..., 9., 10., 12.]))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_openml_data(361234)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dataids = [361234, 361235, 361236, 361237, 361241, 361242, 361243, 361244,\n", + " 361247, 361249, 361250, 361251, 361252, 361253, 361254, 361255,\n", + " 361256, 361257, 361258, 361259, 361260, 361261, 361264, 361266,\n", + " 361267, 361268, 361269, 361272, 361616, 361617, 361618, 361619,\n", + " 361621, 361622, 361623]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.14.2\n" + ] + } + ], + "source": [ + "# get version of openml package downloaded\n", + "print(openml.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "task = openml.tasks.get_task(dataids[0], download_splits=False, download_data=False, download_features_meta_data=False, download_qualities=False)\n", + "dataset = task.get_dataset()\n", + "X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " length diameter height whole_weight shucked_weight cisvera_weight \\\n", + "0 0.455 0.365 0.095 0.5140 0.2245 0.1010 \n", + "1 0.350 0.265 0.090 0.2255 0.0995 0.0485 \n", + "2 0.530 0.420 0.135 0.6770 0.2565 0.1415 \n", + "3 0.440 0.365 0.125 0.5160 0.2155 0.1140 \n", + "4 0.330 0.255 0.080 0.2050 0.0895 0.0395 \n", + "... ... ... ... ... ... ... \n", + "4172 0.565 0.450 0.165 0.8870 0.3700 0.2390 \n", + "4173 0.590 0.440 0.135 0.9660 0.4390 0.2145 \n", + "4174 0.600 0.475 0.205 1.1760 0.5255 0.2875 \n", + "4175 0.625 0.485 0.150 1.0945 0.5310 0.2610 \n", + "4176 0.710 0.555 0.195 1.9485 0.9455 0.3765 \n", + "\n", + " shell_weight sex_F sex_I sex_M \n", + "0 0.1500 False False True \n", + "1 0.0700 False False True \n", + "2 0.2100 True False False \n", + "3 0.1550 False False True \n", + "4 0.0550 False True False \n", + "... ... ... ... ... \n", + "4172 0.2490 True False False \n", + "4173 0.2605 False False True \n", + "4174 0.3080 False False True \n", + "4175 0.2960 True False False \n", + "4176 0.4950 False False True \n", + "\n", + "[4177 rows x 10 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.get_dummies(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset ID: 361234\n", + "uint8\n", + "sex category\n", + "length float64\n", + "diameter float64\n", + "height float64\n", + "whole_weight float64\n", + "shucked_weight float64\n", + "cisvera_weight float64\n", + "shell_weight float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361235\n", + "float64\n", + "frequency float64\n", + "angle_of_attack float64\n", + "chord_length float64\n", + "free_stream_velocity float64\n", + "displacement_thickness float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361236\n", + "float64\n", + "process.b1.capacity uint8\n", + "process.b2.capacity uint8\n", + "process.b3.capacity uint8\n", + "process.b4.capacity uint8\n", + "property.price uint8\n", + "property.product category\n", + "property.winner category\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361237\n", + "float64\n", + "cement float64\n", + "blast_furnace_slag float64\n", + "fly_ash float64\n", + "water float64\n", + "superplasticizer float64\n", + "coarse_aggregate float64\n", + "fine_aggregate float64\n", + "age float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361241\n", + "float64\n", + "F1 float64\n", + "F2 float64\n", + "F3 float64\n", + "F4 float64\n", + "F5 float64\n", + "F6 float64\n", + "F7 float64\n", + "F8 float64\n", + "F9 float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361242\n", + "float64\n", + "number_of_elements uint8\n", + "mean_atomic_mass float64\n", + "wtd_mean_atomic_mass float64\n", + "gmean_atomic_mass float64\n", + "wtd_gmean_atomic_mass float64\n", + " ... \n", + "wtd_entropy_Valence float64\n", + "range_Valence uint8\n", + "wtd_range_Valence float64\n", + "std_Valence float64\n", + "wtd_std_Valence float64\n", + "Length: 81, dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361243\n", + "float64\n", + "V1 float64\n", + "V2 float64\n", + "V3 float64\n", + "V4 float64\n", + "V5 float64\n", + " ... \n", + "V112 float64\n", + "V113 float64\n", + "V114 float64\n", + "V115 float64\n", + "V116 float64\n", + "Length: 116, dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361244\n", + "uint8\n", + "class category\n", + "largest_spot_size category\n", + "spot_distribution category\n", + "activity category\n", + "evolution uint8\n", + "previous_activity category\n", + "complex category\n", + "complex_path category\n", + "area category\n", + "area_largest uint8\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361247\n", + "float64\n", + "lever_position float64\n", + "ship_speed uint8\n", + "gas_turbine_shaft_torque float64\n", + "gas_turbine_rate_of_revolutions float64\n", + "gas_generator_rate_of_revolutions float64\n", + "starboard_propeller_torque float64\n", + "port_propeller_torque float64\n", + "hp_turbine_exit_temperature float64\n", + "gt_compressor_outlet_air_temperature float64\n", + "hp_turbine_exit_pressure float64\n", + "gt_compressor_outlet_air_pressure float64\n", + "gas_turbine_exhaust_gas_pressure float64\n", + "turbine_injecton_control float64\n", + "fuel_flow float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361249\n", + "uint8\n", + "fixed_acidity float64\n", + "volatile_acidity float64\n", + "citric_acid float64\n", + "residual_sugar float64\n", + "chlorides float64\n", + "free_sulfur_dioxide float64\n", + "total_sulfur_dioxide float64\n", + "density float64\n", + "pH float64\n", + "sulphates float64\n", + "alcohol float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361250\n", + "uint8\n", + "fixed_acidity float64\n", + "volatile_acidity float64\n", + "citric_acid float64\n", + "residual_sugar float64\n", + "chlorides float64\n", + "free_sulfur_dioxide float64\n", + "total_sulfur_dioxide float64\n", + "density float64\n", + "pH float64\n", + "sulphates float64\n", + "alcohol float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361251\n", + "float64\n", + "tau1 float64\n", + "tau2 float64\n", + "tau3 float64\n", + "tau4 float64\n", + "p1 float64\n", + "p2 float64\n", + "p3 float64\n", + "p4 float64\n", + "g1 float64\n", + "g2 float64\n", + "g3 float64\n", + "g4 float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361252\n", + "float64\n", + "duration float64\n", + "codec category\n", + "width float64\n", + "height float64\n", + "bitrate float64\n", + "framerate float64\n", + "i float64\n", + "p float64\n", + "b float64\n", + "frames float64\n", + "i_size float64\n", + "p_size float64\n", + "size float64\n", + "o_codec category\n", + "o_bitrate float64\n", + "o_framerate float64\n", + "o_width float64\n", + "o_height float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361253\n", + "float64\n", + "x1 float64\n", + "x2 float64\n", + "x3 float64\n", + "x4 float64\n", + "x5 float64\n", + "x6 float64\n", + "x7 float64\n", + "x8 float64\n", + "x9 float64\n", + "x10 float64\n", + "x11 float64\n", + "x12 float64\n", + "x13 float64\n", + "x14 float64\n", + "x15 float64\n", + "x16 float64\n", + "y1 float64\n", + "y2 float64\n", + "y3 float64\n", + "y4 float64\n", + "y5 float64\n", + "y6 float64\n", + "y7 float64\n", + "y8 float64\n", + "y9 float64\n", + "y10 float64\n", + "y11 float64\n", + "y12 float64\n", + "y13 float64\n", + "y14 float64\n", + "y15 float64\n", + "y16 float64\n", + "energy1 float64\n", + "energy2 float64\n", + "energy3 float64\n", + "energy4 float64\n", + "energy5 float64\n", + "energy6 float64\n", + "energy7 float64\n", + "energy8 float64\n", + "energy9 float64\n", + "energy10 float64\n", + "energy11 float64\n", + "energy12 float64\n", + "energy13 float64\n", + "energy14 float64\n", + "energy15 float64\n", + "energy16 float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361254\n", + "float64\n", + "V1 float64\n", + "V2 float64\n", + "V3 float64\n", + "V4 float64\n", + "V5 float64\n", + "V6 float64\n", + "V7 float64\n", + "V8 float64\n", + "V9 float64\n", + "V10 float64\n", + "V11 float64\n", + "V12 float64\n", + "V13 float64\n", + "V14 float64\n", + "V15 float64\n", + "V16 float64\n", + "V17 float64\n", + "V18 float64\n", + "V19 float64\n", + "V20 float64\n", + "V21 float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361255\n", + "float64\n", + "longitude float64\n", + "latitude float64\n", + "housingMedianAge uint8\n", + "totalRooms float64\n", + "totalBedrooms float64\n", + "population float64\n", + "households float64\n", + "medianIncome float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361256\n", + "uint8\n", + "lread float64\n", + "lwrite float64\n", + "scall float64\n", + "sread float64\n", + "swrite float64\n", + "fork float64\n", + "exec float64\n", + "rchar float64\n", + "wchar float64\n", + "pgout float64\n", + "ppgout float64\n", + "pgfree float64\n", + "pgscan float64\n", + "atch float64\n", + "pgin float64\n", + "ppgin float64\n", + "pflt float64\n", + "vflt float64\n", + "runqsz float64\n", + "freemem float64\n", + "freeswap float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361257\n", + "float64\n", + "carat float64\n", + "cut category\n", + "color category\n", + "clarity category\n", + "depth float64\n", + "table float64\n", + "x float64\n", + "y float64\n", + "z float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361258\n", + "float64\n", + "theta1 float64\n", + "theta2 float64\n", + "theta3 float64\n", + "theta4 float64\n", + "theta5 float64\n", + "theta6 float64\n", + "theta7 float64\n", + "theta8 float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361259\n", + "float64\n", + "theta1 float64\n", + "theta2 float64\n", + "theta3 float64\n", + "theta4 float64\n", + "theta5 float64\n", + "theta6 float64\n", + "thetad1 float64\n", + "thetad2 float64\n", + "thetad3 float64\n", + "thetad4 float64\n", + "thetad5 float64\n", + "thetad6 float64\n", + "tau1 float64\n", + "tau2 float64\n", + "tau3 float64\n", + "tau4 float64\n", + "tau5 float64\n", + "dm1 float64\n", + "dm2 float64\n", + "dm3 float64\n", + "dm4 float64\n", + "dm5 float64\n", + "da1 float64\n", + "da2 float64\n", + "da3 float64\n", + "da4 float64\n", + "da5 float64\n", + "db1 float64\n", + "db2 float64\n", + "db3 float64\n", + "db4 float64\n", + "db5 float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361260\n", + "float64\n", + "LATITUDE float64\n", + "LONGITUDE float64\n", + "LND_SQFOOT float64\n", + "TOT_LVG_AREA float64\n", + "SPEC_FEAT_VAL float64\n", + "RAIL_DIST float64\n", + "OCEAN_DIST float64\n", + "WATER_DIST float64\n", + "CNTR_DIST float64\n", + "SUBCNTR_DI float64\n", + "HWY_DIST float64\n", + "age uint8\n", + "avno60plus uint8\n", + "month_sold uint8\n", + "structure_quality uint8\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361261\n", + "float64\n", + "education uint8\n", + "experience float64\n", + "ethnicity category\n", + "smsa category\n", + "region category\n", + "parttime category\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361264\n", + "float64\n", + "fathers_occupation category\n", + "sons_occupation category\n", + "family_structure category\n", + "race category\n", + "counts_for_sons_first_occupation float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361266\n", + "float64\n", + "bedrooms uint8\n", + "bathrooms float64\n", + "sqft_living float64\n", + "sqft_lot float64\n", + "floors float64\n", + "waterfront uint8\n", + "view uint8\n", + "condition uint8\n", + "grade uint8\n", + "sqft_above float64\n", + "sqft_basement float64\n", + "yr_built float64\n", + "yr_renovated float64\n", + "zipcode category\n", + "lat float64\n", + "long float64\n", + "sqft_living15 float64\n", + "sqft_lot15 float64\n", + "date_year category\n", + "date_month category\n", + "date_day category\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361267\n", + "float64\n", + "city category\n", + "area float64\n", + "rooms uint8\n", + "bathroom uint8\n", + "parking_spaces uint8\n", + "floor category\n", + "animal category\n", + "furniture category\n", + "hoa float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361268\n", + "float64\n", + "CpuName category\n", + "CpuNumberOfCores uint8\n", + "CpuNumberOfThreads uint8\n", + "CpuBaseClock uint8\n", + "CpuCacheL1 float64\n", + "CpuCacheL2 float64\n", + "CpuCacheL3 uint8\n", + "CpuDieSize float64\n", + "CpuFrequency float64\n", + "CpuMultiplier uint8\n", + "CpuMultiplierUnlocked category\n", + "CpuProcessSize uint8\n", + "CpuTDP uint8\n", + "CpuNumberOfTransistors float64\n", + "CpuTurboClock float64\n", + "GpuName category\n", + "GpuArchitecture category\n", + "GpuBandwidth float64\n", + "GpuBaseClock float64\n", + "GpuBoostClock float64\n", + "GpuBus.interface category\n", + "GpuNumberOfComputeUnits float64\n", + "GpuDieSize float64\n", + "GpuDirectX category\n", + "GpuNumberOfExecutionUnits object\n", + "GpuFP32Performance float64\n", + "GpuMemoryBus float64\n", + "GpuMemorySize float64\n", + "GpuMemoryType category\n", + "GpuOpenCL category\n", + "GpuOpenGL category\n", + "GpuPixelRate float64\n", + "GpuProcessSize uint8\n", + "GpuNumberOfROPs uint8\n", + "GpuShaderModel category\n", + "GpuNumberOfShadingUnits float64\n", + "GpuNumberOfTMUs float64\n", + "GpuTextureRate float64\n", + "GpuNumberOfTransistors float64\n", + "GpuVulkan category\n", + "GameName category\n", + "GameResolution float64\n", + "GameSetting category\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361269\n", + "uint8\n", + "hhi category\n", + "whi category\n", + "hhi2 category\n", + "education category\n", + "race category\n", + "hispanic category\n", + "experience float64\n", + "kidslt6 uint8\n", + "kids618 uint8\n", + "husby float64\n", + "region category\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361272\n", + "float64\n", + "age uint8\n", + "height_cm uint8\n", + "weight_kg uint8\n", + "nationality_name category\n", + "overall uint8\n", + "potential uint8\n", + "attacking_crossing uint8\n", + "attacking_finishing uint8\n", + "attacking_heading_accuracy uint8\n", + "attacking_short_passing uint8\n", + "attacking_volleys uint8\n", + "skill_dribbling uint8\n", + "skill_curve uint8\n", + "skill_fk_accuracy uint8\n", + "skill_long_passing uint8\n", + "skill_ball_control uint8\n", + "movement_acceleration uint8\n", + "movement_sprint_speed uint8\n", + "movement_agility uint8\n", + "movement_reactions uint8\n", + "movement_balance uint8\n", + "defending_standing_tackle uint8\n", + "defending_sliding_tackle uint8\n", + "goalkeeping_diving uint8\n", + "goalkeeping_handling uint8\n", + "goalkeeping_kicking uint8\n", + "goalkeeping_positioning uint8\n", + "goalkeeping_reflexes uint8\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361616\n", + "int64\n", + "Team category\n", + "League category\n", + "Year int64\n", + "RA int64\n", + "W uint8\n", + "OBP float64\n", + "SLG float64\n", + "BA float64\n", + "Playoffs category\n", + "RankSeason category\n", + "RankPlayoffs category\n", + "G category\n", + "OOBP float64\n", + "OSLG float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361617\n", + "float64\n", + "relative_compactness float64\n", + "surface_area float64\n", + "wall_area float64\n", + "roof_area float64\n", + "overall_height float64\n", + "orientation uint8\n", + "glazing_area float64\n", + "glazing_area_distribution uint8\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361618\n", + "float64\n", + "X uint8\n", + "Y uint8\n", + "month object\n", + "day object\n", + "FFMC float64\n", + "DMC float64\n", + "DC float64\n", + "ISI float64\n", + "temp float64\n", + "RH uint8\n", + "wind float64\n", + "rain float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361619\n", + "uint8\n", + "school category\n", + "sex category\n", + "age uint8\n", + "address category\n", + "famsize category\n", + "Pstatus category\n", + "Medu uint8\n", + "Fedu uint8\n", + "Mjob category\n", + "Fjob category\n", + "reason category\n", + "guardian category\n", + "traveltime uint8\n", + "studytime uint8\n", + "failures uint8\n", + "schoolsup category\n", + "famsup category\n", + "paid category\n", + "activities category\n", + "nursery category\n", + "higher category\n", + "internet category\n", + "romantic category\n", + "famrel uint8\n", + "freetime uint8\n", + "goout uint8\n", + "Dalc uint8\n", + "Walc uint8\n", + "health uint8\n", + "absences uint8\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361621\n", + "float64\n", + "CIC0 float64\n", + "SM1_Dz float64\n", + "GATS1i float64\n", + "NdsCH uint8\n", + "NdssC uint8\n", + "MLOGP float64\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361622\n", + "float64\n", + "Mileage float64\n", + "Cylinder uint8\n", + "Doors uint8\n", + "Cruise uint8\n", + "Sound uint8\n", + "Leather uint8\n", + "Buick uint8\n", + "Cadillac uint8\n", + "Chevy uint8\n", + "Pontiac uint8\n", + "Saab uint8\n", + "Saturn uint8\n", + "convertible uint8\n", + "coupe uint8\n", + "hatchback uint8\n", + "sedan uint8\n", + "wagon uint8\n", + "dtype: object\n", + "-----------------------------------\n", + "Dataset ID: 361623\n", + "float64\n", + "pop float64\n", + "education float64\n", + "houses float64\n", + "income float64\n", + "xcoord float64\n", + "ycoord float64\n", + "dtype: object\n", + "-----------------------------------\n" + ] + } + ], + "source": [ + "# get the dataset from openml\n", + "for id in dataids:\n", + " task = openml.tasks.get_task(id, download_splits=False, download_data=False,\n", + " download_features_meta_data=False,\n", + " download_qualities=False)\n", + " dataset = task.get_dataset()\n", + " X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute)\n", + " print('Dataset ID:', id)\n", + " # print('Dataset Name:', dataset.name)\n", + " # print('Number of Features:', X.shape[1])\n", + " # print('Number of Instances:', X.shape[0])\n", + " # print('Type of Target Values:')\n", + " print(y.dtype)\n", + " # do they have a column that is non-numeric?\n", + " print(X.dtypes)\n", + " # plt.hist(y, bins=20)\n", + " # plt.show()\n", + " print('-----------------------------------')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "transform_ids = [361236, 361242, 361244, 361252, 361257, 361260, 361261, 361264, 361266, 361267, 361272, 361618, 361622]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset ID: 361236\n", + "Dataset Name: auction_verification\n", + "Number of Features: 7\n", + "Number of Instances: 2043\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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Qa956t0dAE3AFBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmNPkgNmyZYvGjRun5ORkRUREaM2aNSHbHcfRggULlJSUpJiYGGVlZWnv3r0h+xw5ckR5eXnyer2Ki4vT9OnTVVdX16IHAgAAOo5OTf2BY8eOafDgwZo2bZomTJhw2vYlS5boscce01NPPaW0tDTNnz9f2dnZ2r17t7p06SJJysvL08GDB7Vp0yadOHFCU6dO1cyZM1VSUtLyRwQAaLd6zVvv9ghoI5ocMDk5OcrJyTnjNsdx9Oijj+ruu+/W1VdfLUn629/+psTERK1Zs0aTJk3SRx99pA0bNmjHjh0aNmyYJOnxxx/X2LFj9dBDDyk5ObkFDwcAAHQErfoamIqKCvn9fmVlZQXXxcbGKiMjQ+Xl5ZKk8vJyxcXFBeNFkrKyshQZGalt27ad8bj19fUKBAIhCwAA6LhaNWD8fr8kKTExMWR9YmJicJvf71dCQkLI9k6dOik+Pj64z3cVFxcrNjY2uKSkpLTm2AAAwBgT70IqKipSbW1tcDlw4IDbIwEAABe1asD4fD5JUnV1dcj66urq4Dafz6dDhw6FbD958qSOHDkS3Oe7PB6PvF5vyAIAADquVg2YtLQ0+Xw+lZaWBtcFAgFt27ZNmZmZkqTMzEzV1NRo586dwX02b96shoYGZWRktOY4AACgnWryu5Dq6uq0b9++4O2Kigrt2rVL8fHx6tmzp2bNmqX77rtPF1xwQfBt1MnJyRo/frwkqW/fvhozZoxmzJih5cuX68SJEyooKNCkSZN4BxIAAPhBmhww77zzjn7xi18EbxcWFkqSpkyZolWrVumOO+7QsWPHNHPmTNXU1GjEiBHasGFD8DNgJGn16tUqKCjQqFGjFBkZqYkTJ+qxxx5rhYcDAAA6giYHzMiRI+U4TqPbIyIitHjxYi1evLjRfeLj4/nQOgAA0Gwm3oUEAADwbQQMAAAwh4ABAADmEDAAAMAcAgYAAJhDwAAAAHMIGAAAYA4BAwAAzCFgAACAOQQMAAAwp8lfJQAAANqGXvPWh+3Ynz6QG7ZjtwauwAAAAHMIGAAAYA5PIZ1BuC7JtfXLcUBHwJ9voH3gCgwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAObwVQIAgFYXzm9JBiSuwAAAAIMIGAAAYA4BAwAAzCFgAACAOQQMAAAwh4ABAADmEDAAAMAcAgYAAJhDwAAAAHMIGAAAYA4BAwAAzCFgAACAOQQMAAAwh4ABAADmEDAAAMAcAgYAAJhDwAAAAHMIGAAAYA4BAwAAzCFgAACAOQQMAAAwh4ABAADmEDAAAMAcAgYAAJhDwAAAAHMIGAAAYA4BAwAAzCFgAACAOZ3cHgAdV695690eAQBgFFdgAACAOQQMAAAwh4ABAADmEDAAAMAcAgYAAJjT6u9Cuueee7Ro0aKQdRdddJE+/vhjSdLXX3+t2267Tc8995zq6+uVnZ2tJ554QomJia09CgD8aML5rrpPH8gN27EBq8JyBaZ///46ePBgcNm6dWtw2+zZs7V27Vq9+OKLKisrU1VVlSZMmBCOMQAAQDsVls+B6dSpk3w+32nra2tr9eSTT6qkpES//OUvJUkrV65U37599fbbb+uyyy4LxzgAAKCdCcsVmL179yo5OVm9e/dWXl6eKisrJUk7d+7UiRMnlJWVFdw3PT1dPXv2VHl5eaPHq6+vVyAQCFkAAEDH1eoBk5GRoVWrVmnDhg1atmyZKioqdMUVV+jo0aPy+/2Kjo5WXFxcyM8kJibK7/c3eszi4mLFxsYGl5SUlNYeGwAAGNLqTyHl5OQEfz1o0CBlZGQoNTVVL7zwgmJiYpp1zKKiIhUWFgZvBwIBIgYAgA4s7G+jjouL04UXXqh9+/bJ5/Pp+PHjqqmpCdmnurr6jK+Z+YbH45HX6w1ZAABAxxX2gKmrq9P+/fuVlJSkoUOHqnPnziotLQ1u37NnjyorK5WZmRnuUQAAQDvR6k8h3X777Ro3bpxSU1NVVVWlhQsXKioqSpMnT1ZsbKymT5+uwsJCxcfHy+v16pZbblFmZibvQAIAAD9YqwfMZ599psmTJ+uLL77QueeeqxEjRujtt9/WueeeK0l65JFHFBkZqYkTJ4Z8kB0AAMAP1eoB89xzz33v9i5dumjp0qVaunRpa981AADoIPguJAAAYA4BAwAAzCFgAACAOQQMAAAwJyxf5ogz6zVvfdiO/ekDuWE7NgAAbQ1XYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDl8Em87Ea5P+eUTfgEAbRFXYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMzhg+zwvcL1AXkAALQEV2AAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgTie3BwAAfL9e89a7PQLQ5nAFBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwJxObg8AAN/Va956t0cA0MZxBQYAAJhDwAAAAHNcDZilS5eqV69e6tKlizIyMrR9+3Y3xwEAAEa4FjDPP/+8CgsLtXDhQv3zn//U4MGDlZ2drUOHDrk1EgAAMMK1gHn44Yc1Y8YMTZ06Vf369dPy5cvVtWtX/fWvf3VrJAAAYIQr70I6fvy4du7cqaKiouC6yMhIZWVlqby8/LT96+vrVV9fH7xdW1srSQoEAmGZr6H+q7AcFwAAK8Lxb+w3x3Qcp8XHciVg/vvf/+rUqVNKTEwMWZ+YmKiPP/74tP2Li4u1aNGi09anpKSEbUYAADqy2EfDd+yjR48qNja2Rccw8TkwRUVFKiwsDN5uaGjQkSNH1KNHD0VERLg4WdsQCASUkpKiAwcOyOv1uj1Om8P5aRznpnGcm8ZxbhrHuWncN+dm9+7dSk5ObvHxXAmYc845R1FRUaqurg5ZX11dLZ/Pd9r+Ho9HHo8nZF1cXFw4RzTJ6/XyB+Z7cH4ax7lpHOemcZybxnFuGnfeeecpMrLlL8F15UW80dHRGjp0qEpLS4PrGhoaVFpaqszMTDdGAgAAhrj2FFJhYaGmTJmiYcOG6ec//7keffRRHTt2TFOnTnVrJAAAYIRrAXPttdfq8OHDWrBggfx+vy6++GJt2LDhtBf24uw8Ho8WLlx42tNs+D/OT+M4N43j3DSOc9M4zk3jWvvcRDit8V4mAACAHxHfhQQAAMwhYAAAgDkEDAAAMIeAAQAA5hAwht1zzz2KiIgIWdLT090eq834/PPPdd1116lHjx6KiYnRwIED9c4777g9lut69ep12u+biIgI5efnuz2a606dOqX58+crLS1NMTEx6tOnj+69995W+d6W9uDo0aOaNWuWUlNTFRMTo8svv1w7duxweyxXbNmyRePGjVNycrIiIiK0Zs2akO2O42jBggVKSkpSTEyMsrKytHfvXneG/ZGd7dy89NJLGj16dPDT9Hft2tWs+yFgjOvfv78OHjwYXLZu3er2SG3Cl19+qeHDh6tz58567bXXtHv3bv3pT3/ST3/6U7dHc92OHTtCfs9s2rRJknTNNde4PJn7HnzwQS1btkx//vOf9dFHH+nBBx/UkiVL9Pjjj7s9Wptw0003adOmTXr66af1/vvva/To0crKytLnn3/u9mg/umPHjmnw4MFaunTpGbcvWbJEjz32mJYvX65t27apW7duys7O1tdff/0jT/rjO9u5OXbsmEaMGKEHH3ywZXfkwKyFCxc6gwcPdnuMNmnu3LnOiBEj3B7DhFtvvdXp06eP09DQ4PYorsvNzXWmTZsWsm7ChAlOXl6eSxO1HV999ZUTFRXlrFu3LmT9JZdc4tx1110uTdU2SHJefvnl4O2GhgbH5/M5f/zjH4PrampqHI/H4zz77LMuTOie756bb6uoqHAkOe+++26zjs0VGOP27t2r5ORk9e7dW3l5eaqsrHR7pDbh73//u4YNG6ZrrrlGCQkJGjJkiP7yl7+4PVabc/z4cT3zzDOaNm0aX4wq6fLLL1dpaak++eQTSdK//vUvbd26VTk5OS5P5r6TJ0/q1KlT6tKlS8j6mJgYrvx+R0VFhfx+v7KysoLrYmNjlZGRofLychcna18IGMMyMjK0atUqbdiwQcuWLVNFRYWuuOIKHT161O3RXPfvf/9by5Yt0wUXXKCNGzfq5ptv1u9//3s99dRTbo/WpqxZs0Y1NTW68cYb3R6lTZg3b54mTZqk9PR0de7cWUOGDNGsWbOUl5fn9miu6969uzIzM3XvvfeqqqpKp06d0jPPPKPy8nIdPHjQ7fHaFL/fL0mnfbJ8YmJicBtazrWvEkDLfft/hYMGDVJGRoZSU1P1wgsvaPr06S5O5r6GhgYNGzZM999/vyRpyJAh+uCDD7R8+XJNmTLF5enajieffFI5OTmt8tX27cELL7yg1atXq6SkRP3799euXbs0a9YsJScn8/tG0tNPP61p06bpvPPOU1RUlC655BJNnjxZO3fudHs0dEBcgWlH4uLidOGFF2rfvn1uj+K6pKQk9evXL2Rd3759eYrtW/7zn//o9ddf10033eT2KG3GnDlzgldhBg4cqOuvv16zZ89WcXGx26O1CX369FFZWZnq6up04MABbd++XSdOnFDv3r3dHq1N8fl8kqTq6uqQ9dXV1cFtaDkCph2pq6vT/v37lZSU5PYorhs+fLj27NkTsu6TTz5RamqqSxO1PStXrlRCQoJyc3PdHqXN+OqrrxQZGfrXYlRUlBoaGlyaqG3q1q2bkpKS9OWXX2rjxo26+uqr3R6pTUlLS5PP51NpaWlwXSAQ0LZt25SZmeniZO0LTyEZdvvtt2vcuHFKTU1VVVWVFi5cqKioKE2ePNnt0Vw3e/ZsXX755br//vv1m9/8Rtu3b9eKFSu0YsUKt0drExoaGrRy5UpNmTJFnTrx18A3xo0bpz/84Q/q2bOn+vfvr3fffVcPP/ywpk2b5vZobcLGjRvlOI4uuugi7du3T3PmzFF6erqmTp3q9mg/urq6upCr3RUVFdq1a5fi4+PVs2dPzZo1S/fdd58uuOACpaWlaf78+UpOTtb48ePdG/pHcrZzc+TIEVVWVqqqqkqSgv/Z9Pl8TbtC1ez3RsF11157rZOUlORER0c75513nnPttdc6+/btc3usNmPt2rXOgAEDHI/H46SnpzsrVqxwe6Q2Y+PGjY4kZ8+ePW6P0qYEAgHn1ltvdXr27Ol06dLF6d27t3PXXXc59fX1bo/WJjz//PNO7969nejoaMfn8zn5+flOTU2N22O54o033nAknbZMmTLFcZz/v5V6/vz5TmJiouPxeJxRo0Z1mD9vZzs3K1euPOP2hQsXNul+IhyHj5gEAAC28BoYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADDnf6tyFZTO70ZFAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361242\n", + "Dataset Name: superconductivity\n", + "Number of Features: 81\n", + "Number of Instances: 21263\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361244\n", + "Dataset Name: solar_flare\n", + "Number of Features: 10\n", + "Number of Instances: 1066\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361252\n", + "Dataset Name: video_transcoding\n", + "Number of Features: 18\n", + "Number of Instances: 68784\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361257\n", + "Dataset Name: diamonds\n", + "Number of Features: 9\n", + "Number of Instances: 53940\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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o6Gj967/+q9577z1t3LhRZ86ccc6R6dmzp7xer4qKirRr1y6NGzdO3bt3V1FRkebNm6d77rnHCTBTp07VokWLlJqaqoyMDJWWlmrlypVasWKF87wPPfSQbrrpJi1btkwpKSl64YUXtHv3btdl5gAA4NLlMcaYljygsLBQ48aNa9I+Y8YMZWVlNTlhuNG2bdt0880367333tPPfvYzHThwQLW1tYqLi9O9996r9PR01/k4e/fuVVpamt59912Fh4fr5z//uTIyMlxjbtiwQQsWLNDhw4d11VVXacmSJbrlllsueC3BYFChoaGqqalp9U91+j62qVXHO9vhnJQ2GxsAgIvdhb5/tzjk2ISQAwBAx3Oh79/87SoAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAK7U45Gzfvl233nqrYmJi5PF4lJeX5+o3xmjhwoWKjo5W165dlZSUpI8++shVc/ToUU2bNk1+v19hYWFKTU3V8ePHXTV79+7VmDFjFBISotjYWC1ZsqTJXDZs2KBBgwYpJCRE8fHx2rx5c0uXAwAALNXikHPixAkNHz5cq1atarZ/yZIl+v3vf681a9Zo165duvzyy5WcnKyvvvrKqZk2bZr27dun/Px8bdy4Udu3b9fs2bOd/mAwqAkTJqhPnz4qLi7W0qVLlZWVpaefftqp2blzp6ZMmaLU1FTt2bNHkydP1uTJk1VaWtrSJQEAAAt5jDHmGz/Y49Err7yiyZMnS/q/T3FiYmL0yCOP6Je//KUkqaamRpGRkcrNzdXdd9+tDz/8UEOGDNG7776ra6+9VpK0ZcsW3XLLLfrss88UExOj1atX69e//rUCgYC8Xq8k6bHHHlNeXp4OHDggSbrrrrt04sQJbdy40ZnP9ddfrxEjRmjNmjUXNP9gMKjQ0FDV1NTI7/d/05ehWX0f29Sq453tcE5Km40NAMDF7kLfv1v1nJzy8nIFAgElJSU5baGhoUpISFBRUZEkqaioSGFhYU7AkaSkpCR16tRJu3btcmrGjh3rBBxJSk5OVllZmb788kun5uznaaxpfJ7m1NbWKhgMujYAAGCnVg05gUBAkhQZGelqj4yMdPoCgYAiIiJc/V26dFHPnj1dNc2NcfZznKumsb852dnZCg0NdbbY2NiWLhEAAHQQl9TVVZmZmaqpqXG2Tz/9tL2nBAAA2kirhpyoqChJUlVVlau9qqrK6YuKitKRI0dc/fX19Tp69Kirprkxzn6Oc9U09jfH5/PJ7/e7NgAAYKdWDTlxcXGKiopSQUGB0xYMBrVr1y4lJiZKkhITE1VdXa3i4mKn5vXXX1dDQ4MSEhKcmu3bt6uurs6pyc/P18CBA9WjRw+n5uznaaxpfB4AAHBpa3HIOX78uEpKSlRSUiLp/042LikpUUVFhTwejx5++GH9+7//u/7617/qgw8+0PTp0xUTE+NcgTV48GBNnDhRs2bN0jvvvKO33npLc+fO1d13362YmBhJ0tSpU+X1epWamqp9+/bpxRdf1MqVK5Wenu7M46GHHtKWLVu0bNkyHThwQFlZWdq9e7fmzp377V8VAADQ4XVp6QN2796tcePGOfuNwWPGjBnKzc3Vo48+qhMnTmj27Nmqrq7WjTfeqC1btigkJMR5zPr16zV37lyNHz9enTp10p133qnf//73Tn9oaKhee+01paWlafTo0QoPD9fChQtd99K54YYb9Pzzz2vBggX61a9+pauuukp5eXkaOnToN3ohAACAXb7VfXI6Ou6TAwBAx9Mu98kBAAC4WBByAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALBSq4ecvn37yuPxNNnS0tIkSTfffHOTvjlz5rjGqKioUEpKirp166aIiAjNnz9f9fX1rprCwkKNGjVKPp9PAwYMUG5ubmsvBQAAdGBdWnvAd999V2fOnHH2S0tL9S//8i/6t3/7N6dt1qxZeuKJJ5z9bt26OT+fOXNGKSkpioqK0s6dO1VZWanp06frsssu029/+1tJUnl5uVJSUjRnzhytX79eBQUFuv/++xUdHa3k5OTWXhIAAOiAWj3kXHHFFa79nJwc9e/fXzfddJPT1q1bN0VFRTX7+Ndee0379+/X1q1bFRkZqREjRmjx4sXKyMhQVlaWvF6v1qxZo7i4OC1btkySNHjwYO3YsUMrVqwg5AAAAEltfE7O6dOn9dxzz+mnP/2pPB6P075+/XqFh4dr6NChyszM1MmTJ52+oqIixcfHKzIy0mlLTk5WMBjUvn37nJqkpCTXcyUnJ6uoqKgtlwMAADqQVv8k52x5eXmqrq7WzJkznbapU6eqT58+iomJ0d69e5WRkaGysjK9/PLLkqRAIOAKOJKc/UAgcN6aYDCoU6dOqWvXrs3Op7a2VrW1tc5+MBj81msEAAAXpzYNOc8884wmTZqkmJgYp2327NnOz/Hx8YqOjtb48eN16NAh9e/fvy2no+zsbC1atKhNnwMAAFwc2uzrqk8++URbt27V/ffff966hIQESdLBgwclSVFRUaqqqnLVNO43nsdzrhq/33/OT3EkKTMzUzU1Nc726aeftmxRAACgw2izkLNu3TpFREQoJSXlvHUlJSWSpOjoaElSYmKiPvjgAx05csSpyc/Pl9/v15AhQ5yagoIC1zj5+flKTEw873P5fD75/X7XBgAA7NQmIaehoUHr1q3TjBkz1KXL//9G7NChQ1q8eLGKi4t1+PBh/fWvf9X06dM1duxYDRs2TJI0YcIEDRkyRPfee6/ef/99vfrqq1qwYIHS0tLk8/kkSXPmzNHHH3+sRx99VAcOHNBTTz2ll156SfPmzWuL5QAAgA6oTULO1q1bVVFRoZ/+9Keudq/Xq61bt2rChAkaNGiQHnnkEd15553629/+5tR07txZGzduVOfOnZWYmKh77rlH06dPd91XJy4uTps2bVJ+fr6GDx+uZcuW6U9/+hOXjwMAAIfHGGPaexLtJRgMKjQ0VDU1Na3+1VXfxza16njfhcM55/9qEQCAi8GFvn/zt6sAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEpd2nsCuHj0fWxTm419OCelzcYGAKA5fJIDAACsRMgBAABWIuQAAAArtXrIycrKksfjcW2DBg1y+r/66iulpaWpV69e+t73vqc777xTVVVVrjEqKiqUkpKibt26KSIiQvPnz1d9fb2rprCwUKNGjZLP59OAAQOUm5vb2ksBAAAdWJt8knPNNdeosrLS2Xbs2OH0zZs3T3/729+0YcMGvfHGG/r88891xx13OP1nzpxRSkqKTp8+rZ07d+rZZ59Vbm6uFi5c6NSUl5crJSVF48aNU0lJiR5++GHdf//9evXVV9tiOQAAoANqk6urunTpoqioqCbtNTU1euaZZ/T888/rRz/6kSRp3bp1Gjx4sN5++21df/31eu2117R//35t3bpVkZGRGjFihBYvXqyMjAxlZWXJ6/VqzZo1iouL07JlyyRJgwcP1o4dO7RixQolJye3xZIAAEAH0yaf5Hz00UeKiYlRv379NG3aNFVUVEiSiouLVVdXp6SkJKd20KBB6t27t4qKiiRJRUVFio+PV2RkpFOTnJysYDCoffv2OTVnj9FY0zjGudTW1ioYDLo2AABgp1YPOQkJCcrNzdWWLVu0evVqlZeXa8yYMTp27JgCgYC8Xq/CwsJcj4mMjFQgEJAkBQIBV8Bp7G/sO19NMBjUqVOnzjm37OxshYaGOltsbOy3XS4AALhItfrXVZMmTXJ+HjZsmBISEtSnTx+99NJL6tq1a2s/XYtkZmYqPT3d2Q8GgwQdAAAs1eaXkIeFhenqq6/WwYMHFRUVpdOnT6u6utpVU1VV5ZzDExUV1eRqq8b9r6vx+/3nDVI+n09+v9+1AQAAO7V5yDl+/LgOHTqk6OhojR49WpdddpkKCgqc/rKyMlVUVCgxMVGSlJiYqA8++EBHjhxxavLz8+X3+zVkyBCn5uwxGmsaxwAAAGj1kPPLX/5Sb7zxhg4fPqydO3fqxz/+sTp37qwpU6YoNDRUqampSk9P17Zt21RcXKz77rtPiYmJuv766yVJEyZM0JAhQ3Tvvffq/fff16uvvqoFCxYoLS1NPp9PkjRnzhx9/PHHevTRR3XgwAE99dRTeumllzRv3rzWXg4AAOigWv2cnM8++0xTpkzRF198oSuuuEI33nij3n77bV1xxRWSpBUrVqhTp0668847VVtbq+TkZD311FPO4zt37qyNGzfqwQcfVGJioi6//HLNmDFDTzzxhFMTFxenTZs2ad68eVq5cqWuvPJK/elPf+LycQAA4PAYY0x7T6K9BINBhYaGqqamptXPz2nLv+jdEfFXyAEAreVC37/521UAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFZq9fvkAN+ltrxUn8veAaBj45McAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBK3AwQ34m2vGkfAADN4ZMcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJW6tPcEgItV38c2tcm4h3NS2mRcAIAbn+QAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFip1UNOdna2fvCDH6h79+6KiIjQ5MmTVVZW5qq5+eab5fF4XNucOXNcNRUVFUpJSVG3bt0UERGh+fPnq76+3lVTWFioUaNGyefzacCAAcrNzW3t5QAAgA6q1UPOG2+8obS0NL399tvKz89XXV2dJkyYoBMnTrjqZs2apcrKSmdbsmSJ03fmzBmlpKTo9OnT2rlzp5599lnl5uZq4cKFTk15eblSUlI0btw4lZSU6OGHH9b999+vV199tbWXBAAAOqBWvxngli1bXPu5ubmKiIhQcXGxxo4d67R369ZNUVFRzY7x2muvaf/+/dq6dasiIyM1YsQILV68WBkZGcrKypLX69WaNWsUFxenZcuWSZIGDx6sHTt2aMWKFUpOTm7tZQEAgA6mzc/JqampkST17NnT1b5+/XqFh4dr6NChyszM1MmTJ52+oqIixcfHKzIy0mlLTk5WMBjUvn37nJqkpCTXmMnJySoqKjrnXGpraxUMBl0bAACwU5v+WYeGhgY9/PDD+uEPf6ihQ4c67VOnTlWfPn0UExOjvXv3KiMjQ2VlZXr55ZclSYFAwBVwJDn7gUDgvDXBYFCnTp1S165dm8wnOztbixYtatU1AgCAi1Obhpy0tDSVlpZqx44drvbZs2c7P8fHxys6Olrjx4/XoUOH1L9//zabT2ZmptLT0539YDCo2NjYNns+AADQftrs66q5c+dq48aN2rZtm6688srz1iYkJEiSDh48KEmKiopSVVWVq6Zxv/E8nnPV+P3+Zj/FkSSfzye/3+/aAACAnVo95BhjNHfuXL3yyit6/fXXFRcX97WPKSkpkSRFR0dLkhITE/XBBx/oyJEjTk1+fr78fr+GDBni1BQUFLjGyc/PV2JiYiutBAAAdGStHnLS0tL03HPP6fnnn1f37t0VCAQUCAR06tQpSdKhQ4e0ePFiFRcX6/Dhw/rrX/+q6dOna+zYsRo2bJgkacKECRoyZIjuvfdevf/++3r11Ve1YMECpaWlyefzSZLmzJmjjz/+WI8++qgOHDigp556Si+99JLmzZvX2ksCAAAdkMcYY1p1QI+n2fZ169Zp5syZ+vTTT3XPPfeotLRUJ06cUGxsrH784x9rwYIFrq+PPvnkEz344IMqLCzU5ZdfrhkzZignJ0dduvz/04gKCws1b9487d+/X1deeaV+85vfaObMmRc812AwqNDQUNXU1LT6V1d9H9vUquMBF+JwTkp7TwEA2tyFvn+3esjpSAg5sA0hB8Cl4ELfv/nbVQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgpS5fXwKgo2jLPyfCn4wA0NHwSQ4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwErcJwfABWmre/Bw/x0AbYWQA6BdcQNDAG2Fr6sAAICVCDkAAMBKhBwAAGAlQg4AALASJx4DsBZXhAGXNkIOALQQV4QBHQNfVwEAACsRcgAAgJX4ugoALiKcRwS0HkIOAFwCOI8IlyK+rgIAAFbikxwAwLfCV2y4WBFyAAAXpbb8iq2tEMwuLoQcAABaCec+XVwIOQAAdAAEqJYj5AAAcImz9bwqrq4CAABW6vAhZ9WqVerbt69CQkKUkJCgd955p72nBAAALgIdOuS8+OKLSk9P1+OPP6733ntPw4cPV3Jyso4cOdLeUwMAAO2sQ4ec5cuXa9asWbrvvvs0ZMgQrVmzRt26ddPatWvbe2oAAKCdddgTj0+fPq3i4mJlZmY6bZ06dVJSUpKKioqafUxtba1qa2ud/ZqaGklSMBhs9fk11J5s9TEBAOhI2uL99exxjTHnreuwIecf//iHzpw5o8jISFd7ZGSkDhw40OxjsrOztWjRoibtsbGxbTJHAAAuZaG/a9vxjx07ptDQ0HP2d9iQ801kZmYqPT3d2W9oaNDRo0fVq1cveTyeCx4nGAwqNjZWn376qfx+f1tM9aLDmlmzrVgza7aVzWs2xujYsWOKiYk5b12HDTnh4eHq3LmzqqqqXO1VVVWKiopq9jE+n08+n8/VFhYW9o3n4Pf7rfuH83VY86WBNV8aWPOlwdY1n+8TnEYd9sRjr9er0aNHq6CgwGlraGhQQUGBEhMT23FmAADgYtBhP8mRpPT0dM2YMUPXXnutrrvuOv3ud7/TiRMndN9997X31AAAQDvr0CHnrrvu0t///nctXLhQgUBAI0aM0JYtW5qcjNzafD6fHn/88SZffdmMNV8aWPOlgTVfGi7FNf8zj/m6668AAAA6oA57Tg4AAMD5EHIAAICVCDkAAMBKhBwAAGAlQs43sGrVKvXt21chISFKSEjQO++8095TuiDZ2dn6wQ9+oO7duysiIkKTJ09WWVmZq+bmm2+Wx+NxbXPmzHHVVFRUKCUlRd26dVNERITmz5+v+vp6V01hYaFGjRoln8+nAQMGKDc3t62X16ysrKwm6xk0aJDT/9VXXyktLU29evXS9773Pd15551NbjDZkdYrSX379m2yZo/Ho7S0NEl2HOPt27fr1ltvVUxMjDwej/Ly8lz9xhgtXLhQ0dHR6tq1q5KSkvTRRx+5ao4ePapp06bJ7/crLCxMqampOn78uKtm7969GjNmjEJCQhQbG6slS5Y0mcuGDRs0aNAghYSEKD4+Xps3b2719UrnX3NdXZ0yMjIUHx+vyy+/XDExMZo+fbo+//xz1xjN/dvIycm5KNf8dcd45syZTdYyceJEV41Nx1hSs7/XHo9HS5cudWo60jH+Thi0yAsvvGC8Xq9Zu3at2bdvn5k1a5YJCwszVVVV7T21r5WcnGzWrVtnSktLTUlJibnllltM7969zfHjx52am266ycyaNctUVlY6W01NjdNfX19vhg4dapKSksyePXvM5s2bTXh4uMnMzHRqPv74Y9OtWzeTnp5u9u/fb/7whz+Yzp07my1btnyn6zXGmMcff9xcc801rvX8/e9/d/rnzJljYmNjTUFBgdm9e7e5/vrrzQ033OD0d7T1GmPMkSNHXOvNz883ksy2bduMMXYc482bN5tf//rX5uWXXzaSzCuvvOLqz8nJMaGhoSYvL8+8//775rbbbjNxcXHm1KlTTs3EiRPN8OHDzdtvv23efPNNM2DAADNlyhSnv6amxkRGRppp06aZ0tJS8+c//9l07drV/PGPf3Rq3nrrLdO5c2ezZMkSs3//frNgwQJz2WWXmQ8++OA7XXN1dbVJSkoyL774ojlw4IApKioy1113nRk9erRrjD59+pgnnnjCdezP/v2/mNb8dcd4xowZZuLEia61HD161FVj0zE2xrjWWllZadauXWs8Ho85dOiQU9ORjvF3gZDTQtddd51JS0tz9s+cOWNiYmJMdnZ2O87qmzly5IiRZN544w2n7aabbjIPPfTQOR+zefNm06lTJxMIBJy21atXG7/fb2pra40xxjz66KPmmmuucT3urrvuMsnJya27gAvw+OOPm+HDhzfbV11dbS677DKzYcMGp+3DDz80kkxRUZExpuOttzkPPfSQ6d+/v2loaDDG2HeM//nNoKGhwURFRZmlS5c6bdXV1cbn85k///nPxhhj9u/fbySZd99916n5n//5H+PxeMz//u//GmOMeeqpp0yPHj2cNRtjTEZGhhk4cKCz/5Of/MSkpKS45pOQkGAeeOCBVl3jP2vuDfCfvfPOO0aS+eSTT5y2Pn36mBUrVpzzMRfrms8Vcm6//fZzPuZSOMa33367+dGPfuRq66jHuK3wdVULnD59WsXFxUpKSnLaOnXqpKSkJBUVFbXjzL6ZmpoaSVLPnj1d7evXr1d4eLiGDh2qzMxMnTx50ukrKipSfHy864aLycnJCgaD2rdvn1Nz9mvUWNNer9FHH32kmJgY9evXT9OmTVNFRYUkqbi4WHV1da65Dho0SL1793bm2hHXe7bTp0/rueee009/+lPXH6G17Rifrby8XIFAwDW/0NBQJSQkuI5rWFiYrr32WqcmKSlJnTp10q5du5yasWPHyuv1OjXJyckqKyvTl19+6dRcrK9DTU2NPB5Pk7/Pl5OTo169emnkyJFaunSp62vIjrbmwsJCRUREaODAgXrwwQf1xRdfOH22H+Oqqipt2rRJqampTfpsOsbfVoe+4/F37R//+IfOnDnT5I7KkZGROnDgQDvN6ptpaGjQww8/rB/+8IcaOnSo0z516lT16dNHMTEx2rt3rzIyMlRWVqaXX35ZkhQIBJpdf2Pf+WqCwaBOnTqlrl27tuXSXBISEpSbm6uBAweqsrJSixYt0pgxY1RaWqpAICCv19vkTSAyMvJr19LYd76a9ljvP8vLy1N1dbVmzpzptNl2jP9Z4xybm9/Z84+IiHD1d+nSRT179nTVxMXFNRmjsa9Hjx7nfB0ax2gvX331lTIyMjRlyhTXH2b8xS9+oVGjRqlnz57auXOnMjMzVVlZqeXLl0vqWGueOHGi7rjjDsXFxenQoUP61a9+pUmTJqmoqEidO3e2/hg/++yz6t69u+644w5Xu03HuDUQci5RaWlpKi0t1Y4dO1zts2fPdn6Oj49XdHS0xo8fr0OHDql///7f9TS/tUmTJjk/Dxs2TAkJCerTp49eeumldn0j/q4888wzmjRpkmJiYpw2244x3Orq6vSTn/xExhitXr3a1Zeenu78PGzYMHm9Xj3wwAPKzs7ucLf+v/vuu52f4+PjNWzYMPXv31+FhYUaP358O87su7F27VpNmzZNISEhrnabjnFr4OuqFggPD1fnzp2bXH1TVVWlqKiodppVy82dO1cbN27Utm3bdOWVV563NiEhQZJ08OBBSVJUVFSz62/sO1+N3+9v92ARFhamq6++WgcPHlRUVJROnz6t6upqV83Zx7Mjr/eTTz7R1q1bdf/995+3zrZj3DjH8/2eRkVF6ciRI67++vp6HT16tFWOfXv996Ax4HzyySfKz893fYrTnISEBNXX1+vw4cOSOuaaG/Xr10/h4eGuf8c2HmNJevPNN1VWVva1v9uSXcf4myDktIDX69Xo0aNVUFDgtDU0NKigoECJiYntOLMLY4zR3Llz9corr+j1119v8pFlc0pKSiRJ0dHRkqTExER98MEHrv94NP7HdMiQIU7N2a9RY83F8BodP35chw4dUnR0tEaPHq3LLrvMNdeysjJVVFQ4c+3I6123bp0iIiKUkpJy3jrbjnFcXJyioqJc8wsGg9q1a5fruFZXV6u4uNipef3119XQ0OCEvsTERG3fvl11dXVOTX5+vgYOHKgePXo4NRfL69AYcD766CNt3bpVvXr1+trHlJSUqFOnTs7XOh1tzWf77LPP9MUXX7j+Hdt2jBs988wzGj16tIYPH/61tTYd42+kvc987mheeOEF4/P5TG5urtm/f7+ZPXu2CQsLc12JcrF68MEHTWhoqCksLHRdXnjy5EljjDEHDx40TzzxhNm9e7cpLy83f/nLX0y/fv3M2LFjnTEaLy+eMGGCKSkpMVu2bDFXXHFFs5cXz58/33z44Ydm1apV7XZJ9SOPPGIKCwtNeXm5eeutt0xSUpIJDw83R44cMcb83yXkvXv3Nq+//rrZvXu3SUxMNImJiR12vY3OnDljevfubTIyMlztthzjY8eOmT179pg9e/YYSWb58uVmz549zpVEOTk5JiwszPzlL38xe/fuNbfffnuzl5CPHDnS7Nq1y+zYscNcddVVrsuLq6urTWRkpLn33ntNaWmpeeGFF0y3bt2aXGrbpUsX85//+Z/mww8/NI8//nibXWp7vjWfPn3a3HbbbebKK680JSUlrt/vxqtodu7caVasWGFKSkrMoUOHzHPPPWeuuOIKM3369Ityzedb77Fjx8wvf/lLU1RUZMrLy83WrVvNqFGjzFVXXWW++uorZwybjnGjmpoa061bN7N69eomj+9ox/i7QMj5Bv7whz+Y3r17G6/Xa6677jrz9ttvt/eULoikZrd169YZY4ypqKgwY8eONT179jQ+n88MGDDAzJ8/33UPFWOMOXz4sJk0aZLp2rWrCQ8PN4888oipq6tz1Wzbts2MGDHCeL1e069fP+c5vmt33XWXiY6ONl6v13z/+983d911lzl48KDTf+rUKfOzn/3M9OjRw3Tr1s38+Mc/NpWVla4xOtJ6G7366qtGkikrK3O123KMt23b1uy/5RkzZhhj/u8y8t/85jcmMjLS+Hw+M378+CavxRdffGGmTJlivve97xm/32/uu+8+c+zYMVfN+++/b2688Ubj8/nM97//fZOTk9NkLi+99JK5+uqrjdfrNddcc43ZtGnTd77m8vLyc/5+N94fqbi42CQkJJjQ0FATEhJiBg8ebH7729+6QsHFtObzrffkyZNmwoQJ5oorrjCXXXaZ6dOnj5k1a1aT/9m06Rg3+uMf/2i6du1qqqurmzy+ox3j74LHGGPa9KMiAACAdsA5OQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABY6f8BUonwJfU6MTsAAAAASUVORK5CYII=", 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bRRhjTF13/vbbb5WQkKB169bpiiuuUFlZmc455xwtWLBAt956qyTps88+U5cuXVRUVKQ+ffpo5cqVuvHGG7Vv3z4lJiZKkubMmaNx48bp22+/VUxMjMaNG6e8vDx98skn/u81cOBAlZaWatWqVbWam8/nk8vlUllZmZxOZ12XaA1+UQLA6ePjIkIvFL+/T+s5O2VlZZKk1q1bS5KKi4t15MgRpaen+8d07txZ7dq1U1FRkSSpqKhIXbt29YeOJGVkZMjn82nbtm3+Mcceo3pM9TEAAABqq84fBFpVVaWcnBxddtlluvDCCyVJXq9XMTExio+PDxibmJgor9frH3Ns6FTfX33fqcb4fD4dPnxYcXFxJ8ynvLxc5eXl/q99Pl9dlwYAACxS5ys72dnZ+uSTT7Rw4cL6nE+dTZs2TS6Xy39LSUlp7CkBAIAmoE6xM2LECC1fvlxr1qxR27Zt/dvdbrcqKipUWloaML6kpERut9s/5vhXZ1V//UtjnE5njVd1JGnChAkqKyvz3/bu3VuXpQEAAMsEFTvGGI0YMUJLly7V6tWrlZqaGnB/z5491axZMxUWFvq37dixQ3v27JHH45EkeTwebd26VQcOHPCPKSgokNPpVFpamn/MsceoHlN9jJo4HA45nc6AGwAAQFDP2cnOztaCBQv0+uuvq1WrVv7n2LhcLsXFxcnlcmno0KEaPXq0WrduLafTqfvvv18ej0d9+vSRJPXr109paWkaPHiwZsyYIa/Xq0mTJik7O1sOh0OSNHz4cD311FMaO3ashgwZotWrV2vx4sXKy+MVRQAAIDhBXdl55plnVFZWpquuukpJSUn+26JFi/xjZs6cqRtvvFEDBgzQFVdcIbfbrddee81/f1RUlJYvX66oqCh5PB7dcccduvPOO/XQQw/5x6SmpiovL08FBQXq1q2bHnvsMb3wwgu8xw4AAAjaab3PTlPG++wE4n12AOD08T47odfk3mcHAACgqSN2AACA1YgdAABgtTq/gzIAAGeaUD3/kecChRZXdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWi27sCQAAcKbrMD4vZMf+anpmyI4dLriyAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBofBAoAgMVC9SGj4fQBo1zZAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI0PAm1iQvWBbQAAnKm4sgMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwWtCx88477+imm25ScnKyIiIitGzZsoD7jTGaPHmykpKSFBcXp/T0dO3cuTNgzPfff69BgwbJ6XQqPj5eQ4cO1aFDhwLGfPzxx/rNb36j2NhYpaSkaMaMGcGvDgAAnPGCjp0ff/xR3bp10+zZs2u8f8aMGXriiSc0Z84cbdy4US1atFBGRoZ+/vln/5hBgwZp27ZtKigo0PLly/XOO+/o3nvv9d/v8/nUr18/tW/fXsXFxXr00Uf197//Xc8991wdlggAAM5kEcYYU+edIyK0dOlS3XLLLZL+d1UnOTlZf/nLX/TXv/5VklRWVqbExETl5uZq4MCB+vTTT5WWlqbNmzfrkksukSStWrVKN9xwg7755hslJyfrmWee0cSJE+X1ehUTEyNJGj9+vJYtW6bPPvusVnPz+XxyuVwqKyuT0+ms6xIbXIfxeY09BQAAftFX0zNDctxQ/P6u1+fs7N69W16vV+np6f5tLpdLvXv3VlFRkSSpqKhI8fHx/tCRpPT0dEVGRmrjxo3+MVdccYU/dCQpIyNDO3bs0A8//FDj9y4vL5fP5wu4AQAA1GvseL1eSVJiYmLA9sTERP99Xq9XCQkJAfdHR0erdevWAWNqOsax3+N406ZNk8vl8t9SUlJOf0EAACDsWfNqrAkTJqisrMx/27t3b2NPCQAANAH1Gjtut1uSVFJSErC9pKTEf5/b7daBAwcC7j969Ki+//77gDE1HePY73E8h8Mhp9MZcAMAAKjX2ElNTZXb7VZhYaF/m8/n08aNG+XxeCRJHo9HpaWlKi4u9o9ZvXq1qqqq1Lt3b/+Yd955R0eOHPGPKSgoUKdOnXTWWWfV55QBAIDlgo6dQ4cOacuWLdqyZYuk/z0pecuWLdqzZ48iIiKUk5Ojf/zjH3rjjTe0detW3XnnnUpOTva/YqtLly767W9/q3vuuUebNm3S+vXrNWLECA0cOFDJycmSpD/+8Y+KiYnR0KFDtW3bNi1atEizZs3S6NGj623hAADgzBAd7A7vv/++rr76av/X1QGSlZWl3NxcjR07Vj/++KPuvfdelZaW6vLLL9eqVasUGxvr32f+/PkaMWKErr32WkVGRmrAgAF64okn/Pe7XC699dZbys7OVs+ePXX22Wdr8uTJAe/FAwAAUBun9T47TRnvswMAQOicse+zAwAA0NQQOwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAatGNPYFw1GF8XmNPAQAA1BJXdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWK1Jx87s2bPVoUMHxcbGqnfv3tq0aVNjTwkAAISZJhs7ixYt0ujRozVlyhR98MEH6tatmzIyMnTgwIHGnhoAAAgjTTZ2Hn/8cd1zzz26++67lZaWpjlz5qh58+Z66aWXGntqAAAgjEQ39gRqUlFRoeLiYk2YMMG/LTIyUunp6SoqKqpxn/LycpWXl/u/LisrkyT5fL56n19V+U/1fkwAAMJJKH6/HntcY0y9HbNJxs53332nyspKJSYmBmxPTEzUZ599VuM+06ZN04MPPnjC9pSUlJDMEQCAM5nr36E9/sGDB+VyuerlWE0ydupiwoQJGj16tP/rqqoqff/992rTpo0iIiIacWbB8fl8SklJ0d69e+V0Oht7OvXO5vXZvDbJ7vXZvDaJ9YUzm9cm1bw+Y4wOHjyo5OTkevs+TTJ2zj77bEVFRamkpCRge0lJidxud437OBwOORyOgG3x8fGhmmLIOZ1OK//Drmbz+mxem2T3+mxem8T6wpnNa5NOXF99XdGp1iSfoBwTE6OePXuqsLDQv62qqkqFhYXyeDyNODMAABBumuSVHUkaPXq0srKydMkll6hXr17697//rR9//FF33313Y08NAACEkSYbO7fffru+/fZbTZ48WV6vV927d9eqVatOeNKybRwOh6ZMmXLCn+RsYfP6bF6bZPf6bF6bxPrCmc1rkxpufRGmPl/bBQAA0MQ0yefsAAAA1BdiBwAAWI3YAQAAViN2AACA1YidBvbf//5Xd9xxh9q0aaO4uDh17dpV77///in3Wbt2rXr06CGHw6GOHTsqNze3YSZbB8Gub+3atYqIiDjh5vV6G3DWv6xDhw41zjM7O/uk+yxZskSdO3dWbGysunbtqhUrVjTgjIMT7Ppyc3NPGBsbG9vAs66dyspKPfDAA0pNTVVcXJzOP/98TZ069Rc/dydcHnd1WV+4PO6k/31kQE5Ojtq3b6+4uDj17dtXmzdvPuU+4XLupODX15TP3TvvvKObbrpJycnJioiI0LJlywLuN8Zo8uTJSkpKUlxcnNLT07Vz585fPO7s2bPVoUMHxcbGqnfv3tq0aVPwkzNoMN9//71p3769ueuuu8zGjRvNrl27TH5+vvniiy9Ous+uXbtM8+bNzejRo8327dvNk08+aaKiosyqVasacOa1U5f1rVmzxkgyO3bsMPv37/ffKisrG3Dmv+zAgQMB8ysoKDCSzJo1a2ocv379ehMVFWVmzJhhtm/fbiZNmmSaNWtmtm7d2rATr6Vg1zd37lzjdDoD9vF6vQ076Vp6+OGHTZs2bczy5cvN7t27zZIlS0zLli3NrFmzTrpPOD3u6rK+cHncGWPMbbfdZtLS0sy6devMzp07zZQpU4zT6TTffPNNjePD6dwZE/z6mvK5W7FihZk4caJ57bXXjCSzdOnSgPunT59uXC6XWbZsmfnoo4/M7373O5OammoOHz580mMuXLjQxMTEmJdeesls27bN3HPPPSY+Pt6UlJQENTdipwGNGzfOXH755UHtM3bsWHPBBRcEbLv99ttNRkZGfU6tXtRlfdUP3B9++CE0kwqRkSNHmvPPP99UVVXVeP9tt91mMjMzA7b17t3b/OlPf2qI6Z22X1rf3LlzjcvlathJ1VFmZqYZMmRIwLb+/fubQYMGnXSfcHrc1WV94fK4++mnn0xUVJRZvnx5wPYePXqYiRMn1rhPOJ27uqwvXM7d8bFTVVVl3G63efTRR/3bSktLjcPhMP/5z39OepxevXqZ7Oxs/9eVlZUmOTnZTJs2Laj58GesBvTGG2/okksu0e9//3slJCTo4osv1vPPP3/KfYqKipSenh6wLSMjQ0VFRaGcap3UZX3VunfvrqSkJF133XVav359iGd6eioqKjRv3jwNGTLkpB8yG07n7Xi1WZ8kHTp0SO3bt1dKSopuvvlmbdu2rQFnWXt9+/ZVYWGhPv/8c0nSRx99pHfffVfXX3/9SfcJp/NXl/VVa+qPu6NHj6qysvKEP5HGxcXp3XffrXGfcDp3dVlftaZ+7o63e/dueb3egHPjcrnUu3fvk56biooKFRcXB+wTGRmp9PT0oM8nsdOAdu3apWeeeUa/+tWvlJ+fr/vuu09//vOf9fLLL590H6/Xe8K7RicmJsrn8+nw4cOhnnJQ6rK+pKQkzZkzR6+++qpeffVVpaSk6KqrrtIHH3zQgDMPzrJly1RaWqq77rrrpGNOdt6awt/Vf0lt1tepUye99NJLev311zVv3jxVVVWpb9+++uabbxpuorU0fvx4DRw4UJ07d1azZs108cUXKycnR4MGDTrpPuH0uKvL+sLlcdeqVSt5PB5NnTpV+/btU2VlpebNm6eioiLt37+/xn3C6dzVZX3hcu6OV/2zL5ifi999950qKyvr5Wdpk/24CBtVVVXpkksu0SOPPCJJuvjii/XJJ59ozpw5ysrKauTZnb66rK9Tp07q1KmT/+u+ffvqyy+/1MyZM/XKK680yLyD9eKLL+r6669XcnJyY08lJGqzPo/HE/ChvH379lWXLl307LPPaurUqQ0xzVpbvHix5s+frwULFuiCCy7Qli1blJOTo+TkZCsed3VZXzg97l555RUNGTJE5557rqKiotSjRw/94Q9/UHFxcWNPrV4Eu75wOndNCVd2GlBSUpLS0tICtnXp0kV79uw56T5ut1slJSUB20pKSuR0OhUXFxeSedZVXdZXk169eumLL76oz6nVm6+//lpvv/22hg0bdspxJztvbrc7lNM7bbVd3/Gqryg0xfM2ZswY/9WPrl27avDgwRo1apSmTZt20n3C6XFXl/XVpKk+7s4//3ytW7dOhw4d0t69e7Vp0yYdOXJE5513Xo3jw+ncScGvryZN9dwdq/pnXzA/F88++2xFRUXVy89SYqcBXXbZZdqxY0fAts8//1zt27c/6T4ej0eFhYUB2woKCgL+r7qpqMv6arJlyxYlJSXV59Tqzdy5c5WQkKDMzMxTjgun83as2q7veJWVldq6dWuTPG8//fSTIiMDf9RFRUWpqqrqpPuE0/mry/pq0pQfd5LUokULJSUl6YcfflB+fr5uvvnmGseF07k7Vm3XV5Omfu4kKTU1VW63O+Dc+Hw+bdy48aTnJiYmRj179gzYp6qqSoWFhcGfz6CezozTsmnTJhMdHW0efvhhs3PnTjN//nzTvHlzM2/ePP+Y8ePHm8GDB/u/rn4Z5ZgxY8ynn35qZs+e3WRfRlmX9c2cOdMsW7bM7Ny502zdutWMHDnSREZGmrfffrsxlnBKlZWVpl27dmbcuHEn3Dd48GAzfvx4/9fr16830dHR5l//+pf59NNPzZQpU5r0S8+NCW59Dz74oMnPzzdffvmlKS4uNgMHDjSxsbFm27ZtDTnlWsnKyjLnnnuu/6XZr732mjn77LPN2LFj/WPC+XFXl/WF0+Nu1apVZuXKlWbXrl3mrbfeMt26dTO9e/c2FRUVxpjwPnfGBL++pnzuDh48aD788EPz4YcfGknm8ccfNx9++KH5+uuvjTH/e+l5fHy8ef31183HH39sbr755hNeen7NNdeYJ5980v/1woULjcPhMLm5uWb79u3m3nvvNfHx8UG/1QWx08DefPNNc+GFFxqHw2E6d+5snnvuuYD7s7KyzJVXXhmwbc2aNaZ79+4mJibGnHfeeWbu3LkNN+EgBbu+f/7zn+b88883sbGxpnXr1uaqq64yq1evbuBZ105+fr7//S2Od+WVV5qsrKyAbYsXLza//vWvTUxMjLngggtMXl5eA820boJZX05OjmnXrp2JiYkxiYmJ5oYbbjAffPBBA8629nw+nxk5cqRp166diY2NNeedd56ZOHGiKS8v948J58ddXdYXTo+7RYsWmfPOO8/ExMQYt9ttsrOzTWlpqf/+cD53xgS/vqZ87qpfFn/8rfpnR1VVlXnggQdMYmKicTgc5tprrz3h50379u3NlClTArY9+eST/p83vXr1Mu+9917Qc4sw5hfeRhQAACCM8ZwdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1f4PbyEteMGuqKYAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361260\n", + "Dataset Name: miami_housing\n", + "Number of Features: 15\n", + "Number of Instances: 13932\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361261\n", + "Dataset Name: cps88wages\n", + "Number of Features: 6\n", + "Number of Instances: 28155\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361264\n", + "Dataset Name: socmob\n", + "Number of Features: 5\n", + "Number of Instances: 1156\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361266\n", + "Dataset Name: kings_county\n", + "Number of Features: 21\n", + "Number of Instances: 21613\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361267\n", + "Dataset Name: brazilian_houses\n", + "Number of Features: 9\n", + "Number of Instances: 10692\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361272\n", + "Dataset Name: fifa\n", + "Number of Features: 28\n", + "Number of Instances: 19178\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361618\n", + "Dataset Name: forest_fires\n", + "Number of Features: 12\n", + "Number of Instances: 517\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "Dataset ID: 361622\n", + "Dataset Name: cars\n", + "Number of Features: 17\n", + "Number of Instances: 804\n", + "Histogram of Target Values:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n" + ] + } + ], + "source": [ + "for id in transform_ids:\n", + " task = openml.tasks.get_task(id, download_splits=False, download_data=False,\n", + " download_features_meta_data=False,\n", + " download_qualities=False)\n", + " dataset = task.get_dataset()\n", + " X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute)\n", + " print('Dataset ID:', id)\n", + " print('Dataset Name:', dataset.name)\n", + " print('Number of Features:', X.shape[1])\n", + " print('Number of Instances:', X.shape[0])\n", + " print('Histogram of Target Values:')\n", + " plt.hist(y, bins=20)\n", + " plt.show()\n", + " # if min value of y is greater than 0, then log transform y\n", + " if np.min(y) > 0:\n", + " y = np.log(y)\n", + " if np.min(y) <= 0:\n", + " y = np.log(y - np.min(y) + 1)\n", + " plt.hist(y, bins=20)\n", + " plt.show()\n", + " print('-----------------------------------')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/current/subgroup copy.ipynb b/feature_importance/subgroup/current/subgroup copy.ipynb new file mode 100644 index 0000000..2d56787 --- /dev/null +++ b/feature_importance/subgroup/current/subgroup copy.ipynb @@ -0,0 +1,1048 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from os.path import join as oj" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['361617',\n", + " '361250',\n", + " '361249',\n", + " '361234',\n", + " '361243',\n", + " '361257',\n", + " '361622',\n", + " '361261',\n", + " '361259',\n", + " '361253',\n", + " '361237',\n", + " '361247',\n", + " '361619',\n", + " '361254',\n", + " '361621',\n", + " '361266',\n", + " '361256',\n", + " '361623',\n", + " '361264',\n", + " '361242',\n", + " '361235',\n", + " '361269',\n", + " '361251',\n", + " '361255',\n", + " '361618',\n", + " '361236',\n", + " '361241',\n", + " '361260',\n", + " '361252',\n", + " '361258']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get all of the dataids in results\n", + "dataids = []\n", + "for filename in os.listdir('results/'):\n", + " # get last six characters of filename\n", + " dataids.append(filename[6:])\n", + "dataids" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# set the path we want to look at\n", + "seeds = np.arange(1, 11, 1)\n", + "metric = \"rmse\"\n", + "pipeline = 2\n", + "clustertype = \"hierarchical\"\n", + "paths = []\n", + "for dataid in dataids:\n", + " for seed in seeds:\n", + " paths.append(oj(\"results\", f\"dataid{dataid}\", f\"seed{seed}\", f\"{metric}\", str(clustertype)))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "0 0.210224\n", + "1 0.220228\n", + "2 0.186688\n", + "3 0.161838\n", + "4 0.166085\n", + "5 0.173663\n", + "6 0.160990\n", + "7 0.151103\n", + "8 0.148487\n", + "Name: lasso_l2_signed_normed_noleafavg_norank, dtype: float64\n", + "2\n", + "0 0.226198\n", + "1 0.227379\n", + "2 0.160510\n", + "3 0.163259\n", + "4 0.171894\n", + "5 0.162313\n", + "6 0.187754\n", + "7 0.182449\n", + "8 0.154820\n", + "Name: lasso_l2_signed_normed_noleafavg_norank, dtype: float64\n", + "3\n", + "0 2.120058e-01\n", + "1 2.052050e-01\n", + "2 1.440494e-01\n", + "3 1.578458e-01\n", + "4 1.601082e-01\n", + "5 1.602225e-01\n", + "6 3.941527e+11\n", + "7 7.520470e+11\n", + "8 2.908754e-01\n", + "Name: lasso_l2_signed_normed_noleafavg_norank, dtype: float64\n", + "4\n", + "0 0.212301\n", + "1 0.232746\n", + "2 0.198438\n", + "3 0.161851\n", + "4 0.153588\n", + "5 0.145414\n", + "6 0.147955\n", + "7 0.154010\n", + "8 0.167754\n", + "Name: lasso_l2_signed_normed_noleafavg_norank, dtype: float64\n", + "5\n", + "0 2.233610e-01\n", + "1 2.163500e-01\n", + "2 1.692817e-01\n", + "3 1.907662e-01\n", + "4 1.593582e-01\n", + "5 3.354207e+11\n", + "6 1.283680e+11\n", + "7 1.523750e-01\n", + "8 1.517850e-01\n", + "Name: lasso_l2_signed_normed_noleafavg_norank, dtype: float64\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 17\u001b[0m\n\u001b[1;32m 15\u001b[0m method_results\u001b[38;5;241m.\u001b[39mappend(method_result)\n\u001b[1;32m 16\u001b[0m seed_result \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(method_results, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 17\u001b[0m seed_result \u001b[38;5;241m=\u001b[39m \u001b[43mseed_result\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mseed_result\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontains\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m^Unnamed\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 19\u001b[0m seed_result \u001b[38;5;241m=\u001b[39m seed_result\u001b[38;5;241m.\u001b[39mloc[:, \u001b[38;5;241m~\u001b[39mseed_result\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mduplicated()]\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dataid \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m361617\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/indexing.py:1147\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[1;32m 1146\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[0;32m-> 1147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1149\u001b[0m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m 1150\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/indexing.py:1339\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 1336\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take_opportunity(tup):\n\u001b[1;32m 1337\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take(tup)\n\u001b[0;32m-> 1339\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple_same_dim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/indexing.py:994\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_tuple_same_dim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_null_slice(key):\n\u001b[1;32m 992\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 994\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mretval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 995\u001b[0m \u001b[38;5;66;03m# We should never have retval.ndim < self.ndim, as that should\u001b[39;00m\n\u001b[1;32m 996\u001b[0m \u001b[38;5;66;03m# be handled by the _getitem_lowerdim call above.\u001b[39;00m\n\u001b[1;32m 997\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m retval\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/indexing.py:1375\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1373\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_slice_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m-> 1375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getbool_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1376\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like_indexer(key):\n\u001b[1;32m 1377\u001b[0m \u001b[38;5;66;03m# an iterable multi-selection\u001b[39;00m\n\u001b[1;32m 1378\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(labels, MultiIndex)):\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/indexing.py:1173\u001b[0m, in \u001b[0;36m_LocationIndexer._getbool_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1171\u001b[0m key \u001b[38;5;241m=\u001b[39m check_bool_indexer(labels, key)\n\u001b[1;32m 1172\u001b[0m inds \u001b[38;5;241m=\u001b[39m key\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m-> 1173\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_with_is_copy\u001b[49m\u001b[43m(\u001b[49m\u001b[43minds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/generic.py:4088\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[0;34m(self, indices, axis)\u001b[0m\n\u001b[1;32m 4077\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 4078\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_with_is_copy\u001b[39m(\u001b[38;5;28mself\u001b[39m, indices, axis: Axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[1;32m 4079\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4080\u001b[0m \u001b[38;5;124;03m Internal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[1;32m 4081\u001b[0m \u001b[38;5;124;03m attribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4086\u001b[0m \u001b[38;5;124;03m See the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[1;32m 4087\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4088\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4089\u001b[0m \u001b[38;5;66;03m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n\u001b[1;32m 4090\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m result\u001b[38;5;241m.\u001b[39m_get_axis(axis)\u001b[38;5;241m.\u001b[39mequals(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis(axis)):\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/generic.py:4068\u001b[0m, in \u001b[0;36mNDFrame.take\u001b[0;34m(self, indices, axis, **kwargs)\u001b[0m\n\u001b[1;32m 4063\u001b[0m \u001b[38;5;66;03m# We can get here with a slice via DataFrame.__getitem__\u001b[39;00m\n\u001b[1;32m 4064\u001b[0m indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\n\u001b[1;32m 4065\u001b[0m indices\u001b[38;5;241m.\u001b[39mstart, indices\u001b[38;5;241m.\u001b[39mstop, indices\u001b[38;5;241m.\u001b[39mstep, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mintp\n\u001b[1;32m 4066\u001b[0m )\n\u001b[0;32m-> 4068\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4069\u001b[0m \u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4070\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_block_manager_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4071\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 4072\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\u001b[38;5;241m.\u001b[39m__finalize__(\n\u001b[1;32m 4074\u001b[0m \u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4075\u001b[0m )\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/internals/managers.py:877\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[0;34m(self, indexer, axis, verify)\u001b[0m\n\u001b[1;32m 874\u001b[0m indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[1;32m 876\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m--> 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreindex_indexer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_axis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 881\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_dups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 882\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 883\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/internals/managers.py:663\u001b[0m, in \u001b[0;36mBaseBlockManager.reindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)\u001b[0m\n\u001b[1;32m 660\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRequested axis not found in manager\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 662\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 663\u001b[0m new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_slice_take_blocks_ax0\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 664\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 665\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 666\u001b[0m \u001b[43m \u001b[49m\u001b[43monly_slice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43monly_slice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 667\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_na_proxy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_na_proxy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 668\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 669\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 670\u001b[0m new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 671\u001b[0m blk\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[1;32m 672\u001b[0m indexer,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 678\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[1;32m 679\u001b[0m ]\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/internals/managers.py:809\u001b[0m, in \u001b[0;36mBaseBlockManager._slice_take_blocks_ax0\u001b[0;34m(self, slice_or_indexer, fill_value, only_slice, use_na_proxy, ref_inplace_op)\u001b[0m\n\u001b[1;32m 805\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 806\u001b[0m \u001b[38;5;66;03m# GH#32779 to avoid the performance penalty of copying,\u001b[39;00m\n\u001b[1;32m 807\u001b[0m \u001b[38;5;66;03m# we may try to only slice\u001b[39;00m\n\u001b[1;32m 808\u001b[0m taker \u001b[38;5;241m=\u001b[39m blklocs[mgr_locs\u001b[38;5;241m.\u001b[39mindexer]\n\u001b[0;32m--> 809\u001b[0m max_len \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(\u001b[38;5;28mlen\u001b[39m(mgr_locs), \u001b[43mtaker\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 810\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m only_slice \u001b[38;5;129;01mor\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 811\u001b[0m taker \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_indices_to_slice(taker, max_len)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/numpy/core/_methods.py:41\u001b[0m, in \u001b[0;36m_amax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 40\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_maximum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "data_results = []\n", + "for dataid in dataids:\n", + " data_result = []\n", + " for seed in seeds:\n", + " path = oj(\"results\", f\"dataid{dataid}\", f\"seed{seed}\", f\"{metric}\", str(clustertype))\n", + " # if path exists\n", + " if not os.path.exists(path):\n", + " continue\n", + " files = os.listdir(path)\n", + " method_results = []\n", + " for file in files:\n", + " if file.endswith(\".csv\"):\n", + " method_result = pd.read_csv(oj(path, file))\n", + " method_result = method_result.rename(columns={\"rmse\":file[:-4]})\n", + " method_results.append(method_result)\n", + " seed_result = pd.concat(method_results, axis=1)\n", + " seed_result = seed_result.loc[:,\n", + " ~seed_result.columns.str.contains('^Unnamed')]\n", + " seed_result = seed_result.loc[:, ~seed_result.columns.duplicated()]\n", + " if dataid == \"361617\":\n", + " print(seed)\n", + " print(seed_result[\"lasso_l2_signed_normed_noleafavg_norank\"])\n", + " data_result.append(seed_result)\n", + " # average all of the dataframes in data_result\n", + " data_result = sum(data_result) / len(data_result)\n", + " data_results.append(data_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# in each data_result, keep only nclust, lmdi_lasso, lmdi_ridge, shap, lime, rawdata\n", + "for i in range(len(data_results)):\n", + " data_results[i] = data_results[i][[\"nclust\", \"lasso_l2_signed_normed_noleafavg_norank\", \"shap\", \"lime\", \"rawdata\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# for each data_result, only keep the first four rows\n", + "# for i in range(len(data_results)):\n", + "# data_results[i] = data_results[i].iloc[0:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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EqIFDhw7RtGnTSo/x60TFtVX8oUOHiIiI8Oi5bTYb8+fPZ8SIEej1eo+e2xdI+/xfoLcx0NsHgd9GaZ//81Ybs7OzSUxMdH+PV8avExXXcE9ERIRXEhWDwUBERERA/gJK+/xfoLcx0NsHgd9GaZ//83YbqzJtQybTCiGEEMJnSaIihBBCCJ8liYoQQgghfJZfz1GpKrvdjs1mq9ZrbDYbQUFB5OfnY7fbvRSZeqR9/i/Q2xjo7YPAaqNery+zO70QnhDQiYqiKKSnp5OVlVWj18bHx3Po0KGAXKNF2uf/Ar2Ngd4+CLw2RkVFER8fHxBtEb4joBMVV5ISGxuLwWCo1j8eh8OB1WrFaDSeczEafyTt83+B3sZAbx8EThsVRSE3N5eMjAwAEhISVI5IBJKATVTsdrs7SWnQoEG1X+9wOCgsLCQ0NNSvP0AqIu3zf4HexkBvHwRWG8PCwgDIyMggNjZWhoGEx/j3v4xKuOakGAwGlSMRQoj6wfV5W905gUJUJmATFRcZKxVCiLohn7fCGwI+URFCCCGE/5JExQcNGjSIRx55RO0wKjV58mS6du1aJ9fypffDl2KpC/v370ej0bB58+Y6u6ZGo+Hnn3/26DnT09MZPnw44eHhREVFefTcgaxFixa8+eabaoch6jlJVIRHfPzxx/Tv35/o6Giio6MZNmwYf//9t0fO/dNPP/Hcc8955FyifnrjjTc4duwYmzdvZvfu3WqHI4SoBklUhEekpKRwww03sGTJElavXk1iYiIjRozgyJEjtT53TExMlXbYrA8KCwvVDsEvpaam0qNHD1q3bk1sbKza4XiVL01kzS/Kx6E41A5D+DlJVHzcl19+Sc+ePTGZTMTHx3PjjTe61yoAyMzMZOzYsTRq1IiwsDBat27NjBkzAOeX2gMPPEBCQgKhoaE0b96cl156yf3aQ4cOccUVV2A0GomIiODaa6/l+PHjNYpz1qxZ3HfffXTt2pV27drxySef4HA4WLRoUZVe//7779O6dWtCQ0OJi4vj6quvdj939nDLsWPHuOSSSwgLCyMpKYnZs2eX6aLW6XR88cUXjBkzBoPBQOvWrZk7d26pa27fvp2LL74Yo9FIXFwcN998MydPnnQ/n5OTwy233ILRaCQhIYHXX3+9Wu9JixYtePHFF7n99tsxmUw0a9aMjz76qNQx27ZtY8iQIYSFhdGgQQPuuusurFar+/lbb72VK664ghdeeIHGjRvTtm1b93DMd999x8UXX0x4eDi9evVi9+7drFu3jp49e2I0Grn44os5ceJEqet98skntG/fntDQUNq1a8f7779f6vm///6bbt26ERoaSs+ePdm0aVOV25uSkoJGo2HRokX07NkTg8FA37592bVrV6njpk+fTsuWLQkODqZt27Z8+eWXlZ730KFDXHvttURFRRETE8Pll1/O/v373c+vW7eO4cOH07BhQyIjIxk4cCAbN250P9+iRQt+/PFHvvjiCzQaDbfeeisA06ZNo1OnToSHh5OYmMh9993nfu+zs7MJCwvjjz/+KBXLnDlzMJlM5ObmArBq1Sq6du3qfr9+/vnnKg+VpaSkoNPpWLp0Keeff36N3y+NRsP06dO57LLLCA8P54UXXsBut3PHHXeQlJREWFgYbdu25a233ir1Otfv1muvvUZCQgINGjTg/vvvrzTR+eSTT4iKiqrSv+vswmwu+eUSZubMPOexQlRK8WNms1kBFLPZXOa5vLw8ZceOHUpeXp77MYfDoeQU2Kp0s+QVKEePn1QseQVVfk1FN4fDUa12DRw4UHn44YcVRVGUTz/9VJk3b56SmpqqrF69WunTp49y8cUXu4+9//77la5duyrr1q1T0tLSlAULFihz585VFEVRXn31VSUxMVFZtmyZsn//fmX58uXK7NmzFUVRFJvNpnTq1Em58MILlfXr1ytr1qxRevTooQwcOLBKMT7zzDNKly5dKnw+OztbCQ0NVX799ddznmvdunWKTqdTZs+erezfv1/ZuHGj8tZbb5X7fiiKogwbNkzp2rWrsmbNGmXDhg3KwIEDlbCwMOWNN95wHwMojRs3Vr766itlz549ykMPPaQYjUbl1KlTiqIoSmZmptKoUSNlwoQJys6dO5WNGzcqw4cPVwYPHuw+x7333qs0a9ZMWbhwobJ161bl0ksvVUwmU6lYKtO8eXMlJiZGee+995Q9e/YoL730kqLVapV///1XURRFsVqtSkJCgjJmzBhl27ZtyqJFi5SkpCRl3Lhx7nOMGzdOMRqNys0336xs375d2b59u5KWlqYASrt27ZQffvhB2b59u9K7d2+lR48eyqBBg5QVK1YoGzduVFq1aqXcc8897nN99dVXSkJCgvLjjz8q+/btU3788UclJiZGmTlzpqIoimKxWJRGjRopN954o7J9+3bl119/VZKTkxVA2bRp0znbu2TJEgVQLrjgAiUlJUX5559/lP79+yt9+/Z1H/PTTz8per1eee+995Rdu3Ypr7/+uqLT6ZTFixeX+tnNmTNHsdvtSkZGhtK+fXvl9ttvV7Zu3ars2LFDufHGG5W2bdsqBQUFiqIoyqJFi5Qvv/xS2blzp7Jjxw7ljjvuUOLi4pTs7GxFURQlIyNDueiii5Rrr71WOXbsmJKVlaUoiqK88cYbyuLFi5W0tDRl0aJFStu2bZV7773XHcfVV1+t3HTTTaXaeNVVV7kfM5vNSkxMjHLTTTcp//zzjzJv3jylTZs21X6/evbsqSxevLhW71dsbKzy2WefKampqcqBAweUwsJCZdKkScq6deuUffv2KV999ZViMBiUb7/91v26cePGKREREco999yj7Ny5U/n1118Vg8GgfPTRR+5jmjdv7v539fLLLysNGjRQ1q5dW2GbSn7urji8Quk4s6PSaWYnxZxb9jM6EBQWFio///yzUlhYqHYoXuOtNlb2/X22epWo5BTYlOZP/Fbnt5wCW7XadfYXc0nr1q1TAMVisSiKoiijR49WbrvttnKPffDBB5UhQ4aUmyj9+eefik6nU/bv3+9+7J9//lEA5e+//z5njOdKVO69914lOTm51PtfkR9//FGJiIhwf7GcreT7sXPnTgVQ1q1b535+z549ClAmUXnssccUu92uKIozKQCUP/74Q1EURXnuueeUESNGlLrOoUOHFEDZtWuXYrFYlODgYOW7775zP3/q1CklLCysWolKyS86h8OhxMbGKtOnT1cURVE++ugjJTo6WrFare5jfv/9d0Wr1Srp6emKoji/TOLi4txfyoqiuBOVjz76SMnMzFTsdrvy9ddfK4CyaNEi93EvvfSS0rZtW/f9li1buhNVl+eee07p06ePoiiK8uGHHyoNGjQo9TObPn16tb94Fy5cWKo9gPucffv2Ve68885Sr7vmmmuUUaNGue+XTFQ++OADpW3btqV+hwsKCpSwsDDlr7/+KjcOu92umEymUkny5ZdfXioBLM/333+vNGjQwH1/zpw5itFoVHJychRFcX7ehIaGun+Hpk+fXub9+vjjj6v9fv3888/u39Oavl+PPPLIOa93//33K1dddZX7/rhx45TmzZsrRUVFpc593XXXue+7EpXHH39cSUhIULZv317pNUp+7s7cPlPpOLOj0nFmR2Vz+uZzxuePJFGpueokKjL04+M2bNjA6NGjadasGSaTiYEDBwJw8OBBAO69916++eYbunbtyuOPP86qVavcr7311lvZvHkzbdu25aGHHmL+/Pnu5/7991+aNGlCYmKi+7EOHToQFRXFzp07axXz1KlT+eabb5gzZw6hoaHnPH748OE0b96c5ORkbr75ZmbNmuXuWj/brl27CAoKonv37u7HWrVqRXR0dJljzzvvPPf/h4eHExER4R4227JlC0uWLMFoNLpv7dq1A5zzGVJTUyksLOSCCy5wnyMmJoa2bdtW7U0o1rlzZ/f/azQa4uPj3THs3LmTLl26EB4e7j6mX79+OByOUt3/nTp1Ijg4uNJzx8XFuY8t+ZjrWjk5OaSmpnLHHXeUavPzzz9PamqqO57OnTuX+pn16dOnWu09Oy7XUuol29yvX79Sx/fr16/C37nt27ezd+9eTCaTO+aYmBjy8/PdcR8/fpw777yT1q1bExkZSUREBFar1f1vpCILFy5k6NChNGnSBJPJxM0338ypU6fcv3ujRo1Cr9e7hwx//PFHIiIiGDZsGOD8XTz7/Tr//POr/D65lPw9ren71bNnzzLnfe+99+jRoweNGjXCaDTy0UcflXlPzjvvvFIryCYkJJQaWgZ4/fXX+fjjj1mxYkWpWM8lNSvV/f/7svZV+XVCnC1gl9AvT5hex45nR1bpWIfDgSXbginCVOulrcP0NVtKOicnh5EjRzJy5EhmzZpFo0aNOHjwICNHjnRPqrz44os5cOAA8+bNY8GCBQwdOpT777+f1157je7du5OWlsYff/zBwoULufbaaxk2bBg//PBDrdpTmddee42pU6eycOHCUl9YlTGZTGzcuJGUlBTmz5/PpEmTmDx5MuvWratVKalery91X6PR4HA4J/ZZrVZGjx7Nyy+/XOZ1CQkJ7N27t8bXrWoMVVUykano3K6Fts5+rGR7wVmdVTL5Ajy+1Hl5cVW3zS45OTn06NGDWbNmlXmuUaNGAIwbN45Tp07x1ltv0bx5c0JCQujTp0+lE4/379/PpZdeyr333ssLL7xATEwMK1as4I477qCwsBCDwUBwcDBXX301s2fP5vrrr2f27Nlcd911BAV59mPTE+/X2b8j33zzDY899hivv/46ffr0wWQy8eqrr7J27doKr+26/tnX7t+/P7///jvfffcdTz75ZJVjKpmopJpTKzlSiMrVqx4VjUaDITioyrewYF21jq/oVtPVGv/9919OnTrF1KlT6d+/P+3atSvz1w44P7DHjRvHV199xZtvvllqwmZERATXXXcdH3/8Md9++y0//vgjp0+fpl27dhw5coRDhw65j92xYwdZWVl06NChRvG+8sorPPfcc/z555/l/oVXmaCgIIYNG8Yrr7zC1q1b2b9/P4sXLy5zXNu2bSkqKio1yXPv3r1kZmZW63rdu3fnn3/+oUWLFrRq1arULTw8nJYtW6LX60t9sGdmZnq0tLV9+/Zs2bKFnJwc92MrV65Eq9VWu+fmXOLi4mjcuDH79u0r096kpCR3PFu3biU/P9/9ujVr1ng0jvbt27Ny5cpSj61cubLC37kuXbqwZ88eYmNjy8QdGRnpfv1DDz3EqFGjOO+88wgJCSk1Kbo8GzZswOFw8Prrr9O7d2/atGnD0aNHyxw3duxY/vzzT/755x8WL17M2LFj3c+1bduWbdu2UVBQ4H5s3bp1VX4vqqK671fJY/r27ct9991Ht27daNWqlbsHqrrOP/98/vjjD1588UVee+21Kr1GUZRSyYkkKqI26lWi4m+aNWtGcHAw77zzDvv27WPu3Lll1hOZNGkSv/zyC3v37uWff/7ht99+o3379oCzquHrr7/m33//Zffu3Xz//ffEx8cTFRXFsGHD6NChAzfffDMbN27k77//5pZbbmHgwIHVTjIAXn75ZSZOnMhnn31GixYtSE9PJz09vVQFS0V+++033n77bTZv3syBAwf44osvcDgc5X5Zt2vXjmHDhnHXXXfx999/s2nTJu666y7CwsKqlRDef//9nD59mhtuuIF169aRmprKX3/9xW233YbdbsdoNHLHHXfwv//9j8WLF7N9+3ZuvfVWj24cN3bsWEJDQxk3bhzbt29nyZIlPPjgg9x8883uoRxPmjJlCi+99BJvv/02u3fvZtu2bcyYMYNp06YBcOONN6LRaLjzzjvZsWMH8+bNq/IXU1X973//Y+bMmUyfPp09e/Ywbdo0fvrpJx577LFyj7/mmmto2LAhl19+OcuXLyctLY2UlBQeeughDh8+DEDr1q358ssv2blzJ2vXrmXs2LHuDfIq0qpVK2w2m/vf1pdffskHH3xQ5rgBAwYQHx/P2LFjSUpKKtUbdeONN+JwOLjrrrvYuXMnf/31l/v98tRS8tV9v1xat27N+vXr+euvv9i9ezcTJ06sVRLVt29f5s2bx5QpU6q0ANyJ3BPk2M4k4PvMMvQjak4SFR/WqFEjZs6cyffff0+HDh2YOnVqmS+O4OBgJkyYQOfOnRkwYAA6nY5vvvkGcA6pvPLKK/Ts2ZNevXqxf/9+5s2bh1arRaPRMGvWLKKiohgwYADDhg0jOTmZb7/9tkaxTp8+ncLCQq6++moSEhLct6p80UVFRfHTTz8xZMgQ2rdvzwcffMDXX39d4Xj4F198QVxcHAMGDODKK6/kzjvvxGQyVWk+jEvjxo1ZuXIldrudESNG0KlTJx555BGioqLcycirr75K//79GT16NMOGDePCCy+kR48eVb7GuRgMBv766y9Onz5Nr169uPrqqxk6dCjvvvuux65R0n/+8x8++eQTZsyYQadOnRg4cCAzZ85096gYjUZ+/fVXtm3bRrdu3XjqqafKHRqrjSuuuIK33nqL1157jfPOO48PP/yQGTNmMGjQoHKPNxgMpKSk0KxZM8aMGUP79u254447yM/PJyIiAoBPP/2UzMxMunfvzs0338xDDz10zrVSunTpwrRp03j55Zfp2LEjs2bNKlW676LRaLjhhhvYsmVLqd4UcPZW/vrrr2zevJmuXbvy1FNPMWnSJIBq/S5Wprrvl8vdd9/NmDFjuO6667jgggs4deoU9913X61iufDCC/n99995+umneeeddyo99qDFORemYVhDAI7mHCXXVv68MyHORaMoiqJ2EDWVnZ1NZGQkZrPZ/aHlkp+fT1paGklJSTX60HA4HGRnZxMREeH326+XJ5Dad/jwYRITE92TIyGw2leRQG+jP7Zv1qxZ3HbbbZjN5nP26oB/trEyrs/dzbbNvLjpRYY3G86qQ6vIUXL4+pKv6diwo9ohepTNZmPevHnuideByFttrOz7+2z1ajKtCAyLFy/GarXSqVMnjh07xuOPP06LFi0YMGCA2qGJeuaLL74gOTmZJk2asGXLFp544gmuvfbaKiUpgeyA5QAAyZHJ7D26l7SiNPZm7Q24REXUDf9P4YVXnHfeeaXKWEveyqvAqMzy5csrPJfRaKx2bDabjf/7v//jvPPO48orr6RRo0akpKTU6V80nm6TP7jnnnsqbO8999yjdniqSE9P56abbqJ9+/Y8+uijXHPNNe7J7PX5/TqU7Zyk3zKyJXFa53yrklVAQlSH9KiIcs2bN6/CpbSrO9GzZ8+eHt1911WyrSZPt8kfPPvssxVO4jxX122gevzxx3n88cfLfa6+vl8KCgetzjkqyZHJxOqc84X2Znmm5F/UP5KoiHI1b97cY+cKCwujVatWHjufLwjENp1LbGxswG/o50n19f2yO+wUFBUQpA0i0ZToTlSkR0XUlAz9CCGE8JgiRxEALSJaoNfqidU6E5VjOcdKlSwLUVWSqAghhPAYV6LSKsrZ42jQGmgY6ixTlqX0RU1IoiKEEMJjXIlKy6iW7seSI5MBmaciakbVRKVFixZoNJoyt/vvv1/NsIQQQtSQTXFOwnf1qICz+gdknoqoGVUn065btw673e6+v337doYPH84111yjYlRCCCFqQlGU8ntUoop7VMzSoyKqT9VExbX7qcvUqVNp2bIlAwcOVCki33brrbeSlZXFzz//rHYoQghRhs1hQ1EUd8WPYncufJ4c4UxUpEdF1ITPlCcXFhby1VdfMX78+Ao39CooKCi1U2l2djbgXADs7DU/bDbnPxiHw1GjLeZdOwu4zuELFEXxWDy+2D5PCvT2QeC3MdDbB4HXxkJ7IQCNjY1R7Ir7c7lZeDMA0nPSyczNxKgPjEURXe2raM2pQOCtNlbnfD6TqPz8889kZWVx6623VnjMSy+9xJQpU8o8Pn/+fAwGQ6nHgoKCiI+Px2q1UlhYWOO4LBZLjV/raTabjaKiIneC5gm+1D5vCPT2QeC3MdDbB4HTRku+sx2heaHMmzfP/fjapWsxaUxYFAuz/phFYlCiWiF6xYIFC9QOwes83cbc3KpvUukzmxKOHDmS4OBgfv311wqPKa9HJTExkZMnT5a7KeGhQ4do0aJFjTYlVBQFi8WCyWTy2JbtVfXDDz/w3HPPsXfvXgwGA926dWPOnDk88MADZGVlceGFFzJt2jQKCwu57rrreOONN9zLx3/55Ze888477Nq1i/DwcAYPHswbb7zhXngqJSWFoUOH8ssvvzBhwgRSU1Pp2rUrH330ER07Bs4+HGr+/OpKoLcx0NsHgdfGg5kHSd2XykHdQW7qdBM2m40FCxYwfPhwHlr+EGvT1zLpgklc0fIKtUP1iJLtC+RNCb3RxuzsbBo2bOg/mxIeOHCAhQsX8tNPP1V6XEhICCEhIWUe1+v1Zd5Au92ORqNBq9We2ZVUUaCKW407HA6w5aKx6Wq/q6neAFX8EDp27Bhjx47llVde4corr8RisbB8+XJ3RVRKSgqNGzdmyZIl7N27l+uuu45u3bpx5513As52P/fcc7Rt25aMjAzGjx/P7bff7v7rxtWWJ598khdeeIHk5GSefvppLr/8cnbv3h0w/9hc3eiu34FAFOhtDPT2QeC10TX00yKyRanPEr1eT+vo1qxNX0uaJS1gPmdcyvsOCjSebmN1zuUTicqMGTOIjY3lkksu8e6FbLnwYuMqHaoFojx13f87CsHhVTr02LFjFBUVMWbMGPcy9p06dXI/Hx0dzbvvvotOp6Ndu3ZccsklLFq0yJ2o3H777e5jk5OTefvtt+nVqxdWq7XUZnkTJ05k8ODBRERE8Pnnn9O0aVPmzJnDtdde64kWCyHqGUVR3IlKs4hmZZ53VQHJhFpRXaqn8A6HgxkzZjBu3DiCgnwib1JVly5dGDp0KJ06deKaa67h448/JjMz0/38eeedh06nc99PSEggIyPDfX/Dhg2MHj2aZs2aYTKZ3BVUBw8eLHWdPn36uP8/JiaGtm3bsnPnTm81SwgR4GwOGwoKGjTEh8eXed61roos+iaqS/XMYOHChRw8eLBUT4DX6A3O3o0qcDgcZFssRJhMnhn6qSKdTseCBQtYtWoV8+fP55133uGpp55i7dq1zlOd1V2m0Wjc3cc5OTnunYVnzZpFo0aNOHjwICNHjqzVhGIhhDiX/KJ8AIK0Qei0ujLPu9ZSycjNILswm4jgwN1BWniW6onKiBEjqLP5vBpNlYdgcDhAb3ceX8djxxqNhn79+tGvXz8mTZpE8+bNmTNnzjlf9++//3Lq1CmmTp1KYqJzVv369evLPXbNmjVcdNFFAGRmZrJ7927at2/vuUYIIeqVAruz0CFIW/7XSkRwBLGGWDJyM9iXtY+usV3rMDrhz1Qf+hGlrV27lhdffJH169dz8OBBfvrpJ06cOFGlJKJZs2YEBwfzzjvvsG/fPubOnctzzz1X7rHPP/88S5cuZfv27dx66600bNiQK664wsOtEULUF+dKVECGf0TNSKLiYyIiIli2bBmjRo2iTZs2PP3007z++utcfPHF53xto0aNmDlzJt9//z0dOnRg6tSpvPbaa+Ue++KLL/Lkk0/Sq1cv0tPT+fXXXwkODvZ0c4QQ9YQrUdFrK67mkAm1oiZUH/oRpbVv354///yz3OdmzpxZ5rE333yz1P0bbriBG264odRj5Q2tXXjhhaxevZqIiIiAKIsUQqhHURTpURFeI4mKEEKIWil0FKIozoofrabiP3ykR0XUhPwpLYQQolYKipy9KcG64EpX2G0Z6UxUTuSdwFxgrpPYhP+TRKWeGTRoEIqiEBUVpXYoQogA4Rr2CdZVPs/NGGx0r7Gyz7zP63GJwCCJihBCiFpxJyrac0/Idw3/yDwVUVWSqAghhKgVV6ISoiu7F9vZWkU6J9TKPBVRVZKoCCGEqLGSFT963bk3mpMeFVFdkqgIIYSosUJ7ccWPRlPpGiouUvkjqksSFSGEEDVWctinsoofF1eicjLvpFT+iCqRREUIIUSNVWd+CkC4PpyE8ARAhn9E1Uii4oMGDRrEI488AkCLFi3KrD4rhBC+orqJCsjwj6geWZnWx61bt47w8Cru+CyEEHUs354PQGhQKDiq9ppWUa1YcWSF9KiIKpFExcc1atRI7RCEEKJciqJQaC8EnD0qDkfVMhXpURHVIUM/Pu7soR+NRsOHH37IpZdeisFgoH379qxevZq9e/cyaNAgwsPD6du3L6mppT8AfvnlF7p3705oaCjJyck8++yzFBUV1XFrhBCBxFXxo9Voq1Tx4yKbE4rqqFeJiqIo5Npyq3zLK8qr1vEV3crbvbg2nnvuOW655RY2b95Mu3btuPHGG7n77ruZMGEC69evR1EUHnjgAffxy5cv55ZbbuHhhx9mx44dfPjhh3z++ee8/vrrHo1LCFG/lFw6vyoVPy7JkckAnM4/TWZ+pldiE4GjXg395BXlccHsC+r8umtvXItBb/DY+W677TauvfZaAJ544gn69OnDxIkTGTlyJAAPP/wwt912m/v4KVOm8OSTTzJu3DgAkpOTmTJlCk888QQvvPCCx+ISQtQv7vkputBqvc6gN9DE2IQj1iPszdpLr/he3ghPBIh6lagEis6dO7v/Py4uDoBOnTqVeiw/P5/s7GwiIiLYsmULK1euLJWU2O128vPzyc3NxWg01l3wQoiA4a74Cap6xY9Ly6iWHLEeITUrVRIVUal6laiEBYWx9sa1VTrW4XBgsVgwmUxotbUbIQsLCqvV68+m158ZC3Z1t5b3mGtim9VqZcqUKYwZM8Z9jMPhwGq1Ehpavb+EhBDCpSalyS4to1qy7PAymacizqleJSoajabKQzAOh4OioCIMekOtExW1de/enV27dtGqVSv3Yw6Hg+zsbL9vmxBCHQ7FUarip7pcE2ql8kecS71KVOqrSZMmcemll9KsWTOuvvpqtFotmzZtYuPGjbzyyitqhyeE8EM2u61GFT8uUqIsqkr+nK4HRo4cyW+//cb8+fPp1asXvXv35q233iIxMVHt0IQQfso1kbaqe/ycLTkyGQ0aMgsyOZV3ytPhiQAiPSo+KCUlxf3/+/fvL/Xc2aXOLVq0KPPYoEGDyjw2cuRId1UQnBn6EUKImqjNRFpwzt1rYmzCYeth9pn30SCsgSfDEwFEelSEEEJUW20m0rrIwm+iKiRREUIIUW0FRbVPVGSeiqgKSVSEEEJUS8mKn+ou9laSK1GRHhVRGUlUhBBCVEuhvRAFZ8VPkLbmUx1L9qh4eqsRETgkURFCCFEtJeen1KTixyUpMgkNGrIKsjiVL5U/onySqAghhKiW2lb8uIQFhdHU1BSQeSqiYpKoCCGEqBZPVPy4yDwVcS6SqAghhKiW/KKa7ZpcHllKX5yLJCpCCCGqrLZ7/JxNSpTFuUiiItw0Gg0///yz2mEIIXyYK0mpbcWPS8lF36TyR5RH9UTlyJEj3HTTTTRo0ICwsDA6derE+vXr1Q5LVMHkyZPp2rWr2mEIIepQyYm0tan4cUmKTEKr0ZJdmM3JvJO1Pp8IPKomKpmZmfTr1w+9Xs8ff/zBjh07eP3114mOjlYzLJ9TWFiodghCCAGc2YzQE/NTwDl8lGhybpAqE2pFeVRNVF5++WUSExOZMWMG559/PklJSYwYMYKWLVuqGZbqBg0axAMPPMAjjzxCw4YNGTlyJNOmTaNTp06Eh4eTmJjIfffdh9VqBZwbFTZq1IgffvjBfY6uXbuSkJDgvr9ixQpCQkLIzc0FYM+ePYwaNQqDwUCHDh1YsGBBmTieeOIJ2rRpg8FgIDk5mYkTJ2Kz2QCYOXMmU6ZMYcuWLWg0GjQaDTNnzgSoNFYhhH/zxNL5Z2sZKfNURMVU3T157ty5jBw5kmuuuYalS5fSpEkT7rvvPu68885yjy8oKKCgoMB937X7r81mc3+ButhsNhRFweFw4HA4AOcXupKXV6XYFEXBkZeHXafDUcvuTU1YWLW7SD///HPuueceli9fDsCff/7Jm2++SVJSEvv27eOBBx7gf//7H++99x4A/fv3Z8mSJYwZM4bMzEx27txJWFgYO3bsoF27dqSkpNCrVy9CQ0MpKiri6quvpkGDBqxatYrs7GzGjx8PUOr9MhqNfPbZZzRu3Jht27Zx9913YzQa+d///sc111zDtm3b+Ouvv5g/fz4AkZGROBwONBpNpbHWBddYt+t3IBAFehsDvX3gn210Df0Ea4PLxOxwOFAUBZvNhk6nc38un/35fLakiCQAdp/efc5jfUlV2+fPvNXG6pxPo6g4eyk01Nl1OH78eK655hrWrVvHww8/zAcffMC4cePKHD958mSmTJlS5vHZs2djMBhKPRYUFER8fDyJiYkEBwcD4MjL4/jgIV5oSeXilixGGxZW5eMvvfRSLBYLS5curfCYX375hfHjx5Oa6vwL5KOPPmLGjBmsXr2aefPm8cYbbxAbG8vQoUO5/fbbufLKK+nevTsTJ05k8eLFXHfddWzdutXd67Jw4UKuueYavvrqKy655JJyr/nOO+/w008/sWTJEgCmTp3K77//7k6mqhqrEMI/KYrCUftRAOJ18eg0ulLPFxYWcujQIdLT0ykqKqryebcUbuH73O9ppmvGXaa7PBqz8E25ubnceOONmM1mIiIiKj1W1R4Vh8NBz549efHFFwHo1q0b27dvrzBRmTBhgvsvf3D2qCQmJjJixIgyDc3Pz+fQoUMYjUZ3QuQICuK4F9tTkQiTCe1ZiVRlgoKC6NWrV6k2LVy4kJdffpl///2X7OxsioqKyM/PJygoCIPBwIgRI3jyyScpKChg3bp1DBkyhPj4eNasWcP999/P33//zZNPPklERAQHDx4kMTGRhIQETCYTGo2GoUOHAhAWFua+7rfffsu7775LamoqVquVoqIiIiIi3M+HhISg0+nKvPfnirUuKIqCxWJxty8QBXobA7194H9tLLAXgNlZ8RMdWXYuYX5+PmFhYQwYMIDQ0FBsNhsLFixg+PDh6PX6Cs/bKrMV3//xPae1p7n44ov94r0Aqtw+f+atNrpGRKpC1UQlISGBDh06lHqsffv2/Pjjj+UeHxISQkhI2XFRvV5f5g202+1oNBq0Wi1arXMqjiY8nLYbN1QpNofDQbbF4kwytLWbylOToR+j0ei+7v79+7nsssu49957eeGFF4iJiWHFihXccccdFBUVodVq6dKlCzExMSxfvpxly5bxwgsvEB8fzyuvvMKGDRuw2WxceOGFaLXaUrGc/R65/n/16tXcfPPNTJkyhZEjRxIZGck333zD66+/fub9LD5PyfenKrHWBVeXtKt9gSjQ2xjo7QP/a2OhrXjH5KDQcuN1fb6c/Zlc3md0Sa0btEan0WG1Wcm0ZRIXHuf54L3oXO0LBJ5uY3XOpWqi0q9fP3bt2lXqsd27d9O8eXOvXE+j0aCp6l/0DgfaoiK0BoPqHyAbNmzA4XCUShK+++67UsdoNBr69+/PL7/8wj///MOFF16IwWCgoKCADz/8kJ49exIeHg44k0FX96yrN2TNmjWlzrdq1SqaN2/OU0895X7swIEDpY4JDg7GbrdXO1YhhH9yrUjryYm0AMG6YBJNiezP3k+qOdXvEhXhXap+Az/66KOsWbOGF198kb179zJ79mw++ugj7r//fjXD8jmtWrXCZrPxzjvvsG/fPr788ks++OCDMscNGjSIr7/+mq5du7p7ZAYMGMCsWbMYOHCg+7hhw4bRpk0b7rvvPrZs2cLy5ctLJSQArVu35uDBg3zzzTekpqby9ttvM2fOnFLHtGjRgrS0NDZv3szJkycpKCiocqxCCP/jyT1+ziZL6YuKqJqo9OrVizlz5vD111/TsWNHnnvuOd58803Gjh2rZlg+p0uXLkybNo2XX36Zjh07MmvWLF566aUyxw0cOBC73c6gQYPcjw0aNKjMY1qtlh9//JG8vDx69+7Nf/7zH1544YVS57rssst49NFHeeCBB+jatSurVq1i4sSJpY656qqruOiiixg8eDCNGjXi66+/rnKsQgj/481ERZbSFxVRteqntrKzs4mMjCx31nB+fj5paWkkJSW5J9NWh8PhIDs7m4iICNWHfrxB2uf/Ar2Ngd4+8K82OhQHO0/tBKBNdBv0urJzDM7+3LXZbMybN49Ro0adc07CH2l/8Piyx+nSqAtfjfrKK23wtOq0z195q42VfX+fzbf/ZQghhPAJrt4UnUbnkT1+zlayR8WP/34WXiCJihBCiHPy9B4/Z2sR0cJd+XM8V42FJISvkkRFCCHEOXlj6fySgnXBNItoBsg8FVGaJCpCCCHOyZsTaV1clT+yOaEoKeATFRnrFEKI2qtKolLbz1up/BHlCdhExTU72bVbsBBCiJpxKA4K7c5VaUOCKk5UXJ+3Na0OkURFlEfVlWm9SafTERUVRUZGBgAGg6FaE8AcDgeFhYXk5+f7fNlgTUj7/F+gtzHQ2wf+08b8onwcNgc6jY6igiLsmtIrUiuKQm5uLhkZGURFRaHT6So4U+VaRRYv+mZ2Vv74y54/wrsCNlEBiI+PB3AnK9WhKAp5eXmE1WCfHn8g7fN/gd7GQG8f+E8bc225ZBVkEawLRpNZcZxRUVHuz92aaB7RnCBNEDm2HNJz0kkwJtT4XCJwBHSiotFoSEhIIDY2FpvNVq3X2mw2li1bxoABAwJyIR9pn/8L9DYGevvAf9o485+Z/LT3Jy5Ouph7O9xb7jF6vb7GPSnuc+j0NI9oTqo5lb1ZeyVREUCAJyouOp2u2v+AdDodRUVFhIaG+vQHSE1J+/xfoLcx0NsH/tPG7VnbOVZ4jPio+Bqt9F0dLaNakmpOJTUrlf5N+3v1WsI/+O6gqBBCCJ/gmtzqKh/2JilRFmeTREUIIUSF8oryOGI9ApypyvEmqfwRZ5NERQghRIX2mfehoBATGkNMaIzXr+fqUUk1p+JQHF6/nvB9kqgIIYSokKtnoy56UwASIxIJ0gaRV5THsZxjdXJN4dskURFCCFEh11yRlpF1k6jotXpaRLQAZPhHOEmiIoQQokJ1OZHWxT38I4mKQBIVIYQQlajroZ+S15LKHwGSqAghhKhAri23Tit+XKTyR5QkiYoQQohy7TPvAyAmNIbo0Og6u64rUdln3ieVP0ISFSGEEOVzDb3U5fwUgGamZui1evKK8jhqPVqn1xa+RxIVIYQQ5VJjfgpAkDaIFpEtSsUg6i9JVIQQQpRLrR4VgFaRspS+cJJERQghRLnU6lEpeU3pURGSqAghhCgjx5bjXhlWlR4V2ZxQFJNERQghRBmunoyGYQ2JDIms8+u7elTSzGlS+VPPSaIihBCiDDWHfQASTYkEa4PJt+dzxHJElRiEb5BERQghRBlqTqQF0Gl1JEUmlYpF1E+SqAghhCgj1axuj0rJa7tiEfWTJCpCCCHKUGMzwrPJhFoBkqgIIYQ4i7XQSnpOOuAjPSpSolyvSaIihBCiFNdQS2xYLBHBEarF4epRSTOnYXfYVYtDqEsSFSGEEKWoXfHj0sTYhBBdCAX2Ag5bD6sai1CPJCpCCCFKcc0JUTtR0Wl1JEcmAzL8U59JoiKEEKIUX5hI6yLzVISqicrkyZPRaDSlbu3atVMzJCGEqPd8pUelZAxS+VN/BakdwHnnncfChQvd94OCVA9JCCHqrezCbDJyMwBIjkpWORpoGSk9KvWd6llBUFAQ8fHxaochhBAC2Je1D4BYg7oVPy5nV/7otDqVIxJ1TfU5Knv27KFx48YkJyczduxYDh48qHZIQghRb6m9dP7ZmpiaEKoLpdBRyCHLIbXDESpQtUflggsuYObMmbRt25Zjx44xZcoU+vfvz/bt2zGZTGWOLygooKCgwH0/OzsbAJvNhs1m82hsrvN5+ry+Qtrn/wK9jYHePvDNNu4+vRuAJFNSrePyVPtaRLTg38x/2XVqF00MTWp1Lk/yxZ+fp3mrjdU5n0ZRFMWjV6+FrKwsmjdvzrRp07jjjjvKPD958mSmTJlS5vHZs2djMBjqIkQhhAhoM6wzSC1K5YqwK+gZ0lPtcAD4IecHNts2MzR0KINDB6sdjvCA3NxcbrzxRsxmMxERlQ8xqj5HpaSoqCjatGnD3r3lz+6eMGEC48ePd9/Pzs4mMTGRESNGnLOh1WWz2ViwYAHDhw9Hr9d79Ny+QNrn/wK9jYHePvDNNr45500ogiv6X0Hnhp1rdS5PtS9jRwabN28mKC6IUf1G1SomT/LFn5+neauNrhGRqvCpRMVqtZKamsrNN99c7vMhISGEhISUeVyv13vtl8Sb5/YF0j7/F+htDPT2ge+00Vxg5mTeSQDaNmjrsZhq2742MW0A2Je9zyfep7P5ys/PmzzdxuqcS9XJtI899hhLly5l//79rFq1iiuvvBKdTscNN9ygZlhCCFEvuUqA48PjMQYbVY7mDNdaKvvN+ylyFKkcjahrqvaoHD58mBtuuIFTp07RqFEjLrzwQtasWUOjRo3UDEsIIeolX1roraTGxsaEBYWRV5THQctB97L6on5QNVH55ptv1Ly8EEKIEvaZnWuotIr0jdJkF61GS3JkMv+c+ofUrFRJVOoZ1ddREUII4Rt8tUcFZCn9+kwSFSGEEIBvbUZ4NldMspR+/SOJihBCiFIVP77coyKJSv0jiYoQQqhAKSoid/UaKPKNKhbXkErj8MYY9L63gKarR2V/9n5sjsBdCVaUJYmKEEKo4NQnn3D0rrtosHix2qEAZ3oqfLE3BSAhPAFDkIEiRxEHs2VPuPpEEhUhhFCB+bffADBu265yJE6+thnh2TQajQz/1FOSqAghRB0rSEujcK/zyzYkIwPb0aMqR+T7PSog81TqK0lUhBCijlkXLSp1P3fFCpUiOcPXe1TgTGxSoly/SKIihBB1zLLQmajoExMByF2ubqKSmZ/J6fzTACRFJqkaS2VcC71Jj0r9IomKEELUIVtGBnmbNwPQ8MknAMhduxZHYaFqMbl6KJoYm/hkxY+Lq0flQPYBbHap/KkvJFERQog6ZF28BIDQLp0xXHghRUYjSl4eeRs2qBaTP8xPAedmieH6cIqUIg5kH1A7HFFHJFERQog6ZCmen2IaNgyNVktO2zYAWJcuUy0mX146vySNRkPLyOKl9M0yT6W+kERFCCHqiN1iIWfNGgBMQ4cBkNO2HQDW5ctVi8uXl84/m1T+1D+SqAghRB2xLlsGNhvBycmEJDsnrea2bgVaLYWpqdiOHFElLn8Z+gFJVOojSVSEEKKOWEsM+7g4DAZCu3RxPq9Cr8qpvFNkFmSiQeOuqvFlUqJc/0iiIoQQdcBRWOieh2IaNrTUc4YL+wFgXVb3iYqrZ6KJsQlhQWF1fv3qcvWoHMw+SKFdvUopgMzPvyDx/enY0tNVjSPQSaIihBB1IHfNGhw5OQTFxhLasWOp5wwXXghAzpo1dV6mnGr2n/kpAHGGOIx6I3bFzv7s/arFoRQWkvnhh4QdOMCJ555HURTVYgl0kqgIIUQdcC3yZho2FI229EdvSLt26Bo2RMnNrfMyZX+anwK+s+dP7oYNOCwW5/8vW4blzz9ViyXQSaIihBBeptjtWIp3STYOHVrmeY1Wi7G4V6Wuh3/8pTS5JF+Yp2IpXg/HHhYKQPoLL2I3m1WLJ5BJoiKEEF6Wt2Ur9pMn0ZpMhPfqVe4xxgH9geLKoDqiKIpflSa7qN2joigK1iXOROX4lWPQt2iB/eRJMl6fpko8gU4SFSGE8DLLwoUAGAcNQhMcXO4x4f361XmZ8qn8U2QVZKHVaH16j5+zqZ2oFOzZg+3wYTTBweS0b0fspEkAZH33HbkqrjAcqCRREUIIL1IUxZ2omMoZ9nHRRUYS1rUrUHdlyq4v+qbGpoQGhdbJNT3B1ftz0HKQAntBnV/fuiQFgLDevVGCgwnr1ZOoa64G4NikZ1TdtykQSaIihBBeVLh3L7aDB9EEB2Psf2Glx54Z/qmbRMUf56cANAprhCnYhENxsN+8v86vby2ebxQ+cKD7sdjHHkPXoAGFqamc+vjjOo8pkEmiIoQQXuTqTQnv2xdteHilx4b3dyYqdVWm7I/zU8BZ+eOKua6Hf4pOniRv61YAwgcOcD+ui4wk7v8mAHDqgw8p2JdWp3EFMklUhBDCi0qWJZ9LaPv2dVqm7G+lySW5Yq7ryh/r0qWgKIR27EhQXFyp5yJGjSK8f38Um430Z56RtVU8RBIVIYTwEtuxY+T/8w9otRgHDz7n8XVZpqwoit8O/QDuXZTrukfFVZZsHDyozHMajYb4ZyahCQsjd906zD/9VKexBSpJVIQQwktcvSlh3bsR1KBBlV7jnqey3LtlyifzTpJdmO13FT8u7sofc90lKo78fHJWrgTANGRIuccEN21KowcfBOD4K69SdPJkncUXqCRREUIIL7G4NiEcOuwcR57hLlPe690yZVdvSqIpkRBdiNeu4y2uOSqHLIfqrPInZ80alPx8ghISCGnXrsLjYm65mZAO7XGYzRx/aWqdxBbIJFERQggvsGdlkbtuHVC1+SkudVWm7J6fEul/wz4ADcMaEhEcgUNxkGaum4mr1uJhH9PgQWg0mgqP0wQFkTDlWdBqyf79d1V2xQ4kkqgIIYQXWFJSwG4npG1bghMTq/XauihT9uf5KVC68qcuJtQqDgfWlBQAjIPLH/YpKaxTR2JuvgmA9MlTcOTmejO8gCaJihBCeIHVPexT9d4Ul7ooU/bX0uSS6nKF2vx/dlCUkYHWYMBwwflVek2jhx4iqHECtiNHOPHue16OMHBJoiKEEB7myMvDunwFAKbhVZ+f4uLtMuWSe/z4a48K1G2JsnVJ8SJvF16ItoJtEM6mDQ8nvnh5/dOff07+jh1eiy+QSaIihBAelrNqFUp+PvrGjSuddFkRb5cpZ+RmYLFZ0Gl0flnx41KXi75ZipfNNw45d5l5SaZBgzBddBHY7Ryb9AyK3e6F6AKbJCpCCOFhrrJk47ChlU66rIw3y5RdJb2JpkSCdVXrHfBFrh6Vw5bD5BXlee06tqNHKdi507keToll86sq7v8moDWZyN++ncxZs7wQYWCTREUIITxIKSrCuqS4OmRY9Yd9XML79vVamXIgzE8BaBDagKiQKBQUr1b+WIp/nmHduhEUHV3t1+tjY4n9738ByHjzLWxHj3o0vkDnM4nK1KlT0Wg0PPLII2qHIoQQNZa7YSP2rCx0UVEYunev8Xl0UVFeK1MOhPkp4Kz8qYsJta7dkk3VHPYpKeraawjr3h0lN5f0Z5+T5fWroVqJSkZGRqXPFxUV8ffff1c7iHXr1vHhhx/SuXPnar9WCCF8iWWRcxNC4+DBaIKCanUub5Upuyaf+nuPCuD1EmW71UrO2rUAVdoGoSIarZaEZ6eAXo81JQXLX/M9FWLAq1aikpCQUCpZ6dSpE4cOHXLfP3XqFH369KlWAFarlbFjx/Lxxx8TXYMuNSGE8BWKomB1bUJYg2qfs3mjTDlQKn5cvN2jkrNiJdhsBDdvTnBS7SYeh7RqRcM7/wNA+gvPY8/O9kSIAa9a6f7ZXVX79+/HZrNVesy53H///VxyySUMGzaM559/vtJjCwoKKCg4s1RydvEP2WazlYmjtlzn8/R5fYW0z/8Fehv9sX0FO3diO3oUTVgowb16nTP2c7VR16oVugYNsJ86hWXtWgy9e9c6xuO5x7HarARpgmgS1sSr729d/AxbGFsAsDdzr1euk73YmXgaBg2kqKio1HM1aV/E7bdjnjcP2/4DpL/2GrETJ3ouWC/w1s+wOuerXb9kOaozw/2bb75h48aNrCteZvpcXnrpJaZMmVLm8fnz52MwGKp83epYsGCBV87rK6R9/i/Q2+hP7WswfwENgOzklvy5eHGVX1dZG+NaNCfy1Cm2f/ElJ0+frnWMe2x7AIjWRLPgr7p5b735M8xx5ABwJOcIP//+M8EaD1YxORy0XLgIHbAtNJS8efPKPay67QsbMYLEjz4m+7vv2R7TgPykFrWP1cs8/TPMrcZKvR5PVKrq0KFDPPzwwyxYsIDQ0NAqvWbChAmMHz/efT87O5vExERGjBhBRESER+Oz2WwsWLCA4cOHo9frPXpuXyDt83+B3kZ/bN/BTz+jEGg59ka6jRp1zuOr0kaLVsvxDRuJP3qU86twznP5audXsAm6NOnCqP61P19l6upn+MGPH5BZkEmb3m3o0KCDx86bt3EjR3Jz0UZEMOiee8rMOapN+46fOIllzhxaLVhA4vffofHR33Fv/QyzqzHsVa1ERaPRYLFYCA0NRVEUNBoNVqvVfcHqXHjDhg1kZGTQvcSseLvdzrJly3j33XcpKChAp9OVek1ISAghIWV3+dTr9V77R+DNc/sCaZ//C/Q2+kv7Cg8donD3btDpiBoyBF01Yq6sjZH9+3Ncq6UwNRVOnEDfuHGt4kyzOMt4W8e0rrP31ds/w1bRrViXvo4DOQfoEt/FY+c9vcy5ho1x0ECCw8IqPK4m7Ut44nFyly6lMDWV7M8/p+G999YqVm/z9M+wOueq1mRaRVFo06YN0dHRxMTEYLVa6datG9HR0URHR9O2bdsqn2vo0KFs27aNzZs3u289e/Zk7NixbN68uUySIoQQvsy1yJuhVy90UVEeO68uKoqwLs4vX09U/wTSRFoX1w7Qnq78ObNbcs2rfSqii4oibsIEAE5O/4CCtLrZAdofVatHZUnxojeeYDKZ6NixY6nHwsPDadCgQZnHhRDC17nKkmuzyFtFjAMHkLdpE9Zly4i+/roan0dRFPeqtK4v90DgjcqfgrQ0CtPSQK8nvHg7A0+LuPQSzL/8Qs6KFaQ/M5lmn8+s8UrGgaxaicrAGiwdLIQQga7o1CnyNmwEwDR0iMfPH96/PyfefMtdplzVTfHOlp6TTo4thyBNEM0jmns4SvV4I1FxLfIW3qsXOpPJY+ctSaPRED/5GfZdOprcv//G/NMcoq4a45Vr+bNqDf0UFRWVKg8GOH78OFOmTOHxxx9nxYoVtQomJSWFN998s1bnEEKIumZdsgQUhdDzzkOfkODx83tqN2XX0EjziObodb4/76eqXIu+HbEeIddW9WqSyliLq7Zqs8hbVQQ3bUqjBx8A4Pgrr1B06pRXr+ePqpWo3HnnnTz00EPu+xaLhV69evHee+/x119/MXjwYOZVUL4lhBCByuLBRd7K46ndlANxfgpAdGg0MaExAOwz76v1+YoyM8ndtAnwfqICEDNuHCHt2+Mwmzk+9WWvX8/fVCtRWblyJVdddZX7/hdffIHdbmfPnj1s2bKF8ePH8+qrr3o8SCGE8FV2aw45q1YBYBo61GvX8cRuyoG0dP7ZPLmUfs7y5WC3E9KmDcFNm9T6fOeiCQpyLq+v1ZL9669Yl9dudCLQVCtROXLkCK1bt3bfX7RoEVdddRWRkZEAjBs3jn/++cezEQohhA/LWbECpbAQffNmBLfyXgJQajflGu6+G6g9KuDZeSqW4mofYy02IayusE6diL5pLADpU6bgyMurs2v7umolKqGhoeSVePPWrFnDBRdcUOp5q9XqueiEEMLHWRYVD/sMG+bVio3alik7FIe74kd6VCqmFBY6e1QA0xDPT4yuTKOHHiYoIQHb4cOcfO+9Or22L6tWotK1a1e+/PJLAJYvX87x48cZUuIHmZqaSuNaLkYkhBD+QiksxJqSAoBpqHfmp5RkHDgAAOvy6icq6Tnp5BXlEaQNIjEi0dOhqc5TPSo569bhyMlB16ghoXW8VIbOGE588d4/p2bMJH/nzjq9vq+qVqIyadIk3nrrLVq2bMnIkSO59dZbSSgxw33OnDn069fP40EKIYQvylm3DofFgq5hQ8K6em5F1Iq4d1Nevbrauym7ehpaRLRArw2cih8XV4/KsZxj5NhyanweV1myadAgNNpqfUV6hGnIYEwjR4LdzrFJz6DY7XUeg6+p1k9h4MCBbNiwgYceeogZM2bw8ccfl3q+a9euPProox4NUAghfJVlYfEib0OG1MmXWm3KlF09DYE47AMQGRJJw7CGQM17VRRFKVGWXLfDPiXF/d//oTUayd+2jcxZs1WLw1dU+19W+/btefjhh7nuuuvQnvUP86677qJr166eik0IIXyW4nBgXeT8UjMN8161T0m1KVN29agE4kRal9oO/xTs3o3t6FE0ISGE9+ntydCqRR8XS+xj/wXgxJtvYjt2TLVYfEG1VqZdtqxqZXEDBgyoUTBCCOEv8rdvpygjA214OIbedfelZhzQH/PPP2Ndvoy4Jx6v8usCvUcFnG1be2xtjSfUWou3iQnv2xdtJZsQ1oWoa6/F/Mtc8jZtIv3Z52j6/nv1dnn9aiUqgwYNcr9RiqKUe4xGo8EuY2pCiABnWeAc9jEOHFDjJe1r4uwy5arspuxQHO6F0KRHpWJqlCVXRKPVkvDsFPaNuQrrkiVY5i8gYuQItcNSRbWGfqKjo0lMTGTixIns2bOHzMzMMrfTp097K1YhhPAZrrJkoxcXeStPTcqUj1qPkleUh16rJ9EUeBU/LrUpUbZlZJC/dSsARh/Z1y6kdWsa/OcOAI4//zx2i0XliNRRrUTl2LFjvPzyy6xevZpOnTpxxx13sGrVKiIiIoiMjHTfhBAikBXs20fhvn2g12NUYaj7zCq1VUtUXD0MSZFJBGmr1ZHuV1w9Ksdzj2MprN6XunXpUgBCO3dGHxvr8dhqquE99xDcvDlFJ06QMW2a2uGoolqJSnBwMNdddx1//fUX//77L507d+aBBx4gMTGRp556iqKiIm/FKYQQPsO1t094795e21m3MuHFyVFuFcuU68NEWoCI4Ahiw5xJRnWHf6zFwz6mwYM8HFXtaENCiJ8yBYCsb74ld+MmlSOqezWup2vWrBmTJk1i4cKFtGnThqlTp5Kdne3J2IQQwidZFhWXJdfxsI+Lq0zZUcUy5fowkdbFlYxVZ3NCR16ee78mYx2vRlsV4b0vIHLMGFAU0p+ZhFLNNXT8XY0SlYKCAmbPns2wYcPo2LEjDRs25PfffycmJsbT8QkhhE+xHc8gf8tW0GhUm3RZ3TLl+tKjAmfaWJ15Kjmr16AUFKBv3JiQNm28FVqtxP7vMXTR0RTs2cupzz5TO5w6Va1E5e+//+bee+8lPj6eV199lcsuu4xDhw7x3XffcdFFF3krRiGE8BnWxc5hn7AuXVSdy1DV3ZQdioM0cxoALSPrT6JSnaEf6xLXIm+DfbYEOCg6mrj/mwDAyfenU7h/v7oB1aFqzarq3bs3zZo146GHHqJHjx4ArFhRdjvqyy67zDPRCSGEj3HNT6mrRd4qUtUy5SOWI+Tb8wnWBgd0xY9LdSt/FIcDS/F+Tb5QllyZiEsvxfzzL+SsXMmxZybTbOYMn02sPKna078PHjzIc889V+Hzso6KECJQ2bOzyVm7Fqj7suSzucqU8zZtwrpsOdHXX1fuca4v7KTIJHRaXV2GqIrkqGQAMnIzyC7MJiI4otLj87dvx37iJNrwcMJ79aqLEGtMo9EQP/kZ9o2+jNy1azHP+ZmoMVeqHZbXVWvox+FwnPNmqad13kKIwGddugyKighu1ZKQpCS1w6lSmXKq2TkEUh/mp0Bx5Y/BOSS3L+vcE2otxXv7hPfvj6YOF+6rqeDERBo9cD8AGS+/TFE9WLvMY7toFRQUMG3aNJKTkz11SiGE8CmuRd5MQ4epHIlTeP9zlym7elTqQ8WPS3WGf9y7Jfv4sE9JMePGEdK2LXazmeNTp6odjtdVK1EpKChgwoQJ9OzZk759+/Lzzz8D8Nlnn5GUlMQbb7whuycLIQKSo6CAnOL9ztSen+IS2uHcZcquSaX1pUcFqj6htvDwEQp27QKdTpWF+2pKo9eT8NyzoNGQPfdXrCtXqh2SV1UrUZk0aRLTp0+nRYsW7N+/n2uuuYa77rqLN998k2nTprF//36eeOIJb8UqhBCqyVm9GkduLkFxcYR27Kh2OMC5y5TtDru74kd6VMpybUJo6NYNXVSUt8PyqLDOnYm+6SYA0idPwZGXp3JE3lOtROX777/niy++4IcffmD+/PnY7XaKiorYsmUL119/PTpd4E/UEkLUT1b3sM9Qn6q0qKxM+Yj1CAX2AkJ0ITQxNqnr0FRT1R4VV6Lii4u8VUWjhx8mKD4e26FDnHz/fbXD8ZpqJSqHDx92lyV37NiRkJAQHn30UZ/6RyuEEJ6m2O3unXV9ZdjH5ewy5ZJcPQrJkcn1ouLHxbVezIm8E5gLzOUeY7dYyFm3DgDj4EF1FJln6YzhxE+aCMCpz2aQv2uXyhF5R7USFbvdTnCJWdFBQUEYjUaPByWEEL4kb/Nm7KdOoY2IwOBjJayV7aZcH+enABiDjcSHxwMV96rkrFgBNhvBSUk+UcFVU6YhQzANHw52O8cmTkIJwOVBqrWOiqIo3HrrrYSEhACQn5/PPffcQ3h4eKnjfvrpJ89FKIQQKnMt8mYcNBCNXq9yNGUZB/R3rqeyvPR6KvVp6fyztYxqSXpOOnuz9tI9rnuZ5y3uYR//qfapSNzTT5GzejX5W7eS+fU3xNw0Vu2QPKpaPSrjxo0jNjaWyMhIIiMjuemmm2jcuLH7vusmhBCBQlEUnytLPlvJMuWSG9bVp80Iz9Yq0tnm8npUlKIi55o4gGmw/ycq+rg4Yv87HoAT06ZhO3ZM5Yg8q1o9KjNmzPBWHEII4ZMKdu/BdvAgmpAQjP0vVDuccrnKlO0nT5K7cSPhvXuXqviprz0qUH6ikrdpEw6z2Tls1rVrHUfmHVHXXYf5l7nkbd5M+vMvkPjeu2qH5DEeW/BNCCECkWXRQsA5aVVrMKgcTflKlSkX9xQcshyi0FFIWFBYvar4camsRNk1Mdo4cCCaoGrvJOOTNFot8c9OgaAgrIsWkb1ggdoheYwkKkIIUQmrj2xCeC5nlym7ehKSIpPQaurfR72rR+VU/imy8rNKPWddfGa35EAS2qYNDe64A4Djzz2PPUC2tKl/v71CCFFFtiNHyN+xA7Ran/9SO7tMuT4unV+SQW+gcbhzR+mSvSoF+9IoPHAAjV5P+IW+OZRXGw3vvQd982YUZWRw4o031Q7HIyRREUKIClgWOf/yNnTvTlBMjMrRVO7sMuX6WppcUnnzVKxLin+mF1yAzhhe7uv8mTY0lIQpUwDI/PprcjdtUjmi2pNERQghKuCq9jH6+LCPS8ndlPeai0uTIyVRce0gDSXmpwwepEJEdSO8d28ir7gCFIX0Sc+g2Gxqh1QrkqgIIUQ5ijIzyV2/HgDTMN8sSz6bq0w5Z/VqDp2uvxU/Lmf3qBRlZpJX3MMQCGXJlYl94nF00dEU7NnDqc/8u2JX1URl+vTpdO7cmYiICCIiIujTpw9//PGHmiEJIQQA1pSlYLcT0q4dwU2bqh1OlYR2aI+uQQOU3FxaHnBW/DQ2NlY7LNWcXfljXboUHA5C2rdH3ziw35eg6GjinnRuEnzyvfcoPHBA5YhqTtVEpWnTpkydOpUNGzawfv16hgwZwuWXX84///yjZlhCCOEuSzYN9Y9hHyguU+7vHP7ptk8hOTK5Xlb8uCRHJgNwOv80mfmZWF37NQ0epF5QdSjisssI79sHpbCQY5MnoyiK2iHViKq/waNHj2bUqFG0bt2aNm3a8MILL2A0GlmzZo2aYQkh6jlHXh45K1YCYBruH8M+Lq55Kt1SlXo97APOyh/XGjJ7M3Y69/cBjIP9c7fk6tJoNMRPnowmJITc1Wsw//KL2iHViM+sdGO32/n+++/JycmhT58+5R5TUFBAQUGB+352djYANpsNm4cnC7nO5+nz+gppn/8L9Daq2T7r0mUo+fkENWmMNjnZazF4o43B55+PQwOJJ6FtQQNVfz984Xc0KSKJI9YjHFu2EGNuLrpGjdC1ae2RmHyhfeeiSUgg5p57OPXWWxyf+jKhffuii46u8uu91cbqnE+jqNwXtG3bNvr06UN+fj5Go5HZs2czatSoco+dPHkyU4rLrkqaPXs2Bh9dMVII4X/ivv2OyI0bybywHydGj1Y7nGrTvTORlodtbL2sD6H9Llc7HFX9lfcXywuWM2FhQ7qtSyfr/PPJuGqM2mHVLbud5m+/Q0h6Oubu3Tl+3bVqR0Rubi433ngjZrOZiIiISo9VPVEpLCzk4MGDmM1mfvjhBz755BOWLl1Khw4dyhxbXo9KYmIiJ0+ePGdDq8tms7FgwQKGDx+O3gd3S60taZ//C/Q2qtU+paiItIGDcGRn02TGZ4T17Om1a3mjjTaHjVcfuoBrlxahG9CHpPc+9Mh5axSLD/yO/rbvNyatnsgnH+iIyCok4b13CR8wwCPn9oX2VVX+1q0cvulmUBQaf/QRhj69q/Q6b7UxOzubhg0bVilRUX3oJzg4mFatnDOze/Towbp163jrrbf48MOy/7hCQkIICQkp87her/faL4k3z+0LpH3+L9DbWNfty9mwEUd2NrroaEznn49Gp/P6NT3ZxkNZh9iQpHDtUlDWbyFIUdAEB3vk3DWl5u9om4ZtaJ4BEVmFaEJDiejXD62HY/GHf4P6Hj2IvvFGMmfN4sTzz5M89xe0oaFVf72H21idc/ncdHCHw1Gq10QIIeqSZaGz2sc4ZHCdJCmetjdrL/vjwWoMwpGbS+7GjWqHpKrkyGR67nEOHOh796rWl3OgafToIwTFxWE7eJCT709XO5wqUzVRmTBhAsuWLWP//v1s27aNCRMmkJKSwtixY9UMSwhRTymK4l6N1jTUv6p9XFKzUlE0GjI6O6tdrMuWqxyRusKCwuizzzl4YO7VRuVo1KUzGomf+DQApz77jPxdu1WOqGpUTVQyMjK45ZZbaNu2LUOHDmXdunX89ddfDB8+XM2whBD1VP6OHRQdO4bGYCC8b/nVh77OtbiZo3dXAHKKd1Our2zHM2h2pBAHsLeDZ+cy+iPTsGHOkvuiIo5Nmohit6sd0jmpOkfl008/VfPyQghRinvY58IL/XaIwLVcfEz/wfDmrxTs2Yvt6NGAX4m1ItaUFAD2NoZdHFc3GB8R9/TT5KxaTf6WrWR+8w0xPj6K4XNzVIQQQi3WhcXDPn6yCeHZbHYbB7KdS6W3bNal1G7K9ZV1sXO35PWtte7epvpOHxdHo/GPAnBi2hvYjvt2AieJihBCAIUHDlCwZw8EBWEcOFDtcGrkQPYBipQijHojcYa4Ursp10eO3FxyVq8GYENrDanmVL9dRt7Toq+/nrAuXXDk5HD8+efVDqdSkqgIIQRgKe5NCT+/F7rISJWjqZm9ZmePQXJUMhqNxr2bcu7q1SiFhWqGpoqc4nYHNW3CkUZazAVmTuWfUjssn6DR6Yh/9lkICsKyYKF72NMXSaIihBDgrvYx+tEmhGdzzU9x7Rrs2k25vpYpW4qHfUyDh9DUlAggwz8lhLZtQ4Pbbwcg/bnnsVutKkdUPklUhBD1XtHJk+Rt2gT4127JZ3MlKi0jnZsRarRajBdeCNS/eSqKw4E1ZSkApiGD3Rs0ut4j4dTwvnvRN2tG0fHjnHjjTbXDKZckKkKIes+yeDEoCqGdOqGPj1c7nBpz9RaU3DXZONA5/FPfypTzt27FfuoUWpMJQ8+ekqhUQBsaSsKUyQBkzp5N3pYt6gZUDklUhBD13plF3vy3N6XQXsjB7INA6UQlvG9f0GrdZcr1hWXxEgCM/S9Eo9dLolKJ8D59iLz8clAUjk2chOJju0FLoiKEqNfs1hxyVzkrQ/y1LBlgf/Z+7IrdXfHjoouKqpdlytYlxYnK4CHAmXk7e7P2SuVPOWKffAJdVBQFu3dzasZMtcMpRRIVIUS9lrN8GYrNRnCLFgS3bHnuF/go9/yUqJZoNJpSz9W3MuXCQ4ecpeY6nbvtSZFJaDVasguzOZl3UuUIfU9QdDSxTz4BwMn33qPw4EGVIzpDEhUhRL1mKbHI29lf8P7ENT/F1XNQUn0rU3b1phh69HCXmofoQkiUyp9KRV5+OYY+vVEKCkifPNlnep4kURFC1FtKYSHWpc7KEH8uS4bSPSpnq29lyhbXsM+QwaUed1VDyTyV8mk0GhImT0YTEkLOqtVkz52rdkiAJCpCiHosZ+3fOKxWdI0auudx+KvKEpX6VKZsz84md916AExDhpR6zvXeSI9KxYKbN6fhffcBcHzqy9gzM1WOSBIVIUQ9ZlnkXI3TNGQoGq3/fhwW2gs5ZDkElD/0AxBePFcj0MuUrcuXQ1ERwS1bEtysWannXO+N9KhUrsHttxHSujX2zExOvva62uFIoiKEqJ8UhwProuKVS/242gcgzZyGXbFjCjbRKKxRuccY+/U7U6Z87FgdR1h3rEtSAOcib2crWaLsK/MvfJFGryf+2Smg0WCZO5ewver2QEmiIoSol/K3bqXoxAm0RiPhF1ygdji1UnLp/IomBNeHMmXFZsO6zNljZBxcNlFJikxCp9FhsVnIyM2o6/D8iqFbN6JvuAGA2N9+UzWxk0RFCFEvuff2GTAATXCwytHUTnkr0pbHXaa8LDCHf3I3bsKRnY0uOrrcOUfBumB35Y8M/5xbo/GPYhp9KUduuUXVijhJVIQQ9VLJsmR/d/ZmhBUJ9DJla/EmhMZBg9DodOUeU3LhN1E5ndFI3IsvUhQTo2ockqgIIeqdgtRUCtPS0Oj1hA8YoHY4tZZqrrjip6RALlNWFOVMWfLgQRUe556nYpYeFX8hiYoQot5x9aYY+vRGZzSqHE3tFNgLzlnx4xLIZcqF+/ZhO3gQjV7vnDhcAelR8T+SqAgh6p0zmxAOUzmS2kszp+FQHESGRNIgtME5jw/UMmVL8bCPoU9vtOHhFR7n6lHZl7VPKn/8hCQqQoh6xXb8OPlbt4JGU24Jq79xT6SNLLvHT3kCtUzZWrxbsqmcap+SWkS0IEgThNVm5Xju8boITdSSJCpCiHrF1ZsS1rUrQY3KX3PEn1R1Iq1LIJYpF50+Td7mzUD5Zckl6XV6mkU4F4KT4R//IImKEKJesQZQtQ9UvTS5pDO7KQfG8I81ZSkoCqEdOqCPjz/n8SUXfhO+TxIVIUS9YTebyfn7bwBMfr4JoUtle/xUxF2mvCowypSt7mqfqg3lSaLiXyRREULUG9Zly6CoiJDWrQhu0ULtcGotryiPw5bDQPUSlUAqU3YUFGBduRIou1tyRSRR8S+SqAgh6g3LAucmhMYA6U1JM6ehoBAVElWlih+XQCpTzl27FiU3l6C4OEI7dKjSa1pFFm9OaJY9f/yBJCpCiHrBkZ+PdcUKAEzDhqscjWeUHPap7hLngVKmXHKRt6q+B80jmhOkCSLHlkN6TroXoxOeIImKEKJeyFm92vmXd3w8oedV7S9vX+eaSFvVip+SAqFMWVGUM2XJQ4ZU+XV6nZ7mEc0BqfzxB5KoCCHqBctC57CPaehQVTdY86R9WfuA6s1PcQmEMuX8HTsoOn4cjcGAoZo7YMs8Ff8hiYoQIuApdvuZv7yH+/9qtC616VEB/y9Tti5JAcDYry/akJBqvVaW0vcfkqgIIQJe3qZN2DMz0UZGYujRQ+1wPCKvKI8j1iNAzXpUwP/LlN27JQ+u+rCPi/So+A9JVIQQAc9V7WMaNBCNXq9yNJ6xz7wPBYWY0BhiQmNqdA5/LlO2paeTv2MHaDQYB1Z/B2xXj0qqORWH4vB0eMKDJFERQgQ0RVHcy+YbhwXOsE9NFno7mz+XKVtTUoDirRAaVL002yUxIpEgbRB5RXkcy/HPycT1hSQqQoiAVrB7N7bDh9GEhDgrXQJEyc0Ia8Nfy5RduyVXdZG3s+m1elpEtABk+MfXqZqovPTSS/Tq1QuTyURsbCxXXHEFu3btUjMkIUSAcQ37hPfrh9ZgUDkaz6nuZoQV8ccyZUdODrlr1gLn3i25MjKh1j+omqgsXbqU+++/nzVr1rBgwQJsNhsjRowgJydHzbCEEAHENexjCqBhH/DM0A8Ulyl37gz4z/CPddUqlMJC9M2aEdyy5u2XCbX+IUjNi//555+l7s+cOZPY2Fg2bNjAgAHVnxwlhBAlFR4+QsHOnaDVYhw8SO1wPCbXluuu+KltjwqAceAA8jZvxrp8GdHXXVvr83mbu9R88OBarYkjPSr+QdVE5WxmsxmAmJjyZ7AXFBRQUFDgvp+dnQ2AzWbDZrN5NBbX+Tx9Xl8h7fN/gd5GT7TPPP8vAEK7d0cxGn3uvappG3ef2g1Ag9AGhOvCa92ukD594a23yVm1msLcXI9VRnnjd1Sx27EuTQEgdED/Wp27udG5Ou2+rH0UFBag1VRvkCHQ/w2C99pYnfNpFB/ZkcnhcHDZZZeRlZXFiuL9OM42efJkpkyZUubx2bNnYwigsWchhGc0/fBDDPvSyBh9KVnF1S2BYGPBRn7K+4nkoGRuN95e+xM6HCS/8CJBViuH7rqTvFoMp3hb6P4DNJs+HXtYKKkTJ4JOV+Nz2RU7z5qfxY6d8abxxOhqVuYtqi83N5cbb7wRs9lMREREpcf6TI/K/fffz/bt2ytMUgAmTJjA+PHj3fezs7NJTExkxIgR52xoddlsNhYsWMDw4cPRB8i6CyVJ+/xfoLextu2zZ2aSNuH/AOh1//3omzTxdIi1VtM27tq0C3bC+cnnM6rnKI/Ecnz1aixzf6VTYSENR3nmnN74HT35xptkAZGDBjNq9Ohan++reV+xJ2sPid0SGdh0YLVeG+j/BsF7bXSNiFSFTyQqDzzwAL/99hvLli2jadOmFR4XEhJCSDnLJOv1eq/9knjz3L5A2uf/Ar2NNW1fzvIV4HAQ0r49hhYtPB+YB1W3jWnZaQC0jmntsZ+9aeBALHN/JW/lSvRPPumRc7p48nc0d9lSACKGDfXIOVtFtWJP1h72W/czTF+zCdeB/m8QPN/G6pxL1aofRVF44IEHmDNnDosXLyYpKUnNcIQQAeRMtc9QlSPxPE9V/JTkD2XKhQcOULg3FYKCMPbv75Fzut5D1waPwveomqjcf//9fPXVV8yePRuTyUR6ejrp6enk5eWpGZYQws85cnPJWbkSCLyy5BxbjnslVU9U/Lj4Q5myZYmz2sfQsyc6Dw33S+WP71M1UZk+fTpms5lBgwaRkJDgvn377bdqhiWE8HPWFStQCgrQN21KSJs2aofjUa7elIZhDYkMifToucN9fDdl127JphquRlseV49KmjlN9vzxUaoP/ZR3u/XWW9UMSwjh56wlFnmrzTobvsgbwz4uxgHOyaS+uJuy3Wwmd/16AIy1WI32bImmRIK1weTb8zliOeKx8wrPkb1+hBABRbHZsKQ4J1wG8vwUTw77uJTeTXmTx89fG9blK8BuJ6R1K4ITEz12Xp1WR1Kkc36kDP/4JklUhBABJXf9ehxmM7qYGMK6dVM7HI/bay7ejNALPSqldlP2seEfq2sTwsFDPH5u91L6ZllK3xdJoiKECCiWhc5hH+OQwWhqsRiYr/JmjwqU2E15me8kKorNhnW5c4KvJ+enuMiEWt8miYoQImAoinKmLHlo4A37WAutpOekA97pUQHfLFPO3bABh8WCrkEDQosrkzxJNif0bZKoCCECRv72fyhKT0djMBDet6/a4Xica2giNiyWiGDPrsbt4otlyhbXsM+ggWi0nv/acvWopJnTsDvsHj+/qB1JVIQQAcOyaCEAxv790ZazirW/82bFT0m+VKasKEqp3ZK9oYmxCSG6EArsBRy2HvbKNUTNSaIihAgY1gBejRbOzKHwdqLiS2XKhXv3Yjt8GE1wsNd6yXRaHcmRyYDMU/FFkqgIIQJC4f79FOzZ61xefWD1NpfzF96eSOviS2XKluLelPA+fdAaDF67jsxT8V2SqAghAoJrEm34+ed7bHl1X1NXPSq+VKZ8pizZO8M+Lq73VHpUfI8kKkKIgOAuSw7QYZ/swmwycjMA7ycq4BtlykUnT5K3dSsAxsGDvHotVy+V9Kj4HklUhBB+r+jECfI2bwYCsywZzuzuG2eIwxRs8vr1fKFM2bp0KSgKoR07oo+L8+q1Su75U+Qo8uq1RPVIoiKE8HuWxUucX2idO3v9C00triEJb89PcfGFMmXXbsne7k0BZ+VPWFAYNoeNQ5ZDXr+eqDpJVIQQfs+y0FmWHKi9KVB3pcklqVmm7MjPJ2flKgBMQzy/bP7ZtBqte88fGf7xLZKoCCH8mt1qJWfNGgBMw4epHI331NVE2pKM/QcA6pQp56xZg5KXR1BCAiHt2tXJNVtGyoRaXySJihDCr+UsWwY2G8FJSYQkJ6sdjteo0aMSel4H1cqUrUtSADANHoRGo6mTa7reW9d8IOEbJFERQvg197BPgFb7AJgLzJzIOwGc+au/LqhVpqw4HFjd81O8P+zj4t6c0Cw9Kr5EEhUhhN9yFBZiXer8AjUNC9xhH1dvSnx4PMZgY51e+0yZct1NqM3/ZwdFGRloDQYMF5xfZ9d19ajsN++Xyh8fIomKEMJv5a5diyMnh6BGjQjt1EntcLzGtRlhXQ77uJwpU95TZ2XKrt6U8AsvRBscXCfXBGhsbOyu/DloOVhn1xWVk0RFCOG3LAuKNyEcOsQru+r6CvfS+ZF1U5pckhplyu6y5CHeXY32bFqN1r3nj1T++I7A/ZcthAhoisOBpXh5ddOw4SpH411qVPyUVJdlyrajRynYuRO0WlX2bJKl9H2PJCpCCL+Ut2UL9pMn0RqNhJ/fS+1wvKquNiOsSF2WKVtSUgAI69aNoOhor16rPLKUvu+RREUI4Zdc1T7GgQPR1OE8hrpmLjBzMu8koF6PSl2WKVuLd0s21fGwj4vsoux7JFERQvgdRVHOlCUH8CJvcGYIonF4Ywx6gyox1FWZst2aQ+7atYD3d0uuiKtHZX/2fmwOmyoxiNIkURFC+J3C1FRsBw6i0esJv7C/2uF4lRoLvZWnLsqUc1auRLHZCG7enOCkJK9dpzIJ4QkYggwUOYo4mC2VP75AEhUhhN9x9aYY+vZBZwxXORrvquvNCCtSF2XK1uLJ0cYhQ+psNdqzaTQamVDrYyRREUL4HcvCRUBgL/Lm4is9Kt4uU1bsdqxLlwJ1s1tyZWSeim+RREUI4Vdsx46Rv307aDSYVJrHUJd8pUcFvFumnLd5M/asLLSRkRi6d/f4+avDvZS+9Kj4BElUhBB+xbLIOTwQ1q0bQQ0bqhyNd2XmZ3I6/zQASZHqzNkoyZtlyq41cYwDBqAJCvLouatLelR8iyQqQgi/Ylnk2oQw8Id9XH/RNzE2Ua3ipyRvlim7d0tWqSy5JFePysHsg9jsUvmjNklUhBB+w242k/v3OgBMQ+tuV121qL3Q29m8VaZckJZG4b59oNcTXnx+NcUZ4jDqjRQpRezP3q92OPWeJCpCCL9hTUkBu52Q1q0Jbt5c7XC8Tu2l88vjjTJlV29KeK+e6Ewmj523pjQaDclRsuePr5BERQjhN9zVPgG+yJuLr1T8lOSNMmXXbsnGwb7TS9YyUkqUfYUkKkIIv+DIz8e6YgUAxqFDVY6mbvhiolKqTHl57XtV7FlZ5G7cCKi3Gm15XO/5PvM+lSMRkqgIIfxCzqpVKHl5BDVOILRDB7XD8bpTeafILMhEg4bkyGS1wynFXaa8rPbzVKzLlzuH89q0Ibhpk1qfz1OkRNl3qJqoLFu2jNGjR9O4cWM0Gg0///yzmuEIIXyYe9hn6DDVVi2tS66/5JsYmxAWFKZyNKV5skzZXZbsA9U+Jbl6VA5mH6TQ7t0do0XlVE1UcnJy6NKlC++9956aYYgAVLh/Pw0WLCAnJQW7NUftcEQtKUVF7uXVTfVk2MeXFno7m6fKlJXCQvekXNMQ35mfAmcqf+yKXSp/VKbqqjoXX3wxF198sZohiABkXbGSI48+SgOLhWMLF3EsKIiwLl0I79sHY79+hHbsqPqCUqJ6cjduxJ6VhS4yEkPPHmqHUyd8cX6Ki7NMuR/mX+ZiXb6M8N4X1Og8uevX48jJQdeoIaEdO3o4ytpx7fmz5cQWUrNSaRPdRu2Q6i2ZoyICyulZszh09904LBYK4uPRJyZCURF5GzZw8p132X/9Dezu05fDDz5I5tdfU3jgAIqiqB22OAfrIuewj3Hw4HqTZPpiaXJJ4QOcwz+1KVO2LHZW+5gGDUKj9b2vI5mn4hv86l98QUEBBQUF7vvZ2dkA2Gw2bDbPrh7oOp+nz+srAq19SlERJ195FfPXXwMQfukl7OnTh2EXXwzHj5O7eg25q1eTt3YtjuxsLAsWYlngXOE0qEljDH36YujTm7ALLkAXGalmU6os0H6GZ3O1q7CwkOzi3ZLDBg0KqPZW9DNUFMXdo9LC2MIn2xxywQXuMuXcQ4fQx8eXOaay31FFUdzzU8IGDPDJNrYwtQBgz+k95cYX6P8GwXttrM75NIqP/Dmp0WiYM2cOV1xxRYXHTJ48mSlTppR5fPbs2RgM6i8vLdShzcsnYfZswnfvBuDERReROWgglDfh0uEg9MgRDHv2YNizh7ADB9HY7e6nFY2GgiZNyGndmtw2rclr1gzqyV/wvirk6FGav/U2Dr2e1EkTUYKD1Q7J66wOK1Ozp6JBw6TISeg1erVDKlfie+8TdvAgx8eMwXzB+dV6bfCxdFq8+SaOoCBSn5nkkz/Xvba9zMyZSUNtQx6JeETtcAJKbm4uN954I2azmYiIiEqP9atP4AkTJjB+/Hj3/ezsbBITExkxYsQ5G1pdNpuNBQsWMHz4cPR63/yQqI1AaZ/t0GGOPfgghampaMJCiXvxRVoNG1bl9jlyc8lbv97Z27J6DYWpqYQePkzo4cM0WLIETVgYYT17Ontb+vQhuGVLn6k4CZSfYUVc7eucl48ZMPXvz8WV/CHjjyr6Gf6d/jcshqbGplx+yeUqRli50wcPcfq990g2m0kYNarM85X9jp7+6CNOA8YL+/nsz/VE7glm/jyT08ppho4cSogupNTzgf5vELzXRteISFX4VaISEhJCSEhImcf1er3Xfkm8eW5f4M/ty12/nsMPPoQ9M5Og2FiaTn+fsPPOK3XMOdsXGUnI0KFEFVeS2I4fJ2fVanJWrSJn1Srsp06Ru3w5ucULWwXFxhLety/h/foS3qePT+ze688/w6rIW7oUgIgA/jI4+2e437ofgFbRrXy6zRGDBnH6vffIW72aIEVBU0GvSHm/o7lLnWuwRAwZ4rNtTIhIwBRswlJo4UjuEdrGtC33uED/Nwieb2N1zqVqomK1Wtm798wkpbS0NDZv3kxMTAzNmjVTMTLh67Lm/MyxSZPAZiP0vPNo+v576OPian1efVwcUVdeQdSVV6A4HBTs3k3OSmfSkrt+PUUZGZh//hlz8Zo/IW3bFicu/TD07IE2NLTWMYgzgk6fpnDXLtDpMA4aqHY4npVvRrNzHmGFuWWe8rXNCCviKlO2nzpF7sZNVa7+KTpxgvytWwEwDhrkxQhrR6PR0CqqFZsyNrE3a2+FiYrwLlUTlfXr1zO4xJLJrmGdcePGMXPmTJWiEr5McTg48cabnPr4YwBMI0bQ+OWpaMM8vyCWRqsltF07Qtu1o8Edt+MoKCBv40ZyVq7EumoVBTt2UrBrFwW7dnF6xgw0wcGE9ejuTFz69iW0fXufrGTwJ8Z//gHA0LMnQdHRKkfjQQfXwE93EpR1kOFoUPLnQc9boc1FoNP7dGlySTUtU7akpAAQ2rkz+thYL0ZYey2jWrIpY1P93ZzQB6axqpqoDBo0SEpDRZU5cnM5+sSTWBYsAKDB3XfT6OGH6iwZ0IaEEN6nD+F9+hALFJ0+Tc7q4mGilasoSk8vri5aw4nXp6GLjnYe38+ZuOgTEuokzkDiSlQCZpE3exEsexWWvQKKAyU0Ck1+FprUhZC6EMJjUbrcwN5M58RwX+9RAQjvPwDzL3OdZcr/+1+VXuPaLdk0eJDX4vKUel2ifCoV3c/30SBkMFB2DlJd8as5KqL+sh0/zuF77yN/xw40ej0Jzz9H5OXqTjIMiokh8pJLiLzkEhRFoTAt7cww0dq12DMzyZ43j+x58wAITko6M0x0/vnojOGqxu/rik6dImz/AQBMQ31r1dIaydwPP90Fh9Y673e+nqIRL7H09+8ZHHUY3dZvICeDk2vfIbtZU7QKtDi0CUzNQe+7Q4rh/fqW2k35XAm5Iy+PnFWrADD62Gq05XH1atWrHhVFgfWfwvyJaG25dAo9Asr4c7/OSyRRET4vb/s/HL7vPooyMtBFR9P0vXcxdO+udlilaDQaQpKTCUlOJubmm1BsNvK2biVn5UpyVq4ib9s2CtPSKExLI3PWLCixWm54376EdepUbxYyq6rcZcvQKAoh7dujb+I7m9XVyNbv4Pf/QkE2hETApW9Ap6vBZiMnNB7HkNvRDZsEu/9k7/r3wXGYZjYbIXPuhj+fhC43QPdxENtO7ZaUERQdTVjnzuRt3ox1+XKir7220uNzVq9Byc9H37gxIW18f7VXV4/KIcsh8ovyCQ3y3aTRI8xHYO4DkOpc48bRvB9rjVcxWMVqR/lkFD4t+6/5HH3iCZT8fIJbtSTxgw8IbtpU7bDOSaPXY+jRA0OPHjR66CHs2dnkrF3rHiayHTxI3oYN7hVztSYT4b0vcM9v0Tdr5jNl0J6iOBw4cnJwZGdjt1hwWCzYLRbs2dk4LFbslmwc2RbsVguObAt5xZMtw/3gr+4K5Zvh98dg23fO+4m9YcxHEN287LE6PbQfTapihnUv0zIyCSxayD4Ca9533hIvgO63wHlXQrDv9MiFD+hP3ubN5FQhUbEuca5Gaxw82C9+xxuENiAyJBJzgZk0cxrtG7RXOyTvUBRnQj3vf1BghqBQGDYFe/fbyPvjT1VDk0RF+CRFUTj14UecePNNAML796fJtNfRmUzqBlZDuogIIoYPJ2L4cAAKDx92DxPlrFmDw2wutVquvkkT9zBReO8L0EVFqRi9k1JYiN1qdSYY2RYclmzsFqvzv9kWHNazHj8rIXFYrdWemKdoNBhHjPBSi7zs4Fr46T+QdRA0Ohj4BPT/L+gq/9h1L53fehRcfR/sXQQbP4ddfziHjQ6thT8nOHtkuo+Dxl3roDGVM/YfwMm33yGneDflisqUFYcDS0pxouJjuyVXRKPR0DKyJRszNrI3a29gJio5J+G3R2Dnr877TXrAlR9Cw9bgA6vuSqIifI6jsJD0iRMx/zIXgOibbybuiccDamgkuGlTgq+7lujrrkWx28n/5x93b0vu5s3Yjhwh6/vvyfr+e9BoCO3Y0d3bYujWtcIvgoooioKSn18ikbCcSThKJRjO3oxSCUZ2NnarFSUvzyNt1wQHo42IQGcyoTWZnP+NMKEzFv/XFIHWZARDOH8fO0rr5CSPXLfO2Itg+Wuw9BVQ7BDVHK76BBKrtnKru+InsiVoddBmhPNmSYfNs2HjF5CZBus/c94SujgTlk7XQKhnF76sqqqWKedv3479xEm04eGE9+pVx1HWXMsoZ6ISkPNUdv4Gvz4MuSdBGwSDnoR+j54zoa5LvhOJEDgraQ4/8CB5GzeCTkf8008RfcMNaoflVRqdjrDOnQnr3JmG99yDIyeH3PXryVm1CuvKlRTuTSV/2zbyt23j1IcfojEYMPTqSdgFF2Dav5+szEzIzS1OMMofRrFbLFBU5JF4teHhzkTDaCybcJhM6CJK3o9w3jeeeVxbzqKN5bHZbBQUT0T2G5kH4Kc7S0yYvQ5GvVblBKLkHj9lSpNN8dB/PPR7BPYvd/ay7PwVjm2B38fD/KfhvDHOoaHE88vfQsJLqlqmbCke9gnv37/aybaa3BNqzQGUqORlOec/bXHuj0ZsB2cvSkJnVcMqjyQqwmcU7NnDoXvvw3b4MFqTiSZvvoGxXz+1w6pz2vBwjAMHYhw4kDjKXy03Z+kycpYuIwE4WZ2T63RlE4yI4oTCZCwnsSh+3HW80YhGp/NOw/3d1u+dCYNrwuwl06DzNdU6RUZuBhabBZ1GR1JkBT1JWi0kD3Teck7B1m9gw+dwchds/sp5a9TembB0uR4MMR5o3LlVpUzZ6tot2U+GfVxcE2oDpkcldQn8cr9z/pNGC30fgsH/B0FV+yOirkmiInyCdflyjjw6HofVir5ZMxI/mE5IcrLaYfmEilbLta5dy4ljx4hLTiYoMhKtyegcNikxfKI7q8dDYzD4xQRGv5KfDfMeg63fOu8nXlA8YbZFtU/l+os90ZRIsK4KPQ7hDaDP/dD7PmcvzsYvYPtPcGIn/DUBFj4D7S9zJi0t+juTHC85V5my7cgRCnbtAq2W8P79vRaHN7h6VA5bDpNXlEdYkOcXmKwThTmw4BlY51wwk5hkuOIDaFa1hfrUIomKUJWiKGR+NYvjL70EDgeGnj1p8s7bgbUKqQeVXC034pab2TxvHl1GjQr4fUZ81qG/4cf/QNYB51+mA5+A/o/VeHy/xkvnazTQrLfzdtFLsO17Zy9L+lbY/oPzFp3kTFi6jgVT7bebONu5ypQtxYu8Gbp397t/3w1CGxAVEkVWQRZp5jQ6NOigdkjVd3At/HwPnN7nvN/rThg+xaeqxyoi63sL1Sg2G+nPPsvxF14Ah4PIMWNo9tmnfvchJuohexGkvAyfXeRMUqKawW1/Oici1mISokeWzg+NhF7/gXuWw10p0OM2CDY5J+AumgJvdIBvxsKeBeCw1/w65Qgf4OwpySnexLMk62Lnuhz+sMjb2TQajf8u/FZUAAsnw4yLnElKRBO4eQ5c8ppfJCkgPSpCJfbsbI488qhzhUqNhtj/jifmjjtkWEL4vswDxSvMrnHe73St80M/NLLWp3aVJnts6fzG3Zy3Ec/DP3OcQ0OH/4Z/f3PeIhOh203OXpaoxFpf7uwyZdeEXofVSs66dc5jBg+q9XXU0CqqFRuOb/CvpfSPbYU590CGcysKutwAF02FsChVw6ouSVREnSs8eJBD99xL4b59aMLCaPLqK5iGDVM7LCHObdsP8NujzgmzwSa4dBp0rnyBs6qqtOKntkKM0P1m5+34DmfCsuVrMB+ClJcgZSq0GgY9xrk3RqyJUmXKmzYT3L0bALkrV4HNRnBSEiFJflZuXsyvelTsRbDyDWevn8MGhoYw+i1of6nakdWIJCqiTuWuW8fhBx/CnpVFUHw8ie+/R2gHPxzvFfVLfrZzxc6t3zjvNz0frvq4RhNmK3I89zhWm5UgTRAtIjx33jLiOsDFU2HYZGd588bPneXOexc4b8Y46Hqjcz5LTPUmtJcqU162lJjiRCWneLdkf1nkrTx+sznhyT3OXpQj6533210Kl74JxkaqhlUbMkdF1JmsH3/iwO13YM/KIrRTJ1p8960kKcL3HVoHH/Z3JikaLQx8Em77w6NJCpz5S71ZRDP0NezRqBZ9qLN8+tbf4MGN0O9hCG8E1uOw4g14uxt8PtrZi1RUUOXThvcfAOAsUwaw291zVkyD/TdRcfWoHLEeIdeWq3I05XA4YM0H8EF/Z5ISEulcF+W6r/w6SQHpURF1QHE4ODFtGqc++RQA00UX0filF9GG+WmJn6gfHHZY/rpzWESxQ2QzZy9Ks95euZx76XxPD/tURYOWMPxZGDLRuVT/xs+dS/enLXPewqKrvDFiqTLl9HTCDhzAYTaji4oirGvXummPF8SExhATGsPp/NOkmdM4r+F5aod0RtZB+Pk+Z88YQPJguPw9iPTzzTyLSaIivMqRk8ORx5/AumgRAA3vu5eGDzyAxovrOQhRa1kHnRNmD6523u90DVzyukcmzFakxqXJnqTTQ4fLnLesg7DpK+etzMaI44o3RjSUOUXJMuXcFSsI37ETAOPAgX6/DUbLqJacTj/N3qy9vpGoKApsngV/PAmFFtAbYMRz0POOOl2Z2Nv8+7dG+DRbejqH7r2Pgp070QQHk/DC80SOHq12WEJUbtsP8Nt45w6ywSZngtLlOq9f1msTaWsqqplztdKBT8Dehc51WXb/WWJjxCedCVyPcc79hkpw7aacu2Ilxp3FiYofD/u4tIxsybr0db4xodZy3LlHz+4/nPcTL4Arpjt7xwKMJCrCK/K2bePQffdhP3ESXYMGNH33HQzduqkdlhAVK7A4J8y69j5p2gvGfAwx3q9SURTFvSqtqj0q5dHqoM1I582S7vwLfuMXkLkf1n/qvJ21MaK7THnZMoJtNtDrCb/wQrVbUms+M6H2nznOZDrvNOiCYfBT0PdB588qAEmiIjwu+48/OPrkBJSCAkJat6bp9OkENw2MsVIRoA6vhx/vcH75arQw4H8w4PE620E2PTedHFsOQdogmkU0q5Nr1ogpHvr/17m77v5lzl6Wf38rszFiaLeb3WXKAGG9eqEz+sfiYpVRvUQ597Qzmd7+g/N+fCfnhNk4HxiG8iJJVITHKIrCqQ8+4MRbbwMQPnAATV5/HZ3RqHJkQlTAYYfl05xriSh25wJoYz6G5n3qNIx9Zuey5i0iWqDX+sF2CFotJA9y3nJOOXuhNn4OJ3fD5q/QbP4KY8PmmJ15CuGDBqkYrOe4elSO5hwl15aLnjr8We1ZAL88ANZ00OicO2kPeByC/GcX6pqSREV4hKOggGNPTyT7118BiBl3C7GPPy477QrflXWoeMLsKuf9jlc5dzxWYdVO17CPz8xPqY7wBtD3AefmiIfWOntZ/plDeEwGZpzbYURkfgV/50O7SyCiscoB11xUaBQNQhtwKv8UqVmptIuqvALKIwoszp6qDTOd9xu0dvaiNO3h/Wv7CElURK0VnTrF4fsfIG/zZggKIv7pp4m+3vuTD4Wose0/wq+PFk+YNcKo16DL9apVSrgTlUg/TFRcztoY0fj3V4Ts/oCQcAshmWtg3hrnLtNNe0H70c5bNReU8wUto1pyKv0Ue7P2ej9R2b8Sfr7XuZ8UOHfJHjoJ9PVraQdJVESt5O/ezeF77sV29CjaiAiavvUm4X3qtttciCorsMC8x2HLbOf9pr1gzEeqf2Huy3IO/fhlj0p5wqLQDXyAxJS7SZkzk/iEHHS75zl7XA6vc94WTIK4TmeSltj2flFS2zKqJX+n/+3deSq2fFj8HKx+D1Cca/hc8T4k9ffeNX2YJCqixqxLl3Jk/H9x5OSgb96MxOkfEJLsn/t4iHrg8IbiCbNpzgmz/R+DgY/XeF8bT3EoDvZlOxMVn6v48YDckFgcvUeh6/8IZB9zTr7d+SvsXwHHtzlvKS9CTMvipOUyaNLdZ5MW18/I1QvmcUc2OpfAP7nLeb/bzTDyRQiN8M71/IAkKqLaFEUh84svOP7yK+BwYDj/fJq89SZB0dFqhyZEWQ47rJgGS0pOmP0ImvdVOzIAzIqZvKI8grRBJEbUfgdjnxaRAOff6bzlnnaugrvzV0hdDKdTYeWbzltEkzM9Lc36+FTZrdcqf+w2WPYaLHvV+XtqjIPRb0Pbizx7HT8kiYqoFsVmI/35F8j69lsAIq++ioRJk9AEB/7Mc+GHsg7BnLvhwErnfRUnzFYkw54B+FHFj6cYYqDbWOetwAJ75juTlt3znSvhrv3AeTM0hHajnD0tSQMgKETVsF09KsdyjpFjy/HMSTP+df6eHtvsvH/elc7fU0OMZ87v5yRREVVmN5s5/Mgj5K5eAxoNsf/7HzG33YrGR7toRT23/Sf47RHI940JsxVxJSqBOOxTZSEmZxLZ8Sqw5cG+FGfS8u/vkHvSucDcxi8gJALaXOTsaWk1FILrfm2WyJBIGoY15GTeSXdZeY057M5tCRY9B/YCCI1yroTc6WqPxBooJFERVVK4fz+H7rmXwv370RgMNHntNUx+vGW7CGAFFvjjCecKqgBNejo3E/TRChNXohIwE2lrSx8GbS923uw251yWnb8657ZYj8O275y3oDBnstL+MuequXXYS9YyqiUn806Sak4lmBr2Jp9Oc24k6CqPbzUcLnvHOTwmSpFERZxTztq/OfzQQzjMZoISEkic/j6h7epg/QAhqqvkhFk0zlVUBz2p+oTZymQ4pEelQjo9tBzsvI16zVkttHOu85Z10Jm8/PsbaIMgaaCzp6XdpWBs5NWwWkW1Yu2xtewz76Md1fwsVBTYMAP+ehpsOc7evpEvQvdbfK63z1dIoiIqlfn996RPeRaKigjt3JnE994lqJF3PwSEqDaHHVa84Vxh1lEEEU2dE2Zb9FM7sko5FIf0qFSVVgvNLnDeRjwP6ducPS0758KJfyF1kfP226POidKupCXK8xOU3RNqzanVS1Syj8LcB52bPAI07+csO45u4fEYA4kkKqJcit1Oxmuvc3rGDAAiRl1Mwosvog0NVTkyIc5iPgw/3Q0HVjjvn3clXPoGhPl+FdqxnGPYsKHX6kk0BXjFjydpNJDQ2Xkb8hSc2A3//upMXI5uck6ePrDSucNz425nyp4btvbI5V29X/vM+6jSyI+iOHflnvdf55wpXQgMewYuuNeZgIlKSaIiynDk5HDksf9hXbIEgIb330/DB+6XSbPC9/wzx7nVfb4Z9OEw6lXoeqPfdKG71uJoEdGCIK18HNdYozbQ6L/Oob6sg85JuDt/hQOrnInL0U2w6Flo1M6ZsLQf7dzQr4a/J64eleO5x8nX51d+cM4p+P1R2PGL837jbs4l8Bu1rdG16yP5lyFKsR09yqH77qfg33/RBAeT8NKLRF5yidphCVFagbV4wuxXzvuNu8NVn0AD/xo+cVWN+PXS+b4mqhn0vtd5s2acSVrSljqHiE78C8tegajmZ3pamvaqVs9GRHAEsWGxZORluIfuyrXrD5j7EORkOOfRDHjcuZmgD8+Z8kWSqAi3vC1bOHT/A9hPnkTXsCGJ775DWNeuaoclRGlHNsCP/4HT+3BOmB0Pgyb45Ye/a9Gw5EjfrEjye8ZY6Hmb85aXBbv/cs5p2bvIuX/O6nedN2O8c8PE9qOhxYVV+l1qGdWy4kQl3wx/TjhTedaoHVz5gbM3RVSbJCoCAPPvv3Nswv+hFBYS0rYtidPfR9/Yf3c5FQHIYXeuWrrkxRITZj90frH4qYDYjNBfhEVBl+uct8IcZ7Ky81fY/SdY02H9p85bWDS0HeVMWpIHg778eXkto1qy+thqd9WW276l8Mv9YD4EaKDvgzD4qQrPI87NJxKV9957j1dffZX09HS6dOnCO++8w/nnn692WPWCoiicePc9Tr77LgDGQYNo/Npr6Ix1v5CSEBXy4wmzFXEoDvZn7wckUalzweHQ4TLnragA0pY5e1r+/R1yTzl7QjbPcpYOtx7uTFpaj3AuTFfMNaH2uP2484HCXFg4Gf7+0Hk/ugVc8QE0l01aa0v1ROXbb79l/PjxfPDBB1xwwQW8+eabjBw5kl27dhEbG6t2eAFNY7Nx/Iknsf7xBwAxt91G7GP/RaPznX01hNDsnAvzxkN+VvGE2Veg61i/mTBbkSOWI+Tb8wkiiCbGJmqHU38FhTiTkdbD4ZI34NAa2DHX2dtiOeqcsP3PHGelTsshzqSl7cXuCbUZ9gw0R9bDrw/Aqb3Oc/a8HYY/ByFGFRsWOFRPVKZNm8add97JbbfdBsAHH3zA77//zmeffcaTTz6pSkyWkwc5umcT+Ue3sG89BAWp/jZ5XJE1h4Tp72E9kgE6HUEP3ULOxQNJO7Je7dA8oshmIzt3H2kH1xKk97+5C1UR6G0sKiwk7sAHZGxbC4A9oTO2ES+hRDVz7uHj59YfXwdAA20jCooUHJoilSPyPJutiAI75BYWoVf8JLFs3Nt5G/o82mOb0O36Dd2/v6HN3Ae7/4Ddf6BodLRo3gc0YFEspM2+jHCHHUdEPLahU3A06+vcdDH3tNqtqbUiWxGnbTkoiqJaDBpFxasXFhZiMBj44YcfuOKKK9yPjxs3jqysLH755ZdSxxcUFFBQUOC+n52dTWJiIidPniQiwnNbYC+b9h8az/jbY+fzZdZQeH2Mln+aSy2/EGqwmbuSf/R6tcMQlVJooznMRdp1XKRbRwftAQCGJTbmeAD+IXs2m7kLK26ZTmS45+bZZGdn07BhQ8xm8zm/v1V9h0+ePIndbicuLq7U43Fxcfz7779ljn/ppZeYMmVKmcfnz5+PwWDwWFz5pzJpGPi/exxqBNMv1XI8RkOIQ71sWYiKKICNIBz4yV/j1aQ4grGZu6odhjgnDbuVRHbbE3nbPoZmmuNcpP2bftlr+D2qCBs67ATwH3uKjsWLFxPiwVkBubm5VT7Wr76OJ0yYwPjx4933XT0qI0aM8GiPinLxxWT/XwGLFy9myJAh6PV+9TZVic1WxK7Fi/k8gNsXyD8/CPw2/n97dx8UVb2HAfw5LuxCuCq+8SK4gooibyKUIWWpJDFcrk6NGkOGkTO9LMmbTFRjoKaAZaWGmnbHvN6r1VRwTa8goeJopoiuoQkhoVkCWkG8mAq7v/tH41430NQOnmV9PjM74/7O2/M9i+yXc87usfX6gGtrnGiTNdr2axiL9vYO+Nlsfb+7+hpGR0ZArb7NGzB2obm5+abnVXTPDhw4ECqVCg0NDRbjDQ0NcHV17TS/RqOBRqPpNG5vbw97mc/R95UkaFRAXycH2ddtDdrb21lfD2frNdp6fYDt18j6er6rNarVallrvJV1KXqsSq1WIyQkBCUlJeYxk8mEkpIShIXxI11ERER3O8WPVaWmpiI+Ph6hoaG477778M4776Ctrc38KSAiIiK6eyneqMyaNQsXLlzAa6+9hvr6eowdOxaFhYWdLrAlIiKiu4/ijQoAJCYmIjExUekYREREZGVs+PNURERE1NOxUSEiIiKrxUaFiIiIrBYbFSIiIrJabFSIiIjIarFRISIiIqvFRoWIiIisFhsVIiIislpsVIiIiMhqWcU3094uIQSAW7td9M1qb2/HxYsX0dzcbJN3xWR9PZ+t12jr9QG2XyPr6/m6q8ar79tX38dvpEc3Ki0tLQAAT09PhZMQERHRrWppaUHfvn1vOI8kbqadsVImkwnnzp2DVquFJEmyrru5uRmenp44e/Ys+vTpI+u6rQHr6/lsvUZbrw+w/RpZX8/XXTUKIdDS0gJ3d3f06nXjq1B69BGVXr16wcPDo1u30adPH5v9AQRYny2w9RptvT7A9mtkfT1fd9T4Z0dSruLFtERERGS12KgQERGR1WKjch0ajQaZmZnQaDRKR+kWrK/ns/Uabb0+wPZrZH09nzXU2KMvpiUiIiLbxiMqREREZLXYqBAREZHVYqNCREREVouNChEREVktNirXyM7Oxr333gutVovBgwdj+vTpqKqqUjqWrNasWYPAwEDzl/eEhYVhx44dSsfqNjk5OZAkCcnJyUpHkUVWVhYkSbJ4jB49WulYsvvxxx/x5JNPYsCAAXB0dERAQAAOHz6sdCxZDBs2rNNrKEkS9Hq90tFkYTQasWDBAnh5ecHR0RHDhw/H4sWLb+qeLj1JS0sLkpOTodPp4OjoiAkTJqCsrEzpWLdl7969iImJgbu7OyRJQkFBgcV0IQRee+01uLm5wdHREREREaiurr5j+dioXKO0tBR6vR5fffUViouL0d7ejqlTp6KtrU3paLLx8PBATk4OysvLcfjwYUyePBnTpk3DiRMnlI4mu7KyMrz33nsIDAxUOoqs/Pz8UFdXZ37s27dP6UiyamxsRHh4OOzt7bFjxw588803WL58OZydnZWOJouysjKL16+4uBgAMGPGDIWTySM3Nxdr1qzBu+++i5MnTyI3NxfLli3DqlWrlI4mq7lz56K4uBibNm1CRUUFpk6dioiICPz4449KR7tlbW1tCAoKQl5eXpfTly1bhpUrV2Lt2rU4ePAgnJycEBkZiUuXLt2ZgIKu6/z58wKAKC0tVTpKt3J2dhbvv/++0jFk1dLSIkaOHCmKi4vFQw89JJKSkpSOJIvMzEwRFBSkdIxu9dJLL4kHHnhA6Rh3TFJSkhg+fLgwmUxKR5FFdHS0SEhIsBh77LHHRFxcnEKJ5Hfx4kWhUqnEtm3bLMbHjRsnXn31VYVSyQOAyM/PNz83mUzC1dVVvPHGG+axpqYmodFoxJYtW+5IJh5RuYFff/0VANC/f3+Fk3QPo9GIDz/8EG1tbQgLC1M6jqz0ej2io6MRERGhdBTZVVdXw93dHd7e3oiLi8P333+vdCRZbd26FaGhoZgxYwYGDx6M4OBgrF+/XulY3eLKlSv417/+hYSEBNlvrKqUCRMmoKSkBN9++y0A4NixY9i3bx+ioqIUTiafjo4OGI1GODg4WIw7Ojra3BHO2tpa1NfXW/wu7du3L8aPH48DBw7ckQw9+qaE3clkMiE5ORnh4eHw9/dXOo6sKioqEBYWhkuXLqF3797Iz8/HmDFjlI4lmw8//BBHjhzpseeLb2T8+PH44IMPMGrUKNTV1WHhwoV48MEHcfz4cWi1WqXjyeK7777DmjVrkJqaildeeQVlZWWYN28e1Go14uPjlY4nq4KCAjQ1NWHOnDlKR5FNRkYGmpubMXr0aKhUKhiNRixZsgRxcXFKR5ONVqtFWFgYFi9eDF9fX7i4uGDLli04cOAARowYoXQ8WdXX1wMAXFxcLMZdXFzM07obG5Xr0Ov1OH78uM11xwAwatQoGAwG/Prrr/jkk08QHx+P0tJSm2hWzp49i6SkJBQXF3f6a8cWXPtXaWBgIMaPHw+dToePP/4YzzzzjILJ5GMymRAaGoqlS5cCAIKDg3H8+HGsXbvW5hqVf/zjH4iKioK7u7vSUWTz8ccf49///jc2b94MPz8/GAwGJCcnw93d3aZev02bNiEhIQFDhgyBSqXCuHHjEBsbi/LycqWj2Rye+ulCYmIitm3bht27d8PDw0PpOLJTq9UYMWIEQkJCkJ2djaCgIKxYsULpWLIoLy/H+fPnMW7cONjZ2cHOzg6lpaVYuXIl7OzsYDQalY4oq379+sHHxwenTp1SOops3NzcOjXNvr6+NneK68yZM/jiiy8wd+5cpaPIKj09HRkZGXjiiScQEBCA2bNnIyUlBdnZ2UpHk9Xw4cNRWlqK1tZWnD17FocOHUJ7ezu8vb2VjiYrV1dXAEBDQ4PFeENDg3lad2Ojcg0hBBITE5Gfn49du3bBy8tL6Uh3hMlkwuXLl5WOIYspU6agoqICBoPB/AgNDUVcXBwMBgNUKpXSEWXV2tqKmpoauLm5KR1FNuHh4Z2+FuDbb7+FTqdTKFH32LBhAwYPHozo6Gilo8jq4sWL6NXL8q1FpVLBZDIplKh7OTk5wc3NDY2NjSgqKsK0adOUjiQrLy8vuLq6oqSkxDzW3NyMgwcP3rFrG3nq5xp6vR6bN2/Gf/7zH2i1WvP5t759+8LR0VHhdPJ4+eWXERUVhaFDh6KlpQWbN2/Gnj17UFRUpHQ0WWi12k7XFDk5OWHAgAE2ca3R/PnzERMTA51Oh3PnziEzMxMqlQqxsbFKR5NNSkoKJkyYgKVLl2LmzJk4dOgQ1q1bh3Xr1ikdTTYmkwkbNmxAfHw87Oxs69dwTEwMlixZgqFDh8LPzw9Hjx7FW2+9hYSEBKWjyaqoqAhCCIwaNQqnTp1Ceno6Ro8ejaefflrpaLestbXV4qhsbW0tDAYD+vfvj6FDhyI5ORmvv/46Ro4cCS8vLyxYsADu7u6YPn36nQl4Rz5b1EMA6PKxYcMGpaPJJiEhQeh0OqFWq8WgQYPElClTxM6dO5WO1a1s6ePJs2bNEm5ubkKtVoshQ4aIWbNmiVOnTikdS3aff/658Pf3FxqNRowePVqsW7dO6UiyKioqEgBEVVWV0lFk19zcLJKSksTQoUOFg4OD8Pb2Fq+++qq4fPmy0tFk9dFHHwlvb2+hVquFq6ur0Ov1oqmpSelYt2X37t1dvvfFx8cLIX7/iPKCBQuEi4uL0Gg0YsqUKXf0Z1cSwsa+LpCIiIhsBq9RISIiIqvFRoWIiIisFhsVIiIislpsVIiIiMhqsVEhIiIiq8VGhYiIiKwWGxUiIiKyWmxUiKiT06dPQ5IkGAwGpaOYVVZW4v7774eDgwPGjh37l9YlSRIKCgpkyUVE3YuNCpEVmjNnDiRJQk5OjsV4QUEBJElSKJWyMjMz4eTkhKqqKov7jvxRfX09XnzxRXh7e0Oj0cDT0xMxMTE3XOav2LNnDyRJQlNTU7esn+hux0aFyEo5ODggNzcXjY2NSkeRzZUrV2572ZqaGjzwwAPQ6XQYMGBAl/OcPn0aISEh2LVrF9544w1UVFSgsLAQkyZNgl6vv+1t3wlCCHR0dCgdg8jqsFEhslIRERFwdXVFdnb2defJysrqdBrknXfewbBhw8zP58yZg+nTp2Pp0qVwcXFBv379sGjRInR0dCA9PR39+/eHh4cHNmzY0Gn9lZWVmDBhAhwcHODv74/S0lKL6cePH0dUVBR69+4NFxcXzJ49Gz/99JN5+sMPP4zExEQkJydj4MCBiIyM7LIOk8mERYsWwcPDAxqNBmPHjkVhYaF5uiRJKC8vx6JFiyBJErKysrpczwsvvABJknDo0CE8/vjj8PHxgZ+fH1JTU/HVV191uUxXR0QMBgMkScLp06cBAGfOnEFMTAycnZ3h5OQEPz8//Pe//8Xp06cxadIkAICzszMkScKcOXPMNWVnZ8PLywuOjo4ICgrCJ5980mm7O3bsQEhICDQaDfbt24djx45h0qRJ0Gq16NOnD0JCQnD48OEusxPdDdioEFkplUqFpUuXYtWqVfjhhx/+0rp27dqFc+fOYe/evXjrrbeQmZmJv/3tb3B2dsbBgwfx3HPP4dlnn+20nfT0dKSlpeHo0aMICwtDTEwMfv75ZwBAU1MTJk+ejODgYBw+fBiFhYVoaGjAzJkzLdaxceNGqNVq7N+/H2vXru0y34oVK7B8+XK8+eab+PrrrxEZGYm///3vqK6uBgDU1dXBz88PaWlpqKurw/z58zut45dffkFhYSH0ej2cnJw6Te/Xr9/t7DoAv99Z/fLly9i7dy8qKiqQm5uL3r17w9PTE59++ikAoKqqCnV1dVixYgUAIDs7G//85z+xdu1anDhxAikpKXjyySc7NXsZGRnIycnByZMnERgYiLi4OHh4eKCsrAzl5eXIyMiAvb39bWcn6vHu2O0PieimxcfHi2nTpgkhhLj//vtFQkKCEEKI/Px8ce1/28zMTBEUFGSx7Ntvvy10Op3FunQ6nTAajeaxUaNGiQcffND8vKOjQzg5OYktW7YIIYSora0VAEROTo55nvb2duHh4SFyc3OFEEIsXrxYTJ061WLbZ8+etbgr8EMPPSSCg4P/tF53d3exZMkSi7F7771XvPDCC+bnQUFBIjMz87rrOHjwoAAgPvvssz/dHgCRn58vhPj/nWMbGxvN048ePSoAiNraWiGEEAEBASIrK6vLdXW1/KVLl8Q999wjvvzyS4t5n3nmGREbG2uxXEFBgcU8Wq1WfPDBB39aA9Hdwk6xDomIbkpubi4mT57c5VGEm+Xn54devf5/ANXFxQX+/v7m5yqVCgMGDMD58+ctlgsLCzP/287ODqGhoTh58iQA4NixY9i9ezd69+7daXs1NTXw8fEBAISEhNwwW3NzM86dO4fw8HCL8fDwcBw7duwmK/z9Go/uMm/ePDz//PPYuXMnIiIi8PjjjyMwMPC68586dQoXL17EI488YjF+5coVBAcHW4yFhoZaPE9NTcXcuXOxadMmREREYMaMGRg+fLh8xRD1MDz1Q2TlJk6ciMjISLz88sudpvXq1avTG3R7e3un+f546kCSpC7HTCbTTedqbW1FTEwMDAaDxaO6uhoTJ040z9fVaZjuMHLkSEiShMrKylta7moDd+1+/OM+nDt3Lr777jvMnj0bFRUVCA0NxapVq667ztbWVgDA9u3bLfbNN998Y3GdCtB5/2RlZeHEiROIjo7Grl27MGbMGOTn599STUS2hI0KUQ+Qk5ODzz//HAcOHLAYHzRoEOrr6y3eZOX87pNrL0Dt6OhAeXk5fH19AQDjxo3DiRMnMGzYMIwYMcLicSvNSZ8+feDu7o79+/dbjO/fvx9jxoy56fX0798fkZGRyMvLQ1tbW6fp1/v48KBBgwD8fh3MVV3tQ09PTzz33HP47LPPkJaWhvXr1wMA1Go1AMBoNJrnHTNmDDQaDb7//vtO+8bT0/NPa/Hx8UFKSgp27tyJxx57rMsLnYnuFmxUiHqAgIAAxMXFYeXKlRbjDz/8MC5cuIBly5ahpqYGeXl52LFjh2zbzcvLQ35+PiorK6HX69HY2IiEhAQAv19g+ssvvyA2NhZlZWWoqalBUVERnn76aYs37ZuRnp6O3NxcfPTRR6iqqkJGRgYMBgOSkpJuOa/RaMR9992HTz/9FNXV1Th58iRWrlxpcRrrWlebh6ysLFRXV2P79u1Yvny5xTzJyckoKipCbW0tjhw5gt27d5sbNp1OB0mSsG3bNly4cAGtra3QarWYP38+UlJSsHHjRtTU1ODIkSNYtWoVNm7ceN38v/32GxITE7Fnzx6cOXMG+/fvR1lZmXlbRHcjNipEPcSiRYs6nZrx9fXF6tWrkZeXh6CgIBw6dOgvXcvyRzk5OcjJyUFQUBD27duHrVu3YuDAgQBgPgpiNBoxdepUBAQEIDk5Gf369bO4HuZmzJs3D6mpqUhLS0NAQAAKCwuxdetWjBw58pbW4+3tjSNHjmDSpElIS0uDv78/HnnkEZSUlGDNmjVdLmNvb48tW7agsrISgYGByM3Nxeuvv24xj9FohF6vh6+vLx599FH4+Phg9erVAIAhQ4Zg4cKFyMjIgIuLCxITEwEAixcvxoIFC5CdnW1ebvv27fDy8rpufpVKhZ9//hlPPfUUfHx8MHPmTERFRWHhwoW3tB+IbIkkuvMKNCIiIqK/gEdUiIiIyGqxUSEiIiKrxUaFiIiIrBYbFSIiIrJabFSIiIjIarFRISIiIqvFRoWIiIisFhsVIiIislpsVIiIiMhqsVEhIiIiq8VGhYiIiKwWGxUiIiKyWv8DmRWnzoNmiAgAAAAASUVORK5CYII=", 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", 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", 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", 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30ZK8KnwIbNq0aYwbN462bdvSvn175s+fT3p6OhMmTADgySefpHbt2syePRuAIUOGMG/ePFq1amUeAnv33XcZMmSIORC6X56lxWAwmMcbi0qn02FlZUVWVhYGg6FU61MZyPZVfdW9jdW9fVC92qhWq82/2yWpNFV4ADRy5Ehu377NjBkziImJISQkhM2bN5snMUdGRubq8XnnnXdQKBS888473LhxA09PT4YMGcKsWbOKnGdJCSGIiYkhKSmpWPf6+PgQFRVVLfcYku2r+qp7G6t7+6D6tdHFxQUfH59q0Rap8lAIIURFV6KySUlJwdnZmeTk5HznAEVHR5OUlISXlxcajcai/5RGo5G0tDQcHBzuv0lTFSTbV/VV9zZW9/ZB9WmjEIKMjAxu3bqFi4sLvr6+gKmHa9OmTQwcOLBaDhFV9/ZB2bXxfp/f96rwHqCqxmAwmIMfd3d3i+83Go1kZ2dja2tbpX8xFUS2r+qr7m2s7u2D6tXGnK1Ibt26hZeXlxwOk0pN1f6fUQFy5vyU1fJ4SZIkKbec37eWzrmUpMLIAKiY5Fi0JElS+ZC/b6WyIAMgSZIkSZJqHBkA1SA9evTgpZdequhqFOr9998nJCSkXMqqTO9HZapLebh69SoKhYITJ06UW5kKhYI///yzVPOMiYmhT58+2Nvb4+LiUqp5V2cBAQHMnz+/oqsh1XAyAJIqtUWLFtG1a1dcXV1xdXWld+/eHD58uFTyXrNmDR9++GGp5CXVTF988QXR0dGcOHGCixcvVnR1JEmygAyApEotNDSUJ554gl27dnHgwAHzESU3btwocd5ubm5FOjG4JsjOzq7oKlRJ4eHhtGnThoYNG+Ll5VXR1SlTlWoCsiEb5A4uUgnJAKiGWrZsGW3btsXR0REfHx9GjRrFrVu3zK8nJiYyevRoPD09sbOzo2HDhixZsgQwfVhOnToVX19fbG1t8ff3N+/UDRAVFcWwYcNwcHDAycmJESNG5DmbraiWL1/Oc889R0hICI0bN+bHH380H21SFN999x0NGzbE1tYWb29vHnvsMfNr/x12io6OZtCgQdjZ2VGvXj1WrFiRp6tepVLx888/88gjj6DRaGjYsCHr1q3LVWZYWBgDBgzAwcEBb29vxo4dS1xcnPn19PR0nnzySRwcHPD19eXzzz+36D0JCAjg448/5qmnnsLR0ZG6devyww8/5Epz+vRpevXqhZ2dHe7u7jzzzDOkpaWZXx8/fjzDhg1j1qxZ1KpVi6CgIPOw1G+//caAAQOwt7enXbt2XLx4kSNHjtC2bVscHBwYMGAAt2/fzlXejz/+SJMmTbC1taVx48Z89913uV4/fPgwrVq1wtbWlrZt23L8+PEitzc0NBSFQsGOHTto27YtGo2GTp06ceHChVzpFixYQP369bG2tiYoKIhly5YVmm9UVBQjRozAxcUFNzc3HnroIa5evWp+/ciRI/Tp0wcPDw+cnZ3p3r07x44dM78eEBDAH3/8wc8//4xCoWD8+PEAzJs3j+DgYOzt7fHz8+O5554zv/cpKSnY2dnx999/56rL2rVrcXR0NB/kuH//fkJCQszv159//lnkIcPQ0FBUKhW7d++mffv2xX6/FAoFCxYsYOjQodjb2zNr1iwMBgMTJ06kXr162NnZERQUxJdffpnrvpyfrc8++wxfX1/c3d15/vnnCw2gfvzxR1xcXIr2/zrtNlbftuHBs6+hiNx///SSVBAh5ZGcnCwAkZycnOe1zMxMcfbsWZGZmWm+ZjQaRbpWV6RHaqZW3IyNE6mZ2iLfU9DDaDRa1K7u3buLF198UQghxOLFi8WmTZtEeHi4OHDggOjYsaMYMGCAOe3zzz8vQkJCxJEjR8SVK1fEtm3bxLp164QQQnz66afCz89P/PPPP+Lq1atiz549YsWKFUIIIXQ6nQgODhZdunQR//77rzh48KBo06aN6N69e5Hq+N5774mWLVsW+HpKSoqwtbUV69evv29eR44cESqVSqxYsUJcvXpVHDt2THz55Zf5vh9CCNG7d28REhIiDh48KI4ePSq6d+8u7OzsxBdffGFOA4hatWqJX375RVy6dEm88MILwsHBQcTHxwshhEhMTBSenp5i+vTp4ty5c+LYsWOiT58+omfPnuY8pkyZIurWrSu2b98uTp06JQYPHiwcHR1z1aUw/v7+ws3NTXz77bfi0qVLYvbs2UKpVIrz588LIYRIS0sTvr6+4pFHHhGnT58WO3bsEPXq1RPjxo0z5zFu3Djh4OAgxo4dK8LCwkRYWJi4cuWKAETjxo3F77//LsLCwsQDDzwg2rRpI3r06CH27t0rjh07Jho0aCAmT55szuuXX34Rvr6+4o8//hARERHijz/+EG5ubmLp0qVCCCFSU1OFp6enGDVqlAgLCxPr168XgYGBAhDHjx+/b3t37dolANGhQwcRGhoqzpw5I7p27So6depkTrNmzRqhVqvFt99+Ky5cuCA+//xzoVKpxM6dO3N979auXSsMBoO4deuWaNKkiXjqqafEqVOnxNmzZ8WoUaNEUFCQ0Gq1QgghduzYIZYtWybOnTsnzp49KyZOnCi8vb1FSkqKEEKIW7duif79+4sRI0aI6OhokZSUJIQQ4osvvhA7d+4UV65cETt27BBBQUFiypQp5no89thjYsyYMbna+Oijj5qvJScnCzc3NzFmzBhx5swZsWnTJtGoUSOL36+2bduKnTt3luj98vLyEj/99JMIDw8X165dE9nZ2WLGjBniyJEjIiIiQvzyyy9Co9GIVatWme8bN26ccHJyEpMnTxbnzp0T69evFxqNRvzwww/mNP7+/ub/V3PnzhXu7u7i0KFDBbYp1+/dPV8I8Z6TEO85CeN7zkJsfkuI7MwC762KsrOzxZ9//imys7MruiplpqzaWNjn93/JACgflgZA6Vqd8H9jQ7k/0rU6i9r13w/8ex05ckQAIjU1VQghxJAhQ8SECRPyTft///d/olevXvkGYJs3bxYqlUpcvXrVfO3MmTMCEIcPH75vHe8XAE2ZMkUEBgbmev8L8scffwgnJyfzB9Z/3ft+nDt3TgDiyJEj5tcvXbokgDwB0KuvvioMBoMQwhRsAOLvv/8WQgjx4Ycfir59++YqJyoqSgDiwoULIjU1VVhbW4vffvvN/Hp8fLyws7OzKAC69wPUaDQKLy8vsWDBAiGEED/88INwdXUVaWlp5jQbN24USqVSxMTECCFMH1Le3t7mD3shhDkA+uGHH0RiYqIwGAzi119/FYDYsWOHOd3s2bNFUFCQ+Xn9+vXNAXCODz/8UHTs2FEIIcT3338v3N3dc33PFixYYPEH+vbt23O1BzDn2alTJzFp0qRc9w0fPlwMHDjQ/PzeAGjhwoUiKCgo18+wVqsVdnZ2YsuWLfnWw2AwCEdHx1zB90MPPZQrsMzP6tWrhbu7u/n52rVrhYODg0hPTxdCmH7f2Nramn+GFixYkOf9WrRokcXv159//mn+OS3u+/XSSy/dt7znn39ePProo+bn48aNE/7+/kKv1+fKe+TIkebnOQHQ66+/Lnx9fUVYWFihZZh/72ZkCPFVayHecxKJc1qYAyHxTXshbhy/b12rChkAFZ8lAZAcAquhjh49ypAhQ6hbty6Ojo50794dMJ29BjBlyhRWrlxJSEgIr7/+Ovv33+1qHj9+PCdOnCAoKIgXXniBrVu3ml87f/48tWvXxs/Pz3ytadOmuLi4cO7cuRLVec6cOaxcuZK1a9dia2t73/R9+vTB39+fwMBAxo4dy/Lly81DDP914cIFrKysaN26tflagwYNcHV1zZO2WbNm5q/t7e1xcnIyDx+ePHmSXbt24eDgYH40btwYMM0XCQ8PJzs7mw4dOpjzcHNzIygoqGhvwh0tWrQwf61QKPDx8THX4dy5c7Rs2RJ7e3tzms6dO2M0GnMNgwQHB2NtbV1o3jnn5wUHB+e6llNWeno64eHhTJw4MVebP/roI8LDw831adGiRa7vWceOHS1q73/rlXMkwr1t7ty5c670nTt3LvBnLiwsjMuXL+Po6Gius5ubG1lZWeZ6x8bGMmnSJBo2bIizszNOTk6kpaWZ/48UZPv27Tz44IPUrl0bR0dHxo4dS3x8vPlnL2fr/5yh0z/++AMnJyd69+4NmH4W//t+tW/fvsjvU457f06L+361bds2T77ffvstbdq0wdPTEwcHB3744Yc870mzZs1y7djs6+uba4gd4PPPP2fRokXs3bs3V10LdfMExF9GqO3Z13A6+uG/gL0n3D4PPz4Iuz8Bg75oeUk1njwKoxTYqVWc/aBfkdIajUZSU1JxdHIs8Rb1duribQmfnp5Ov3796NevH8uXL8fT05PIyEj69etnngw7YMAArl27xqZNm9i2bRsPPvggzz//PJ999hmtW7fmypUr/P3332zfvp0RI0bQu3dvfv/99xK1pzCfffYZc+bMYfv27bk+CAvj6OjIsWPHCA0NZevWrcyYMYP333+fI0eOlGjJ8n/PrVEoFBiNRgDS0tIYMmQIc+fOzXOfr68vly9fLna5Ra1DUd0bIBWUd84GdP+9dm97wbRa796gDij1Iwvyq5elbc6Rnp5OmzZtWL58eZ7XPD09ARg3bhzx8fF8+eWX+Pv7Y2NjQ8eOHQudMH716lUGDx7MlClTmDVrFm5ubuzdu5eJEyeSnZ2NRqPB2tqaxx57jBUrVvD444+zYsUKRo4ciZVV6f46Lo33678/IytXruTVV1/l888/p2PHjjg6OvLpp59y6NChAsvOKf+/ZXft2pWNGzfy22+/8eabbxatQmf/AkA0HYZeaYdo1B8COsKGl+HcOtg1Cy78DQ9/D56NLGqrVPPIHqBSoFAo0FhbFflhZ62yKH1Bj+Lujnr+/Hni4+OZM2cOXbt2pXHjxnn+OgPTB8G4ceP45ZdfmD9/fq6Jtk5OTowcOZJFixaxatUq/vjjDxISEmjcuDE3btwgKirKnPbs2bMkJSXRtGnTYtX3k08+4cMPP2Tz5s35/kVaGCsrK3r37s0nn3zCqVOnuHr1Kjt37syTLigoCL1en2ty7uXLl0lMTLSovNatW3PmzBkCAgJo0KBBroe9vT3169dHrVbn+sBITEws1SXUTZo04eTJk6Snp5uv7du3D6VSaXFP0/14e3tTq1YtIiIi8rS3Xr165vqcOnWKrKws830HDx4s1Xo0adKEffv25bq2b9++An/mWrZsyaVLl/Dy8spTb2dnZ/P9L7zwAgMHDqRZs2bY2Njkmsyen6NHj2I0Gvn888954IEHaNSoETdv3syTbvTo0WzevJkzZ86wc+dORo8ebX4tKCiI06dPo9VqzdeOHDlS5PeiKCx9v+5N06lTJ5577jlatWpFgwYNzD1mlmrfvj1///03H3/8MZ999tn9bxBGCDdNkjaG3H2/sPeAET/DI4vAxhluHoPvu8LBBVDMAFmqGWQAVAPVrVsXa2trvv76ayIiIli3bl2e/XBmzJjBX3/9xeXLlzlz5gwbNmygSZMmgGmVy6+//sr58+e5ePEiq1evxsfHBxcXF3r37k3Tpk0ZO3Ysx44d4/Dhwzz55JN0797d4uAFYO7cubz77rv89NNPBAQEEBMTQ0xMTK4VTQXZsGEDX331FSdOnODatWv8/PPPGI3GfIOAxo0b07t3b5555hkOHz7M8ePHeeaZZ7Czs7Mo0Hz++edJSEjgiSee4MiRI4SHh7NlyxYmTJiAwWDAwcGBiRMn8tprr7Fz507CwsIYP358qR5YOXr0aGxtbRk3bhxhYWHs2rWL//u//2Ps2LHmIa3SNHPmTGbPns1XX33FxYsXOX36NEuWLGHevHkAjBo1CoVCwaRJkzh79iybNm0q2geeBV577TWWLl3KggULuHTpEvPmzWPNmjW8+uqr+aYfPnw4Hh4ePPTQQ+zZs4crV64QGhrKCy+8wPXr1wFo2LAhy5Yt49y5cxw6dIjRo0ebD+YsSIMGDdDpdOb/W8uWLWPhwoV50nXr1g0fHx9Gjx5NvXr1cvWejRo1CqPRyDPPPMO5c+fYsmWL+f0qrSMhLH2/cjRs2JB///2XLVu2cPHiRd59990SBWedOnVi06ZNzJw58/4bI2ZngD4TPBoharfL/ZpCAS1GwHMHILAn6LNg85vw81BIKnzIUqq5ZABUA3l6erJ06VJWr15N06ZNmTNnTp4PJGtra6ZPn06LFi3o1q0bKpWKlStXAqahpU8++YS2bdvSrl07rl69yqZNm1AqlSgUCpYvX46LiwvdunWjd+/eBAYGsmrVqmLVdcGCBWRnZ/PYY4/h6+trfhTlA9TFxYU1a9bQq1cvmjRpwsKFC/n1118LnG/w888/4+3tTbdu3Xj44YeZNGkSjo6ORZpvlKNWrVrs27cPg8FA3759CQ4O5qWXXsLFxcUc5Hz66ad07dqVIUOG0Lt3b7p06UKbNm2KXMb9aDQatmzZQkJCAu3ateOxxx7jwQcf5Jtvvim1Mu719NNP8+OPP7JkyRKCg4Pp3r07S5cuNfcAOTg4sH79ek6fPk2rVq14++238x0iLIlhw4bx5Zdf8tlnn9GsWTO+//57lixZQo8ePfJNr9FoCA0NpW7dujzyyCM0adKEiRMnkpWVhZOTEwCLFy8mMTGR1q1bM3bsWF544YX77vXTsmVL5s2bx9y5c2nevDnLly/PtUVEDoVCwRNPPMHJkydz9f6AqXd1/fr1nDhxgpCQEN5++21mzJgBYNHPYmEsfb9yPPvsszzyyCOMHDmSDh06EB8fz3PPPVeiunTp0oWNGzfyzjvv8PXXXxecMPtOj2brJ00BT36ca8PYtTDoc1Br4Ooe+K4THF8u9w2S8lAIIX8q/islJQVnZ2eSk5PNvwxzZGVlceXKFerVq1esX0ZGo5GUlBScnJxK9a/+yqI6te/69ev4+fmZJ7VC9WpfQap7G6ti+5YvX86ECRNITk6+by8UVM02FiYrNYkrYYeot/8NbJ/dhs7GhU2bNpknlOcrPhzWTobrd3aODxoIQ74Eh8q/YaVOp7t/+6q4smpjYZ/f/yUnQUvSHTt37iQtLY3g4GCio6N5/fXXCQgIoFu3bhVdNamG+fnnnwkMDKR27dqcPHmSN954gxEjRhQp+KmWspJM/9brCg6eUJRdqd3rw1ObYd+XsOtjuLAJog7B4PnQdGhZ1laqIqr+nwZSldKsWbNcy6XvfeS3Iqcwe/bsKTAvBwcHi+um0+l46623aNasGQ8//DCenp6EhoaW619gpd2mqmDy5MkFtnfy5MkVXb0KERMTw5gxY2jSpAkvv/wyw4cPNy9CqHHvlzBCZrLp6yYPWXavUgVdp8Ezu8C7OWTEw29jYc2zkJlU6lWVqhY5BJYPOQRWfPdr37Vr1wrcEt/b29uis7kyMzMLPROsQYMGRc6rqMr6+1cRbfqv8v4ZvXXrFikpKfm+5uTkVOpnbFX1/4NFeb+qehtzyUwk6/YVrkQnUi/4AWw19sUbPtFrIXQO7JtvCqqcasND30D9XmVa/eKQQ2DFJ4fApErL39+/1PKys7Mrl4CgPFXHNt2Pl5dXtT9ItDTVuPcrI970r7XG1KNTXFY20Ps9aNQf/pwMCRGw7GFoNwn6zATr/PfFkqqvKv6ngSRJklRt6bNBm2r62rqUhoDrdoDJe6Hd06bnRxbBwq4QVbp7LUmVnwyAJEmSpMop807vj1oDylIcsLC2Ny2VH7MGHGtBQjj81Bd2fGAKuqQaQQZAkiRJUuUjBGQkmL62dSmbMho8CM/thxYjTfOC9nwOi3pB7JmyKU+qVGQAJEmSJFU+2lQwZINCBTaFT2YtETtXeOQH03Eadm4Qexp+6AF7vwCjoezKrckMehSXtuCYeb1CqyEDIEmSJKnyyen9sXOF8ljJ1vQheO4gNBpgCry2vw9LBpomS0ulIz7c9L5+0Qyr30ZT/9bmCq2ODIAkxo8fz7Bhwyq6GpIkSSYG/d3NDzXueV7ed3Mf0fro0i/X0Rue+BUe+hasHSHqICzoAkcWy6M0iis7A078agomv25t6llLi0Fo3Mmydq3Qqsll8JIkSVLlkpkICLCyMy1/z8oyv3Qh4QL/F/p/aBQaxhjGlP4+OQoFtBoDAV3hr+dN54ltnAbnN5r2DXKqVbrlVUdCwM1jcGwZhP0B2jv7VimU0KA3tBqDPrA357dsJ7ACqykDIEmSJKnyEOLu3j/59P5sj9wOQIbIYO+NvfSv379s6uHqD0+ug0MLYcdMCN8B3z0Ag+ZB80cLPpC1JstIgFOrTIHPrXsmkrsGmILKlqNMB9ZC0Y4zKWNyCKwG+f333wkODsbOzg53d3d69+5Nenq6+fXPPvsMX19f3N3def7553Pt2Lxs2TLatm2Lo6MjPj4+jBo1ilu3bplfDw0NRaFQsHHjRjp37oxGo+GBBx4gLCysXNsoSVIVp8sEfSagAE3eIZKdkTvNX2+8urFs66JUQsfn4Nl/oFYryEqGPybC6vGQHl+2ZVcVRgNc3mF6Tz4Pgs1vmoIfK1sIHgHj1sP/HYdur90NfioJ2QNUGoQAXUbR0hqNprTZqpJP7FNrivxXSHR0NE888QSffPIJDz/8MKmpqezZs4eck1B27dqFr68vu3bt4vLly4wcOZKQkBAmTZoEmLYt//DDDwkKCuLWrVtMmzaN8ePHs2nTplzlvPHGG8yaNYvAwEDeeecdhgwZwsWLF6vtdu6SJJWynN4fW5c8e/9Ep0dzMfEiChQIBHtv7iUpKwmXslomn8MzCCZugz3z4J9P4OyfEHkAhn4NjfqVbdmVVeI1OLEcTqyA5Ki7131bQquxEPyYaQJ7JSYDoNKgy4CPizYurARcSqvct24Wefv26Oho9Ho9jzzyiPk4iuDgYPPrrq6ufPPNN6hUKho3bsygQYPYsWOHOQB66qmnzGkDAwP56quvaNeuHWlpabkO6Xz33Xfp2bMnTk5O/O9//6NOnTqsXbuWESNGlEaLJUmqzozGO/N/APu8w18Hbx4EoK13W67HXSfaEM2Wq1sY2Xhk2ddNpYYeb0CjvrB2Mtw+DytGmD7s+30MtmW4VL+y0GXB+Q1wfBlE7AbuTAy3dYEWd94L3xYVWUOLyCGwGqJly5Y8+OCDBAcHM3z4cBYtWkRiYqL59WbNmqFS3T1nx9fXN9cQ19GjRxkyZAh169bF0dGR7t27AxAZGZmrnI4dO5q/dnNzIygoiHPnzpVVsyRJqk6ykkAYQGWd79EXh6IPAdCzTk9C1CEAbIjYUI4VxDQU9sxu6DgVUJiCgYWd4ere8q1HeYo5DZteNw1x/TERIkIBAfW6w6OL4ZULMPDTKhX8gOwBKh1qjak3pgiMRiMpqak4OTqW/JRmtabISVUqFdu2bWP//v1s3bqVr7/+mrfffptDh0y/UP47RKVQKDAajQCkp6fTr18/+vXrx/Lly/H09CQyMpJ+/fqRnS23jZckqZTcO/n5P8P7BmHgXILpj6nudbqjuKRgi3YLJ26fIColCj8nv/Krp9oW+s2CoAHw5xRIioSlg6Hj89DrXdPrVV1mEoT9bprQHH3i7nWn2hAyGlqNNk1ursJkAFQaFIqinyRsNILaYEpfHpt73UOhUNC5c2c6d+7MjBkz8Pf3Z+3atfe97/z588THxzNnzhz8/Ey/ZP7999980x48eJD+/U2rMhITE7l48SJNmjQpvUZIklQ96bMgO830tZ1bnpe1ei1CCJq4NcHX3hdHpSPtvdtzMOYgG65sYErLKeVcYSCgC0zZD1vegmM/w4Fv4PJ2eHihqaeoqhHC1JN1fBmc/cv0PQFQqqHxQGj1JNTvCUpV4flUEZViCOzbb78lICAAW1tbOnTowOHDhwtM26NHDxQKRZ7HoEGDzGnS0tKYOnUqderUwc7OjqZNm7Jw4cLyaEqldejQIT7++GP+/fdfIiMjWbNmDbdv3y5ScFK3bl2sra35+uuviYiIYN26dXz44Yf5pv3oo4/YvXs3YWFhjB8/Hg8PD7nJoiRJ95ez87ONI1hZ53k5y2D6MO5Vt5f52qB6pt/7G8I3mBd0lDsbR9Nk6FG/gb2XaW7Qj70hdC4YKn6pd5Gk3IR/PoOvWsH/BpuWsuuzwLOJaX7TK+dNR4U07F1tgh+oBAHQqlWrmDZtGu+99x7Hjh2jZcuW9OvXL9f8k3utWbOG6Oho8yMsLAyVSsXw4cPNaaZNm8bmzZv55ZdfOHfuHC+99BJTp05l3bp15dWsSsfJyYl//vmHgQMH0qhRI9555x0+//xzBgwYcN97PT09Wbp0KatXr6Zp06bMmTOHzz77LN+0H3/8MW+++Sbt2rUjJiaG9evXY22d95eZJEmS2b0Hn+az94/BaECr1wK5A6CedXpiZ2VHZGokp+NOl0tVC9Son+kojabDwKiH0I9hcR+4faFi61UQfTacXQfLR8AXzWDnh5B4xbQDdpvx8PROeO6AaVjP3qOia1smKnwIbN68eUyaNIkJEyYAsHDhQjZu3MhPP/3Em2++mSe9m1vurtGVK1ei0WhyBUD79+9n3Lhx9OjRA4BnnnmG77//nsOHDzN06NCya0wl1qRJEzZvzv/claVLl+a5Nn/+/FzPn3jiCZ544olc1/L7i6tLly4cOHAAJyenks9xkiSpZtCmgFFnWvZu65zn5Ux9JgKBt703DV0aotfrAdCoNTxY90E2RGxgffh6WnhW8CRce3cYvtS0+/HGaXDzOHzfDR58DzpMLvdpD/m6fcE0XHdyJWTE3b1etxO0Hms6E62oUzqquAoNgLKzszl69CjTp083X1MqlfTu3ZsDBw4UKY/Fixfz+OOPY29/9xvWqVMn1q1bx1NPPUWtWrUIDQ3l4sWLfPHFF/nmodVq0Wq15ucpKaZtu3U6Xa7NAHOuCSEwGo3mScKWyAkacvKoLnLaUl3bl6O6tw+qfxure/ug6rVRkR6PAhB2rqYjt0TuOqfpTHOD2nu3R6/Xm38v63Q6+tftz4aIDWy+upmXW72MWlkJ9hxr/BDUao9q44soI3bClukYz2/AMOQbcL7/ZO1721cqstNQnP0T5YnlKG8cMV8W9l4YWzyOseUocG9wbwVKp9xClHob/5NvUVRoABQXF4fBYMDb2zvXdW9vb86fP3/f+w8fPkxYWBiLFy/Odf3rr7/mmWeeoU6dOlhZWaFUKlm0aBHdunXLN5/Zs2czc+bMPNe3bt2KRpN7pZWVlRU+Pj6kpaWVaAVUampqse+tjDIyTBtBpqWl4ezsXO3a91/VvX1Q/dtY3dsHVaONCqMeJ20yAKkGa4x3/gDNIYQgPdu0Y71NrE2uzVe3bduGQRhwUDiQpE3i6/Vf01jduPwqfz9O4/D3q0vzGyuwurYPw3cdCaszhki3rkXaxHbbtm3FL1sIXNMv4x+/m9pJh7Aymv7IN6Ik1rklke7diXVqgciygkMXgYvFL6sEStTGfOR8FhVFhQ+BlcTixYsJDg6mffv2ua5//fXXHDx4kHXr1uHv788///zD888/T61atejdu3eefKZPn860adPMz1NSUvDz86Nv3744OeXe3CorK4uoqCgcHBywtbV8qaMQgtTUVBwdHVFUo7NkBg4ciMFgqLbty1Hd2wfVv43VvX1QtdqoSL+FIguEWoODS965Jhn6DIxZRpQKJSN7jERjp0Gn07Ft2zb69OmDWq3m4tGLLL+wnFj3WKZ1mZZPKRVpECLxeYzrpqK+fohWkT/S0iYKw8AvwMEr3zv+2z6LpN9GeXoVyhPLUcRfMl8WbvUxthyNMXgEHo4+VPSsnhK1sRAp/wmgC1OhAZCHhwcqlYrY2Nhc12NjY/Hx8Sn03vT0dFauXMkHH3yQ63pmZiZvvfUWa9euNa8Ma9GiBSdOnOCzzz7LNwCysbHBxsYmz3W1Wp3nG2MwGFAoFCiVymLNccnpjs7Jo7qR7av6qnsbq3v7oAq18Z7JzwqNO4p86pqaberFsrWyxcbaJtfv5Jzf0Q81fIjlF5az+/puskQWjtaO5VP/ovJqBE/9Dfu/hl2zUF7agnJRVxj8hWnOTQHy+wzKl0FvOqz12M9wcbNpEjaY9oprOgxaj0VRtyMqhYLKtoaryG20IL+iqtD/GdbW1rRp04YdO3aYrxmNRnbs2JFrR+H8rF69Gq1Wy5gxY3Jdz5m389//9CqVqkqMhUuSJNUY2elg0IJCme+5UUKIuwGQquAe98ZujanvXJ9sYzbbr20vs+qWiFIFXV6CZ0LBO9i06eNvT8KaZ0ybDhZHfDhsnwnzm5uO5Ti/wRT81G4Dg+ebdmh+eAH4d5Kn1+ejwofApk2bxrhx42jbti3t27dn/vz5pKenm1eFPfnkk9SuXZvZs2fnum/x4sUMGzYMd/fcSyadnJzo3r07r732GnZ2dvj7+7N7925+/vln5s2bV27tkiRJku4j18GnefsmsgxZ6Iw6lCixUeXtpc+hUCgYXH8wXx77kvUR63m44cNlVOFS4N0MJu2E3XNh7zzTnjtX9sCwb6F+r/vfn50B59aZdmi+ds/xGxp3aPE4tBoD3k3Lrv7VSIUHQCNHjuT27dvMmDGDmJgYQkJC2Lx5s3lidGRkZJ7enAsXLrB37162bt2ab54rV65k+vTpjB49moSEBPz9/Zk1axaTJ08u8/ZIkiRJRWA0mM7+gnz3/gFIyTbN59BYa8hSZBWa3aB6g/jy2JcciTlCdFo0vg6+pVnb0mVlDQ++C436w9pnISEclj0M7Z6GPh+A4j97pwlhWlJ/fBmc/gPuTBoHBTR40HQIadDAfDeQlApW4QEQwNSpU5k6dWq+r4WGhua5FhQUVOiunz4+PixZsqS0qidJkiSVtsxE03J3lU2B+87kDH/ZW9mTReEBkK+DL+182nEk5ggbr2zk6eCnS73Kpc6vHUzeC9vfg8M/wJEfIXwniiHfml7PSIBza02BT2zY3ftc6pqCnpBR4FynYupeDVSKAEiSJEmqYXKGv+zzHnwKoDVozbs/a9Qa4om/b5aDAwdzJOYIG8I3MLH5xEq/Ag4Aa43pJPWggfDX85AQgernQXR0aILVqUtguLPdisoGmgwxbVYY0K1ybKpYxcl3sAbp0aMHL730EgABAQF5dnuWJEkqF7pM0GUAinwPPoV7en/U9lgpi/a3eh//PlgrrQlPDud8wv33kqtU6vc0Haza8gkUwohX6hkUhmzwCYYBn5rO43psMQT2kMFPKZE9QDXUkSNHcu2eLUmSVG5yzv2ydQJV/suWcwIgS5a0O1o70sOvB1uvbWVDxAaauN//sOdKxc4FHl6IvtEgruz+lYDB01DXbVvRtaq2ZBhZQ3l6eubZ5VqSJKnMCSNkFnzwKYDeqCdDZ9rR18naKd80BRlSfwgAm65sQp+zH04VIxr152ztkeDbsqKrUq3JAKiG+u8QmEKh4Pvvv2fw4MFoNBqaNGnCgQMHuHz5Mj169MDe3p5OnToRHh6eK5+//vqL1q1bY2trS2BgIB988IH5oEJJkqQ8slJMe9Uo1WCTf3Bz7+aH6gJ6iArSuXZnXGxciMuM43D04RJXV6q+ZABUCoQQZOgyivzI1GdalL6gR2Er4Yrjww8/5Mknn+TEiRM0btyYUaNG8eyzzzJ9+nT+/fdfhBC5Vuvt2bOHJ598khdffJGzZ8/y/fff87///Y/PP/+8VOslSVI1kjP5WeNW4OZ8OQGQpb0/AGqlmv4B/QFYH7G+eHWUagQ5B6gUZOoz6bCiQ7mXe2jUITTq0hvGmjBhAiNGjADgjTfeoGPHjrz77rv069cPgBdffNG8QSXAzJkzefPNNxk3bhwAgYGBzJw5kzfeeINZs2aVWr0kSaom9NmgvXNWkyb/yc8Go8F8+ntxj7QYUn8IKy+sZEfkDjJ0GaX6e1KqPmQPkGTWokUL89c5G1EGBwfnupaVlWU+bO7kyZN88MEHODg4mB/PPvssMTExFp3IK0lSDZEz98faAazyP9oiXZeOEAJrlXWhuz8XJtgjGH8nfzL1meyI3HH/G6QaSfYAlQI7KzsOjTpUpLRGo9F8SnNJDym0s7Ir0f3/de8hcjn7Z+R3LedMtbS0NGbOnMkjjzxiTmM0GklLS8PWtuBzeyRJqoGEuGf4K//Jz3B392dH6+KfZK9QKBgUOIjvTnzHxoiN5onRknQvGQCVAoVCUeQuVqPRiN5Kj0atqdynNBdB69atuXDhAg0aNDBfMxqNpKSkVPm2SZJUyrLTTJv6KVRg65xvEqMwlmj+z70G1xvMdye+40D0AW5n3MZT41mi/KTqR35KScU2Y8YMfv75Z2bOnMmZM2c4d+4cK1eu5KOPPqroqkmSVNmk3+n9sXPN9+BTgAxdBkZhRKVUlbiH28/JjxDPEIzCyN9X/i5RXlL1JHuApGLr168fGzZs4IMPPmDu3Lmo1Wrz6jFJkiQzo/6eg0/zn/wMuVd/lcYxFoMDB3Pi9gk2RGzgyWZPlji/8iB0OpJXrsR33XpuHfkXtYc7Vm7uqNzcsHJ3Q+XmhsrVFStXVxRqy7YIkHKTAVANcu/BslevXs312n+X1AcEBOS51qNHjzzX+vXrZ14lBneHwCRJkswyEgEBVnZQwHQBIUSu+T+loV9AP+YcmcO5hHNcTrxMA9cG97+pAqXt2UPsnLlkh4fjCKScPl1oeqWzM1aurqjc3bFyc0Xl6obK3Q2rnH/dTAGT1Z2gSWElP/LvJd8NSZIkqWzdO/m5gJ6dLH0WeqMepUKJvbp0julxsXWha+2u7IraxYaIDbzU5qVSybe0aSOucGvuXNJ27wZA6erKrbZtaNigASQno09IxBAfjz4xAUNCIobERBACY3Iy2cnJ8J8/aAuidHa+JyhyReXmjsrN9U4Pk+ud13KCqeofMFXv1kmSJEkVKzsD9JmYDj51LTBZTu+Pg7UDSkXpTU8dUn8Iu6J2sfHKRl5o/UKp5l1ShpQU4r79joTly0GvBysr3MaMwXnS05zfuxf3gQNzrcTNIQwGDMnJGBIS0McnYEhMQJ+QgMH89X0CpitXilQ/lbOzacjN7W5vUq6Ayd0dlatblQ2YqlZtJUmSpKolp/fH1gVUBX/kpOosP/y0KLrV6Yaj2pGY9BiOxh6lnU+7Us2/OITBQNLq37n95Zem4ARw6N4drzfewCawHjqdrtD7FSoVVneCEpsijOoVN2AyJCdjKE7AlDMMlzNvydUtd8Dk7oaoBIdxywBIkiRJKhtGI2SaPuALm/ysNWjR6rUoUOCgdijVKtiobOgb0Jc/Lv3B+vD1FR4ApR88ROzs2WgvXADAun59vN98E4euXcqszGIHTPHxpuAoMQF9fPyd4OhOEJWQYAqY4hMwJCUVK2DyCWkJAweWrHElIAMgSZIkqWxkJYEwgMoabAru2clZ/aVRa7BSlv7H0uDAwfxx6Q+2XdvGWx3ewraAXajLUnZUFLc++ZTUbdsAUDo54Tl1Kq5PPF7pVnPlCpiKkL64AZOwqth2ywBIkiRJKhtFOPgU7gZApT38laO1d2tq2dfiZvpNQq+Hmg9LLQ+GtHTif/iBhCVLEDodKJW4Pv44Hv83FSvXgudEVSXFCZi0cXFs3769zOtWmMozG0ySJEmqPvRa0+7PAHYFH32hM+rI0JnODizp7s8FUSqUDAocBMDG8I1lUsZ/CaORpDVrCR/Qn/gffkDodNh36ki9P9fiM+PdahP8FIdCpULl5obBoXSHOy0le4AkSZKk0pfT+2PjCFbWBSZLuxMk2VrZolaV3ZDI4MDBLDq9iL039pKQlYCbbcFzkkoq49hxYj/+mKywMADU/nXxfuMNHHr2LJUNHqXSIXuAJEmSpNIlBGTcOfm9kINP4e7y97Lq/ckR6BJIU/em6IWeLVe3lEkZuuhobrzyKtdGjSIrLAylvT1er71K4Pr1OPbqJYOfSkYGQFKZUygU/PnnnxVdDUmSyos2BYy6Qg8+BTAYDaTr0oGym/9zryGBplPhN4RvKNV8jZmZ3P7mW8IHDCRl40ZQKHAZ/hj1t2zGfeJElNYF94BJFUcGQFKl8/777xMSElLR1ZAkqbhyTX4u+GMmTZeGEAJrlTU2qqJMny2Z/vX6o1KoOBV3imsp10qcnxCC5A0bCR84iLhvvkFkZWHXtg0Bv6/G98MPsfLwKIVaS2VFBkA1WHZ2dkVXQZKk6sagg6w75wHeZ/jr3tVf5TE85GHnQcdaHQHYEFGyXqDM02FcGzWam6++ij46GqtavtT+Yh7+y5Zh16xZaVRXKmMyAKpBevTowdSpU3nppZfw8PCgX79+zJs3j+DgYOzt7fHz8+O5554jLc00KVEIgaenJ7///rs5j5CQEHx9fc3P9+7di42NDRkZplUcly5dYuDAgWg0Gpo2bcq2O3te3OuNN96gUaNGaDQaAgMDeffdd807ny5dupSZM2dy8uRJFAoFCoWCpUuXAhRaV0mSKonMBECYDj1V2xWYzCiMuU5/Ly+DAwcDpmGw/x7uXBS6W7e4Of0trg4fTubx4yjs7PB88QXqb9qE04ABcp5PFSJXgZUCIQQiM7NIaY1GI8bMTIxWVqAsWfypsLOz+D/b//73P6ZMmcK+ffsA+Pvvv/nqq6+oV68eERERPPfcc7z++ut89913KBQKunXrRmhoKI899hiJiYmcO3cOOzs7zp8/T+PGjdm9ezft2rVDo9FgNBp57LHHcHd358CBA6SmpvLSSy/lqYOjoyNLly6lVq1anD59mkmTJuHo6Mjrr7/OyJEjCQsLY/PmzeY9IpydTXMIlEplgXWVJKkSsGDyc4YuA6MwYqW0ws6q4ECptPWq2wuNlYbradc5efskIV4hRbrPqNWS8L+fiV+4EOOdP/ichg7B65VXUHt7l2GNpbIiA6BSIDIzudC6jUX3xJZCuUHHjqLQaCy6p2HDhnzyySd38wgKMn8dEBDARx99xOTJk81BRY8ePfj+++8B+Oeff2jVqhU+Pj6EhobSuHFjQkND6d69OwDbt2/n/PnznDp1iqCgIJRKJR9//DEDBgzIVYd33nknV5mvvvoqK1eu5PXXX8fOzg4HBwesrKzw8fHJdd+9wVR+dZUkqYLpMkCfZZr3U8jBp3B39Vd5DX/lsLOyo7d/b9aFr2NDxIb7BkBCCFK3b+fW3E/QXb8OgG3LFvhMn46dnKtYpckhsBqmTZvcgdr27dt58MEHqV27No6OjowdO5b4+HjzkFb37t05e/Yst2/fZvfu3fTo0YMePXoQGhqKTqdj//799OjRA4Bz587h5+eXa4isY8eOeeqwatUqOnfujI+PDw4ODrzzzjtERkbet+73q6skSRUsI870r60LKFUFJhNClPnuz4XJGQbbfHUzOkPBB49mXbhA5PgJ3Pi/F9Bdv46Vlxe1PplLwK+/yuCnGpA9QKVAYWdH0LGjRUprNBpJSU3FydERZSkMgVnK/p4TeK9evcrgwYOZMmUKs2bNws3Njb179zJx4kSys7PRaDQEBwfj5ubG7t272b17N7NmzcLHx4e5c+dy5MgRdDodnTp1KnL5Bw4cYPTo0cycOZN+/frh7OzMypUr+fzzzwu9ryh1lSSpAhkNkJlk+vo+w19Z+iz0Rj1KhRJ7dfmfCt7epz1edl7cyrzFnht76FW3V67X9QkJ3P7yK5JWrwajEYWNDW5PTcDj6adRVoJTzKXSIQOgUqBQKIo+FGU0otTrUWo0JQ6ASuro0aMYjUY+//xzc11+++23XGkUCgVdu3blr7/+4syZM3Tp0gWNRoNWq+X777+nbdu25qCqSZMmREVFERMTg5OTaVLjwYMHc+W3f/9+/P39efvtt83Xrl3LvRzV2toag8FgcV0lSapAmYkgjKCyAevCg4Sc4S8HaweUhSyTLysqpYqBgQNZemYpGyI2mAMgkZ1NwooVxH37HcbUOz1U/fvj9eqrWNepXe71lMqWHAKrwRo0aIBOp+Prr78mIiKCZcuWsXDhwjzpevTowa+//kpISAgODg4olUq6devG8uXLzfN/AHr37k2jRo147rnnOHnyJHv27MkV6IBpDlJkZCQrV64kPDycr776irVr1+ZKExAQwJUrVzhx4gRxcXFotdoi11WSpApy7+Tn+8zpqcjhrxw5w2ChUaGkZKeQtns3EUMf4tacuRhTU7Fp2gT/ZT9TZ/4XMvippmQAVIO1bNmSefPmMXfuXJo3b87y5cuZPXt2nnTdu3fHYDCY5/qAKSj67zWlUskff/xBZmYmDzzwAE8//TSzZs3KldfQoUN5+eWXmTp1KiEhIezfv5933303V5pHH32U/v3707NnTzw9Pfn111+LXFdJkiqALgvu7OiMpvAztrR6LVqDFgUKHNUVFwAFuQXR0LUhXreyOT9+DFHPTib76lVU7u74fvQh9VavRtOuXYXVTyoHohL45ptvhL+/v7CxsRHt27cXhw4dKjBt9+7dBZDnMXDgwFzpzp49K4YMGSKcnJyERqMRbdu2FdeuXStSfZKTkwUgkpOT87yWmZkpzp49KzIzMy1r5B0Gg0EkJiYKg8FQrPsrO9m+qq+6t7G6t0+ICmhj0nUhbhwTIi78vklvZ9wWYbfDxJWkK0XO/r+/d7Ozs8Wff/4psrOzi1tjoU9KEjteHiVON24szgY1FmebB4uYTz4R+tTUYudZWkqjfZVdWbWxsM/v/6rwHqBVq1Yxbdo03nvvPY4dO0bLli3p168ft27dyjf9mjVriI6ONj/CwsJQqVQMHz7cnCY8PJwuXbqYl2mfOnWKd999F1tb2/JqliRJUs0gjHc2P+S+k5+h/A4/LYjQ60lYsYLwfv3x3XQMlYAjDRXYr1qE92uvoXJwqJB6SeWvwidBz5s3j0mTJjFhwgQAFi5cyMaNG/npp594880386R3c8vdvbpy5Uo0Gk2uAOjtt99m4MCBufa7qV+/fhm1QJIkqQbLSgGjHpRqsC08qNEZdGTqTJvGVsT8n/QDB4j9eDbaS5cAsGnYgOW9bfjN5QJa/Skm0aHc6yRVnAoNgLKzszl69CjTp083X1MqlfTu3ZsDBw4UKY/Fixfz+OOPm1ciGY1GNm7cyOuvv06/fv04fvw49erVY/r06QwbNizfPLRaLVqt1vw8JcX0F4pOpzMf0ZBDp9MhhDDt6Gw0WtJcAPPW6zl5VDeyfVVfdW9jdW8flG8bFRlxKABh52oqt5DjJXImP9tZ2aFSqIpcN6PRiBACnU6HSqUy/17+7+/ngmRHRhL/2eek79oFgNLZGbfnn8d5+GM0jdwEB99nXfg6xjUeVymOsrC0fVVRWbXRkvwqNACKi4vDYDDg/Z9txL29vTl//vx97z98+DBhYWEsXrzYfO3WrVukpaUxZ84cPvroI+bOncvmzZt55JFH2LVrV65VSzlmz57NzJkz81zfunVrnv1lcnYoTktLK9Fhoql3llhWV7J9VV91b2N1bx+UfRsVRj1OWlMZqQZrjHf+eCxIgsE0VKY2qs1/aBZFdnY2mZmZ/PPPP+j1evP1/M4avJcyKwu3nbtw2bsXpcGAUCpJ6vgA8b17Y9RoYOtW9EKPFVZcTbnKj+t/pLZV5Vnxdb/2VQel3UZLNsat8CGwkli8eDHBwcG0b9/efC3nL4qHHnqIl19+GcC82mjhwoX5BkDTp09n2rRp5ucpKSn4+fnRt29f8342ObRaLZGRkdjb22NXjI0IhRCkpqbi6Fi+27+XF9m+qq+6t7G6tw/Kr42KtFhT74+1PQ4uhc//MQojN5NuAuDp6Im1yrrI5WRmZmJnZ0f37t2xsbFBp9Oxbds2+vTpg1qtzpNeGAyk/vUX8V99jSE+HgBN5054vPYaDfOZDnFk3xG2XNtCUu0kJrWZVOR6lZX7ta86KKs2WhJYV2gA5OHhgUqlIjY298lYsbGxec6B+q/09HRWrlzJBx98kCdPKysrmjZtmut6kyZN2Lt3b7552djYYGNjk+e6Wq3O841RKpUoFAqysrJy7apcVDkBmkKhqPCNEMuCbF/VV93bWN3bB+XURiHMk58VGncU9yknVZuKEAJrlTU2VjYWBWZZWVkoFArs7OxQqe4esZHf7+iMf/8l9uPZZJ09C4B1QABeb76BQ/fuBZY5tMFQtlzbwpZrW3i9/etYKStH30B+7atuSruNluRVod9la2tr2rRpw44dO8zzc4xGIzt27GDq1KmF3rt69Wq0Wi1jxozJk2e7du24cOFCrusXL17E39+/xHVWqVS4uLiYV6lpNBqL/iMbjUays7PJysqqlr98ZfuqvurexurePiinNmang1YLqABbyMoqNHlieiJGnRFbpW2uOZeFEUKQkZHBrVu3cHFxyRX8/Jfuxg1iP/uM1L83A6B0dMTjuedwGz0KhXXhvU0da3XEzdaNhKwEDtw8QNc6XYtUP6l4MnQZfHX0K/yMfhVajwoPc6dNm8a4ceNo27Yt7du3Z/78+aSnp5tXhT355JPUrl07z6Z3ixcvZtiwYbi75+12fe211xg5ciTdunWjZ8+ebN68mfXr1xMaGloqdc7pnSpoqX5hhBDm7tzq2P0u21f1Vfc2Vvf2QTm1MSPeFARZO0B64YcZCyGIzYjFKIwY7YykqdIsKsrFxaXAUQFjRgbxP/5I/OKfEFotKJW4DB+O54svYOVW+KaMOdRKNQPqDWD5ueVsiNggA6AytjhsMcsvLMdH6cNwMfz+N5SRCg+ARo4cye3bt5kxYwYxMTGEhISwefNm88ToyMjIPH/BXLhwgb1797J169Z883z44YdZuHAhs2fP5oUXXiAoKIg//viDLl26lEqdFQoFvr6+eHl5WTyDXafT8c8//9CtW7dq2bUp21f1Vfc2Vvf2QTm0MSsFlowBgxaG/w+86xWa/HjscWafnI2LjQtLByy16PwvtVqdf8+PEKRu2Ej8/Pno70yj0LRvj/db07Ft3Nii5oDpaIzl55azM3In6br0CjmktSaISo1iadhSAHra9qzQP0IqPAACmDp1aoFDXvn12gQFBZmXeRbkqaee4qmnniqN6hVIpVIV2iVb0D16vR5bW9tq+ctXtq/qq+5trO7tg3Jo46llkHwZvJpB3db3PftrR/QOorOj6RzQGY1dEQ+OLkRWWBh+3y0gNtLU86SuUwev11/DsU+fYn+gNnNvRoBTAFdTrrIjcgdD6w8tcT2lvD498inZxmx81MEEKJre/4YyVD0HwCVJkqSyc3yZ6d/WY+8b/BiFkV2Rpv13evn1KnHR2ogr3Bg3HrvISBR2dni+/DKBGzfg1LdviXoTFAqF+YDU9eHrS1xPKa99N/axK2oXClSEn+/Nd+esMBoL78woSzIAkiRJkoou+hREnwSVNbQYed/kYXFh3Mq8hcZKQwffku+0HP/9QkR2NhkBAfhvWI/Hs8+gzGcVb3EMChwEwKHoQ9zKsHyOp1QwnUHHnMNzANDGd8SY7U2/OkaUyoobApMBkCRJklR0Ob0/jQfd9+R3gJ2ROwHoWqerRXv/5Cc7MpLkDRsBuD1kMFZeXiXK77/qONahtVdrBIJNEZtKNe+absX5FVxNuQoGB7RxvRnXsS4t3Cqu9wdkACRJkiQVlS4LTv1m+rrV2CLdsjPKFAA9WPfBEhcf98MPYDCg6dIFbZ06Jc4vP4Prm4bBNkRsKJP8a6LbGbdZcHIBAJmx/WlRy5vX+zaq4FrJAEiSJEkqqvMbICsJnP0gsOd9k0ckR3Al+QpWSiu61C7ZKlzdjRsk//kXAK7PPluivArT178vaqWaC4kXuJh4sczKqUnmH5tPui4dQ6Yfdlnt+eaJ1lhbVXz4UfE1kCRJkqqGY/8z/RsyGoqwwWLO5OcOPh1KfPp73I8/gl6PpuMD2IW0LFFehXG2caZ7HdORSbIXqORO3DrBuvB1AGTFDGXuYyHUdS/5SsDSIAMgSZIk6f4SrsCVfwAFtBpdpFtyhr961S3Z6i9dbCzJv/8BgMeUKSXKqyhyVoNtjNiIwWgo8/KqK6Mw8sGBjwHQJbVhdEgXBgb7VnCt7pIBkCRJknR/J5ab/g3sAS5175v8dsZtTt0+BUAPvx4lKjr+x8UInQ5N27bY33P4dVnpWqcrTtZO3Mq4xZHYI2VeXnX1+4U1XEo6hzDY4K8YztuDmlR0lXKRAZAkSZJUOKMBTqwwfd26aJOfd0WZhr9aeLTAS1P81Vr627dJ+s008drjubLv/QGwVlnTL6AfABvC5TBYcaRkp/DJ4S8AEIl9WTiqB7ZqyzYOLmsyAJIkSZIKF74TUm6AnSs0HlykW3KGv3rWvf9k6cLEL1mK0Gqxa9kSTceOJcrLEkPqDwFg27VtZOozy63c6uLt0M/RihQMWi8+enAy9Twq39EiMgCSJEmSCnfsZ9O/LUaC1f03HUzLTuNQ9CGgZPN/9ImJJK5cCZh6f8rz3KgQzxBqO9QmQ59BaFRouZVbHRy6HkbozT8B6OQykYdD7j9kWhFkACRJkiQVLD0OLvxt+rqIe//svbEXvVFPgFMAgc6BxS46Yen/EBkZ2DZrhn23bsXOpzjk0RjFozcYeXHb+6AwYpsdwjcP33+38IoiAyBJkiSpYCdXglEHtVqBT/Mi3ZKz+3NJen8Myckk/vILAB5TJlfIqeE5AdD+m/uJz4wv9/Krolc2LSNdeQFhtOLLvjMq3byfe8kASJIkScqfEPccfPpkkW7JNmTzz41/gJIFQAnLfsGYno5No0Y49Cr5IarFEeAcQLBHMAZhYPPVzRVSh6ok9OJ1tsf+CEBPn5F08m9YwTUqnAyAJEmSpPxd/xdunwcrO2j+aJFuORxzmHRdOp52ngR7BBerWENaGgk/m+YdeUyZjKIImy6WlZwDUuVqsMLdTtXy0pYvUKqTsFW480mfFyu6SvclAyBJkiQpf8fvTH5uNgxsnYt0S87wV0+/nigVxfuISVy+AmNKCtaBgTj27VusPErLgHoDUClUhMWHcSX5SoXWpbIyGgXP/7YNvYPpe/9e5zexs7Kr4FrdnwyAJEmSpLy0aRC2xvR1ESc/G4XRvP9PcYe/jBkZJCxdCoDH5GdRqCp2DombrRuda3cG5NEYBfku9DKnMn5BodQT7NaGQYH9KrpKRSIDIEmSJCmvs39Cdhq41Qf/TkW65XTcaeIy43BQO9Dep3g7NieuXIUhMRF13bo4DRxYrDxK25BA055AGyM2YhTGCq5N5XIoIp75+zaidjqDAiUfdHmnQiasF4cMgCRJkqS8jt2Z/NxqDBTxAy1n+Ktr7a6oVWqLizRmZRH/008AeDz7DAorK4vzKAs9/Hpgr7bnRtoNTtw6UdHVqTTi07T838p/sfY2HXY6qskTNHBtUMG1KjoZAEmSJEm53b4IUQdBoYKQUUW+raTL35N+W40hLg51rVo4Dx1arDzKgq2VLX38+wCwPkLuCQSmeT/TfjtJotUuVDa3cbVx5bmQ5yq6WhaRAZAkSZKUW87S94Z9wdGnSLdEJEdwNeUqVkorutTuYnGRxuxs4hcvBsD9mWdQqC3vQSpLOXsCbbm6hWxDdgXXpuJ9/08E/4RHYOOxHYCX2ryEk7VTBdfKMjIAkiRJku4y6ODkr6avi3jwKdzt/eng2wEHaweLi01eswZ9bCxWPj44P/KwxfeXtXY+7fDWeJOanco/1/+p6OpUqKPXEvhs6wVsvDajUGlp5t6MYQ2GVXS1LCYDIEmSJOmui1sg/TbYe5l6gIpoV+Sd1V9+lg9/CZ2O+B8WAeA+cSJKa2uL8yhrSoWSgYGmSdk1eTVYYno2/7fiOML6GmqXowC81eGtYm95UJGqXo0lSZKkspMz/BXyBBRxIvOtjFucijsFmPb/sVTyunXobt5E5eGBy/DHLL6/vOSsBtt9fTfJ2uQKrk35E0Lw2u8nuZmcgVMdUxD4UP2HaOHZooJrVjwyAJIkSZJMUm7Cpa2mr4u49w9gPi29hWcLPDWeFhUp9Hrivv8BAPennkJpa2vR/eWpoWtDglyD0Bv1bLm6paKrU+4W773C9nO3sHU9hkEdib3anpfavFTR1So2GQBJkiRJJidWgDBC3Y7gUfRznHZE7gCKN/yVsmkTushIVK6uuD5eeU8OzzGkvqkXqKYNgx2PTGTO3+dBmYlz7W0ATGk5BQ87jwquWfHJAEiSJEkCoxGOm05ft6T3JyU7hcPRhwHLl78Lg4G4hd8D4DZ+PEqNxqL7K8KAegNQKpQcv3WcqNSoiq5OuUjO0DF1xXH0RkFQ4/1kGJKp51yPUY2LvkVCZSQDIEmSJAmu7YPEK2DtaDr7q4j2Xt+LXuip51yPes71LCoydetWsiMiUDo74zq6anyYemm86ODTATDtDF3d5cz7uZGUSW3PZGIxrfZ7s/2bxdrssjKRAZAkSZJ0d/Jz80fA2r7It+2MMn0gPlj3QYuKE0YjcQsWAuA2diwqB8uXzleUwfVNewJtjNiIEKKCa1O2lu6/ytazsahV4NdwCwZhoJdfLzrVKtrxKJWZDIAkSZJquswkOPuX6evWTxb5tmxDNnuu7wEsn/+TtnMn2osXUdrb4zZ2jEX3VrTedXtjZ2XH1ZSrhMWFVXR1ysyp60l8vOkcAI91S+JM4jGslda81u61Cq5Z6ZABkCRJUk0X9jvos8CzCdRuU+TbDkUfIkOfgZedF808mhX5PiEEcd8tAMB1zBhUzs4WV7kiadQa83L/6joZOiXLNO9HZxD0burC0dT/ATCh+QTqONap4NqVDhkASZIk1XQ5B5+2Hlvkg0/h7vBXz7o9LdoIL233brLOnkWh0eA2fpxFVa0sclaDbb66GZ1RV8G1KV1CCKb/cZrIhAzquNrRtMkxotOj8bH3YWLwxIquXqmRAZAkSVJNFnMaok+AUg0tHi/ybUZhLNbuz0II4hbc6f15/HGsXF0tqm5l8YDvA7jbupOQlcCBmwcqujql6pdDkWw8HY2VUsGMYT6suGDq/Xm17avYWdlVcO1KT6UIgL799lsCAgKwtbWlQ4cOHD58uMC0PXr0QKFQ5HkMGjQo3/STJ09GoVAwf/78Mqq9JElSFZbT+9N4INi7F/m2U7dPEZ8Vj4PagXY+7Yp8X/r+/WSdPIXCxgb3pyZYWttKw0ppxYB6AwBYH159Tog/czOZDzecBeCN/o35++YitAYt7X3a09e/6EejVAUVHgCtWrWKadOm8d5773Hs2DFatmxJv379uHXrVr7p16xZQ3R0tPkRFhaGSqVi+PDhedKuXbuWgwcPUqtWrbJuhiRJUtWjy4JTq0xftyr65Ge4O/zVtU5Xi5ZD5/T+uIwcgZVH1d1ED+4Og+2K2kVadloF16bk0rR6pq44TrbeyIONvWheP5btkdtRKVS82f5NFBYMj1YFFR4AzZs3j0mTJjFhwgSaNm3KwoUL0Wg0/PTTT/mmd3Nzw8fHx/zYtm0bGo0mTwB048YN/u///o/ly5ejVlftvQokSZLKxPkNkJUETnWgftHP8BJCmE9/t2Tzw/TDh8n89ygKtRr3iVV/LkkTtyYEOgeiNWjZdm1bRVenRIQQvLXmNFfi0qnlbMucR5sy58gcAB5v/DgNXYu+M3hVUaEBUHZ2NkePHqV3797ma0qlkt69e3PgQNHGVBcvXszjjz+Ovf3dfSuMRiNjx47ltddeo1mzoq9MkCRJqlFy9v5pNRqUqiLfdiX5CtdSrqFWqulSq0uR78vp/XF+7FHU3t4WVbUyUigU5l6gqr4p4sojUaw7eROVUsHXo1rxd+QfRCRH4GrjypSWUyq6emXCqiILj4uLw2Aw4P2f/wje3t6cP3/+vvcfPnyYsLAwFi9enOv63LlzsbKy4oUXXihSPbRaLVqt1vw8JSUFAJ1Oh05XurP7c/Ir7XwrC9m+qq+6t7G6tw+K2MakSNQRoQgU6JuPBAvej61XTQemtvduj43CpkjvZeaJk2QcOAhWVjiPH1+i978yfQ/7+PXhy2NfcjjmMNeTr+OtKXlgV97tOx+TyvvrzgDw8oMNqO2azdR9pmB1asupaJSaKvNZaEl+FRoAldTixYsJDg6mffv25mtHjx7lyy+/5NixY0Uer5w9ezYzZ87Mc33r1q1oyuhsmm3bqnZ36f3I9lV91b2N1b19UHgbg6LX0Bi47diUA/vDgKJv6Pdn6p8AeCR5sGnTpiLdU/unJdgDya1C2HbiBJw4UeTyClJZvocBqgCuGq4yf/N8utp2LbV8y6N9WgN8dkqFVq+giYuR2qnneGPjGtJ0adRS1UJ9Qc2mi0X7HhdHabcxIyOjyGkrNADy8PBApVIRGxub63psbCw+Pj6F3puens7KlSv54IMPcl3fs2cPt27dom7duuZrBoOBV155hfnz53P16tU8eU2fPp1p06aZn6ekpODn50ffvn1xcnIqRssKptPp2LZtG3369KmWc5Nk+6q+6t7G6t4+KEIbjQasvp0OgNuDLzKw2cAi5x2bEcuNP2+gQMHz/Z8v0mngWWfOcP3CBVCpaDFzJmo/vyKXl5/K9j3UXtby4eEPCbcJ5+MBH5d4snB5tu/1P05zKysab0cblkzuSHTmRY5tPQbArF6zaOnZskzKLas25ozgFEWFBkDW1ta0adOGHTt2MGzYMMA0f2fHjh1MnTq10HtXr16NVqtlzJjcW6iPHTs215wigH79+jF27FgmTMh/yaWNjQ02NjZ5rqvV6jL74SvLvCsD2b6qr7q3sbq3Dwpp4+XdkHIDbF2wavYQWPA+7I3eC0BLz5b4OvkW6Z6YRT8C4Dx4EJrAwCKXdT+V5XvYv35/5v47l8vJl7mSdoUgt6BSybes27f63yjWnohGqYCvnmiFl7MdL+/9BICh9YfStlbbMis7R2m30ZK8KnwIbNq0aYwbN462bdvSvn175s+fT3p6ujlYefLJJ6lduzazZ8/Odd/ixYsZNmwY7u65961wd3fPc02tVuPj40NQUOn8UEqSJFVpOXv/tBgJaluLbrV09VfWhQuk7dgBCgXuzz5rUVlVhZO1E939urPt2jbWh68vtQCoLF2KTWXGX6Z5P9P6NKJDoDtrL60lLD4Me7U9L7V+qWIrWA4qPAAaOXIkt2/fZsaMGcTExBASEsLmzZvNE6MjIyNRKnMvVrtw4QJ79+5l69atFVFlSZKkqis9Hs7fWbHUeqxFt6Zkp3Ak5ghQ9AAo58R3pwH9sSnF3p/KZkjgELZd28amK5t4uc3LqCxYVVfeMrMNPL/iGJk6A10aeDClRwNSs1OZf2w+AJNbTMZT41mxlSwHFR4AAUydOrXAIa/Q0NA814KCghBCFDn//Ob9SJIk1UinVoFRB74h4BNs0a17ru9BL/TUd66Pv5P/fdNrL18mdcsWANyfnVyc2lYZXWp3wcXGhduZtzkUc4hOtTpVdJUK9N66MC7GpuHpaMMXI0NQKRUsOLmAhKwEApwCGN1kdEVXsVxU+EaIkiRJUjkR4u7ePxb2/oDlw19xC78HIXDs0xvboEYWl1eVqFVq+gX0A2BDeOU9IX7t8ev89u91FAr4cmQIno42hCeF8+u5XwF4s/2bFu3sXZXJAEiSJKmmuHEMbp0FK1to/phFt2oNWvbeME2ALkoAlH31Kil3lsi7T67evT85BgcOBmB75HYydEVfjl1eLt9K4+21pu0OXujVkE4NPBBCMPvwbPRCT0+/nnSu3bmCa1l+ZAAkSZJUUxwznepN04fAzsWiWw9FHyJDn4GXxoum7k3vmz7u+x/AaMShRw/sasiO/C09W+Ln6EemPtN8VlplkaUzMHXFMTKyDXQMdOeFB01HW+yI3MGh6ENYK615rd1rFVzL8iUDIEmSpJogOx3C1pi+blX84a+efj1RKgr/6Mi+fp3kdesA8Hiueh6jkB+FQmHuBdoQUbmGwWauP8v5mFQ8HKz58nHTvJ8sfRafHvkUgPHNx+PnWLL9maoaGQBJkiTVBGf+hOxUcK0HAUU/vwvAYDSwK2oXULThr/gfFoHBgH3nzti1aFGc2lZZOQHQgZsHiMuMq+DamKw7eZNfD0eiUMAXI0PwcjJtfbAkbAk302/iY+/DxOZV/3BaS8kASJIkqSYwH3w6BizcqfhU3CkSshJwVDvSzrtdoWl10dEkrV0L1Kzenxx1nerSwrMFRmHk7yt/V3R1uBKXzvQ/TgHwfI8GdG1oWt5+M+0mi8NM52i+0vYVNOqyOfapMpMBkCRJUnUXdwkiD4BCCSGjLL49Z/ira52u910hFP/jYtDp0LRvj6ZNm2JVt6obEmg6IX59+PoKrUfOvJ/0bAPtA9x4qXdD82uf/fsZWoOWdj7t6OffrwJrWXFkACRJklSNGIyCQ1cSiMvi7n5pOb0/DfqAUy2L8hNCsCNyB3D/4S/drVskrV4N1Mzenxz9AvphpbDiXMI5wpPCK6weH286x5mbKbhq1Hz5RAhWKtNH/sHog2y7tg2lQsmb7d8s8dllVZUMgCRJkqqR+dsvMuanf/nwuBVtP97Fk4v2knbYFADFNRph0SayAOFJ4USlRmGttKZL7cLnDiX8tASRnY1dq1ZoOnQodhuqOldbV7rUMb1XFTUZetPpaH4+cA2AeSND8HW2A0Bn1DHn0BwARgaNpJFr9d6fqTAyAJIkSaomkjN0LNl3FQClQpCSpcfmyg4cdAncFk488IeaVh9uY+ziQ3yy+Tybw6K5kZRZaFCUs5z7gVoPYK+2LzCdPiGBxFWrAFPvT03tVciRMxl6Y8RGjMJYrmVHxmfwxu+meT/Pdg+kZ5CX+bVV51cRnhyOi40Lz4c8X671qmwqxVEYkiRJUsn978BV0rR6grwdeCYgifqtu+Cx8Qe4BXvseqPQq0nK0LHnUhx7Lt1doeRub03z2s60qONMcG1nWtRxwdvJBoVCcXf3Z7/Ch78SlixFZGZi27w59l0sW2VWHfXw64GD2oHo9GiOxh6lnU/hk8dLi1ZvYOqvx0jV6mnj78qrfe8ezBqfGc93J74D4IXWL+Bs41wudaqsZAAkSZJUDaRr9fy07woAk7vVQ3n9OM0cM1Df3gPAIxOnM8i1PhdiUjl9I5nT15M5dT2Zi7GpxKdns/vibXZfvG3Oz8PBhqA6Bs4ozqBAQTOXBwos25CUROLy5ab7ZO8PADYqG/oG9GXNpTVsjNhYbgHQnL/Pc+p6Ms52ar56ohVq1d2Bnq+Of0WqLpUmbk14pMEj5VKfykwGQJIkSdXAr4cjScrQEeCuYUBzH7ZcB+XpVSAM4NcBPBthA7So40KLOi5wZ4pOls7A+ZhUTl9P4tT1ZE7fSObSrTTi0rQkxx7A1gd0GXXpP+8E3k7nCK7tcqeXyJngOs54ONiQ8PPPGDMysGncGIeePSvybahUBgcOZs2lNWy9upXpHaZjo7Ip0/K2nIkxD4F+PrwltV3szK+FxYWx9pJpe4K3OrxVqU+rLy8yAJIkSaritHoDi/ZEADC5e31USgUIgfKkqVeG1k8WeK+tWkWInwshfi7ma5nZBs5GpzDj0K9EZoIrrdAqIDZFS2xKLNvPxZrT1rcTfLbmf9gCtx4ahUuGDjd767JoZpXTxrsNvva+RKdHsztqN30D+pZZWVEJGby2+iQAT3epR++m3ubXjMLI7EOzEQiGBA4hxCukzOpRlcgASJIkqYr74+gNYlO0+Drb8kjrOiAMuKdfQJEQAdYO0HSYRfnZWato4KPkZtYZAH4d/TSetrU5ezPF3Et06noSEXHptDuxA1ttBtccvZly3hbx4TZqu9iZe4iCa5seLpqaFxQpFUoGBQ7ix9M/sj5ifZkFQDqDkf/79TgpWXpa+rnwev/GuV5fF76OU3Gn0FhpeLnNy2VSh6pIBkCSJElVmN5gZOFu014zk7oGYm2lRKczUDd+tylB80fAxsHifPfc2INe6Gng0gB/J38A2ga40TbAzZwmJSGZqH4fAHCxz3DquTkSEZfOjaRMbiRl8ndYjDltXTeNOSBqUduZZrWdcbYrfFPF6mBw4GB+PP0je6/vJTErEVdb11Iv49MtFzgRlYSTrRXfPNEKa6u7835Ss1OZf3Q+AJNbTsZT41nq5VdVMgCSJEmqwjaejiYyIQM3e2seb3/nMMusFGolHjF93arg4a/C3Hv4aUF0a1ajTE3BOiCA//twMi+oVKRk6Qi7kUzYjWRzb9G1+AwiE0yPjaeizffX87A3rT6rbeotalbLCUfb6hUU1XepTxO3JpxLOMeWq1t4vPHjpZr/zvOx/PCPafjzk8da4ueW+0iLhScXEp8VT4BTAGOajCnVsqs6GQBJkiRVUUaj4NtdlwGY2KUeGmvTr3Tl2TWoRDbCIwhFnbYW55ulz2Lvjb0APFj3wfzLzswkfslSANyffRaFyjSp1slWTaf6HnSq72FOm5yhI+xmTkBkmmx9PTGTK3HpXIlLZ/3Jm4DpiLJ6HvZ3AiLTZOtmtZywt6naH1WDAwdzLuEcGyI2lGoAdDMpk2m/meb9jO8UQP/mPrlej0iKYMW5FQC80f6N+x5jUtNU7Z8qSZKkGmz7uVguxqbhaGPFmAf8zdcVJ0yTn40ho1EVY0n6oehDZOoz8dJ40dS9ab5pkn77DUN8POo6dXAePKjQ/Jw1ajo38KBzg7tBUWJ6tmk5/p0l+advJHMjKZOI2+lE3E7nzxN3g6IGng53h8/qONPQo2od3DkwcCCfH/2ck7dPEpkSSV2nuiXOU2cw8sKvx0nK0BFc25npA3PP+xFCMOfwHPRCT486Pe67i3dNJAMgSZKkKkgIwbehprk/Yzv6351PE3kIZfRxjAoVxuYjKM5i55zdn3v59cp3Tx+jVms69BRwf2YSCrXlPQuu9tZ0a+RJt0Z356TEpWk5fSOZsOvJnLoTGMWkZHHpVhqXbqWx5tgNAJQK8LFTsV93htb+boT4udLAy8G0+q0S8rDzoKNvR/bd3MfGiI1MCSn5OWnztl3k32uJONpY8c2oVthY5f5O74zayYHoA6iVal5v93qJy6uOZAAkSZJUBe27HM/JqCRs1UomtveEY8vgxAqI3A9AjHNrPO097pNLXgajgdCoUKDgw0+T/vgD/e3bWPn64jJsWDFbkJeHgw09g7xyHd1wKzXr7nyiO4HR7VQtNzMUrPr3Bqv+NQVFDjZWtKjjTIifC63quhLi54KnY9nuu2OJwfUHs+/mPtZHrGdyy8kl2iwy9MItFtwJfuc82gJ/99xHlGTps/j0yKcAjG82Hj8nv+JXvBqTAZAkSVIV9N3Oi3RUnuFNz6O4L3gKdBmmFxRKjPV6ctpuCIUfXpG/k7dPkpCVgKO1I2198s4fEtnZxC/6EQD3pyeisC7b5e1ejrb0amxLr8Z397WJik9lyV+7sPKuz6kbpqX5aVo9+8Pj2R8eb05X28WOkLoutPJzoVVdF5rVcsZWXTEbAPby64WdlR1RqVGcijtFS8+WxconJjnLPO9nzAN1GdTCN0+apWeWciPtBt4ab54OfrpE9a7OZAAkSZJUlSREcHP3Ej65uYI61nGQcOe6e0NoNRpajMRg50nWpk3Fyj5n9Vf3Ot1RK/MObSX99Rf66GisPD1xeeyx4raiRHycbGnpLhjYtxFqtRq9wcilW2mciErieGQiJ6KSuHQrzbwcP2flmZVSQRNfJ1rVdTFv/ljPw75cju7QqDX0rtub9RHrWR++vlgBkN5g5IWVx0lIz6aprxPvDMo7P+tm2k0WnzYNT77a9lU06qo1X6o8yQBIkiSpstOmwpk/zUNctQAUkKl0wK7VcGg1Bmq3Mc0YBtDpilWMEOLu/J98hr+ETkf89z8A4DbxKZQ2lWOIyUqlpImvE018nXiivWmCcWqWjtPXkzkelcTxyCRORCWZ5xidvpHMzweuAeBspzYHQyF1XQip44JrGe1kPThwMOsj1rPl6hbeaGf5qqyvdlzi8JUE7K1VfDu6db69WZ//+zlZhizaerelX0C/0qp6tSQDIEmSpMrIaIRre+H4cji3zjzEJRRK/tE35w9jN6b938sE+Fg+z6cgl5MuE5UahbXSms61Oud5PXnDRnTXr6Nyc8N15MhSK7csONqq6dTAg053Vp4JIbiemMmJqCTz4/SNZJIzdXkOgq3nYW8OilrVdaGxj1OuzQWLq4NvBzztPLmdeZu9N/bSs27Rz03beymOr+9sefDxI8HU87DPk+ZQ9CG2XtuKUqHkzfZvykNp70MGQJIkSZVJQgScXAknfoXkyLvX3RtCyCjevdKcX87qGNzCt1SDH7g7/NWxVsc8QyfCYCD+++8BcJswHqWdXZ77KzOFQoGfmwY/Nw1DWtYCIFtv5HxMiikgikzieFSSeW+iK3HprD1ummBtbaWkeS0nQvxczcNndVztLA4wVEoVA+sN5H9n/8eGiA1FDoBupWbx0qoTCAFPtPfjoZDaedLojXrmHJ4DwIhGIwhyC7KobjWRDIAkSZIqmjYVzv5lGuK6tu/udRtn01EWIaOhTluuxGewYlMoAM/1aFDq1Shs+Cvl781kX72KytkZ1ydGlXrZFcHaSkmLOi60qOPCkx1N15Iysu/MJbrbU5ScqeNYZBLHIpPgzrfHw8E614qzFnWci7SL9eD6g/nf2f8RGhVKSnYKTtZOhaY3GAUvrTxBXJqWIG9HZgxulm+6VRdWcTnpMi42LkxtNdWCd6HmsigAunXrFl5eXgW+rtfrOXbsGO3bty9xxSRJkqq1nCGuEytMwU/OKi4UUL8XhIyCxoNAfbenZWFoOEYBDzb2ommtwj84LRWTHsPZ+LMoUNC9TvdcrwmjkfjvFwLgOu5JVA55h1+qCxeNNT2CvOhxZym+EIKr8RnmydUnopI4ezOFuLRstp+7xfZzt4C7GzaaeohMQVEjbwesVLmHzoJcg2jg0oDLSZfZfm07jzR8pND6fLPzMvvD47FTq/h2dCvsrPPO+0nISuDb498C8H+t/g9nG+fSeCuqPYsCIF9fX6Kjo81BUHBwMJs2bcLPz7THQHx8PB07dsRgMJR+TSVJkqqDhCtw8tcCh7hoMRKc8w5x3EzKZM3x6wA817MMen/uDH+18mqFu517rtdSt21He+kySgcH3MbUrPOkFAoF9TzsqedhzyOt6wCQpTNw5mZyrl6i64mZ5g0bf/vX9H3SWKsIru1sXoof4ueKj7MtgwMHM//YfNaHry80ADp0JYEvd1wE4KNhzWng5Zhvuq+OfUWqLpUmbk14tOGjpfwOVF8WBUBCiFzPr169iu4/qw3+m0aSJKnGK+IQF4XMKfnhnwh0BsEDgW608S/9E8VzAqD/Dn8JIYhbaOr9cXtyLCqn0u15qops1Sra+LvRxt/NfO12qvZOMJTI8cgk895Eh64kcOhKgjmdr7MtTer4AQr+jf2XK4nXqedaJ08ZqTqYtfo0RgGPtanDo23ypgE4E3eGNZfWADC9w3RUyorZ56gqKvU5QHLWuSRJEvcZ4uppCnr+M8RVkLg0LSuPmHqLpvZsWOpVTdYm82/sv0De09/TdoWiPXcOpUaD25PFO1m+JvB0tKFPU2/6NDVt2GgwCsJvp5mHzo5HJnExNpXo5Cyik8Gubj2s7CPov/hL6quH5tqwsbaTNb9cUnIrVUtDLwc+eCj/eT9GYeTjwx8jEAwOHEwrr1bl2eQqT06CliRJKk0FDnE1uDPE9Xi+Q1yFWbLvClk6Iy3rONO5gfv9b7DQP9f/wSAMNHBpkOugTiEEcQsWAOA6ehQqF5dSL7u6UikVNPJ2pJG3IyPbmd7TdK2eU9eTORGVxJbIHoSLCFROxzgb0Z2z0SmsOGT6ebFVK8nSKbFVK/l2dGs01vl/VG+I2MCp26fQWGl4uc3L5da26sKiAEihUJCamoqtrS1CCBQKBWlpaaSkpACY/5UkSapRtGn3DHHtvXvdxumeIa52hQ5xFSQlS8fP+02b9j3Xs0GZ9LIXNPyVvncfWadPo7C1xW38+FIvt6axt7GiY313OtZ3Z2z2JHr8thytzS3eftiJ2/GenIhM4tSNJLJ0RgBmDGpCI+/85/2kZacx7995ADzb8lm8NAUvUJLyZ/EcoEaNGuV63qpVq1zP5RCYJEk1gtFoms9jHuJKv/OC5UNchVl24BqpWj2NvB3o08T7/jdYKEufxb6bpnlJD9Z90Hw9V+/PyJFYuZd+z1NN5mDtQE+/nmy+upkExQHeGvgGADqDkTPXE9m9Zy/D2xTcU/j9qe+Jz4rH38mfMU1q1sT00mJRALRr164yqcS3337Lp59+SkxMDC1btuTrr78ucCl9jx492L17d57rAwcOZOPGjeh0Ot555x02bdpEREQEzs7O9O7dmzlz5lCrVq0yqb8kSTVIwhXTRoUnV0BS6QxxFSQz28DivVcA074/SmXp/4F5MPogmfpMfOx9aOLWxHw949BhMo8dQ2FtjdvEp0q9XMl0NMbmq5v5+8rfvNL2FayUVqhVSprVcuJa/h0/AEQkR/DL2V8AeKPdG1iryvZA2urKogCoe/fu909koVWrVjFt2jQWLlxIhw4dmD9/Pv369ePChQv57jm0Zs0asrOzzc/j4+Np2bIlw4cPByAjI4Njx47x7rvv0rJlSxITE3nxxRcZOnQo//77b6nXX5KkGqAMh7gKs/JIJAnp2dR10zA4n1O/S4N5+MuvV64e/JzeH5fHHkNdyP5vUvF1qt0JVxtX4rPiORh9kC61u9z3HiEEcw/PRS/0dK/Tna51upZDTasniwIgvV6PwWDA5p4D8GJjY1m4cCHp6ekMHTqULl3u/w2817x585g0aRITJkwAYOHChWzcuJGffvqJN998M096Nze3XM9XrlyJRqMxB0DOzs5s27YtV5pvvvmG9u3bExkZSd26dZEkSbqvwoa4AnvcHeKyLpvTtrP1Rn74JwKAyd3r59lQrzQYjAZCo0KB3PN/Mo4eJePQIVCrcZ/0dKmXK5molWr61+vPr+d/ZUPEhiIFQLuidrH/5n7USjWvt3u9HGpZfVkUAE2aNAlra2u+v3MeTGpqKu3atSMrKwtfX1+++OIL/vrrLwYOHFik/LKzszl69CjTp083X1MqlfTu3ZsDBw4UKY/Fixfz+OOPY29f8M6kycnJKBQKXApYwaDVatFqtebnOZO5dTpdnn2OSionv9LOt7KQ7av6qnsb79u+pGsoT61EeWoVintWcQm3QIwtnsAYPAKc7hniKqP36fej14lOzsLL0YahLbwt+n4U9Xt47NYxErWJOFk7EewWbE5/69vvAHAaOhQ8PCrdz0J1+hkdUHcAv57/lR3XdpDcJhmNWlNg+7QGLZ8c/gSAMY3H4GvnW2Xfg7L6HlqSn0JYsHNho0aN+Oabb+jbty9gmrvz8ccfc/bsWZydnXnjjTc4fPhwkecK3bx5k9q1a7N//346duxovv7666+ze/duDh06VOj9hw8fpkOHDhw6dKjAOUNZWVl07tyZxo0bs3z58nzTvP/++8ycOTPP9RUrVqDRlM1fd5IkVR4qQxa1ko5QN2EPHmnnzdd1SjtuuHYg0q0rifYNSn2IqyBGAbNOqIjLUjDM30DPWmWzwezfmX+zT7uPEHUIj9k/BoBtVBR1v/kWoVRy9dVX0MnJz2VKCMH81PnEG+N5VPMorawL3stnV9YudmTtwEnhxItOL2KjsCkwbU2VkZHBqFGjSE5Oxuk+m3Za1AN048YNGja8uwnXjh07ePTRR3F2Np07Mm7cOJYsWVKMKhfP4sWLCQ4OLjD40el0jBgxAiEEC+6MZ+dn+vTpTJs2zfw8JSUFPz8/+vbte9830FI6nY5t27bRp08f1Or7H5xX1cj2VX3VvY3m9vV+EOvof1GeWoni3DoUd4a4BApEve4YWzwOQQOprdZQOlOai27DqWjiDp7GxU7N+2N7YW9j2ZZtRfkeCiFYuH4haGH0A6N50M+0Auzm1KlkAE6DB9Nn7NiSNqVMVLef0Zunb7Lg9AJuON3g7V5v59u+mPQYPtrwEQBvdnyT/gH9K7LKJVZW30NLtuOx6H+Vra0tmZmZ5ucHDx7k008/zfV6WlpakfPz8PBApVIRGxub63psbCw+Pj6F3puens7KlSv54IMP8n09J/i5du0aO3fuLDSQsbGxyTWvKYdarS6z/1xlmXdlINtX9VXbNqZEExS9FrtF76JIunb3ult9CBmFouXjKJzrUPozbopGCMH3e64C8FSXerg4FH8ZfWHfw4uJF7medh0blQ3d/LqhVqvJOnuWjN3/gFKJ55TJlf77X11+Roc0HMKC0ws4HHuYRF0irmrTUSf3tu/Lk1+SZciitVdrBjcYXG22nCnt76EleVn0fzwkJIRly5YBsGfPHmJjY+nV6+7EufDwcIuWmltbW9OmTRt27NhhvmY0GtmxY0euIbH8rF69Gq1Wy5h8DubLCX4uXbrE9u3bcZdduJIkAZxajdUPnWgcs9YU/Fg7Qusn4amt8H9Hodur4Jz/mUvlZef5W5yPScXeWsW4jgFlVs6OSNPv3Y6+HdGoTUP9cQtMZ345DRiATb16ZVa2lJufox+tvFphFEb+vvJ3ntePxBxhy9UtKBVK3urwVrUJfiqaRT1AM2bMYMCAAfz2229ER0czfvx4fH3vLs1cu3YtnTt3tqgC06ZNY9y4cbRt25b27dszf/580tPTzavCnnzySWrXrs3s2bNz3bd48WKGDRuWJ7jR6XQ89thjHDt2jA0bNmAwGIiJiQFMK8isreV+CZJU42hTYdNrcPJXFECiph6OfV7HqtmwMlvFVRxCCL7ZdRmAMR39cdaUXe/GrkjTXM2c1V9ZFy+Sum0bKBR4TH62zMqV8jc4cDDHbx1nQ8QGRjUaZb6uN+qZfdj0+Te80XCC3IIqqorVjsX7AB09epStW7fi4+NjXnqeIyQkpMD5OAUZOXIkt2/fZsaMGcTExBASEsLmzZvx9jbteBoZGYlSmbuj6sKFC+zdu5etW7fmye/GjRusW7fOXJ977dq1ix49elhUP0mSqrjrR+GPiZB4BRRKDF1eYU9qUwY0HwKVbPjkQEQ8xyOTsLFS8nSXwDIr52baTc4lnEOpUNLdz7S/W/xC0+pex759sblnrqdUPvoF9GP24dmcTzjP5aTL5uu/XfiNS4mXcLZxZmrI1AqsYfVj8WGoTZo0oUmTJvm+9swzzxSrElOnTmXq1Py/saGhoXmuBQUFUdDitYCAgAJfkySpBjEaYd982DULjHpwqgOPLsJYqx1i06aKrl2+vtsVDsDIdn54OpbdCp9dUabenxDPENxs3dBGXCHlb9PQi8eUyWVWrlQwZxtnutXuxs6onWy6uokGNCAxK5FvTnwDwAutXsDF1qViK1nNWBQA/fPPP0VK161bt2JVRpIkqVSk3IS1z8KVO7+zmg6DIfPBzrXM9u0pqRNRSey9HIeVUsEz3cqu9wfyHn4a//33IAQOvXph27hxmZYtFWxI/SHsjNrJ31f/5nn183x76ltSs1Np7NaYRxs+WtHVq3YsCoB69OhhnnxVUC+LQqHAYDCUvGaSJEnFcX4j/DUVMhNArYEBc6HV2HLbw6e4vr0z92dYq9rUcS27eUlJWUkcjT0KmAKg7KgokjdsAMBjypQyK1e6v251uuFo7UhsRiz7bfez5fIWAKa3n45Kqarg2lU/FgVArq6uODo6Mn78eMaOHYuHh0dZ1UuSJMkyukzY+g4c+dH03KcFPPYTeFT++SwXYlLZdjYWhcJ07EVZ+ufGPxiEgUaujfBz9CP6k3fBYMC+a1fsgpuXadlS4axV1vQL6MfvF39nc9ZmAAbWG0hr79YVXLPqyaJl8NHR0cydO5cDBw4QHBzMxIkT2b9/P05OTjg7O5sfkiRJ5Sr2DPzQ827w03EqPL29SgQ/AN+Fmnp/BjT3oYGXQ5mWde/wl+7GDZLW/gnI3p/KYnDgYPPXdlZ2TGszrZDUUklYFABZW1szcuRItmzZwvnz52nRogVTp07Fz8+Pt99+G71eX1b1lCRJyksIOPSDKfi5fQ7svWDMGug3C6yqxjEB1+LTWX/yJgDP9WhQpmVl6jPZd2MfYDr9Pe7HH0GvR/PAA2haF3wEg1R+Wnm1ora9ae/xp5s9jbe9dwXXqPoq9mandevWZcaMGWzfvp1GjRoxZ84ci7agliSp8tEZdXx38jt2Z+0mOj26oqtTuPQ4+PVx+Ps1MGihYV+Ysh8aPFjRNbPIwt0RGAX0CPKkee2y7UE/ePMgWYYsfO19qa9zJfn3PwDZ+1OZKBVK5nady0C7gYxpknejX6n0FCsA0mq1rFixgt69e9O8eXM8PDzYuHEjbm5upV0/SZLK0brL6/jxzI9sy9rG4L8G89SWp1h7aS1p2UU/4qZchO+CBZ3h4mZQWUP/uTDqN3DwrOiaWSQmOYs/jl4H4PmeZdv7A7Az6u7wV8JPPyF0OuzatEHTvl2Zly0VXVO3pnSy6YRaWbn2qapuLJoEffjwYZYsWcLKlSsJCAhgwoQJ/PbbbzLwkaRqQAjBygsrAXBVupJoTORIzBGOxBxh1qFZ9PLrxeD6g+lUqxNWSou3ECsd+mzY+SHs/8r03CMIHlsMPsEVU58SWrQngmyDkfb13GgXULa/R/VGPaFRoQA8aN+GpFWvAabeH3m0glQTWfRb7IEHHqBu3bq88MILtGnTBoC9e/fmSTd06NDSqZ0kSeXmVNwpziecx1ppzRSHKXTu2ZmtUVtZH76eiOQI/r76N39f/Rs3WzcG1hvI4PqDaerWtPw+POPD4fenIPqE6XmbCdDv40p1lIUlEtKzWXEoEiif3p/jt46TpE3CydoJv43HSdRqsW3RAvvOncq8bEmqjCz+My4yMpIPP/ywwNflPkCSVDWtOr8KgH7+/dAkavC19+Xp4KeZ2HwiZxPOsiF8A5uubCIhK4Ffzv3CL+d+IdA5kCH1hzCo3iB8HXzvU0IxCQEnVpjO8tKlg60LDP0amlbtP7SW7rtCps5A89pOdGtY9luK5Kz+6ufSkaRPTd9rjymTZe+PVGNZFAAZjcb7psnIyCh2ZSRJqhiJWYlsvmrad2R4w+FEHo40v6ZQKGjm3oxm7s2Y1nYaB24eYH34enZF7SIiOYIvj33JV8e+oq1PW4YEDqGPfx8crEtpKXdmEmycBmGmyboEdIWHvwfn2qWTfwVJzdKxdP9VAJ7v0aDMgxAhhPn4i/6H9YiMDGyaNsFBno0o1WClNpCv1Wr59ttv+eSTT8ynr0uSVDWsvbwWnVFHU/emNHNvRiSR+aZTK9V0q9ONbnW6kZqdyvZr21kfsd48V6hU5wtFHoI/nobkSFCooOdb0OVlqAY74v5yMJKULD31Pe3p18ynzMu7mHiRG2k3cNVZ47R+LwI590eSLPrNpNVqef/999m2bRvW1ta8/vrrDBs2jJ9++ol33nkHlUrFyy+/XFZ1lSSpDBiMBn678BsAjwc9XuQPRUdrRx5u+DAPN3yY6LRoNl7ZWDrzhQx62PM57J4LwgAu/qYdneu0LUkzK40snYHFeyMA074/SmXZByE5w18Tz/sg0iKwadgQxwer1nYBklTaLAqAZsyYwffff0/v3r3Zv38/w4cPZ8KECRw8eJB58+YxfPhwVKqq/9eZJNUk+27u40baDZysnehfrz/kf8xfoXwdSmm+UFIUrJkEkQdMz4NHwKDPwdap+A2sZFYdiSIuLZs6rnYMDalVLmXujNqJnVbQJtS0t5PHlMkolMXeBk6SqgWLAqDVq1fz888/M3ToUMLCwmjRogV6vZ6TJ0/KrlRJqqJWXTBNiB3WYBh2VnboSnBaen7zhTaEb2Bn1E7zfKEvj31JO592eecLnfkT1r8AWclg7QCD5kHLkaXQwsojW2/k+93hADzbvT5qVdkHITfSbnA+4TzDjoEqLRPrevVw7NevzMuVpMrOogDo+vXr5uXvzZs3x8bGhpdfflkGP5JURV1Pvc6e63sAGBE0olTzvne+UFp2Gtuubct3vlDP2l0ZEh9Dx7BNqAFqt4FHfwS3wFKtT2Xw14kb3EzOwsPBhuFt6pRLmbsid2GTLRh2xPR72mPysyhkT70kWRYAGQwGrK2t795sZYWDQ9ke3CdJUtlZfXE1AkGnWp3wd/Ivs3IcrB0KnC+0OXI7mwG3urUZ4BTEkG7v09S1HtXtzyqDUbAg1NT7M6lrPWzV5ROE7IzaSZ/jAk26AXXdujgNGlQu5UpSZWdRACSEYPz48djYmA4ZzMrKYvLkydjb2+dKt2bNmtKroSRJZUJr0LL20loARgaV31CTr4MvTzd7iokpmZw9P4sNGhs2OTqSoFKxPP0yy/8eQ6BzIIMDBzM4cHDZ7S9UzjaHxRARl46znZrRD5RdsHmvJG0Sp67/y9eHTFuYeDwzCYVVBe3iLUmVjEX/E8aNG5fr+Zgx8qA2Saqqtl7dSqI2ER97H7rV6VZ+BafGwp9TUITvoBnQLOBBXhn8BfuTzueaL/TV8a/46vhX+c8XqmKEEHy76zIA4zsF4GBTPkHInht76HnCgEs6WNXyxVnu0i9JZhb9L1yyZElZ1UOSpHKWM/l5eKPh5Xe218Wt8NdzkH4brGxNR1m0fQorhYJuDl655gttiNiQd76QX0+G1B9Cx1odq9RBkaEXbnM2OgWNtYrxnQLKrdx/ruzksYN3en8mTUJxzxQGSarpZF+oJNVA5+LPcfL2SayUVjzS8JGyL1CXBdvfh0MLTM+9mpn29vFqnCfpvfOFYtJj2BCxgQ3hGwhPDmfz1c1svroZN1s3BtQbwJDAITR1L8fzyIpBCME3d3p/xjzgj6t9+QQh2SIb6y17cU8FPNxwfqQcvs+SVIXIAEiSaqCc3p8+dfvgYVfG51DdvgC/T4TY06bnHSZD75mgtr3vrT72PgXuL7T83HKWn1tOPed6DAkcUmnnCx2+ksDRa4lYq5Q83aVeuZUbnnWRQfu1AHhNehblnbmbkiSZyABIkmqYlOwUNl3ZBMDIxmU4+VkIOLoUNk8HfSZo3GHYAmhk+R409+4v9ErbV9h/c795vtCV5CuVer5QTu/P8LZ18HK6f9BXWlTH9uKVDFonW1xHDC+3ciWpqpABkCTVMOvD15Opz6SBSwNae7Uum0IyEkybGp5bb3oe2BMeXgiOJT/3ykpplWd/oco6X+jU9ST2XIpDpVQwuXv9citXl51F2z3XTE9GDUNpZ1duZUtSVSEDIEmqQYQQrDy/ErDs3C+LXNkDa56B1JugVEPv9+CB56EMjl4oznyh8vTdLtO+Pw+1rIWfm6bcyj37+yJ8EgWpdgpCJsrzGSUpPzIAkqQa5HDMYa6mXEVjpWFw/cGlm7lBB6FzTAeZIsCtPjy2GGq1Kt1y/r+9+46rqv4fOP66m71lbxX3Xrly5MjMssy0LE3THJiD8pf2rbTlzswyV2k2tVxZmmbOHLn3AFkCIiB7c9f5/XGVJHGgFy7j83w8eHDvued8zvsDF3jzmXdw63ihi+kX+S3qt1LHCz0R+AQYTFtE2FnZoVaosVJYoZKrzJoQXk7OYdv5JADGdq241h/JaMSw2rS5bWTvBrS1rz77qAmCOYkESBBqkJuDn/vV7oetyvYeZ5dBeoxpE9OEo6bnLV6Cx+eApuLH4chkMhq6NqSha0PeaP0GhxIP8Vv0b+yO201MVgyLTy8GYPHmxSWvQ4ZGoSlOiNQKNVZKq5LPy3B8zeFEFHY5tPZ3J1cWyYU0DRrFLR9K02e1XG22xKvw4kXSv/0Wh8Qscq3A/aVh975IEGookQAJQg2RnJfMrrhdgJlXfj7zM/weBtoc0DhCv4XQuHJMuVbKlXT27Uxn387kanP5K+4vNkdu5nzKeYxyI0WGIiQkACQkCg2FFBoKySbbLPe38YMLEgz94+7nlUiM/pMg3eu4TRF4HozEfedprCMTi8v8tYOS/6vd1Sz1EITqSCRAglBDrL+8HoNkoJVHK+o61334AguzYesUOGMaU4TfIzBgBTj5P3zZ5cBObUf/Ov3pG9CXrVu38sQTT6BUKtEb9RQZiig0FKI1aP/9rP/Pc0MhRfoiigylfPzn+LlrqVzLysXBRsLHRUWR/t/yiwxFFOoLixMvoPi6+yZJ1L0KPU4baXZRwkpnOqyXw5F6MnY2k6GvWx9rpRj8LAh3IhIgQagBdEYd6yLWAabBzw8t4TisHwEZsSCTQ5e3oPOboKhav1JkMhkqhQqVQoUd5umuS8kupNPc3Wj1Rla99giPBLvedo4kSQ+UeBkyM3HacxrPnWexS8goLi/T044LHbw429qVTBsJjSSjU34bs9RHEKqrqvXbShCEB7I7bjfXC67jauXKY/6PPXhBRgMcWAi7Z4JRD45+8OwKCGhvtliruq/2x6DVG2kd4Ey7IJdSzylL4iUZjeQfOUrmL7+Qs2MHklZrKkOjweHxx3F6fiD1W7ak/S3jiHQ6HVu3bjVfpQShGhIJkCDUADcHPw8IGYBK8YBr4mQnmqa3x/5tet7oGXhyIVg7mSXG6iAzX8v3/5jW3wntVuehBjfrr18nc+MmMtetQxcXV3xc06ABTgOfw/HJJ1E4iBlegvCgRAIkCNVcdGY0R5KOIJfJGRjygCsCX/wdNo+HggxQ2cITc6H5EKjEe3BZwqoDseRrDTT0cqBrvVplvl4yGMjbv5+MX34hd/ceMBgAkNva4vDkkzg99xxWjRtV6r3PBKGqMP/KZA9g8eLFBAYGYmVlRbt27Thy5Mgdz+3atSsymey2j759+xafI0kS7733Hl5eXlhbW9OjRw8uX75cEVURhErnZutPV9+ueNqWcSVmbT78PhnWDjElP17NYPQ+0zR38Ue4hNwiPd8cjAXK3vqjS0zk+qLPiXysB/Gjx5D7104wGLBu3hyvjz+i7r69eL0/A+smjUXyIwhmYvEWoLVr1xIWFsbSpUtp164dCxcupHfv3oSHh+Pu7n7b+Rs2bEB7ow8cIC0tjWbNmjFw4L//2c6dO5dFixaxevVqgoKCePfdd+nduzcXLlzAyqri9uIRBEvL1+WzOWoz8AD7fiWfh19Hw/VLpucdXofu74GyYnYzr2p+PHyFrAIdwW62PN743ommpNORs3s3mb+sI2//ftPeaYDC0RGHp58ytfaEhJR32IJQY1k8AVqwYAGjRo1i+PDhACxdupQtW7awcuVKpk6detv5Li4lBxWuWbMGGxub4gRIkiQWLlzIO++8w9NPPw3At99+i4eHB5s2bWLwYDPMgBGEKmJLzBZydbkEOATwiNcj93eRJBF0/U+Uq34BQxHYupv28arzEIOnq7lCnYEVf8cAMKZrbRTyO7fSaGNjyVy3jsyNmzCkpRUft2nXDqeBA7Hv2UPs3C4IFcCiCZBWq+X48eNMmzat+JhcLqdHjx4cOnTovsr4+uuvGTx4MLa2plVtY2JiSEpKokePHsXnODo60q5dOw4dOlRqAlRUVERR0b9rcGRnmxZB0+l06HS6B6rbndwsz9zlVhaifpWHJEn8dPEnAAbUGYBBb8CA4b8nQd51ZJmxkBGLLD0a+ZWDNE04CICxTk8MTy4C21pQBep8P8rje7jmSDzXc4rwdrTiycbut5VtLCoib8dfZK1fT+GxY8XHFa6u2Pfvj8Ozz6D2N62fZAAMDxlbVXqfPghRv6qvvOpYlvIsmgClpqZiMBjw8PAocdzDw4NLly7d8/ojR45w7tw5vv766+JjSUlJxWX8t8ybr/3XrFmzeP/99287/ueff2JjUz4bGO7YsaNcyrUoSaJJwnd0yYskM/ozCtRu5KtdyVfXouDGZ53CpsqOHcnTwR/xcs5mKPg1difdvI04VOLeoDh9HJdzL6NChcuFPI6emouNNgXbomRsi1JMH9rrKI2Ft11rkKk47zOIGLuesPeoBaIvf+b6GTQYYdEpBSCjvXMeO7ZvK35NfS0JxyNHcDh5EkVBAQCSTEZevRCy2rYlr359UCjg3DnTh5lVy98ztxD1q/rMXcf8/Pz7PtfiXWAP4+uvv6ZJkya0bdv2ocqZNm0aYWFhxc+zs7Px8/OjV69eOJh5mqm2qIid27fTo08fVKoHnI5cSckubkZ56i8AnApiSz1HUtuBkz+Sgy+Skz84+iI5+oGjn+mzjVulS5AMRok1xxJY+FckmQWm/y52XZNx4LqSwW18GdkpEE8HC48t0xVA5hVkGTHIMmIhI5a3c02tqE/mZvJ0zP/ueKmEDBx8kJwDwTkQvYM/f193oH2/YTSoZu9RMP2HuGPHDnr27GmWn8GNJxNJP3wOV1s1M4Z2Rq0rIuePP8jesIGiM2eLz1N6eeHwzDPYP9MflWcZB6OXkbnrWNmI+lV95VXHmz0498OiCZCbmxsKhYLk5OQSx5OTk/G8xy+IvLw81qxZwwcffFDi+M3rkpOT8fLyKlFm8+bNSy1Lo9GgKaXPXaVSmfUbk7v/AMlz5uAcEIDqqaeq1xtbVwA7ZwBwxbULvq36oMi9CpnxkBkHWfGmrhZtLqRcQJZyofRylNbg5GfaTsHxxudbH9t5gLziJi8ejk5jxm8XuHjN9ENV192W5rbZROhcOJ2QxepDcfx0JIFBbfwY07U2Pk7luPVAfjpkxJg2Hs2IgfTYG5+jIedaiVPT5HJ2+PuATMagrGxQqME5EJyDwCWoxGeZcwAoNdxMO2U6Hblbt5r9/V/ZmKN+RqPEsr9jQJKY5Kcjd9ZMsrdswXjzv1ClEvvu3XEa+By2HTogUyjMEPn9E9/Dqq261w/MX8eylGXRBEitVtOqVSt27txJ//79ATAajezcuZPx48ff9dpffvmFoqIiXnrppRLHg4KC8PT0ZOfOncUJT3Z2NocPH2bs2LHlUY37ZszJRnv5Ms4JCRjz8sDJyaLxmNWBRZAVR4G9N6e8X8C7zXMo/vtG1OZDVsKNhCjO9PnWBCknCfQFkBph+iiNQg2OvqUnR05+YO9tlu0YEjMLmPXHJX47bdpc0sFKyRu96vF8Sy/+3L6Nj/u05fCVLBbtvMzR2Ay+++cKa47GMaClL+O61sHf9QG6To1GUyJTIsmJ+TfJKcy6+/Uah+KkZqNKiy77HE3sA2g0bis4eIO8Yv/41gR/HblMw8PbeTPuCIG/JpJ547g6IMC0WGH//ijd3CwZoiAId2DxLrCwsDCGDRtG69atadu2LQsXLiQvL694VtjQoUPx8fFh1qxZJa77+uuv6d+/P66uJffZkclkTJo0iY8++oi6desWT4P39vYuTrIsxb5XL1SBgRAbS9batbiPHm3ReB5GVlEW0VnRRGdGE51ymphLa4j29SZRpcQ+7ws6FPWgluo/C8GpbaBWiOmjNPoiU4KUFX97cpQZB9lXwaA1JQPp0aWXIVOAg8+/CdF/EyQH37tO4y7UGfjq72gW746iQGdAJoMX2vrzZq96uNiqiwfYyWQyOtetRac6bvwTnc7nuy5zMCqNNUfj+eV4Av2b+xDarTbBtf6zzYFea6pPevTtSU5GLOhvH49Tgp0HuASX2pKDjQvIZBiMBn7Z8AQAg5qOMtVbMBtJkig4doyMX9bhueUPxhluvCfUaux798Zp4HPYtGkj1usRhErO4gnQoEGDuH79Ou+99x5JSUk0b96cbdu2FQ9ijouLQ/6fLo/w8HD279/Pn3/+WWqZ//d//0deXh6vvfYamZmZdOrUiW3btll8DSCZQoHzqJGk/O8dMld/i9vQocitK+9uzZIkkZSXZEp0sqKJyYop/pxemF7yZOt/uxCzpWxWnF3B2+3fLtsNlRpwrW36KI1BDzmJ/0mObmlJykoAo850LCsOrpRWiAzsvW5LjiRHPw6kWjN9XzZRGUYAWgc4M+OpRjT2cbxjyDKZjPa1XWlf25Vjseks2hXJsYh4Lpw8wLzTG3jcu4CutXJxLEgwJTlZCSAZ7/w1kClMsTkHmRKdEklOIKht7/ll3H91P4l5iThqHOkd2Pue5wv3R5+WRtamX8lctw5tjGnKuwq44uBF49dexuu5Z1BUp1ZdQajmLJ4AAYwfP/6OXV579uy57Vi9evWQbiwaVhqZTMYHH3xw2/igysC+Tx+uzv8E0tPJ/GUdLkNftnRI6Aw64nLiihOcmy07sdmxFOgL7nidp60nwWoXgmP/IUinJ+jxT0izcmLK/in8cvkXXmz4IoGOgeYLVKH8t9urNEYD5CaXnhzdbEnSF5qSqJxEiD9cfKkM6ATsBNKtHJE5++PkWhvZeX+4ektXm61n8dRxchJMrTfp0ZARQ+v0GL7NiAGr6//GdP3Gx62U1iUTm1sfO/rBg+7VdcOa8DUAPFPnGayUYuHPhyEZjeQdPGTaiHTXruKlAGQ2NhwLbsV3Ls3p1K8Lj/drZOFIBUEoq0qRANUkMpWK9K5d8di4kbSVK3EaPAi5umLmUudqc4nNjv236+pGa058TjwGyVDqNUqZEn8Hf4IdgwlyDCLIMYhgp2CCHIKwUWhgWRdIz4RWr0Cjweh0Ouop6xGuD2fB8QUs6r6oQuoGmMa4OHibPvzb3f76zcQlMx4yr1CYFsvZc+fISY7Gm1R8ZdexkxXiQhZknDV9/IcKeFKmQnHqHmtNWLuQb+fPuQIXDmU6Emf04IrkTlDdxgzr1Y7Gvk5mqfJ/xWfHc+DqAQCeD3m+XO5RE+iSksjcsIGsdevRJSYWH7dq0gSngc8R2bg973x7BpVCxupH79BiKQhCpSYSIAvIbt0K7wMH0CclkbVpE87Pm+8PlSRJpBWmFSc4t3ZdpeSn3PE6G6VNcZIT7HTjs2Mwvva+qOR3aJE4thKSz4LGEbq/W3y4t3VvInMj2R2/m6NJR2nj2cZs9XsoMhnYuWO0qcW6a+7M/due1Nx6APRo4MG7fetjZ6MtOe4oM/7G4yumx4WZKCQdEjJkDj7/dk39t7vKyhEboC3gkJTN57siOX72Gsci9PwScYDu9d15vXsdWvg7m7WKv0T8goRER5+O+DmIsT9lIen15O7dS+Yv68jdt880KB2QOzjg2K8fTgOfw6p+fQAWrzLtV/hcK188HUUrmyBURSIBsgBJqcT5lWGkzp1H2oqvcHr2WWTKsn0rDEYDV3Ovljo+J0ebc8frXK1cCXYKLtmi4xiMh41H2QZtFmTAzg9Nj7tNA9t/Z7q4K9x5ts6z/HL5F+YdnceaJ9cgl1WKfXc5EZfBjM3nOZNgmlEVXMuW955sSNd6t+w7Z+MC3s1LvV6Xm86erevp2m8wKmv7+7pnfU8HFr/YksiUHL7YFcnm04nsupTCrkspdK7rxoTH6tIm0OXeBd1Dob6QDZEbABhcT2z5cr+08fFkrltP1oYN6K//219p07o1Ts8PxL5XL+S3jB88dzWLPeHXkctgTBfR+iMIVZVIgCzEYcAAMlZ8hS4+nuytW3F86qlSzyvUF5q6rTKjicmOKW7ZuZJ9BZ2x9G4YuUyOj50PwY4lE50gxyAcNXce0Fsmu2dBQTrUqg9tRt728pgmY/gj9g8upl/k9+jfeap26fWrKCnZhczedokNJ64CYKdRMqlHXYa2D0StLENyprEnX+MODzC2po67PQsHt2BijxAW745k48mr/H05lb8vp9I+2JXXH6tD+2DXB5499OeVP8kqysLL1ovOPp0fqIyaQqbXk7NtGzkbNpB/6J/i4woXFxyf6Y/TgOfQBAeVeu2SPVEA9GvmTYDrvQelC4JQOYkEyELkNja4vPIK1z/9lNRlyzH26ERMTmyJgcgxWTEk5iYiUfqAb41CQ6BD4L9JjpOpNSfAIQCNohw3U0y+AEe/Mj1+fHapg3adrZwZ1XQUnx7/lM9OfEbPgJ5YKyt+xptWb2TVgRgW7bxMntY0zmlgK1+mPF4Pd3vLdF0Eudkyf2AzJnSvy5K9kaw7nsCh6DQORafRJtCZ17vXpXNdtzInQmsvrQXg+XrPoxBr/pRKkiQyVq8m+MslJN9crFAmw7ZjR5yeew777t2Q3WVMXmRKLlvPmRadHNe1TkWELAhCOREJUAUySkau5V3jsu4yP1z6gat143jCWgFRUUz5sDOH65feEuGgdjC15vyn68rb1rvi/9BJEmybCpIB6j8Jtbvd8dQhDYbwc/jPXM29yurzqxnTbEwFBgq7w1P48LcLRKfmAdDMz4n3n2pEcz+nCo3jTvxdbZj1bFPGd6/Lsr1RrDkSz9HYDIauPEJzPycmPFaHbvXc7ysROp92njOpZ1DKlTxT55kKiL7qkSSJlNlzSF+9GgWgcHfH+bkBOD47ALWvz32VsXRvFJIEPRt6UM/z/rpABUGonEQCVIHWhq9l5uGZpicnTJ+MLY08dwCeOWgkrqU3QaWMz3Gxcqk8i6pd+h1i9oJCA70+uuupGoWGSS0nMWXfFFaeW8mAugOoZVPrrteYQ0xqHh/+foFdl0yDvt3sNLz1eD0GtPRFLq8kX8db+DhZ88HTjQntVodle6P54fAVTsVnMuKbYzT2ceD17nXp2cDjrrH/HP4zAL0CeuFq7XrH82oqyWgkacb7ZP5s+jqlPNmX9h99hLoMa4MlZOSz6aSpC3VcVzH2RxCqOpEAVaBAh0CUMiXOMmea+DShtlNtajd1Rzoxh+DkQja4v4Ndly6WDvPOdIWw/cammh3Gm2Y73UPvwN58d/E7zlw/wxenvuD9Du+XW3i5RXq+2BXJ1/uj0RkklHIZIzoF8Xr3OthbVf79dDwcrHivX0PGdq3NV39H890/Vzh3NZvR3x2nvqc947vXoU9jLxT/SYSyirLYGr0VgMH1xeDn/5L0ehLffpvszb+BXI77jBlEaNRl3pdr+b5o9EaJjnVczT57TxCEilc5pubUEK09W3Ng0AEmOkxkfuf5TGg5gb4tBuP6wosApC5ZetcFHi3u0Oem6eD2XtAp7L4ukclkTGk9BYCNlzcSnh5u9rAkSWLjyQS6z9/D0r1R6AwSj4bUYtukR3n7iQZVIvm5VS17DdOeaMD+t7oT2q02dholl5JyGP/jSXov3Mevp65iMP77PtkctZlCQyEhziE0r9XccoFXQkatlquTw0zJj1KJz/x5ODzTv8zlpOQUsuZoPACh3cTYH0GoDkQCVIFUclWpa+q4Dn8FmVpNwalT5B8+YoHI7kPWVfh7gelxzw9AY3f382/R3L05jwc+joTEvGPzzJrknU3IYsCSg0xee5qUnCL8XWz4amhrVg9vQx33+4+xMnKxVTOld332v9WNiY/VxcFKSWRKLhPXnKLHgr2sO55AkV7P2nDT4OdB9QZVnq7SSsBYWEhC6HhyduxAplLhu2gRDk888UBlrdwfi1ZvpIW/E+2DRRejIFQHIgGqBJS1auH03HMApC5dauFo7uCv6aDLB7920GRgmS+f2HIiKrmKw9cO8/fVvx86nNTcIqauP8NTi/dzIi4TG7WCKb3r8efkR+nRsIxrGlVyTjZqJvcMYf/U7rzZKwQnGxUxqXm8+ctpunyxgivZV7BV2vJk8JOWDrXSMOTmEf/aaPL+/huZtTV+y5Zi3/3OA/bvJitfx/f/mDaWC+1ap1q9twShJhMJUCXhOvJVUCrJ/+cf8k+etHQ4JcX9A2d/AWTQZ45pReUy8rX35aWGLwEw/9j8O65hdC86g5GV+2PoNn8Pa47GI0nQv7k3u97oSmi3Olipqu/0bwcrFeO712X/W92Z2qc+rrZqMhV7AdBmtmT9sRQKdaVvaVKTGLKyiH/1VfKPHEFua4v/Vyuw7dDhgctbfSiW3CI99T3t6V7f/d4XCIJQJYgEqJJQeXvj+LRpscC0pcssHM0tjAb44/9Mj1u8BN4tHrioUU1G4axxJiYrhvUR68t8/f7LqTzx2d988PsFcgr1NPJ24Jcx7Vk4uEWN2o7ATqNkTJfarH+9EWqHiwBkJrfm3V/P02Xeblbuj6FAWzMTIX16OldeGU7B6dMoHB3x/+YbbFq1euDy8or0rDxg2vl9XLc6lXIWoSAID0YkQJWI26hRIJeTu3cvhRcuWDock5Pfw7XToHGAx6Y/VFH2anvGNR8HwJenvrzrlh23ik/PZ8x3x3np68NcTsnFxVbNrGebsHl8J7NsIVFV/R6zAQkjrdxbM/3x7ng5WpGcXcQHv1+g89xdLN8XRV6R3tJhVhhdcjJXXh5K0cWLKFxd8f/2W6ybNH6oMn86Ekdmvo5AVxv6NvEyU6SCIFQGIgGqRNSBgcWDNFOXLbdwNEBBJuz8wPS4y1tg9/Br+DwX8hxBjkFkFGWw4uyKu99ea2DBn+H0WLCXbeeTUMhlvNIhkN1vdOWFtv63TQevSXQGHesvm1rRXmgwmGEdAtkzpSsfP9MYHydrUnO1zNx6iU5zdrF4dyQ5hQ/W5VhVaBOucuWll9FGRaH09CTg+++wqhfyUGUW6Q0s3xcNmPb8qsnvN0GojkQCVMm4vjYKgJw//6QoKsqyweydC/mp4FoX2r5mliKVciVvtn4TgO8vfE9CTsJt50iSxO9nEnnskz0s2hVJkd5Ih9qubJ3QmRlPNcLRpmpNay8PO+N3klqQSi3rWnT37w6ARqlgSLsA9kzpytznmhLgakNGvo5528PpNGc3n/11mayC6pcIFcXEcOWll9DFx6Py8yPg++/RBN17jap7WX/8Kik5RXg5WvFsS18zRCoIQmUiEqBKxiokBPuePUCSSFtuwVag6+Fw5MZYpMdng/LO+yOVVWefzrTzaofOqOOzE5+VeO3itWwGL/+H8T+eJDGrEB8na5YMackPI9uJrQducXPfr+dCnrttaQWVQs7zrf3YGdaFTwc1I7iWLVkFOj79K4JOs3cxf3s4GXlaS4RtdoXhEVx5eSj6pCTUtWsT8P33972txd3oDUaW7jX9AzKqc3DZNswVBKFKED/VlZDraNOeWVm/b0EbH1/xAUgSbJsGRj2E9IG6Pcxa/M3FEWXI2Ba7jVMpp8jI0/LupnP0XfQ3h2PS0SjlTOpRl7/CutCniZeYenyLyIxIjiUfQyFTMKDugDuep1TIeaaFLzsmd+HzF1oQ4mFHTpGeL3ZH0mnOLmb9cZHU3KIKjNy8Cs6eI27oUAypqWgaNCDgu29ReZhnltbvZ64Rl56Pi62awW39zFKmIAiVi0iAKiHrxo2w7dwZDAbSVnxV8QFEbIOonaBQQ++Py+UW9Vzq0b9OfwDe2v0RXT/ZzXf/XMEoQd8mXux8owuTeoRgra6+09of1M2FD7v7d8fD1uOe5yvkMvo182bbxEdZ+lJLGno5kKc1sGxvNJ3m7OKj3y+Qkl1Y3mGbVf7x48S98gqGrCysmjUl4JtVKF3MMyDeaJT4ck8kAK92CsJGLXYMEoTqSCRAlZTbmNEAZG7ciC4pqeJurC8ytf4APDIOXMtv08eOLkOQSRoSC8PJUxynnoc9P45qx+IhLfF1tim3+1Zlebo8fov+DTCt/FwWcrmMxxt7sWVCJ74a2ppmvo4U6ox8tT+GTnN3M/3Xc1zLKiiPsM0q7+BB4kaOwpiXh02bNvh/vRKFo6PZyt9xMZmI5FzsNUpeeiTAbOUKglC5iASokrJp1QqbNm1ApyNt5cqKu/GhxZARA3ae8Oib5XKLxMwCXv/pJKNXR1KY2hkAj8CdbAhtQ4fabuVyz+ri96jfydPlEegQSFvPtg9Uhkwmo0dDDzaFduSb4W1o6e+EVm9k9aErdJm7h3c3XyAxj0q5L13Ort3Ejx6DVFCAbefO+C1fhsLO1mzlS5LEl7tNrT8vtw/A0VoMuBeE6kokQJWY21jTWKDMn39Bn5ZW/jfMvgb75pse95gBGvMOOi7UGfh852Ue+2Qvv51ORCaD/sEvUsvanWx9CmsjfjLr/aobSZJYE74GMO36/rDjomQyGV3rubN+bAd+GNmOdkEuaA1G1hxNYM4ZJY/O38fU9WfYdu4a2ZVgGn321q0kTJiApNNh37Mnvou/QG5tbdZ7HIhM43RCFlYqOSM6PfxMMkEQKi/RuV2J2bRvj1XTphSeOUP6N6txf+P+dmB/YH/NAF0e+LSGpmXrXrkbSZL480IyH225QHy6qYulTaAz0/s1orGPI5ujJvK//f9jxZkV9K/THxermru44d2cSDlBZGYk1kpr+tXuZ7ZyZTIZHeu40bGOG4ej01i2N4q/I1JIyi5izdF41hyNRymX0TLAmS4hteharxYNvRwqdGB65voNXHv3XTAacXiqH94zZyJTmv/X1+IbrT+D2/jjZqcxe/mCIFQeogWoEpPJZLiNMbUCZfzwA4bMzPK7WfxROGNqXaDPXJCb560RmZLD0JVHGP3dceLTC/B0sOKzwc35eXR7GvuYxm08GfwkDVwakKvL5ctTX5rlvtXRzanvTwQ9gYPaoVzu0S7YlWUvtWBmGwNfD23J8I6BBLvZojdKHIlJZ972cPou2k/bmTt585fT/H4mkaz88m0dSv/hB679739gNOL0/PN4z55dLsnP8SsZHIpOQymX8dqjwWYvXxCEykW0AFVydt26oqlXj6LwcNK//4Fa40PNfxOj8d/9vpoPAd8H3zvpppxCHYu3X2b1wVj0Rgm1Qs5rjwYztmttbDUl33ZymZwpbaYwYvsI1kWs48X6LxLsJP4A3Sq1IJUdcTsAU/dXeVMr4NG6bjzW0Av6QVxaPnsjUtgTfp2DUWlczyli3fEE1h1PQC6DFv7OdA2pRZd6tWjs7Wi2PbNSV6zg+icLAHAZNhT3qVPLreXp5tifZ1v64O1k3q41QRAqH5EAVXKmVqDRXJ0cRvp33+HyyitmHfQJwOkfIfEEqO0fer8vo1HiULKM9xfuJz3P1DLQs6EH7/RtQIDrneNu49mGbn7d2B2/m0+Of8LixxY/VBzVzYbLG9Ab9TSr1Yz6LvUr/P7+rja83D6Ql9sHUqgzcCw2gz3hKeyNuM7llFyOX8ng+JUMPtkRgZudmkfrmpKhznVr4WJb9kU0JUni+qJFpC1ZCoDr2DHUmjCh3JKfC4nZ7LyUglwGY7vWKZd7CIJQuYgEqAqw79ULdVAQ2pgYMtf8hOvIkeYrvDAb/nrf9LjLFLC/+7oyBVoDSdmFXMsqICmrkGtZhSRlFZKUbfqckJFPRr4C0BFcy5bp/RrRJeT+9hALaxXG3wl/sy9hH4cSD9Heu/1DVq560Bv1/BLxC1D2qe/lwUqloFNdNzrVdeMdICEjn30RqewJT+FAZCqpuVo2nLzKhpNXkcmgma9T8dihpr5O99xTS5IkUmbPIX31agBqvRFm2ii4HN1c9+eJJl4EuZn5HwxBEColkQBVATKFAtfXXuPatGmkrfoG55deQm5lZZ7C982FvBRwqU1O85EkJecUJzXXsgpJyr4l0ckuJPM+xntoFBJhPesxvFPtMm0hEOgYyKD6g/jh4g/MPzafn5/8GYVcLIS4L2EfSXlJOGuc6RXYy9Lh3MbX2YYX2/nzYjt/tHojx69ksCcihb3h17mUlMOp+ExOxWfy2c7LONuo6FzXlAx1rluLWvYlBxpLRiNJM94n8+efAfB45x1cXhpSrvFHX89ly9lrAIwTrT+CUGOIBKiKcHyyL6lffIHu6lUyf1mHy8sv3fe1kiSRma8rTmhuJjj6lMu8EbkEJTAubSBbP9xzX+VZqxR4OVnh5WiFp4O16bOj6bmbrZLwY/t5pmMgqgfYP2lM0zFsjtpMREYEm6M280zdZ8pcRnVzc+XnZ+o+g0ZRuWcmqZVy2td2pX1tV6b1aUBSViF7I0xdZX9fTiUjX8fm04lsPp0IQBMfR7rWq0WXkFo087Ij5d13yN78G8jleH34IU4Dni33mJftjUaS4LH67jT0Lp/B5YIgVD4iAaoiZCoVrqNGkjTjfdK+/hrnQc8jU6sxGiVS84pK7Y66tZuqSG+8rcyVqnkoFXp2G5qxVdcUAAcrJV6O1sUJzb+frfF0MD13sFLecSyGTqcj9iEabZysnBjddDTzj81n0clF9A7sjY2q5q4KfSX7CgcTDyJDxsCQgZYOp8w8Ha0Y1MafQW380RmMnIrPZE+4aTD1+cRszl7N4uzVLJbuuMT/Tv5Iu4QzSAoF9h98jNOAp8s9vsTMAjacTABgXDfR+iMINYlIgCoxvcFISk7Rv4mNe3NaOLhgnZTE7MkL+N2vLcnZheiN97dir6utujiheVR2ku7RpzDKlDj2n89O/4Z4OljdNkPLEl6o/wJrw9cSnxPPqvOrCG1eDjPfqoifw01dQZ19O+Nr72vhaB6OSiGnTaALbQJdmNK7Pik5heyLSOXvcwk88s1cml+7iE6u4OPWQzl8VEmDhL+LW4daBTijUph/1Y7l+6LRGSQeCXahVYCz2csXBKHysvxfuxqqSG/kWna+qZUmu7BEC8617EKSsgq4nlPEf3Ob/v6dGH1uM23/+Z0Vtg0xyhXIZOBur8HT0RqvG600xS03DlZ4OVrj7qDBSnWjaUavhSWm9YXk7cfSstWDbalQXtQKNZNbTSZsTxjfnPuGAXUH4GnraemwKlyBvoBNkZuAyjH42dzc7a14pp4zbT77H/nXLiJprDg5chqFMm9kV7O4eC2bi9eyWbInCnuNko513OhSzzR+yMvx4aepp+UWseZoHADju9V96PIEQahaRAJUgf66kMyCHeHEXVcw8dBf93WNUi7D45akxqPNIHSz9uCdm8aGBvm4P/M0tew1Zfvv+PBSSIsEW3d49P8esDblq4d/D1q6t+REygk+P/k5H3cqn13pK7NtMdvI1mbjY+dDR++Olg7H7AxZWcS/NpqC06eR29rit3wZDVu1Yhim5OTvy6aZZfsup5Kep2Xb+SS2nTdtDFzPw96UDIXUolWgMxpl2ftdvzkUR6HOSDNfRzrWcTVz7QRBqOxEAlSB9EYjF67lAKbxM2ql/JZWGtM4m5Ljbqxws9Xctqhc6vURXF+4EPv13+M17HlkZVm1OScZ9s41Pe4xHawq56BPmUzGlDZTeGHLC2yO2syLDV6kkWsjS4dVoW4Ofn6+3vPVbjacPj2duFdHUnTxIgpHR/y++grrJo2LX3e109C/hQ/9W/hgNEqcvZrF3ojr7AlP4VR8JuHJOYQn57B8XzQ2agUdarsVd5f5udx7zFi+Hr4/EQ+Yxv5U5LYegiBUDhZPgBYvXsy8efNISkqiWbNmfP7557Rte+cumczMTP73v/+xYcMG0tPTCQgIYOHChTzxxBMAGAwGZsyYwffff09SUhLe3t688sorvPPOOxb/JdcqwIXlL7Ug8sxRBjzRA3dHmweKyXnIi6R9/TXayChy/voLh15lmBq98wPQ5oB3S2j2YpnvXZEauzWmb3BftkRvYf7R+azsvdLi38OKci71HOfTzqOWq3mmTvWaCadLTiFuxAi0UVEoXF3xX7kSq3ohdzxfLpfRzM+JZn5OTHisLpn52hutQ9fZG3Gd1Nwi/rqYzF8XkwGoXcuWLiHudK1Xi7ZBLv92/d5if5KM3CI9IR529Gxw97WvBEGoniyaAK1du5awsDCWLl1Ku3btWLhwIb179yY8PBx3d/fbztdqtfTs2RN3d3fWrVuHj48PV65cwcnJqficOXPmsGTJElavXk2jRo04duwYw4cPx9HRkQkTJlRg7W5Xy15Dt3q1KIgCF1v1A/8xV9jb4/zSENKWLCV16VLse/a8v7KuHodT35sem3G/r/I0scVE/rryF8eSj7E7fjfd/btbOqQKseaSaV+23oG9cbaqPoNztQlXiRs+HF18PEpPT/xXrUQTVLZd151s1PRr5k2/Zt4YjRIXrmWzN+I6e8Ovczwug6jreURdj2HlgRisVHLaB7vStZ4pIQpwtSVfq2fPNdN7f1zXOmbbtkMQhKrFognQggULGDVqFMOHDwdg6dKlbNmyhZUrVzJ16tTbzl+5ciXp6ekcPHgQlUoFQGBgYIlzDh48yNNPP03fvn2LX//pp584cuRI+VamgrkMHUr66m8punCRvL//xu7RR+9+gdEIf7xletx0MPi1Kf8gzcDLzouhDYey4uwKFhxfQGefzqgUKkuHVa4yCzPZFrsNgEH1q8/g56KYGOKGj0CflITKzw//VatQ+/o8VJlyuYzGPo409nEktFsdsgp0HIz8t3UoKbuQ3eHX2R1+HYBAVxu8Ha3I08vwc7bmyaZe5qiaIAhVkMUSIK1Wy/Hjx5k2bVrxMblcTo8ePTh06FCp12zevJn27dsTGhrKr7/+Sq1atXjxxRd56623UChMzdwdOnRg+fLlREREEBISwunTp9m/fz8LFiy4YyxFRUUUFRUVP8/OzgZMa9rodObd6fpmeQ9drp0djgOfI3P1t1z/cgnqRx65ayuQ7MxalAlHkVS26Lv+D8xcr5vMVr9bDK0/lPWX13Ml+wo/XfyJF+q9YLayy6o86vdfGyI2UGQoor5zfRo4NijXe5WmPOpYFBFB4mujMaSloQoKwmfFcmQe7mavm40SetR3o0d9NySpPhHJuey9nMq+y6kcv5JJbFo+sWn5ALzawR/JaEBnNJg1hsqgIt6nliTqV/WVVx3LUp5MkqT7W0TGzBITE/Hx8eHgwYO0b//vnk//93//x969ezl8+PBt19SvX5/Y2FiGDBnCuHHjiIyMZNy4cUyYMIHp002beBqNRt5++23mzp2LQqHAYDDw8ccfl0i0/mvGjBm8//77tx3/8ccfsbGpvIvwKbKzCZozF7leT/xroyioXbvU85SGAh678H9Y6bO44DWQy579KjjSh3e06Ci/FvyKtcyaMPswrOXVc7duo2RkYc5C0o3p9LfuT2tNa0uH9NA0CQn4fr0SRX4+hd5eXH31VQx2dhUeR6EBIrJkXMyUoZBB/wAjD7BYuSAIlVh+fj4vvvgiWVlZODjcfZKPxQdBl4XRaMTd3Z3ly5ejUCho1aoVV69eZd68ecUJ0M8//8wPP/zAjz/+SKNGjTh16hSTJk3C29ubYcOGlVrutGnTCAsLK36enZ2Nn58fvXr1uucXsKx0Oh07duygZ8+exd14D+N6VDRZa9YQcvoMPq+/Xuo58l0foNBnITkHUXfoQuoqy287BXPX76Zexl6c++McUVlRXPG+QljLsHtfVA7Kq343HUw8SPqedOxUdkzpNwVrZcUneuasY8GJEyR+8CFSfj6apk0I+nIJjR0tO/OwvL+HlUF1r6OoX9VXXnW82YNzPyyWALm5uaFQKEhOTi5xPDk5GU/P0he98/LyQqVSFXd3ATRo0ICkpCS0Wi1qtZopU6YwdepUBg8eDECTJk24cuUKs2bNumMCpNFo0GhuTwpUKlW5vfnMVXat10aRtW4dBYcPoz9/HuvmzUuekBYFR5YCIOs9E5V1xfznbe6vnQoVU9pMYcxfY1gTsYYXGryAv4O/2covczzl9N5YF7UOgP51+uNgbdlE4WHrmHfwIImh45EKCrBp0wbfJUtQ2FWendbL8+e7sqjudRT1q/rM/reiDGVZrAFYrVbTqlUrdu7cWXzMaDSyc+fOEl1it+rYsSORkZEYjf/uaxUREYGXlxdqtRowNX/J/zO7SaFQlLimOlF5e+P49FMApC5ddvsJ2/8HBi3U7g71+lRwdObV0acjHb07ojfqWXhioaXDMbvE3ET2JewDTGv/VGU5u3YTP3oMUkEBtp0747d8WaVKfgRBECzaAx4WFsaKFStYvXo1Fy9eZOzYseTl5RXPChs6dGiJsTtjx44lPT2diRMnEhERwZYtW5g5cyahof/uFdWvXz8+/vhjtmzZQmxsLBs3bmTBggU880z1WkvlVm6jRoFcTu6ePRRevPjvC5F/QcQfIFfC47OhGqyh80brN5DL5Oy4soPjycctHY5ZrYtYh1Ey0s6rHUGOZZsaXplkb91KwoQJSDod9j174Lv4C+TW1XPMliAIVZdFE6BBgwYxf/583nvvPZo3b86pU6fYtm0bHh6mhcni4uK4du1a8fl+fn5s376do0eP0rRpUyZMmMDEiRNLTJn//PPPee655xg3bhwNGjTgzTffZPTo0Xz44YcVXr+Kog4MxKGPqXUnddly00GDDrbdSB7bvga16lkoOvOq61yXZ+s+C8D8o/MxStWjZU9r0LL+8noABtcbbOFoHlzm+g1cfXMK6PU49OuHz6efIr/ROisIglCZWHwQ9Pjx4xk/fnypr+3Zs+e2Y+3bt+eff/65Y3n29vYsXLiQhQsXminCqsF19Gtkb9lCzvbtFEVFoUnZBqkRYOMGXd6ydHhmFdo8lK3RWzmXdo6tMVt5MvhJS4f00P668hfphem4W7vT1a+rpcN5IOk//EDyhx8B4PT883jOmF62bVoEQRAqkPjtVE1YhYRg1+MxkCTSvvwc9sw2vfDYe2DtZNHYzM3N2o2RTUYC8NmJzyjUF1o4ood3c9+v5+o9h1Ju8f9Lyix1xYri5Mdl2FA8358hkh9BECo18RuqGnEbPQaArD/+RJuWB17NoMVLFo6qfLzc8GU8bT1JykviuwvfWTqchxKeHs6JlBMoZUoG1B1g6XDKRJIkUj77jOufmBYadR07BvepU2vMnm2CIFRdIgGqRqybNMa2TVMwSqRdtLux31f12kX8JiulFRNbTgTgq7NfkVqQauGIHtzP4T8D0N2/O+42t++BV1lJkkTK7DmkLTEts1ArLAz3iRNF8iMIQpUgEqDqRJJwq5MEQFasHTp1oGXjKWdPBD1BY9fG5OvzWXxqsaXDeSC52lx+i/4NgMH1q87gZ8loJGn6DNJXrwbA4513cHttlIWjEgRBuH8iAapOzq7DRjqFjbseySCRtnKlpSMqV3KZnCltpgCw4fIGLmdctnBEZfdb9G8U6AsIdgymtUfV2PZC0utJnDqVzJ9/Brkcr48/xuWlIZYOSxAEoUxEAlRdaPNgx3sAuL7YF4DMn39Bn5ZmyajKXUuPlvQM6IlRMvLJsU8sHU6ZSJLE2kumwc+D6g2qEl1HklbL1clhZG/+DZRKfObPw2nAs5YOSxAEocxEAlRd/L0AchLBKQDbEbOwatIEqbCQ9G9WWzqycje55WSUciUHEg+w/+p+S4dz344lHyMqKwprpTX9alf+DWqNhYXEjx9Pzo4dyFQqfBd9hsMTT1g6LEEQhAciEqDqID0GDn5uetz7Y2Rqa9zGmmaEZfz4I4asLAsGV/78HPx4sf6LAHxy7BP0Rr2FI7o/N6e+Pxn8JPZqewtHc3eG3DziXxtN3r6/kVlZ4bt0Cfbdu1s6LEEQhAcmEqDq4M93wFAEQV2gvmlRQLuuXdGEhGDMyyP9++8tHGD5e63pazhqHInMjGRj5EZLh3NP1/Ovs/OKaR+8QfUGWTiauzNkZRH/6qvkHzmC3NYW/69WYNexo6XDEgRBeCgiAarqonbDpd9BpoA+c4r3+5LJ5biNGQ1AxrffYcjNs2SU5c5R48jYZmMB+OLkF+Rqcy0c0d2tv7wevaSnhXsL6rlU3m1K9OnpXHllOAWnT6NwdMT/m1XYtK4ag7UFQRDuRiRAVZlBB9tu7IPWZiS4Nyjxsn3v3qgDAzFkZZG5do0FAqxYz4c8T4BDAOmF6Xx97mtLh3NHeqOeXyJ+ASp3648+JYUrLw+l6OJFFK6u+H/7LdZNmlg6LEEQBLMQCVBVdvRruH4JrF2g27TbXpYpFLi+9hoAaau+wVhY9beMuBuVQkVYqzAAvj3/LYm5iRaOqHR74/eSkp+Ci5ULPQN6WjqcUinT00kY9graqCiUnp4EfP8dVvVCLB2WIAiC2YgEqKrKS4U9M02Pu78D1s6lnubY70lU3t4YUlPJXLe+AgO0jG5+3Wjj2QatUctnJz6zdDilWhNuao17tu6zqBWVb6d0bWwsfkuXoU9IQOXnR8D336MJCrJ0WIIgCGYlEqCqatdHUJgFHk2g1St3PE2mUuE6yrRxaNpXXyFptRUUoGXIZDLebP0mMmRsjdnK2etnLR1SCTFZMfxz7R9kyBgYMtDS4dxGGxvL1VeGo8rKQhUURMD336H29bF0WIIgCGYnEqCq6NoZOP6N6XGfOffc78vx2WdR1qqFPimJrM2byz8+C2vo2rB4XZ15x+YhSZKFI/rXzX2/uvh2wdvO28LRlKS/fp24kaMwpKVR5OWJz6qVqDw8LB2WIAhCuRAJUFUjSfDHW4AEjZ6FwHtPR5ZrNLiMGAFA6vIVSPqqsU7Ow5jQYgJWCitOppzkr7i/LB0OAPm6fH6N/BWAQfUr1+BnQ24ucaNHo7vR7ZXw6qsoXV0tHZYgCEK5EQlQVXN+A8QdBKU19Prwvi9zHvQ8CicndHFxZP+xrRwDrBw8bD14pfErACw4tgCtwfJdf9tit5Gjy8HP3o8O3h0sHU4xSasl4fXXKbpgmu3lvWwpBvvKvTCjIAjCwxIJUFWizYc/Tft90WkyOPre96VyGxtcXnkFgNRlS5GMxnIIsHIZ3mg4btZuJOQm8NOlnywaiyRJrLlkGvw8qN4g5LLK8aMnGY0kTnub/EP/ILOxwW/ZMlR+fpYOSxAEodxVjt/Cwv05sBCyE8DRHzpOKPPlzkNeRG5vjzYyipydO80fXyVjo7JhQgvT12nZmWVkFmZaLJazqWe5mH4RjULD07Wftlgc/5Uybz7ZW7aAUonvokVYN25k6ZAEQRAqhEiAqorMODhwY1p3rw9BZV3mIhT29ji/NASAtCVLK9Xg4PLyVO2nCHEOIUebw9IzSy0Wx819vx4PfBwnKyeLxXGrtJWrSF+1CgDvjz/CrpPY3kIQhJpDJEBVxZ/vgL4QAjtDwwdvQXAZOhSZtTWFFy6Q9/ffZgywclLIFbzZ+k0A1l5aS2xWbIXHkFGYwbYY07irwfUHV/j9S5P12++kzJ0LgPuUN3F8uvK0SgmCIFQEkQBVBTH74MKvIJPD47OL9/t6EEpnZ5wHm/4Ip9aQVqD23u151PdR9JKeBccXVPj9N0VuQmvU0si1EY3dGlf4/f8r7+BBEt9+GwCXYUOLZwgKgiDUJCIBquwMevjjxn5frUeA58P/AXUZ/goytZqCkyfJP3L0ocurCt5o9QYKmYLd8bs5mlRxdTZKxuLur8qw71fhhQskjH8ddDocnuiD+1tvIXuIhFoQBKGqEglQZXd8FaScBysn6PY/sxSpcnfH6bkBAKQts9y4mIoU7BTMcyHPATDv6DyMUsXMgjtw9QBXc6/ioHbg8aDHK+Sed6KNjyfutdEY8/OxadcOr9mzkcnFrwBBEGom8duvMstPh90fmx53fwdsXMxWtOurr4JSSd7BQxScPm22ciuzcc3HYaey42L6RX6L+q1C7nmz9ad/nf5YK8s+cN1c9GlpxI0ciSE1FU39+vh+8TlydeXbh0wQBKGiiASoMtv9MRRkgHsjaDXcrEWrfHxwfOopAFKXLjNr2ZWVi5ULo5qOAmDRyUXk6/LL9X5Xc6+yL2EfAM/Xe75c73U3xrw84seMRXclDpW3N37Ll6EQCx0KglDDiQSosko6B8dWmh73mQ0Kpdlv4TpqJMjl5O7eTeGlS2YvvzIa0mAIPnY+pOSnsPrC6nK91y/hvyAh0cG7AwEOAeV6rzuRdDoSJk+m8OxZFE5O+H31FSp3d4vEIgiCUJmIBKgykiTYNhUkIzR4CoIeLZfbaIKCcHjcNC4ldVnNaAXSKDRMajkJgFXnVpGSn1Iu99EatGy4vAGw3OBnSZK49u575O37G5mVFX5Ll6AJDrJILIIgCJWNSIAqowu/QuzfoLSCXh+V661cR48GIGfbdoqio8v1XpVF78DeNK3VlAJ9AV+c/KJc7vHnlT/JKMrA09aTR33LJ4G9l+ufLiRr0yZQKPD5dAHWzZtbJA5BEITKSCRAlY2uAP581/S440RwLt+uE6t6Idg99hhIEmnLlpfrvSoLmUzGlNZTANMaPeHp4Wa/x9pLpsHPA0MGopSbv/vyXtK//4G05abvp9cH72PfrVuFxyAIglCZiQSosjmwCLLiwMEXOk6qkFu6jTG1AmX9/jvahIQKuaelNXdvzuOBjyMhMe/YPLMuCHkp/RKnrp9CKVfybN1nzVbu/cretp3kj02zB2tNnIDTgAEVHoMgCEJlJxKgyiQzHvZ/anrc6wNQ21TIba2bNMG2Y0cwGEhb8VWF3LMymNhyIiq5isPXDvP3VfNtC3Jz6ntP/564WbuZrdz7kXf4CIlTpoAk4fTCYFzHjKnQ+wuCIFQVIgGqTHa8B/oCCOgIjSq25cBtrOkPZdaGDeiSkyv03pbia+/LSw1fAmD+sfnojLqHLjNHm8OW6C0ADKpfsYOfC8PDSQgNRdLpsO/ZE8933hGrPAuCINyBxROgxYsXExgYiJWVFe3atePIkSN3PT8zM5PQ0FC8vLzQaDSEhISwdevWEudcvXqVl156CVdXV6ytrWnSpAnHjh0rz2o8vNgDcH6DWfb7ehA2rVtj3boVkk5H+sqVFXpvSxrVZBTOGmdismJYF7HuocvbHLWZAn0BdZzq0NK9pRkivD+6xETiR72GMTcX69at8J4/D5lCUWH3FwRBqGosmgCtXbuWsLAwpk+fzokTJ2jWrBm9e/cmJaX0qclarZaePXsSGxvLunXrCA8PZ8WKFfj4+BSfk5GRQceOHVGpVPzxxx9cuHCBTz75BGdn54qqVtkZDfDHW6bHLYeBV1OLhOE2ZiwAGWt/Rp+ebpEYKpq92p5xzccBsOTUErK12Q9cliRJxd1fg+sNrrDWF31GBnEjR6FPSUFTtw5+ixcj12gq5N6CIAhVlUUToAULFjBq1CiGDx9Ow4YNWbp0KTY2Nqy8QwvEypUrSU9PZ9OmTXTs2JHAwEC6dOlCs2bNis+ZM2cOfn5+rFq1irZt2xIUFESvXr2oXbt2RVWr7E6shuSzYOUI3d+1WBi2HTtg1aQJUmEh6d+U7yKBlclzIc8R5BhERlEGX5158DFQR5OOEpMVg43ShidrP2nGCO/MWFBAwthxaKOjUXp64rd8OQpHxwq5tyAIQlVW8fNzb9BqtRw/fpxp06YVH5PL5fTo0YNDhw6Ves3mzZtp3749oaGh/Prrr9SqVYsXX3yRt956C8WN5v7NmzfTu3dvBg4cyN69e/Hx8WHcuHGMGjXqjrEUFRVRVFRU/Dw729QKoNPp0OkeflzIrW6WV1xuQSbKnR8iAwyPvoVR7QBmvmdZOI0cSdLEiaT/8AMOQ4eicHQo0/W31a+KmNR8EhP3TuT7i9/zbO1n8bHzKfW8u9Xvx4s/AtA3qC9q1OX+NZD0epImh1Fw6hRye3u8lnwJbm4Pfd+q+j28X9W9flD96yjqV/WVVx3LUp5MMuf83zJITEzEx8eHgwcP0r59++Lj//d//8fevXs5fPjwbdfUr1+f2NhYhgwZwrhx44iMjGTcuHFMmDCB6dOnA2BlZQVAWFgYAwcO5OjRo0ycOJGlS5cybNiwUmOZMWMG77///m3Hf/zxR2xsyncmVuOE76l9/U+yrXzYU/9DJJnFclITo5GAzxahSUoitWdP0ns8Ztl4KogkSazKW0W0PprGqsYMth1cpuuzjdnMz56PESOv27+Oh8KjnCK9QZJw37ARpyNHMCqVJIwcSWFQYPneUxAEoZLLz8/nxRdfJCsrCweHu/8Db+G/tmVjNBpxd3dn+fLlKBQKWrVqxdWrV5k3b15xAmQ0GmndujUzZ84EoEWLFpw7d+6uCdC0adMICwsrfp6dnY2fnx+9evW65xewrHQ6HTt27KBnz56oMqNQntoJgM2zi+gT1MWs93pQOQoFyf/3Fu5HjtD2ow+RlyEJLFE/laocozS/uhl1eeGPFzinO4dPGx+a1Wp22zl3qt/SM0sxnjPS0r0lw3uYd+Pa0qQvWUL6kSMgl+M9fx4hj5kvUa3K38P7Ud3rB9W/jqJ+VV951fFmD879sFgC5ObmhkKhIPk/U66Tk5Px9PQs9RovLy9UKlVxdxdAgwYNSEpKQqvVolar8fLyomHDhiWua9CgAevXr79jLBqNBk0pg0ZVKlW5vflUSiWqv94ByQD1n0QZ0qNc7vMgnPv2JWPxl2ivXCF33XpcXx1R5jLK82tXXhq5N6J/nf5sjNzIp6c+5fs+399xIPOt9dMZdWyM2gjAC/VfKPd6Z6z9mfQvlwDg+d67ON/Yz83cquL3sCyqe/2g+tdR1K/qM3cdy1KWxQZBq9VqWrVqxc6dO4uPGY1Gdu7cWaJL7FYdO3YkMjISo9FYfCwiIgIvLy/UanXxOeHhJbc2iIiIICDAMrtx34ks4g+I3gMKTbnv91VWMoUC19deAyBt1SqMhYUWjqjijG8xHmulNWeun2F77Pb7umZ33G6uF1zH1cqVx/zLt8swZ+dOkm5017qNG4vz4LJ11QmCIAgmFp0FFhYWxooVK1i9ejUXL15k7Nix5OXlMXy4qQth6NChJQZJjx07lvT0dCZOnEhERARbtmxh5syZhIaGFp8zefJk/vnnH2bOnElkZCQ//vgjy5cvL3GOpcmNWhR/3Zjt1WE8uFS+Hbodn+qH0tsLQ2oqmXdpPatu3G3cGd7Y9P779PinFBmK7nHFvys/DwgZgEpRfv+t5Z84ydWwN8BoxPG5Abi9/nq53UsQBKG6s2gCNGjQIObPn897771H8+bNOXXqFNu2bcPDwzSANC4ujmvXrhWf7+fnx/bt2zl69ChNmzZlwoQJTJw4kalTpxaf06ZNGzZu3MhPP/1E48aN+fDDD1m4cCFDhgyp8PrdSZ2Ubcgyr4C9F3QKu/cFFiBTqXC7MXMu7auvkbRaC0dUcYY1HIa7jTuJeYn8cPGHu54bnRnNkaQjyGVyBoYMLLeYiqKiiB87FqmoCLuuXfGaMUOs8iwIgvAQLD4Ievz48YwfP77U1/bs2XPbsfbt2/PPP//ctcwnn3ySJ5+smHVYyiw7kbrJm02Pe34IGjvLxnMXjs8+S+qXS9Bfu0bWb7/VmE01bVQ2TGgxgXcOvMOKMyvoX6c/LlYupZ57s/Wnq29XPG1LH7v2sHTJycSNHIUxKwvrZs3w+XQBMqXFf3QFQRCqNItvhVHTKHa9j9KoxejbDpo8Z+lw7kqu0eAywjQAOnX5ciS93sIRVZx+tfvRwKUBubpcvjz1Zann5Ovy2RxlSmbLa98vQ3Y28SNHob92DXVQEL5LlyC3ti6XewmCINQkIgGqSHH/ID+/HgkZhl4zK3y/rwfhPOh5FE5O6K7Ekb3t/gYFVwdymZwpbaYAsC5iHdGZ0bedsyVmC7m6XAIcAnjE6xGzx2AsKiIhdDxFly+jqOWG34oVKCvzli6CIAhViEiAKlLqZSSlFVdcHwWv29eYqYzkNja4vGJaPylt2VKkW2bgVXdtPNvQza8bBsnAJ8c/KfGaJEmsubQGgOdDnkcuM++PkmQwkPh/b5F/9ChyOzv8V6xA7Vv66tSCIAhC2YkEqCK1fBn9mENc9Cq/wbLlwfnFF5Hb2VF0OZKcW5YtqAnCWoWhlCnZl7CPQ4n/btFyJvUMERkRWCmseLrO02a9pyRJJH88k5zt25GpVPh+8QVW9eub9R6CIAg1nUiAKpqjH1qVeVeXLm8KBwecXzLNoktbugwL7Z5iEYGOgcXje+Yfm4/BaADg58s/A9AnqA+OGvNuPpq2fAUZP/4IMhnec+dg+0g7s5YvCIIgiARIuE8uw4Yhs7am8Px58vbvt3Q4FWpM0zHYq+2JyIjgt5jfyDXm8lfcX4D5Bz9nbtjI9U8/BcBj2lQc+vQxa/mCIAiCiUiAhPuidHbGeZDpj33qkqU1qhXIycqJ0U1HA/Dl6S85VHQInVFHE7cmNHJtZLb75O7bx7V3TQtkuo58FZehQ81WtiAIglCSSICE++YyfDgytZqCEyfIP3rU0uFUqBfqv4CfvR+phansLdoLwKB65mv9KThzhoSJk8BgwPHpp6gVVjkXyBQEQaguRAIk3DeVhzuOA54FTGOBahK1Qs3kVpOLnzuqHekd2NssZRfFxBA/egxSQQG2nTrh9dFHyOTiR1MQBKE8id+yQpm4vjoSlEryDh6k4PRpS4dToXr496B5reYAPFX7KayUVg9dpv76deJHvYYhIwOrRo3w/Wwhsmq++7MgCEJlIBIgoUzUvj449usHQGoNawWSyWTM7TSXJ6yeYEyTMQ9dniE3l7jRo9ElJKDy98dv2VLktrZmiFQQBEG4F5EACWXm+tookMnI3b2bwkuXLB1OhXKzdqODVQeslQ+3HYWk1ZLw+usUXbiIwtUV/69WoHRzM1OUgiAIwr2IBEgoM01QEA59HgcgdVnNagUyB8loJHHa2+Qf+geZjQ1+y5ah9ve3dFiCIAg1ikiAhAfiOto0LTxn23aKomMsHE3VkjJvPtlbtoBSie+iRVg3Nt9UekEQBOH+iARIeCBW9eph99hjIEmkLV9u6XCqjLSVq0hftQoA748/wq5TRwtHJAiCUDOJBEh4YG5jTK1AWb/9hjbhqoWjqfyyfvudlLlzAXCf8iaOT5t3DzFBEATh/okESHhg1k2aYNuxIxgMpH21wtLhVGp5Bw+S+PbbALgMG4rLiBEWjkgQBKFmEwmQ8FCKW4HWb0CXnGLhaCqnwgsXSBj/Ouh0ODzRB/e33kImk1k6LEEQhBpNJEDCQ7Fp0wbr1q2QdDrSV660dDiVjjY+nrjXRmPMz8emXTu8Zs8WqzwLgiBUAuI3sfDQ3EabFgXMWLsWQ3q6haOpPPTp6cSNHIkhNRVN/fr4fvE5crXa0mEJgiAIiARIMAPbTh2xatwYqbCQzO+/t3Q4lYIxL4/40WPQXYlD5e2N3/JlKOztLR2WIAiCcINIgISHJpPJ/h0L9NMa5AUFFo7IsiSdjoTJkyk8exaFkxN+X32Fyt3d0mEJgiAItxAJkGAWdt27o6lbF2NuLk4HD1o6HIuRJIlr775H3r6/kVlZ4bd0CZrgIEuHJQiCIPyH0tIBCNWDTC7HdcxoEt94E9e/dhI34Dms6tRGHRSMpnYw6uBg1IGByK0efgf1yuz6pwvJ2rQJFAp8Pl2AdfPmlg5JEARBKIVIgASzcXj8cdK++Yais+fQRkSgjYgoeYJMhsrHB3VwEJrg2qbPtWujDg5G6exsmaDNKP37H4pXxfb64H3su3WzcESCIAjCnYgESDAbmUKB7/ff89cPP9DB3x/DlTiKYqLRRkVTFB2NMSsLXUICuoQE8vb9XeJahbMz6uBgNMGm1qKbrUYqb+8qMW08e9t2kj/+GIBaEyfgNGCAhSMSBEEQ7kYkQIJZyeRydK6u2HbpgkqlKj4uSRKG9HS00dEURUWjjbnxOToaXWIihowMCo4fp+D48ZLlaTSog4LQBAehDq5t+ly7NuqAgErTnZZ3+AiJU6aAJOH0wmBcx4yxdEiCIAjCPYgESKgQMpkMpasrSldXbNq0KfGaMT8fbWwsRdExaKOjTJ+jotDGxiIVFVF06RJFly79t0BUvr4lu9NutB5VZHdaYXgECePHI+l02Pfsiec774hVngVBEKoAkQAJFie3scGqYUOsGjYscVwyGNAlJFAUbWopKoq+pTstOxtdfDy6+Hjy9u4rcZ3C2Rl17WA0QcGmz8HBqINro/L2Mmt3mi4xkfhRozDm5GDduhXe8+chUyjMVr4gCIJQfkQCJFRaMoUCdUAA6oAAuGVA8c3utKKoKLTRMRRF//tZn3jN1J127DgFx/7TnWZlZepOCwoqkRipAwOQazRlis2QmcnVkaPQp6SgqVsHv8WLy1yGIAiCYDkiARKqnFu702zbti3xmjE/n6KYmBKJkTY62tSdVlhI0cWLFF28+N8CUfn63jYAWxMcjMLJ6fb7a7VcG/862uholJ6e+C1fjsLRsRxrLAiCIJibSICEakVuY4N1o0ZYN2pU4rik16O7evW2Adj/7U5j794S1ylcXEokRnJ/f7x++onCCxeROzjgv2I5Ki+viqyiIAiCYAYiARJqBJlS+W93Gv/pTktLu32cUUy0qTstPZ389HTyjx0rvsYO0+w0vyVfoqlbt+IrIwiCIDy0SrHAyuLFiwkMDMTKyop27dpx5MiRu56fmZlJaGgoXl5eaDQaQkJC2Lp1a6nnzp49G5lMxqRJk8ohcqGqk8lkKN3csG3bFufBg/F8+238v/6Kurt2Ue/4MQLXr8N73lxcx47Bvndv1HVqo7ezw2PuXGxatbJ0+IIgCMIDsngL0Nq1awkLC2Pp0qW0a9eOhQsX0rt3b8LDw3EvZQNJrVZLz549cXd3Z926dfj4+HDlyhWcShmrcfToUZYtW0bTpk0roCZCdSO3tb2tO02n07F161bqdxerPAuCIFRlFm8BWrBgAaNGjWL48OE0bNiQpUuXYmNjw8qVK0s9f+XKlaSnp7Np0yY6duxIYGAgXbp0oVmzZiXOy83NZciQIaxYsQLnarDNgiAIgiAI5mPRBEir1XL8+HF69OhRfEwul9OjRw8OHTpU6jWbN2+mffv2hIaG4uHhQePGjZk5cyYGg6HEeaGhofTt27dE2YIgCIIgCGDhLrDU1FQMBgMeHh4ljnt4eHDpvyv/3hAdHc2uXbsYMmQIW7duJTIyknHjxqHT6Zg+fToAa9as4cSJExw9evS+4igqKqKoqKj4eXZ2NmDq7tDpdA9StTu6WZ65y60sRP2qvupex+peP6j+dRT1q/rKq45lKc/iY4DKymg04u7uzvLly1EoFLRq1YqrV68yb948pk+fTnx8PBMnTmTHjh1Y3edeUbNmzeL999+/7fiff/6JjY2NuasAwI4dO8ql3MpC1K/qq+51rO71g+pfR1G/qs/cdczPz7/vcy2aALm5uaFQKEhOTi5xPDk5GU9Pz1Kv8fLyQqVSobhly4EGDRqQlJRU3KWWkpJCy5Yti183GAzs27ePL774gqKiohLXAkybNo2wsLDi59nZ2fj5+dGrVy8cHBzMUdViOp2OHTt20LNnzxKbhVYXon5VX3WvY3WvH1T/Oor6VX3lVcebPTj3w6IJkFqtplWrVuzcuZP+/fsDphaenTt3Mn78+FKv6dixIz/++CNGoxH5jX2dIiIi8PLyQq1W89hjj3H27NkS1wwfPpz69evz1ltv3Zb8AGg0GjSlbGOgUqnK7c1XnmVXBqJ+VV91r2N1rx9U/zqK+lV95q5jWcqyeBdYWFgYw4YNo3Xr1rRt25aFCxeSl5fH8OHDARg6dCg+Pj7MmjULgLFjx/LFF18wceJEXn/9dS5fvszMmTOZMGECAPb29jRu3LjEPWxtbXF1db3tuCAIgiAINZPFE6BBgwZx/fp13nvvPZKSkmjevDnbtm0rHhgdFxdX3NID4Ofnx/bt25k8eTJNmzbFx8eHiRMn8tZbb1mqCoIgCIIgVDEWT4AAxo8ff8curz179tx2rH379vzzzz/3XX5pZQiCIAiCUHNZfCFEQRAEQRCEiiYSIEEQBEEQahyRAAmCIAiCUOOIBEgQBEEQhBpHJECCIAiCINQ4lWIWWGUjSRJQthUl75dOpyM/P5/s7OxqucCVqF/VV93rWN3rB9W/jqJ+VV951fHm3+2bf8fvRiRApcjJyQFMaw4JgiAIglC15OTk4OjoeNdzZNL9pEk1jNFoJDExEXt7e2QymVnLvrnPWHx8vNn3GasMRP2qvupex+peP6j+dRT1q/rKq46SJJGTk4O3t3eJRZRLI1qASiGXy/H19S3Xezg4OFTbNzaI+lUH1b2O1b1+UP3rKOpX9ZVHHe/V8nOTGAQtCIIgCEKNIxIgQRAEQRBqHJEAVTCNRsP06dPRaDSWDqVciPpVfdW9jtW9flD96yjqV/VVhjqKQdCCIAiCINQ4ogVIEARBEIQaRyRAgiAIgiDUOCIBEgRBEAShxhEJkCAIgiAINY5IgCrArFmzaNOmDfb29ri7u9O/f3/Cw8MtHZZZLVmyhKZNmxYvatW+fXv++OMPS4dVbmbPno1MJmPSpEmWDsUsZsyYgUwmK/FRv359S4dldlevXuWll17C1dUVa2trmjRpwrFjxywdllkEBgbe9j2UyWSEhoZaOjSzMBgMvPvuuwQFBWFtbU3t2rX58MMP72vPp6okJyeHSZMmERAQgLW1NR06dODo0aOWDuuB7Nu3j379+uHt7Y1MJmPTpk0lXpckiffeew8vLy+sra3p0aMHly9frrD4RAJUAfbu3UtoaCj//PMPO3bsQKfT0atXL/Ly8iwdmtn4+voye/Zsjh8/zrFjx+jevTtPP/0058+ft3RoZnf06FGWLVtG06ZNLR2KWTVq1Ihr164Vf+zfv9/SIZlVRkYGHTt2RKVS8ccff3DhwgU++eQTnJ2dLR2aWRw9erTE92/Hjh0ADBw40MKRmcecOXNYsmQJX3zxBRcvXmTOnDnMnTuXzz//3NKhmdXIkSPZsWMH3333HWfPnqVXr1706NGDq1evWjq0MsvLy6NZs2YsXry41Nfnzp3LokWLWLp0KYcPH8bW1pbevXtTWFhYMQFKQoVLSUmRAGnv3r2WDqVcOTs7S1999ZWlwzCrnJwcqW7dutKOHTukLl26SBMnTrR0SGYxffp0qVmzZpYOo1y99dZbUqdOnSwdRoWZOHGiVLt2bcloNFo6FLPo27evNGLEiBLHnn32WWnIkCEWisj88vPzJYVCIf3+++8ljrds2VL63//+Z6GozAOQNm7cWPzcaDRKnp6e0rx584qPZWZmShqNRvrpp58qJCbRAmQBWVlZALi4uFg4kvJhMBhYs2YNeXl5tG/f3tLhmFVoaCh9+/alR48elg7F7C5fvoy3tzfBwcEMGTKEuLg4S4dkVps3b6Z169YMHDgQd3d3WrRowYoVKywdVrnQarV8//33jBgxwuwbOltKhw4d2LlzJxEREQCcPn2a/fv306dPHwtHZj56vR6DwYCVlVWJ49bW1tWuRTYmJoakpKQSv0sdHR1p164dhw4dqpAYxGaoFcxoNDJp0iQ6duxI48aNLR2OWZ09e5b27dtTWFiInZ0dGzdupGHDhpYOy2zWrFnDiRMnqmx//N20a9eOb775hnr16nHt2jXef/99OnfuzLlz57C3t7d0eGYRHR3NkiVLCAsL4+233+bo0aNMmDABtVrNsGHDLB2eWW3atInMzExeeeUVS4diNlOnTiU7O5v69eujUCgwGAx8/PHHDBkyxNKhmY29vT3t27fnww8/pEGDBnh4ePDTTz9x6NAh6tSpY+nwzCopKQkADw+PEsc9PDyKXytvIgGqYKGhoZw7d67aZfMA9erV49SpU2RlZbFu3TqGDRvG3r17q0USFB8fz8SJE9mxY8dt/51VB7f+F920aVPatWtHQEAAP//8M6+++qoFIzMfo9FI69atmTlzJgAtWrTg3LlzLF26tNolQF9//TV9+vTB29vb0qGYzc8//8wPP/zAjz/+SKNGjTh16hSTJk3C29u7Wn3/vvvuO0aMGIGPjw8KhYKWLVvywgsvcPz4cUuHVu2ILrAKNH78eH7//Xd2796Nr6+vpcMxO7VaTZ06dWjVqhWzZs2iWbNmfPbZZ5YOyyyOHz9OSkoKLVu2RKlUolQq2bt3L4sWLUKpVGIwGCwdolk5OTkREhJCZGSkpUMxGy8vr9uS8QYNGlS7rr4rV67w119/MXLkSEuHYlZTpkxh6tSpDB48mCZNmvDyyy8zefJkZs2aZenQzKp27drs3buX3Nxc4uPjOXLkCDqdjuDgYEuHZlaenp4AJCcnlzienJxc/Fp5EwlQBZAkifHjx7Nx40Z27dpFUFCQpUOqEEajkaKiIkuHYRaPPfYYZ8+e5dSpU8UfrVu3ZsiQIZw6dQqFQmHpEM0qNzeXqKgovLy8LB2K2XTs2PG25SciIiIICAiwUETlY9WqVbi7u9O3b19Lh2JW+fn5yOUl/2QpFAqMRqOFIipftra2eHl5kZGRwfbt23n66actHZJZBQUF4enpyc6dO4uPZWdnc/jw4QobOyq6wCpAaGgoP/74I7/++iv29vbF/ZuOjo5YW1tbODrzmDZtGn369MHf35+cnBx+/PFH9uzZw/bt2y0dmlnY29vfNmbL1tYWV1fXajGW680336Rfv34EBASQmJjI9OnTUSgUvPDCC5YOzWwmT55Mhw4dmDlzJs8//zxHjhxh+fLlLF++3NKhmY3RaGTVqlUMGzYMpbJ6/Xrv168fH3/8Mf7+/jRq1IiTJ0+yYMECRowYYenQzGr79u1IkkS9evWIjIxkypQp1K9fn+HDh1s6tDLLzc0t0YocExPDqVOncHFxwd/fn0mTJvHRRx9Rt25dgoKCePfdd/H29qZ///4VE2CFzDWr4YBSP1atWmXp0MxmxIgRUkBAgKRWq6VatWpJjz32mPTnn39aOqxyVZ2mwQ8aNEjy8vKS1Gq15OPjIw0aNEiKjIy0dFhm99tvv0mNGzeWNBqNVL9+fWn58uWWDsmstm/fLgFSeHi4pUMxu+zsbGnixImSv7+/ZGVlJQUHB0v/+9//pKKiIkuHZlZr166VgoODJbVaLXl6ekqhoaFSZmampcN6ILt37y71b9+wYcMkSTJNhX/33XclDw8PSaPRSI899liFvndlklTNltEUBEEQBEG4BzEGSBAEQRCEGkckQIIgCIIg1DgiARIEQRAEocYRCZAgCIIgCDWOSIAEQRAEQahxRAIkCIIgCEKNIxIgQRAEQRBqHJEACYJQYWJjY5HJZJw6dcrSoRS7dOkSjzzyCFZWVjRv3vyhypLJZGzatMkscQmCUL5EAiQINcgrr7yCTCZj9uzZJY5v2rQJmUxmoagsa/r06dja2hIeHl5iX6L/SkpK4vXXXyc4OBiNRoOfnx/9+vW76zUPY8+ePchkMjIzM8ulfEGo6UQCJAg1jJWVFXPmzCEjI8PSoZiNVqt94GujoqLo1KkTAQEBuLq6lnpObGwsrVq1YteuXcybN4+zZ8+ybds2unXrRmho6APfuyJIkoRer7d0GIJQ6YgESBBqmB49euDp6cmsWbPueM6MGTNu6w5auHAhgYGBxc9feeUV+vfvz8yZM/Hw8MDJyYkPPvgAvV7PlClTcHFxwdfXl1WrVt1W/qVLl+jQoQNWVlY0btyYvXv3lnj93Llz9OnTBzs7Ozw8PHj55ZdJTU0tfr1r166MHz+eSZMm4ebmRu/evUuth9Fo5IMPPsDX1xeNRkPz5s3Ztm1b8esymYzjx4/zwQcfIJPJmDFjRqnljBs3DplMxpEjRxgwYAAhISE0atSIsLAw/vnnn1KvKa0F59SpU8hkMmJjYwG4cuUK/fr1w9nZGVtbWxo1asTWrVuJjY2lW7duADg7OyOTyXjllVeK6zRr1iyCgoKwtramWbNmrFu37rb7/vHHH7Rq1QqNRsP+/fs5ffo03bp1w97eHgcHB1q1asWxY8dKjV0QagKRAAlCDaNQKJg5cyaff/45CQkJD1XWrl27SExMZN++fSxYsIDp06fz5JNP4uzszOHDhxkzZgyjR4++7T5TpkzhjTfe4OTJk7Rv355+/fqRlpYGQGZmJt27d6dFixYcO3aMbdu2kZyczPPPP1+ijNWrV6NWqzlw4ABLly4tNb7PPvuMTz75hPnz53PmzBl69+7NU089xeXLlwG4du0ajRo14o033uDatWu8+eabt5WRnp7Otm3bCA0NxdbW9rbXnZycHuRLB0BoaChFRUXs27ePs2fPMmfOHOzs7PDz82P9+vUAhIeHc+3aNT777DMAZs2axbfffsvSpUs5f/48kydP5qWXXrotiZw6dSqzZ8/m4sWLNG3alCFDhuDr68vRo0c5fvw4U6dORaVSPXDsglDlVdi2q4IgWNywYcOkp59+WpIkSXrkkUekESNGSJIkSRs3bpRu/XUwffp0qVmzZiWu/fTTT6WAgIASZQUEBEgGg6H4WL169aTOnTsXP9fr9ZKtra30008/SZIkSTExMRIgzZ49u/gcnU4n+fr6SnPmzJEkSZI+/PBDqVevXiXuHR8fX2KX8y5dukgtWrS4Z329vb2ljz/+uMSxNm3aSOPGjSt+3qxZM2n69Ol3LOPw4cMSIG3YsOGe9wOkjRs3SpL0707YGRkZxa+fPHlSAqSYmBhJkiSpSZMm0owZM0otq7TrCwsLJRsbG+ngwYMlzn311VelF154ocR1mzZtKnGOvb299M0339yzDoJQUygtlnkJgmBRc+bMoXv37qW2etyvRo0aIZf/25Ds4eFB48aNi58rFApcXV1JSUkpcV379u2LHyuVSlq3bs3FixcBOH36NLt378bOzu62+0VFRRESEgJAq1at7hpbdnY2iYmJdOzYscTxjh07cvr06fusoWkMTXmZMGECY8eO5c8//6RHjx4MGDCApk2b3vH8yMhI8vPz6dmzZ4njWq2WFi1alDjWunXrEs/DwsIYOXIk3333HT169GDgwIHUrl3bfJURhCpGdIEJQg316KOP0rt3b6ZNm3bba3K5/LY//Dqd7rbz/tuFIpPJSj1mNBrvO67c3Fz69evHqVOnSnxcvnyZRx99tPi80rqjykPdunWRyWRcunSpTNfdTAxv/Tr+92s4cuRIoqOjefnllzl79iytW7fm888/v2OZubm5AGzZsqXE1+bChQslxgHB7V+fGTNmcP78efr27cuuXbto2LAhGzduLFOdBKE6EQmQINRgs2fP5rfffuPQoUMljteqVYukpKQSf7zNuXbPrQOH9Xo9x48fp0GDBgC0bNmS8+fPExgYSJ06dUp8lCXpcXBwwNvbmwMHDpQ4fuDAARo2bHjf5bi4uNC7d28WL15MXl7eba/faZp6rVq1ANM4o5tK+xr6+fkxZswYNmzYwBtvvMGKFSsAUKvVABgMhuJzGzZsiEajIS4u7ravjZ+f3z3rEhISwuTJk/nzzz959tlnSx2gLgg1hUiABKEGa9KkCUOGDGHRokUljnft2pXr168zd+5coqKiWLx4MX/88YfZ7rt48WI2btzIpUuXCA0NJSMjgxEjRgCmgcHp6em88MILHD16lKioKLZv387w4cNLJAP3Y8qUKcyZM4e1a9cSHh7O1KlTOXXqFBMnTixzvAaDgbZt27J+/XouX77MxYsXWbRoUYnuvFvdTEpmzJjB5cuX2bJlC5988kmJcyZNmsT27duJiYnhxIkT7N69uzgRDAgIQCaT8fvvv3P9+nVyc3Oxt7fnzTffZPLkyaxevZqoqChOnDjB559/zurVq+8Yf0FBAePHj2fPnj1cuXKFAwcOcPTo0eJ7CUJNJBIgQajhPvjgg9u6qBo0aMCXX37J4sWLadasGUeOHHmosUL/NXv2bGbPnk2zZs3Yv38/mzdvxs3NDaC41cZgMNCrVy+aNGnCpEmTcHJyKjHe6H5MmDCBsLAw3njjDZo0acK2bdvYvHkzdevWLVM5wcHBnDhxgm7duvHGG2/QuHFjevbsyc6dO1myZEmp16hUKn766ScuXbpE06ZNmTNnDh999FGJcwwGA6GhoTRo0IDHH3+ckJAQvvzySwB8fHx4//33mTp1Kh4eHowfPx6ADz/8kHfffZdZs2YVX7dlyxaCgoLuGL9CoSAtLY2hQ4cSEhLC888/T58+fXj//ffL9HUQhOpEJpXnCD9BEARBEIRKSLQACYIgCIJQ44gESBAEQRCEGkckQIIgCIIg1DgiARIEQRAEocYRCZAgCIIgCDWOSIAEQRAEQahxRAIkCIIgCEKNIxIgQRAEQRBqHJEACYIgCIJQ44gESBAEQRCEGkckQIIgCIIg1DgiARIEQRAEocb5f5SY7w8HfYWWAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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09PTsxwkJCYButkqNRvPCOPLiUXn5XW5RIepX/JX0Oor6FX8lvY6ifq9f9suQZFmW8z2ClxQWFoarqytHjx6lcePG2fsnTpzIwYMHOXHixHPPHzFiBMeOHePixYvPPKZy5cq0adOGX3/99ZnHTJs2jenTp+fav3btWkxMTF6iJoIgCIIg6FtKSgp9+vQhPj7+hUtZ6bUFyM7ODqVSSWRkZI79kZGRODk5Pffc5ORk/Pz8+Oqrr555zKFDh7hx4wbr169/blmTJk1iwoQJ2Y8fLabWtm3bAlkLbM+ePbRp06bErvEi6le8lfQ6ivoVfyW9jqJ+r+7RHZyXodcEyMDAgLp16+Lv70+XLl0A0Gq1+Pv7M2bMmOeeu3HjRtLT0+nXr98zj/H19aVu3brUrFnzuWUZGhpiaGiYa79arS6wN19Blq1XmWlIcmbJrd9DJb1+UPLrKOpX/JX0Oor6vVqZL0vvq8FPmDCBgQMHUq9ePRo0aMCcOXNITk7Gx8cHgAEDBuDq6sqMGTNynOfr60uXLl2wtbV9arkJCQls3LiRWbNmFXgdSr30RLjxD1zejOqOP+1RoTA6C41GgJWHvqMTBEEQhFz0ngD17NmT6OhopkyZQkREBLVq1WLnzp3ZHaODgoJQKHIOVrtx4waHDx9m9+7dzyzXz88PWZbp3bt3gcZfamUkw81dcGUz3NoDmWkASIABGXD8NzgxHyq/A41GgUdjkCT9xiwIgiAID+k9AQIYM2bMM295HThwINe+SpUq8aK+28OHD2f48OH5EZ7wiCYVbu+Fy5vh5k7QpDx+zqYcVO+GplInzvpvpr72DIqAQ3Btm25zqqFLhKq/B6rctxsFQRAEoTAViQRIKMIy0+HOPl3Sc+MfyEh8/JyVJ1TvBtW6gZO3roVHoyHCMoCsDl+giL0JJxbCxQ0QcRG2joI9U6DeYKg3BMwdn31dQS/iUjJYdfQeymR9RyIIglCwRAIk5JalgbsHdbe3rv0N6fGPn7Nwg2pddImPS53n39ZyrAadf4VW0+Dscjj5OySGwcHv4dBsXWtQo5HgUruAKyS8jMD7yfgsP8Xd6GQMlUoaNI6nXhk7fYclCIJQIEQCJOhkZULAoYdJz1+Q+uDxc+bOULWLLulxrQeKPE4gbmoLzT6GJmPh6p+6VqGQU3DRT7e5N9IlQpU7gVK8JfXhTGAsw1aeITY5A4UE6VkSPivOsHZoI7zdLPUdniAIQr4TnzalmTYLgo7pbm9d2wbJ0Y+fM7WHqu/qbm95NM570vM0SjV4v6/bQs7AiQVwZQsEH9dtFm7QYCjUGQgmNq9/PeGl/HUhjI83XiAjU4u3qyWzu1dnuO9h7iVm0s/3BOuGNaKqS/7OhyUIgqBvIgEqbbRaCDmpSzyubIWkiMfPGdtA1c5QrSt4vlGwrTFudcHtd2jzNZz2hdNLISEE9k6DA99DzV7QcCQ4VC64GEo5WZaZf+AOP+66AUDrKo780rsWaklmZOUs1kXYcj44PjsJquRkrueIBUEQ8o9IgEoDWYbQs7rbW1e26hKNR4wsdbeeqneFMs11rTSFycIZ3voCmv0PLv8BxxdC5CU4s0y3lW2pGz1Wvk3+tEIJAGiytEzecokNp3XvhcFNyzC5YxWUCgmNRoORCnz718Fn5VkuhsTT9/fj+A1vRHkHkQQJglAyiASopJJl3ciry5t1rT1xgY+fMzCHyh10t7fKvQUqA/3F+YjaCGr3g1p9IfAIHF8AN3bA3f26zaYcNBwBtfqAofgQfh3xqRpGrznDkdv3UUgwtVM1BjbxynWchbGalYMb0GfJCa6GJ9B7yQnWD29EWXuzwg9aEAQhn4kEqBBFJqThe+gOTmkFdAFZhqirj5Oe2DuPn1ObQqX2uqSnfGtdwlEUSRJ4vaHbHgTAySVwdpWuLv9MhH3f6BKlBsPBpoy+oy12gmNT8Fl+ittRSZgYKPmtT23eqvzs6QisTAxYPbQhfZYc53pEIn2WnGD9iEZ42poWYtSCIAj5TyRAhWjNiSAWHwpAQsnR1HMMalqWpuVtkV53huTom7rbW5c3Q8yNx/tVRlCxna5PT4V2YFDMVra39oJ230KLSXBhnW702P3bcHy+roWo0tu6fkJl3hSzTL+E88FxDF1xipikDBwtDFk6qD7VXF48wsvGVJcE9Vp8nNtRSfRZcgK/4Y1wtylm7ydBEIQniASoENXztKZJORuO3oll7/Vo9l6Ppqy9KQMbe9GtjivmRnnof3P/zuM+PZGXs3fLSgPkcq3RVu+KXKEtsoEJWlmLVtaCJkX3M1pkWUaWZbTonpNlGRn5qT8/Oj77X1mLjJz9sxYtyKCVtWRkZpCkTcrfF87QDBoM002eeMdfl/zc8dfdIruxAxyq6YbRe3cHtXH+XruE+OdSOOPWnyc9U0sVZwuWDqqHs+XLv1Z2ZoasfZgE3Y1Jps/vx1k/vDEuVuL1FgSheBIJUCGK4iBJditxN0hEo1CRlK4hUpb54YrMj1dlTAwUGBsoUSrImWQ8TFLQatFmZaDVapBlLTKgNZaQvdzRShIyIANoLsO5y3Dua73UU4GCYweP8V7F92jm1gyVIp/eZgoFVGij26JvwIlFupahqCuw7UPYMxXq+UD9oWDhkj/XLOZkWWbxv3eZufM6sgwtK9nza586mBnm/XfiYGHE2mGN6Ln4GIH3U+i9RJcEOVkW0dupgiAIzyESoEIUlx7HvYR7ugdakNS6xUMfSZUhNf0lCpIAKX9HRCkkBQoUSJKEhIRC0v38aD/S84959DhLm0VESgQHQw9yMPQg9sb2dC7Xma4VuuJp4Zl/AdtXgndmQ6svdX2ETi6B+CA4NAuOzNXNYdRwFLjXz79rFjOZWVqmbLvC2hNBAAxo7MmUd6qiUr76e8fJ8mEStEiXBPVZchy/EY1wMBdJkCAIxYtIgApRhzIdqGZdjRMnTtCkURPUajXSw/8uhyay/WI4J+89wFJOpJniCq3Vl6gs30OBjISuZUVyrYNUsR2K8m1RmNjmSkYe/ZydnPCU/Ug5EpfX7oP0BI1Gw/K/lvPA7QHb720nOjUa38u++F72pa5jXbpV6EYbzzYYq/Lp1omxNTQdC41G626HnVioG0V2eZNuc62re67qu4U/xF+PEtM0fLD2HP/ejEaS4MuOVfFp6pUvv2tXK2PWPUyC7sYk03fJCdYNb4SdmVjkVhCE4kMkQIXIxcwFe0N7olRR1HaojVr9+AO5pkk0fTNvkKbZgmHIMSRZhgzdc6e1lQhyaU+llv2oVqminqJ/eQ5KBwbVHsT4uuM5GHKQzbc2cyTsCGciz3Am8gwzTsygQ5kOdKvQjaq2VfMnAVOqdJM4Vu0M4Rd08wld/gNCz8CmIbD7C6g/BOr6gGnJXt8qNC6VIctPcT0iEWO1krm9atG2mlO+XsPdxoR1wxvRY9ExbkUl0e933WSJ1qZFYEoFQRCElyASIH1KidWtu3VlM9w7BHIWj24kaF3rcdGqFT+HVuVghBoCgGW3qOUezcAmnnTwdsZQpdRj8C+mVqpp7dma1p6tiUiOYNudbWy5tYWQpBA23NzAhpsbqGhdkW4VuvFO2XewNMynNaeca0LXBdBmOpxepptpOjFcN4T+4I9Qo7vu9phT9fy5XhFyKSSewStOEZ2Yjr25Ib4D61HDzapAruVpa6prCVqsGyLfz/cEa4c2wtKk9LS0CYJQfIkEqLClJeB+/xBKv5Vw7wBoMx8/51xLt+Bota4orDyoBSyXZc4Hx7HyWCDbL4ZzPjiO8+vj+Obva/Rq4E7fhp7FYiSOk6kTw2sMZ6j3UE5HnGbTrU3sDdzLzQc3mXlyJrNOz6K1R2u6VuhKQ+eGKPKjj5OZA7T4FN4Yr5sX6cQCCDsH51brNq9mumH0ld4GRdFOJl/G7isRfOR3nlRNFpUczVnqUx/XAn5vlLU3Y92whvRcdJwrYQn0X3qC1UMbYpGXEY2CIAh6IBKgwnTwB1T//kidrIzH+xy9dctQVOsKNmVznSJJErU9rKntYc3nHaqw/lQQq48HEZGQxrz9d1h48C5tqjgyoIknjcvmw5xCBUwhKWjg3IAGzg2IT49nx70dbLm1hWux1/gn4B/+CfgHVzNX3i3/Ll3Ld8XJNB9u3agMoGZPqNEDgk/qEqGr2yDgkG6z8tRNrFinv25pkGJGlmWWHQng6+1XkWVoVsGO+X3r5G1ahddQ3sGctcMa0WvxMS6GxDNw6UlWDm5QaNcXBEF4FSIBKkwWrkhZGSQYuWLaoB/KGt3BrsJLn25vbsiYtyowsnk59lyNZMWxAI7fjWXnlQh2XomggoMZA5p40a22K6avMMy5sFkaWtK7cm96V+7N1ftX2XxrMzvu7iA0KZT55+ez4PwCmrg0oVuFbrR0b4n6dTsxSxJ4NNRt8SFw6nc4s1y3TMjuybD/O91SGw1Hgl35fKljQcvM0vL131dZcUy31EnvBh589W411K8x0utVVHIyfzhj9AnOBcUxePkplvs0KBbvQ0EQSiexumRhqtoZzbBD7K8yA22zT/KU/DxJpVTwtrczfsMbs2vcm/Rt6IGJgZJbUUl8ufUyjb7zZ9q2K9yJzucJCQtQVduqfNHoC/b12MeMZjNo4NQAGZkjYUf4+ODHtNrYih9O/cDtB7fz54KWbtB6Goy/Cp3mgn0V0CTDqSXwW11Y0x1u++uWFymiktIzGbbydHby83mHynzXtXqhJz+PVHOxZPWQhpgbqTgV8IAhK06RmpGll1gEQRBeRCRAhcnQHByq5GuRlZzM+barN8c/b8XUTlUpY2dKYnomy48G0GrWQfr7nmDv1UiytEX3g/xJRioj3in7Dr7tfNnRdQfDvIfhYOzAg/QHrLq6iq7butJ3e1823dxEsib59S9oYAJ1B8HoY9B/K1RsD0hwazes7gbzGsIpX8jIh2vlo/D4VLovPMb+G9EYqhQs6FuH4W+We61boLFpsRwMPsjKaysJywx7pTK83SxZObgBZoYqjt+NZdjK06RpRBIkCELRI9qnSwgLIzU+TcswsLEXh2/HsPJYAP7Xozh0K4ZDt2JwszamfyNPetRzLzZDld0t3BlbZyyja43maNhRNt/azMHgg1yMucjFmIt8f+p72nm1o1uFbtSyr/V6/Z8kCcq11G3378DJxXBujW5tte0TwP8rqDMA6gzOvwq+oith8QxeforIhHTszAxYMqAetT2s81RGelY612Ovcyn6Ehejda9naFJo9vMSEmEnwhhXdxy2xrZ5Kru2hzXLfeozYOlJDt+OYcSqMyweULfIj1oUBKF0EQlQCaNQSLxZ0Z43K9oTHJvC6uOB+J0KJuRBKjP+uc7sPTd5t5YLAxp7Ud21eHT4VSlUvOn2Jm+6vUlMagx/3fmLzbc2E5AQwNbbW9l6eytlLMvQrXw3OpXrlOcP7Fxsy8Hb30PLyXB+jW7JjQf34OgvqI79Rj3LepBYB2zc86eCebDveiRj1p4jJSOL8g5mLBtU/4WLksqyTHBiMBeiL3Ap5hKXoi9x/cF1Mp8cgfhQGcsy2BvZczLyJFvvbMU/yJ/RtUbTs3JP1IqX74NVz8uGpYPqM2jZSQ7ejGb06rMs6FcXA5VodBYEoWgQCVAJ5m5jwqQOVRjXuiJ/XQhj+dEAroYnsOF0CBtOh1DX05oBjT15u7pzsflgsjO2w6e6D4OqDeJ89Hk239rMroBd3Iu/x6wzs5h7di7N3ZvTrUI3mrg0eb11yIwsoNEo3QixW7vh+AKkewdxjTuJvKwt9PHTzTlUSFYeC2DatitoZWhSzpYF/epiaZw7KYlPj89OdC7GXORyzGXi0uNyHWdtaI23vTfedt7UsK9BdbvqWBhYoNFoWLBtAYfUh7j+4Drfn/qeTbc28WmDT2nk3Oil421U1hbfgfUZvPwU/tej+HDdWX7rU0dvfZQEQRCeJBKgUsDYQEmP+u50r+fG2aAHrDgayI5L4ZwJfMCZwAd8bXaNPg096NvQA0eL4rGmkyRJ1HaoTW2H2nxa/1N2Buxky60tXIy5iH+QP/5B/jgYO2QPp3e3eI3WGoVSN1dQpbfRBJ8lbXUfzBPDYGl76LpIN/t0AcrSyny7/RpLj+jWketRz41vunhjoFKgydJw88FN3W3B6ItcirlEYEJgrjLUCjVVbKtQw64G3nbeeNt742bm9szbhp4qT4a3G85fgX/xy9lfuB13m2G7h9HGsw0f1/sYVzPXl4q9aXk7Fg+ox7AVp9l1JZJx688zt2et11qPTBAEIT+IBKgUkSSJup421PW04YuOVVh3Mpg1JwKJSkznF/9bzN9/m3bVnRjY2Iv6XtZFfk6hR8wMzHi/4vu8X/F9bj+4zebbm/nrzl9EpUax5NISllxaQgOnBnSt0JXWHq0xUr1Gkufkzb8Vp/B20noU9w7Ahv7w1hfQ7H+6fkT5LCUjk7HrzrP3WiQgM6KVNTXLhfLz2Z1cirnEtfvXyNBm5DrPw9zjceuOXQ0q2VTCQJm3vl9KhZLuFbvT1rMt88/Px++GH3sC9/BvyL8MqT4En+o+L/VaNq9oz8L+dRix6gzbL4ajUkjM7lELpaJ4vL8EQSiZRAJUSjlYGPFR6wqMblmOXVciWHk0kJMBsWy/GM72i+FUdjJnYBMv3q3lgolB8XmblLcuz8T6ExlXZxz7g/ez5dYWjoYd5WTESU5GnOQ7g+/oWKYj3Sp0o4rtq43Iy1SZktXLD4X/VDi5SLfERvRN6PwrqPOvBe3u/RiGr99CUPJ1TNyDsbAKZ21YHGv/M0DLwsACb3vvx607dt5YGVnlWxyWhpZMajiJ9yq+x8yTMzkVcYr5F+az9fZW/lf/f7T2aP3CZPmtyo7M61OH0WvO8uf5MFQKBT++XwOFSIIEQdCT4vPJJhQItVLBOzVceKeGC1fDElh1PIAt50K5HpHIpM2XmLHjGj3qudO/sSeetqb6DvelGSgNaOfVjnZe7QhPCmfrna1svbWVsOQw/G744XfDjyo2VehaoSsdynTI+zpkChV0+AHsK8GOT+DSBoi9C73WgrljnuPN1GZyO+62bkRW9EVOR1wgNCkQTGUMH77syZmgklRUsqmU3W/H284bTwvPQmmtq2hdEd+2vuwO3M1Pp38iLDmMCQcm0NC5IZ/V/4zy1s+fPLJtNSd+6V2bD9edY9PZENRKie+6eoskSBAEvRAJkJCtqosFM7rV4LP2Vdh4JpiVxwIJik3h98P38D1yjxYV7RnQxIvmFeyL1YeWs5kzo2qOYkSNERwPP86WW1vwD/LnWuw1rp24xqzTs2jl0Yr3KrxHPad6eVuHrP4QsC0PGwZA6GlY8hb0XgfONZ55iizLRKZEZvfZuRh9kWux10jNTM15oASKLBvecK9LI9faeNt5U8W2CoZKw1d8JV6fJEm082pHM9dmLL28lGWXl3Ei/ATv//U+vSv3ZlStUVgYWDzz/A7ezmiytIxffx6/U8GolBJfv1u92NxuFQSh5BAJkJCLpYmaoc3KMrhpGQ7ejGbFsQAO3Ihm/8PN09aE/o086V7P/amjkIoqhaSgiUsTmrg0IS4tju33trPp1iZuPbjFjns72HFvB25mbnSt0JV3y72Lo+lLtuSUbQ7D9sHannD/FixtB90WQ5VOAKRoUrhy/0qOhCc6NTpXMWZqM+zU5bkVbIMmxY1a9jXw7d8SK5OiN2+TidqEMbXH0KV8F3489SP7gvex+tpqdtzbwUd1PqJL+S7PTCTfreVKllbm440XWH08CLVSwZR3qookSBCEQiUSIOGZFAqJlpUdaFnZgYCYZFYdD2TD6WAC76fwzfZrzNp9ky61XRnQ2JMqzs/+1l8UWRlZ0bdKX/pU7sOV+1fYfGsz/9z7h5CkEH499yvzzs+jqUtT3qvwHm+6v/niOXBsy8HQvWRtGMjdkCNc3D6CS1eXcVEFd+LvoJW1OQ5XSkoqWFfI7rPjbVeDDUfTWHwoAIButV2Z+V6NIj89gZu5G3PfmsvR0KPMPDWTe/H3mHp0KhtubGBSw0nUtH/6NAHd6riRmSUzcdNFlh0JQK1UMOntyiIJEgSh0IgESHgpXnamfPlOVT5uW5Gt58JYeSyA6xGJrDsZxLqTQTQoY8PAxl60rGij71DzRJIkqttVp7pddT6p/wl7Avew6eYmzkad5VDoIQ6FHsLGyIbO5TrTtUJX3E1yDqePTonmYsxFLkVf4lLMJS6rIkhxc9Y9mXQr+zgnU6fsEVne9t5UsamCiVo3gWFqRhYTNpznn8sRAIxvXZGxrcoXq2SgiWsTNjlvYt21dSy4sIAr96/Qb0c/OpfrzPi647Eztst1To/67mi0WiZvuczif++iUkh80q5Ssaq3IAjFl0iAhDwxMVDRp6EHvRu4c/JeLCuPB7LzcgQn78Vy8l4sjuaG1LRQ4BGaQC1Pm2L1YWasMqZzuc50LteZgPgAttzewrY724hJjWH5leUsv7KcmnY1sU2z5eDhg1y+f5nw5PCnllPdwBbv8GvUSEvD27oyDt1WP7VzdHRiOkNXnuZCcBwGSgXfv+9N19puhVHdfKdWqBlQbQAdynZg7tm5bL29lW13tuEf5M/IGiPpW6UvamXOlrS+DT3JzJKZuu0K8w/cQa1UML5NRT3VQBCE0kQkQMIrkSSJhmVtaVjWloj4NNaeDGLtiSAiE9PZnahg98LjOFsa0aaqI22qOtKwjG2Rv53zJC9LL8bXHc+Y2mM4HHKYzbc2cyj0EBdiLugOCNL9IyFRzqocNe1rZk8wWM6yHEqFEu4egA0DIfTcUztH34pMZNCyU4TGpWJlomZRv7o0LPuay3gUAXbGdnzd9Gt6VOzBjJMzuBRziVlnZrHp1iY+a/AZTV2b5jh+YBMvNFlavtl+jbn+t1ArJca8VUFP0QuCUFqIBEh4bU6WRkxoU5ExLcuz42Ioy/ee52aSmvD4NFYeC2TlsUDMjVS0rORA22qONK9oj7lR8eg8rVaoaenRkpYeLYlOiWbLzS0cvHqQ5lWbU8uxFtXsqmGqfsb0AGVbPOwc3QPu387ROfrI7RhGrj5DYlomXrYmLB1Un7L2ZoVat4Lmbe/N6g6r+fP2n8w5O4eAhABG7h1JC/cWTKw/EXfzx7cThzYriyZL5vud1/lp903USgUjmpfTY/SCIJR0IgES8o2BSkFHbyekYC2t2rTgVFACu69GsOdqFDFJ6Wy7EMa2C2EYKBU0Lmeb3TpUXJbfsDexx6eaD46BjnSo1gG1+iWSuIedo9k4SNcitL4flyqNZeClRmRqoZ6nNYsH1MPGtOiN9MoPCkmhm4HbszULLyxk7bW1HAg+wNHQowysNpCh3kOz+0KNalGOzCwts/bcZMY/11EpFQx5o4x+KyAIQolVfO5JCMWKoVpJy8oOzOhWg5Oft2LTqCaMaF6WsnamZGRpOXgzmi+2Xqbhd/50mXeEeftvczsqEVmW9R16/jO2hr6bkOsPA8D7xi/8qJxHN29bVg9tWGKTnyeZG5jzSf1P2NR5E42dG5OhzWDJpSV03tqZf+79k/17/7BVBca+pZtQ8eu/r7LyWIAeoxYEoSQTLUBCgVMoJOp6WlPX05pJb1fhdlQSe65GsvtqBOeC4jgfrNt+3HWDMnamtH3YMlTbw7rErBeVppX4X3wfrDSZTFOtoKvyCF1Sv0BKWwPqvM8cXVyVtSrLojaL2Be8jx9P/UhoUigT/53I+hvrmdRgEpVsKjG+TUU0WpkFB+4w5c8rqBQK+jT00HfogiCUMCIBEgpdeQczyjuYMapFOaIS0th7LYrdVyM4evs+92KSWfTvXRb9exc7MwNaV9ElQ03L22GkVuo79FdyPymd4avOcCbwAWplW9o2bsqb5z9GCjn1UjNHlzSSJNHKoxVNXZqy/MpyfC/5cibyDD3+7kH3it35sPaHTGxXCU2mlt8P3+PzLZdQKSV61HN/ceGCIAgvSSRAgl45WBjRp6EHfRp6kJSeycEb0ey5GoH/9ShikjLwOxWM36lgTAyUvFnBnrbVHHmrskORnB35ae5EJ+Gz7BRBsSlYGKlY2L8uTcrZQb3asK7nE52jl0CVd/QdbqEyUhkxsuZI3i33LrPOzGJXwC7W31jPzoCdjK09ls/e7kamVmb50QA+3XQRtVIqtlMECIJQ9IgESCgyzAxVdKzhTMcauvWiTt6LZfeVCHZfjSQ8Po2dVyLYeSUCpUKigZcNbavpWofcrE30HfpTHbtzn5GrzxCfqsHdxphlg+pT3sFc96Rd+f90ju4LrabAGxOgGM2dlB+czZz5qflP2cPmb8fd5uvjX7Px5kY+a/AZmiwP1pwI4uMNF1ApFHSq6aLvkAVBKAH03gl63rx5eHl5YWRkRMOGDTl58uQzj23RogWSJOXaOnbsmOO4a9eu0blzZywtLTE1NaV+/foEBQUVdFWEfKRWKmha3o7p71bn6Gdv8feHbzD2rfJUdjInSytz7O59pv91lTe+30+HuYf4ec9NroTFF5lO1JvOhDBg6QniUzXU9rBiy+imj5OfRx52jqbBcN1j/69gywjQpBV+wEVAA+cGbOy0kUkNJmFuYM712OsM2jWITJvVvFvHFK0M49afZ+fl3JNPCoIg5JVeW4DWr1/PhAkTWLhwIQ0bNmTOnDm0a9eOGzdu4ODgkOv4zZs3k5GRkf34/v371KxZk+7du2fvu3PnDm+88QZDhgxh+vTpWFhYcOXKFYyMisdQayE3SZKo7mpJdVdLJrStRND9lIfD6yM5FRDL1fAEroYnMNf/Fq5WxrSp6kjbao408LJBpSzcHF+WZX7ee4tf/HXLYHT0dmZWj5rP7r+kVEGHH8G+EuyYCBfXQ+xd6LUWzHL/P1DSqRQq+lTpQ/sy7fn13K9surmJHQE7MFbtp7Z3J85drsGYtedY0E9Bm6qlp/O4IAj5T68J0OzZsxk2bBg+Pj4ALFy4kO3bt7N06VI+++yzXMfb2ORcZ8rPzw8TE5McCdDkyZPp0KEDP/zwQ/a+cuXEhGoliYetCUOblWVos7LEJmew73oUu69E8O+taELjUll+NIDlRwOwNFbTqrIDbao68mZFe0wNC/btnp6Zxad/XGTr+TBAN6/NJ20roXiZkWz1h4JNOdg4EEJOweKW0McPnLwLNOaiysbIhqmNp9K9YndmnJjB+ejz3GYDDpUPcD/4bUavgcX969GyculLEgVByB96uwWWkZHBmTNnaN269eNgFApat27NsWPHXqoMX19fevXqhampbiZerVbL9u3bqVixIu3atcPBwYGGDRuydevWgqiCUATYmBrwfl03Fg+ox7kv27JkQD2613XDxtSA+FQNm8+FMmrNWWp/vYfBy0/hdzKI6MT0fI/jQXIG/X8/ydbzYSgVEjO7efNp+8ovl/w8Uq4lDN0HtuUhIQR828G1v/M91uKkqm1VVr69ku/e+A57Y3tSicLEfQUql6WMXL+Tf29G6ztEQRCKKb21AMXExJCVlYWjY85mbEdHR65fv/7C80+ePMnly5fx9fXN3hcVFUVSUhIzZ87km2++4fvvv2fnzp1069aN/fv307x586eWlZ6eTnr64w/FhIQEADQaDRqN5lWq90yPysvvcosKfdZPJUGLCja0qGBDlrYKZ4Pi8L8exZ5rUQTFprLvehT7rkchSZeo7W5F6yr2tK7sQBm7Zyxl8RRPq1/A/WSGrTpHwP0UzAxV/NqrJm+Ut32118DSEwbuRLllCIp7B5HX90Pb8gu0jccWWufoovgebe/RnmbOzfC94svq66vB7Aay6WxGbj/FnMzxtKzw8kPki2L98lNJrx+U/DqK+r1+2S9DkvXUazQsLAxXV1eOHj1K48aNs/dPnDiRgwcPcuLEieeeP2LECI4dO8bFixdzldm7d2/Wrl2bvb9z586Ympqybt26p5Y1bdo0pk+fnmv/2rVrMTEpmiOMhJcnyxCeCpdjJS7GKghOzplIOBrLeFvLeNto8TCDvDTa3EmA328oScmUsDGUGV45C+d8eMtIcibVQ9ZSNmYvAMHWTTnv4YNWUTyG/xekmKwY/k7dwe3MmwDImeY0V7ejjUVNpFI2gk4QhJxSUlLo06cP8fHxWFhYPPdYvbUA2dnZoVQqiYyMzLE/MjISJyen556bnJyMn58fX331Va4yVSoVVatWzbG/SpUqHD58+JnlTZo0iQkTJmQ/TkhIwN3dnbZt277wBcwrjUbDnj17aNOmzcutJVXMFIf6hcense96FHuvR3P8biyRqRCZKrE3TIGDuSFvVbanTRUHGpaxwfA/K9g/Wb+d12JYsPkymiyZGq4WLOxbG3tzw3yMtDNZp5ei2D0J9wdHcDXJIOv9FQXeObo4/A4HMIB9QQf5/NAMMlRR/Cv/Qaj2Gl81/ZwqNlWee25xqN/rKOn1g5JfR1G/V/foDs7L0FsCZGBgQN26dfH396dLly6Arg+Pv78/Y8aMee65GzduJD09nX79+uUqs379+ty4cSPH/ps3b+Lp6fnM8gwNDTE0zP3BpVarC+zNV5BlFwVFuX4edmoGvWHOoDfKkZCm4cCNaHZfieDAjWiiEtPxOxWC36kQzAxVNK9kT9uqjrSo5IClsa4+sgyLjwQzx/82AO2qOTKnZ22MDQpgpurGI8ChImwciCL0FIplbQutc3RR/h0CtCvXmsauTXlv7UzCpb+5k3iFfjv78V7F9/iw9ofYGNk89/yiXr/XVdLrByW/jqJ+r1bmy9LrKLAJEyYwcOBA6tWrR4MGDZgzZw7JycnZo8IGDBiAq6srM2bMyHGer68vXbp0wdbWNleZn3zyCT179uTNN9+kZcuW7Ny5k7/++osDBw4URpWEYsbCSE3nmi50rulCemYWx+/qJl/cey2SyIR0tl8MZ/vFcFQKiUZlbWlV2Y5/7ig4Ga1LfoY1K8Nnb1cp2DXLHnWOXtsDYu/oOkd3W1zqZo5+GgsjY7b1m0LfZQ25mr4OteV5/rj5B7sCdvFBrQ/oWaknKoWY71UQhNz0+pehZ8+eREdHM2XKFCIiIqhVqxY7d+7M7hgdFBSEQpHzFsSNGzc4fPgwu3fvfmqZXbt2ZeHChcyYMYOxY8dSqVIlNm3axBtvvFHg9RGKN0OVkuYV7Wle0Z6v363OxdB49lyNYPeVSG5FJXH4dgyHb8cACpQKiemdq9Gv0bNbFvOVXXkY5g8bBsK9g7C+38OZo8eXupmj/8vYQMmqQW0ZuNSKcwHnMHHZRiJhzDw5kz9u/sGkBpNo4NxA32EKglDE6P2r0ZgxY555y+tprTaVKlV64Wy/gwcPZvDgwfkRnlBKKRQStdytqOVuxSftKnMvJpk9VyPYdTmCOxEPmNWrDq2qOhduUMbW0G8T7PwMTv0O/tMh+gZ0mgvq0j3Rp6mhimU+9envK3P+jgeWjmcxcdzN7bjbDNk9hLaebflfvf/hbFbIvzNBEIosvS+FIQjFQRk7U4a/WQ6/YQ2YXjeLNyvY6ScQpRo6zoIOP4GkhIt+sKITJEXpJ54ixNxIzYrBDfB2tSY+sh4ZARN52+M9FJKC3YG76by1MwsvLCQts3QuNSIIQk4iARKE4qjBMF1rkJElhJyEJW9BxCV9R6V3lsZqVg1pQFVnC+4nqDhw9A1+fmMFdR3rkpaVxrzz83h/+/tc17x4rjFBEEo2kQAJQnH1qHO0TTmID9Z1jr6+Xd9R6Z2ViQGrhzakkqM5UYnpTF5/n+n15vHjmz/iaOJIWHIYa5LX8Pfd0j3LtiCUdiIBEoTi7FHn6DLNQZMMfn3h8M+6sfqlmI2pAWuGNaS8gxnh8Wn0+f0E1a3eZFuXbXQt1xUZmWknprHz3k59hyoIgp6IBEgQirtHnaPrDwVk2DsNto6CzPxf86w4sTMzZO3QhpS1MyU0LpXeS44TnyIxucFk6hrURStrmXRoEvuC9uk7VEEQ9EAkQIJQEvy3c/SFdQ87R5fuxUIdLIxYO6wRnrYmBMem0nvxcaITM3jX+F06eHUgU87kfwf/x5HQI/oOVRCEQiYSIEEoSRoMg35/6DpHB5+AJS0h4rK+o9IrJ0tdEuRmbUzA/RQGLDtNkkbBtEbTaOPZBo1Ww0f7P+JUxCl9hyoIQiESCZAglDTl3oKh/k90jm4L13foOyq9crUyZt2wRrhYGnE3JoXvLygZv+EK5RlODevGpGel84H/B5yPOq/vUAVBKCQiARKEksiuAgzd+0Tn6D5weE6p7hztbmPC2odJUFKmxM4rkfyw8w5HjnUgM7kCqZmp+PwznJ8O+HM5NB5NllbfIQuCUID0PhO0IAgFxMRG1zn6n0/htC/snQrR13UzR6vyc9X64sPLzpRdHzVl0R+7MXarzJXwRC4ExxMa3B9j92Vgeo/ldz5n/v5hqLNcqeZiQc2HM4LXdLPC09YEqZQvPSIIJYVIgAShJFOq4Z3Z4FBFlwhdWAex96DnajCz13d0emGkVlLBUqZDszLZK0dHJaZxMqAWsy59TAy3MPX0JTlgOGeDtJwNiss+19JYTQ03y+yEqKa7FfbmpTOZFITiTiRAglAaNBgGtuVgwyAIPq6bObr3OnCqru/IigQHcyPe8S7Dm5WWM3TXUK7FXsO96ioGen1PUJQJF0LiuBKWQHyqhkO3Yjh0Kyb7XFcrY2q6W2YnRN6ulpgaij+tglDUif9LBaG0KPeWrl/Qup4QexeWtoNuS6ByB31HVmRYGFiwqM0iBu8azO2426wP+YLl7ZfjbFaNjEwtNyISuRASx4XgOC6ExHErKonQuFRC41LZcSkCAIUEFRzMdUnRw5aiSk7mqJWiy6UgFCUiARKE0sS+om6E2MaBcO9fXefo1tOg6Ucg+rYAYG1kzZK2Sxi0cxCBCYEM3T2UZe2X4WDigLebJd5ulvRr5AlAUnoml0LiHydFwXGExadxIzKRG5GJbDgdAoChSiH6EwlCESMSIEEobUxsoN9m0Tn6OeyM7fi97e8M2jmIoMQghu0exrL2y7AxsslxnJmhisblbGlczjZ7X1RCGhdC4rNbiS4Ex5GQlsnZoLhc/YlqultRy03XUlTDTfQnEoTCJBIgQSiNntc52tBK39EVCU6mTvze9ncG7hzI3fi7DN89HN92vlgaWj73PAcLI9pUNaJNVUcAtFqZwNgULgTHcf5hUvSoP9G/N6P59+bj2bpFfyJBKDzi/yxBKM2e1jm6+yp9R1VkuJm74dvWl0E7B3HjwQ1G7hnJkrZLMDMwe+kyFAqJMnamlLEzpUttV4Ds/kTnn7h1djta9CcShMIkEiBBKO3+0zlatbID7k59QG6v78iKBC9LL5a0XcLgXYO5fP8yH/h/wILWCzBRm7xymQYqRXZ/ov4P+xMlpmm4FBrPhWDd7bOLIc/uT1Td9VErkaXoTyQIr0gkQIIg5OgcLd37lzpBS5B9j0Pbr6FcS31Hp3cVrCuwqM0ihu4aytmos4zdN5bfWv2Gkcoo365hbqSmSTk7mpSzy9733/5E54PjSEzL5EzgA84EPsg+zspETQ03XX+iai7mJGnyLSxBKLFEAiQIgs7DztFZh39Be/BH1JGXYFUXKNcK2nxV6ucMqmpblQVtFjB893BORJxgwoEJzG05F7VSXWDXfFp/ooD7yQ87V8dzPjiOq2EJxKXk7E+klJQo3UPp1dCrwGIThOJOJECCIDymVKNtMpa9MY60NbqI8sxSuOMPd/ZBrT7QcjJYuuo7Sr2paV+Tea3mMWrvKA6FHuKTfz/hp+Y/oVIUzp9ShUKirL0ZZe3N6FrbDdD1J7oekZDdUnQ2MJa7MSlM/vMqjlYmtKzkUCixCUJxI3rSCYKQS4bKHG3bb2HMSajWFZDh/Br4tQ7snQ5p8foOUW/qOdVj7ltzUSvU+Af58/nhz8nSZuktHgOVghpuVvRv5MlP3Wuyc2xT6ttpydLKfLDmLBdD4vQWmyAUZSIBEgTh2WzKQvfluv5BHk0gMw0Oz4a5teD4QsjM0HeEetHEpQmzW8xGJan4594/TDs2Da1cNFaPlySJXuW0NClnQ0pGFoOXnyLofoq+wxKEIkckQIIgvJhbPfDZAb3WgV1FSI2FnZ/CvAZwZQvIsr4jLHQt3Fvw/Zvfo5AUbL29le9OfIdcRF4HlQJ+61WLqs4WxCRlMHDZSWKTS2eyKgjPIhIgQRBejiTp1g0bdQzemQOmDvDgHmwcBL+3hsCj+o6w0LX1ass3Tb9BQmL9jfXMOj2ryCRB5kYqlvnUx9XKmHsxyQxZcYrUDP3dqhOEokYkQIIg5I1SBfV8YOw5aDEJ1KYQehqWvQ3r+kD0TX1HWKg6levElMZTAFhxdQXzzs/Tc0SPOVoYsWJwfSyN1ZwLiuPDdefIzCoat+oEQd9EAiQIwqsxNIMWn+kSobo+ICnhxnaY3wj+Hg+JkfqOsNC8X/F9PmvwGQCLLi7i90u/6zmix8o7mPP7wHoYqBTsvRbJ1G1XikwrlSDok0iABEF4PeaO0GkOjD4GlTqCnAWnl8IvteHATEhP0neEhaJvlb6MrzsegLln57LqatFZUqS+lw1ze9ZCkmDNiSDmH7ij75AEQe9EAiQIQv6wrwS914LPP+BaDzTJcGCGLhE6vRSyMvUdYYEbXH0wo2qOAuCHUz+w4cYGPUf02Nvezkx5pyoAP+66waYzIXqOSBD0SyRAgiDkL88murXFui8H6zKQHKW7JbagMVzfUeJHjI2qOQqf6j4AfHP8G7bd2abniB7zaVqG4W+WBeDTTRdzrEQvCKWNSIAEQch/kqSbQPGDk/D2D2BsAzE3wa83LO8IIWf0HWGBkSSJ8XXG06dyH2RkvjzyJTsDduo7rGyfta9Mp5ouZGplRq0+w+XQ0juppVC6iQRIEISCozKAhiPgo/PwxgRQGUHgEfj9Ld3w+di7+o6wQEiSxKcNPuW9Cu+hlbVM+ncS+4L26TssQLecxk/da9C4rC3JGVn4LD9FcKyYKFEofUQCJAhCwTOyhNZT4cOzUKsvIOkmUPytAfzzGSTf13eE+U4hKfiy0Zd0LNuRTDmT/x38H0dCj+g7LAAMVUoWDahLZSdzohPTGbTsJHEpYqJEoXQRCZAgCIXH0hW6zIeRh6F8a9Bq4MQC+KUWHJoNmlR9R5ivlAol3zT9hjaebdBoNXy0/yNORZzSd1gAWBipWeZTH2dLI+5EJzN0xWnSNGKiRKH0EAmQIAiFz6k69NsE/beCkzekJ4D/dPi1LpxfC3pcXDS/qRQqvm/2Pc3dmpOelc4H/h9wPuq8vsMCwNnSmBWDG2BupOJ04APG+Z0nS1uyO6kLwiMiARIEQX/KtYTh/0LXxWDpDgmhsHUULGoOt/31HV2+USvVzGoxi0bOjUjNTGXU3lFcuX9F32EBUNHRnCUD6mGgVLDzSgRf/31VTJQolAoiARIEQb8UCqjZE8achjZfgaElRF6C1d1gZRcIv6jvCPOFodKQuS3nUsehDkmaJEbsGcHNB0Vj2ZBGZW2Z1aMmAMuPBrD435LZOV0QniQSIEEQiga1ETT9SDdirNEHoFDD3f2w6E3YMhLigvUd4WszUZswv/V8atjVID49nmG7h3E3vmgkG51quvBFxyoAzPjnOn+eD9VzRIJQsEQCJAhC0WJiA+2/gw9PQ/X3ARkurNP1D9ozBVLj9B3hazFVmzK/9Xwq21QmNi2WYbuGEZxQNJK7oc3KMrhpGQD+t/ECR2/H6DkiQSg4IgESBKFosvaC931h2D7wfAOy0uHIXN2IsWPzITNd3xG+MktDSxa3WUw5y3JEpUYxdPdQwpPC9R0WAF90rEJHb2c0WTIjVp3hWniCvkMShAJRJBKgefPm4eXlhZGREQ0bNuTkyZPPPLZFixZIkpRr69ixY/YxgwYNyvV8+/btC6MqgiDkN9e6MOhv6LMB7CtD6gPYNQl+qw+XNxXbpTWsjaz5vd3veFp4EpYcxtDdQ4lO0f/SFAqFxKweNWlQxobE9Ex8lp0iLK5kTU8gCFAEEqD169czYcIEpk6dytmzZ6lZsybt2rUjKirqqcdv3ryZ8PDw7O3y5csolUq6d++e47j27dvnOG7dunWFUR1BEAqCJEHFdjDyCHT6BcycIC4Q/hgMS96CgMP6jvCV2Bnb8Xvb33E1cyUoMYhhu4cRmxar77AwUitZ0r8eFRzMiEhIY9Cyk8SnavQdliDkK70nQLNnz2bYsGH4+PhQtWpVFi5ciImJCUuXLn3q8TY2Njg5OWVve/bswcTEJFcCZGhomOM4a2vrwqiOIAgFSamCugNh7FloORkMzCDsrG59sbW9IOq6viPMMydTJ35v+zsOJg7cib/D8N3DiU/X//pcliZqlg9ugKOFITcjkxi+8jTpmSVnfiZB0GsClJGRwZkzZ2jdunX2PoVCQevWrTl27NhLleHr60uvXr0wNTXNsf/AgQM4ODhQqVIlRo0axf37JW+qfUEotQxMoflEGHsO6g8FSQk3/9GtOL9tLCRG6DvCPHEzd8O3rS+2RrbceHCDUXtHkZSRpO+wcLUyZtmgBpgZqjhxL5YJGy6gFRMlCiWESp8Xj4mJISsrC0dHxxz7HR0duX79xd/kTp48yeXLl/H19c2xv3379nTr1o0yZcpw584dPv/8c95++22OHTuGUqnMVU56ejrp6Y87VCYk6Dr9aTQaNJr8bfZ9VF5+l1tUiPoVf8WqjobW0HYm1B2Ccv83KG5sh7MrkC9tRNtwNNpGH4CheY5Timr9XE1cWfDWAob7D+dSzCVG7R3FvJbzMFYZ56mc/K5fBXtj5vWuydBVZ9l+MRxHMwMmvV0pX8p+VUX1d5hfRP1ev+yXIcl6nPIzLCwMV1dXjh49SuPGjbP3T5w4kYMHD3LixInnnj9ixAiOHTvGxYvPnyjt7t27lCtXjr1799KqVatcz0+bNo3p06fn2r927VpMTExesjaCIOibTdJNqoX5YZN8G4A0lSU3nLsSaPsmsqTX73svLSwzjKVJS0kjjbKqsvQ37Y9aUus7LE5HS6y6rfsC2cUzi5YuoiVIKHpSUlLo06cP8fHxWFhYPPdYvf5FsLOzQ6lUEhkZmWN/ZGQkTk5Ozz03OTkZPz8/vvrqqxdep2zZstjZ2XH79u2nJkCTJk1iwoQJ2Y8TEhJwd3enbdu2L3wB80qj0bBnzx7atGmDWq3/P2r5TdSv+CvedewA8kdk3vgb5b6vMHpwj5rBy6mRcpisllOQK76NJjOzyNevQUwDRu8bzd3Mu/ib+jOr2SzUypeLtaB+fx0Ap0P3+HH3LbYGKmnZsAYdvJ//d7qgFO/36IuJ+r26R3dwXoZeEyADAwPq1q2Lv78/Xbp0AUCr1eLv78+YMWOee+7GjRtJT0+nX79+L7xOSEgI9+/fx9nZ+anPGxoaYmhomGu/Wq0usDdfQZZdFIj6FX/Fuo7e3aBqJzi9DA7ORLp/G9UfA8CjMdJbU4GiXb+6znX5rdVvjN47msNhh5l8bDI/Nv8RleLl/2QXRP1Gt6xAVGIGK44F8smmyzhamdCorG2+XiMvivLvMD+I+r1amS9L76PAJkyYwJIlS1ixYgXXrl1j1KhRJCcn4+PjA8CAAQOYNGlSrvN8fX3p0qULtrY5/+dLSkrik08+4fjx4wQEBODv78+7775L+fLladeuXaHUSRCEIkCphobDYex5aPY/UBlD0DFUy9vT8M4spMt/QFrRneSvvlN95raci1qhZm/QXiYfnkyWVr+jsCRJYkqnarSr5khGlpbhK09zMzJRrzEJwqvSewLUs2dPfvrpJ6ZMmUKtWrU4f/48O3fuzO4YHRQURHh4zhlSb9y4weHDhxkyZEiu8pRKJRcvXqRz585UrFiRIUOGULduXQ4dOvTUVh5BEEo4Iwto9SV8eAZq90NGwinhAqo/R8KP5WFdb7jgVySX2Gji2oTZLWajklTsuLeD6cemo5W1eo1JqZCY26s29TytSUjLZODSk0TEp+k1JkF4FUWiV+CYMWOeecvrwIEDufZVqlSJZ/XdNjY2ZteuXfkZniAIJYGlK7w7j8wGH3B36wwqaq4gxd6BGzt0m0IN5VpC1XehUgfdmmRFQAv3Fsx8cyYT/53IlttbMFQa8nnDz5EkSW8xGamVLBlQj/cWHuVudDKDlp1kw8jGWBiV3Ns1Qsmj9xYgQRCEQmVXgesu75M58jiMOgbNPwP7KqDVwK3d8OcH8FMFWNUVziyHZP0vCNrOqx3fNP0GCQm/G37MPjP7mV8CC4u1qQErfBpgb27I9YhERq46Q0amflunBCEvRAIkCELpJEngWBVaToIPjsMHJ6HlF+BYHbSZcGcf/PWRLhla0QlO+ULS05foKQydynViSuMpACy/spz5F+brLZZH3G1MWDaoPqYGSo7euc8nf4iJEoXiQyRAgiAIAPaVoPknMOoIjDkDraaAc02QtXDvX9g+AX6qCMs6wonFkFD4q7e/X/F9PmvwGQALLyzk90u/F3oM/1Xd1ZIF/eqiUkj8eT6M73cVv+VIhNJJJECFSJZlEnfsAK1oJhaEIs2uPDT7GEb8qxtF1uYr3ar0yBB4GP75BGZXAd92cGw+xIcUWmh9q/RlXJ1xAMw9O5fVV1cX2rWf5c2K9sx8rwYAiw7eZcXRAP0GJAgvoUh0gi4t7i9eQvTPP+NSpQrali3BykrfIQmC8CI2ZaDpR7otLgiu/QVX/4TgExB8XLftmgRu9XUdqKt0BmvPAg1piPcQ0rPSWXBhAd+f+h5DlSHdK3Z/8YkF6P26bkTEp/LT7ptM++sKjhaGtK/+9LnXBKEoEC1AhUjt5opkaIjZtWuE9B+AJjRU3yEJgpAXVh7Q+AMYshvGX4X234NHE0CCkFOw+wuYWwMWt4DDP0Ps3QILZVTNUfhU082X9vWxr9l2Z1uBXetlfdCyPH0aeiDL8JHfeU4HxOo7JEF4JpEAFSLLjh1xXepLprk5Gbduca9HT1LOntN3WIIgvApLV2g0Egb/Ax9fhw4/gVczkBQQdg72ToNfasPCN+DfHyHmVr5eXpIkxtcdT+/KvZGR+fLIl+wM2Jmv13iVmL7qXI3WVRxIz9QyZMVpbkfpf1V7QXgakQAVMqMaNQga8wEGlSuTdf8+QQMHEr9N/9/cBEF4DeZO0GAYDPobPr4J78yBsi1BUkLEJdj3DfxWD+Y3hgPfQ1T+dBSWJInPGnzGexXeQytrmfTvJA6GHMyXsl+VSqng1951qOVuRXyqhoFLTxKVICZKFIoekQDpQaaVFW4rlmPepjWyRkPYxE+J+nkOsugcLQjFn5k91POBAVvhk9vQ+Tco3xoUKoi6Cge+g/kN4bcGsO9biLgMrzGnj0JS8GWjL+lYtiOZciYTD0/kliZ/W5vyythAie/AenjZmhAal4rP8lMkpWfqNSZB+C/RCVpPFCYmuM6dS/ScudxfvJj7ixaRcfcuLt/PRGFiou/wBEHIDyY2UKe/bkt9ADf+0XWgvrMPYm7Avz/oNptyug7UVd/VDb3P4yzPSoWSb5p+Q0ZWBnsC97AmeQ1vRr1JA9cGBVSxF7M1M2TF4Aa8t+AoV8ISGLX6DEsH1UetFN+7nyciPo0NpwI5fFvB6e3XsTIxwNxIhbmRGgsj9cOfHz3W/WukVuh1ZvDiSiRAeiQpFDhMGI9hubKEf/EliXv2EBAagvv8+aidnPQdniAI+cnYGmr10W1p8XBzly4ZurUHYu/A4dm6zdrrcTLkUuelkyGVQsX3zb4nVZPK4bDDfHzoY9Z0WIOHhUfB1us5PG1NWTqoPr0WH+fQrRg+3XSRWd1rig/r/8jM0rLvehTrTwWz/0YUurkkFZyMDnqp81UKCXMjFRbGDxMkQ3V2kmRupMpOlHIc80QyZWGkxlBV+pIokQAVAZbvvova3Z2QMR+SfvUa97p3x33ePIxr1NB3aIIgFAQjS6jRQ7elJ+qW4Lj6J9zcDQ8C4Mhc3Wbp/jgZcq0Hiue3nqiVar5/43t6bOpBaHooo/1Hs/rt1VgZWRVKtZ6mhpsV8/rWYeiK02w+G4qLpTH/a1dJb/EUJYH3k1l/Kpg/zoQQlZievb++lzUOWTF4lClPskZLQqqGxLRMEtMySUh79LOGpPRMtDJkamUepGh4kKJ55VjUSilHq9KTLU1PtjhZGOVOoB79bKRW5sfLUmhEAlREmNSpg9eGDYSMGkX6rVsE9h+Ay4zvsOjQQd+hCYJQkAzNofp7ui0jWdcidPVPXQtRfDAc+023mbtA1c5QtQu4N3xmMmSsMqafaT9WZK4gMCGQcQfGsbjNYgyUBoVbrye0rOTAjK7eTNx0kd/238bZyoi+DQt2rqSiKk2Txa4rEaw/FczRO/ez99uaGvB+XTd61HfHw8qQHTt20KFNBdTqZy8wK8syyRlZJKZpSEjVJUU5k6TH+xLTNCTkeKw7Lik9E1kGTZZMbHIGsckZr1w3A6UCC+P/JEeGuRMmUwMFt+5LVIpOprKL1Stf73WJBKgIMXBzxXPdOsL+9z+SDhwgdMLHpN+5i92YD0pd06QglEoGplCti27TpMJtf10ydOMfSAyDEwt1m5kTVOmkaxnybAKKnN+8zRXm/NLiFwbvGcyZyDNMPTqV7974Tq9/R3rUdycsPpU5e2/x5dbLOJgb0aaqo97iKWw3IhLxOxXElnOhxD1sqZEkaFbBnt713WlVxREDlS6p1WheriVHkiTMDFWYGapwtny1uLRameSMzP+0MD1KpHImUNnHpP5n38MO7hlZWmKSMohJepkkSonCIZQvRAIkPKI0M8Vt3m9EzZpN7NKlxMybR8a9uzh/9x0KIyN9hycIQmFRG0OVd3SbJg3uHoCrW+H6DkiKgFNLdJupPVR+R5cMeTXLPr28VXlmtZjF6L2j+fvu33iYezCq1ii9VQfgo1YViIhPw+9UMB+uO8vaYY2o42Gt15gKUnJ6Jn9fDMPvVDDnguKy97tYGtG9njvd67nhZq3fQS8KhfSwdebZLU0votXKJGXkbHF6MklK+E9rVHxqBkHh0XjZ6rfuIgEqgiSlEseJn2BYtgzh06aTsOMfMoJDcJv3G2oHB32HJwhCYVMbQaX2ui0zA+4dfJgMbYfkaDizTLcZ26Cs+Db2Sa4gv00TlyZ80egLph+bzvwL83Ezd6NTuU56q4YkSXzTpTqRCWnsvxHNkOWn2DSqCWXtzfQWU36TZZmLIfH4nQpi2/kwkjOyAF1H5dZVHOnZwJ03K9ijVJScVn2FQsLi4Sg1MH7h8RqNRneLr55bwQf3HCIBKsKs3n8ftYcHoR+OJe3SJQK698B9wXyMqlbVd2iCIOiLygAqtNFt78yBgEO622TX/oKU+ygurKEJoN0RDp3n8n7F9wlKDGLZ5WVMPToVZ1Nn6jnV01/4SgW/9alD7yXHuRgSz8BlJ9k8qin25oZ6iyk/xKdo2Ho+lHUng7gekZi9v4ydKT3ru9OtjisO5qIVvygREzIUcaYNGuC1cQMGZcuSGRlJQN9+JOzere+wBEEoCpRqKPcWdJqrm4F64F9k1R6IjITi/CrYNBQyMxhXZxxtPNug0WoYd2AcAfEBeg3b1FDF0kH18bAxITg2lcHLT5FcDCdKlGWZ43fvM87vHPW/28vUbVe4HpGIgUpBl1ou+A1vxL6PmzOyeTmR/BRBIgEqBgw8PPBa74fpG28gp6YSOvYjYhYuQn6N2WMFQShhlCoo8ybaDrM45fUBskINVzbD+r4oMtP57o3vqGFXg/j0eD7w/4AHaQ/0Gq7dw4kSbUwNuBQazwdrz6LJKh6z4UcnprPw4B3emnWQXouPs/V8GBmZWio7mTO9czVOfd6aOb1q06isrRjAUoSJBKiYUJqb475wAdb9+wMQPWcOYZ9+ijY9/QVnCoJQ2oRbNyCrx2pQGevmGFr9PkaZGcx9ay6uZq4EJQbx0f6PSM/S79+PMnam+A6sh5FawYEb0UzecqnIfrHL0srsvxHFiFWnaTzDn5n/XOdeTDKmBkp6N3Dnzw+a8s9HzRjYxAtLk1fvUCwUHpEAFSOSSoXT5M9xmjYVlEoStv1F0MBBZMbE6Ds0QRCKGLlcK+i/GQwtIPAwrOyMnVZiXqt5mKvNORd1jilHpug94ajtYc1vveugkGDD6RDm7NXvOmb/FfIghZ/33KTZ9/vwWXaKXVciydTK1Paw4of3anBycmtmdKtBTXcr0dpTzIgEqBiy7tULj9+XoLCwIPX8ee716EHajRv6DksQhKLGswkM/AtMbCHsHCzvQDmFCbNazEIlqdhxbwfzL8zXd5S0rurI112qAzDX/xZ+J19uCYiCkpGp5Z9L4QxcepJmP+xnrv8twuLTsDJR49PUi13j3mTL6Kb0qO+OqaEYS1RciQSomDJt3Biv9X4YeHqSGRZOYO8+JO7br++wBEEoalxqgc8/upmko6/D0nY0NnLiy8ZfArDwwkK23dmm3xiBvg09GdOyPACTt15m//WoQo/hTnQSM3Zco8lMf0atOcvBm9HIMjQpZ8svvWtzfFIrpnaqRiUn80KPTch/IgEqxgzLlMFrvR8mjRqhTUkh5IMPuO+7VO9N2oIgFDH2lWDwTrAuA3GBsOxtullWZaj3UACmHp3KqYhTeg4SPm5bkffquJGllRm95iwXguMK/Jppmiw2nw2hx8JjtJp1kEX/3iUmKQN7c0NGtyjHwU9asHZYIzrXdCl2a10JzycSoGJOaWWFx5LFWPXsCbJM1I8/Ej75C+SMV1/PRRCEEsjaU5cE2VeBxHBY9jYfOjSlrWdbMrWZfLT/I+7G39VriJIkMfM9b96saE+qJovBy08ReD+5QK51JSyeKX9epv63e5mw4QInA2JRSNCqsgNLBtTj2GdvMbF9ZTxtTQvk+oL+iQSoBJDUapymTcVx8mRQKIjfvJnAwYPJfKDfYa6CIBQx5k7gswNc60JqLIqVXfjW/R1q2NcgMSORD/Z+QGxarF5DVCsVzO9bh+quFtxPzmDg0pPcT8qf0WqJaRrWnAik82+H6fjLYVYeCyQxLRM3a2M+blORo5+1wndQfdpUdUSlFB+PJZ34DZcQkiRh078f7osWojAzI/X0GQK69yD99m19hyYIQlFiYgMD/tStG5aRiNG6Xvzi2Q1XM1dCkkL4aJ/+h8ebPZwo0c3amID7KQxZcZrUh0tK5JUsy5wJjOWTjRdo8K0/k7dc5mJIPGqlRMcazqwa0oB/P2nJh60q4GQpJissTUQCVMKYNWuGl9861O7uaEJCCOjVm6RDh/QdliAIRYmhOfTdCBXbQ2YatpuGM9+jC+YG5pyPPs8Xh79AK+t3UkIHcyNWDG6AlYma88FxfLjuLJl5mCgxNjmD3w/dpe3P//LegmNsPBNCqiaL8g5mfNGxCscntWJenzo0q2CPogStyyW8PJEAlUCG5cvjtWE9JvXqoU1KInjESGJXrhKdowVBeExtDD1XQ/X3QZtJ2e2fMcflbVSSip0BO/nt3G/6jpBy9mb4DqyHoUrB3mtRTNl25bl/x7RamcO3Yhiz9iyNvvPnm+3XuBWVhJFawft13fhjZGP2jH+Toc3KYmtWvNceE16fSIBKKJW1NR5LfbF8rxtotUR+9x0R06YjazT6Dk0QhKJCqYZui6GuDyDTYP9PTHV4A4All5aw5dYW/cYH1PW0YW6v2kgSrD0RxLz9uW/rRyak8du+WzT/aT/9fE/w98VwMrK0eLta8k2X6pyc3JqfuteknpeNmKxQyCZmcCrBJAMDnL/5BsNy5Yn68Ufi1q8nIzAQtzk/o7Sy0nd4giAUBQolvPMzGFnAkbl0Ob6SoJrtWZJwla+OfYWLmQsNnRvqNcT21Z2Y3rkaU/68wk+7b2JvpkYtg/+1KP44F8a+61FoHzYMmRup6FLLlZ713anuaqnXuIWiTSRAJZwkSdgO9sGgjBdhH/+PlOPHCejZC7cFCzAsW0bf4QmCUBRIErSeDkaW4P8VYy7sJKRyQ/5JD2f8gfGsfns1Za3K6jXEAY29CItLY+HBO0zeehUTpZKE4+ezn6/vZU2v+h508HbG2EDM1yO8mLgFVkqYt2yJ57p1qF1cyAgMJKBXL5KPHtV3WIIgFBWSBM0+hg4/oQC+vnGCWkoLEjMSGe0/mvup9/UdIRPbVaJLLRcytTIJGgkbUzXD3yzL3gnN2TiyCe/VdRPJj/DSRAJUihhVqojXxg0Y166NNiGBoGHDeeDnp++wBEEoShoMg66LMETJ3LtXcZcMCE0KZez+saRlpuk1NIVC4of3a/JFh0r4VMzi0P+a83mHKpR3MNNrXELxJBKgUkZla4vH8mVYvtsZsrKImDadiG++Rc7M1HdogiAUFTV7QY8V2Egq5gUHYIGCi9EXmXx4st6HxxuoFAxs7EktWxkDlfgIE16dePeUQgpDQ5xnzsR+/HgAHqxeTfCIkWQlJOg5MkEQiowqnaDPBspgwJzwcFQy7A7cza/nftV3ZIKQL0QCVEpJkoTdiOG4/jIXydiY5CNHCOjVm4zAQH2HJghCUVGuJQz4k/oYMT1G1wfo90u/s/nWZj0HJgivTyRApZxF27Z4rVmNysmJjLt3CejRk+STJ/UdliAIRYV7Axi0nc6yCSMfxAPw9bGvOBZ2TM+BCcLrEQmQgFHVqnhtWI9RjRpkxccTNHgIcX/8oe+wBEEoKpy8wWcno7XmdEhKJlPOYsL+cdx+INYaFIovkQAJAKgdHPBcuQKLDm9DZibhX3xJ5MzvkbNebQFCQRBKGLvySIN38nWWJXXS0kjKTOGD3cOISY3Rd2SC8EqKRAI0b948vLy8MDIyomHDhpx8zi2YFi1aIElSrq1jx45PPX7kyJFIksScOXMKKPqSQ2FkhMusWdh9OAaA2OXLCRn9AVlJSXqOTBCEIsHKHYPBu5ijtcNDoyEsLYaxO4eQmpmq78gEIc/0ngCtX7+eCRMmMHXqVM6ePUvNmjVp164dUVFRTz1+8+bNhIeHZ2+XL19GqVTSvXv3XMdu2bKF48eP4+LiUtDVKDEkScL+gw9w/Xk2kqEhSQcPEti7DxkhofoOTRCEosDMAetBO5iPM5ZZWVxKuMvkXcP1PjxeEPJK7wnQ7NmzGTZsGD4+PlStWpWFCxdiYmLC0qVLn3q8jY0NTk5O2duePXswMTHJlQCFhoby4YcfsmbNGtRqdWFUpUSxePttPFevQmVvT/qtWwT06EHK2bP6DksQhKLA2BrP/n8xV+mGWpbZE3OeOXvG6jsqQcgTva4FlpGRwZkzZ5g0aVL2PoVCQevWrTl27OVGGPj6+tKrVy9MTU2z92m1Wvr3788nn3xCtWrVXlhGeno66enp2Y8THs6Ho9Fo0OTz6umPysvvcguCqnJl3NauIXzsWNKvXSdw4CAcpk3FonPnZ55TnOr3Kkp6/aDk11HUL58oDKnRYxPTNvVgclYIy8IP4rp3It2af1uw10X8Dou7gqxfXsqUZFmW8z2ClxQWFoarqytHjx6lcePG2fsnTpzIwYMHOXHixHPPP3nyJA0bNuTEiRM0aNAge/+MGTPYv38/u3btQpIkvLy8GDduHOPGjXtqOdOmTWP69Om59q9duxYTE5NXq1wJImVk4LR+PeaXrwAQ26IFMe3agkLvDYiCIOiZJGdxNWIWa4wTUMoy/9PWwtw2d5cEQSgMKSkp9OnTh/j4eCwsLJ57bLFeDd7X1xdvb+8cyc+ZM2eYO3cuZ8+eRZKklypn0qRJTJgwIftxQkIC7u7utG3b9oUvYF5pNBr27NlDmzZtitWtOblzZ2J/m8eDJUuwOXAAd4UCxxnfofhPglhc6/eySnr9oOTXUdQv/7XXdiBuc2e2Z4QzTzrHchMvyraYWmDXE7/D4q0g65eQhxUN9JoA2dnZoVQqiYyMzLE/MjISJyen556bnJyMn58fX331VY79hw4dIioqCg8Pj+x9WVlZfPzxx8yZM4eAgIBcZRkaGmJoaJhrv1qtLrA3X0GWXVCcPp6AccUKhE/+guR9+wgd5IP7/HmonZ1zHVsc65cXJb1+UPLrKOqXr1fjq+5/EbGxPWcyYvgwaBNr92Vi13aGbpX5grqq+B0WawVRv7yUp9d7GAYGBtStWxd/f//sfVqtFn9//xy3xJ5m48aNpKen069fvxz7+/fvz8WLFzl//nz25uLiwieffMKuXbsKpB6liWWnTnisWI7S1pb0a9e4170HqRcu6DssQRD0zEBlyNxuW/FSWxKuUjEmcDOp28eDVowOE4qmPCVAzxqa/khmZuZz5/B5mgkTJrBkyRJWrFjBtWvXGDVqFMnJyfj4+AAwYMCAHJ2kH/H19aVLly7Y2trm2G9ra0v16tVzbGq1GicnJypVqpSn2ISnM6ldmzIb1mNYqRJZMTEE9h9A/N/b9R2WkE/02C1QKOYsDS2Z985arJRGXDE0ZFLQX2i3jICsktmZVyje8pQAOTs750iCvL29CQ4Ozn58//79F7bc/FfPnj356aefmDJlCrVq1eL8+fPs3LkTR0dHAIKCgggPD89xzo0bNzh8+DBDhgzJ07WE/KN2dcVzzRrMWrZEzsgg7H//I/qXX5HFt71iLf6vvwlo0QL7bdvE71J4JR4WHsxtswi1pMTf1ISfQ3bDhoGgSdN3aIKQQ576AP33m2FAQECuIWev8u1xzJgxjBkz5qnPHThwINe+SpUq5ek6T+v3I7w+pZkpbr/9StTs2cT6LiVm/nzSbt9GavaGvkMT8kiWZe4vXkL0zz8DYH3kKNHffYfLtGkvPZhAEB6p41iHb974jk8PfcpyKwvcww7SY20P6LUWDM30HZ4gAAXQB0j8sSxdJKUSx08+wfnbb0GtJmn3btwXLiLzBbdLhaJDzswkYvr07OTHpEVzZEkiYf0GImfMELfEhFfSoWwHxtTSfbH9ztaawxEnYFUXSH2g38AE4SExkYuQL6ze64bnsqUorK0xCg0luHcf0q5e1XdYwgtoU1IIGfMhcX7rQZJwnDwZl19/JfK99wB4sHIVUT/+JJIg4ZUMrzGczuU6kyVJ/M/BnhuR52H5O5AY+cJzBaGg5SkBkiSJxMREEhISiI+PR5IkkpKSSEhIyN6E0sukXj3c16wh3cGBrKgoAvr2I3HvXn2HJTxDZkwMgQMGknTgAJKhIa6/zMWmv25UZUL9ethPmQJA7NKlRM+ZK5IgIc8kSWJa42nUd6pPskJijLMT0TFXYVl7iAvSd3hCKZenBEiWZSpWrIi1tTU2NjYkJSVRu3ZtrK2tsba2FqOsBNTubgSPHo1xkybIqamEfDiWmCVLxIdnEZN+9x4BvXqTdvkySisrPJYvw6JNmxzHWHZ/H8cvvgDg/qJFxMyfr49QhWJOrVTzc4uf8bLwIkIpMcbFjZQH92Dp2xBzS9/hCaVYnjpB79+/v6DiEEoQrbERLvN+I/anWTxYs4boWbPJuHsP5+nTkAwM9B1eqZdy9iwho0aTFR+P2sMDj8WLMPDyeuqxNv36ImdqiJr5PTG//oakUmM3YnjhBiwUe5aGlsxvNZ++O/pylQd85urJzyEBKJe2h/5bwLmGvkMUSqE8JUDNmzcvqDiEEkZSqXD68gsMypQh8rvviN+yBU1wMK6//oLK2lrf4ZVaCTt3ETZxInJGBkY1a+C+YAEqG5vnnmM7aBCyRkP0rNlE//wzklqN7WCfQopYKCncLdz55a1fGLJrCPvVGcxyr8DE4Fu6PkF9N4JHQ32HKJQyeboFlpmZmWPVdNAtWzF9+nQmTpzI4cOH8zU4ofiz6dcX90ULUZiZkXL6NAE9epJ+546+wyqV7i9fTuj48cgZGZi1aoXn8uUvTH4esRs2DLuxHwIQ9cMPxK5aXZChCiVULYdafPuGbrX4Vap01nl6Q3q8bnTYnX36DU4odfKUAA0bNoyxY8dmP05MTKR+/frMmzePXbt20bJlS3bs2JHvQQrFm1mzZnj5rUPt5oYmOJiAXr1JOnJE32GVGnJWFhHffUfUzO9BlrHu2xe3X+aiMDbOUzn2o0djO2okAJHffssDP7+CCFco4dqXac/Y2rrPkZnKRP4t2wg0KbC2J1z7S8/RCaVJnhKgI0eO8N7D4bEAK1euJCsri1u3bnHhwgUmTJjAjz/+mO9BCsWfYfnyeG1Yj3GdOmgTEwkePoIH69bpO6wST5uWRui4cTxYuQoAh4kTcfxiMpJS+Url2Y8di+1Q3QzsEdOmE7dpU77FKpQeQ72H0qV8F7Sylk+Ucdyo3AayMmDDADi/Vt/hCaVEnhKg0NBQKlSokP3Y39+f9957D0tLSwAGDhzIlStX8jdCocRQ2djgsXwZlu++C1lZREz/iohvvkXOzNR3aCVS5oMHBA3yIXHPXiS1GtfZs7Ad7PNak5VKkoT9xx9jM3AAAOFffEn8n3/mV8hCKSFJElMaTaGhU0NSMlMYrXxAZI3uIGth6yg4sVjfIQqlQJ4SICMjI1JTU7MfHz9+nIYNG+Z4PikpKf+iE0ochYEBzjNnYD9+PAAPVq8meNRoshIT9RxZyZIRFERgr96knj+PwtISj2VLsejQIV/KliQJh88+w7pPb5BlwiZ9Tvx2sRiukDdqpZrZLWdT1rIsUSlRfKiOJ6X+MN2T/3wC//4IYvoMoQDlKQGqVasWq1bpmtIPHTpEZGQkb731Vvbzd+7cwcXFJX8jFEocSZKwGzEc17lzkYyMSD50iMA+fcgICdF3aCVC6oULBPTqTUZgIGoXF7zWrsGkXr18vYYkSTh+8QVW3buDVkvYxE9J2LU7X68hlHwWBhbMazUPGyMbrsVeZ6JRKllvTtQ9ue8b2POlSIKEApOnBGjKlCnMnTuXcuXK0a5dOwYNGoSzs3P281u2bKFp06b5HqRQMlm0a4vn6tWoHBxIv3WbgO49SDl7Vt9hFWuJ/v4EDhxEVmwsRlWr4rXeD8Ny5QrkWpJCgdP0aVh27QpZWYR+/DGJ+8RIHiFv3Mzd+OWtXzBUGnIw5F9+NFNCu+90Tx79Ff4eB9osvcYolEx5SoCaN2/OmTNnGDt2LMuWLWPJkiU5nq9VqxbjH97aEISXYVy9Gl4bN2BUtSpZDx4QNHAQ8du26TusYil2zRpCPhyLnJaGafM38Vy1EpW9fYFeU1IocP7mayzeeQcyMwn5aBxJBw8W6DWFkqemfc3s4fFrrq1hjZUVdP4VkODMctg8DLI0+gxRKIHyvBhqlSpV+Oijj+jZsycKRc7Thw8fTq1atfIrNqGUUDs64rl6FeZtWiNrNIRN/JSoOXOQtVp9h1YsyFotkT/+SOTX34BWi1X37rjPm4fC1LRQri8plbjMnIF5+/ag0RDy4VgxzYGQZ+282jGuzjgAfjj1AwftPeH9paBQw+VN4NcXNKnPL0QQ8iBPM0H/+++/L3Xcm2+++UrBCKWXwsQE17lziZ4zl/uLF3N/4SIy7gXgMnNGnuerKU206emET5pEwo5/ALAfNw7bEcNfa6TXq5BUKlx//IGQTA1Je/0JGf0B7osWYdpIzO4rvLzB1QcTnBjMplub+OTfT1jRfgVVeq+D9f3g1i5Y/T70XgdK8TdBeH15SoBatGiR/Yf1WYtbSpJEVpa4XyvknaRQ4DBhPAZlyhA+ZQqJu3YRGBqK27x5qB0d9B1ekZMVF0fImA9JOX0aVCpcvv1GN8WAnkhqNW6zZ+tagA4eJHjUKDyWLM73DthCySVJEpMbTSY0KZTj4ccZ4z+GNR3X4NRvs26ixMDDsPJd6Ckm4RReX55ugVlbW+Pu7s6XX37JrVu3ePDgQa4tNja2oGIVSgmrrl3wXLYUpZUVaZcvE9CjB2lXr+o7rCIlIySUgL79SDl9GoWZGR5LFus1+XlEMjDA9Ze5mL7xBnJqKsHDR5By7py+wxKKEbVCzewWsylnWY6o1Cg+3Pchya61YNBfYGwDYWdRre6MRUoQaMUcYsKry1MCFB4ezvfff8+xY8fw9vZmyJAhHD16FAsLCywtLbM3QXhdJvXq4bVhPQblypEZGUlA334k7t2r77CKhNQrVwjo3YuMO3dQOTnhuWYNpo0b6zusbApDQ9x++xWTRo3QpqQQPGw4qZcu6zssoRgxNzBnXmvd8Pjrsdf55OAnZDp5g88/YO6MFH2dlje+QPWDByxoCpuGwqFZcGMnPAgE0X9QeAl5SoAMDAzo2bMnu3bt4vr169SoUYMxY8bg7u7O5MmTyRQz+gr5yMDDAy+/dZg2bYqcmkrIh2OJWbLkmbdfS4Okf/8lsP8AsqJjMKxUCS+/dRhVqqjvsHJRGBnhPn8eJvXqoU1KImjIENGKJ+SJq5krv731G4ZKQw6FHuL7k98j21eCwTvRlmlOpsIQKSsDIi/DpY3g/xWs6wlza8BMd1jSCv4cA8fmw539kBQl5hQScsjzKLBHPDw8mDJlCnv37qVixYrMnDmThISE/IxNEFCam+O+aCHWffqALBM9azbhn09GzsjQd2iF7sGGDQSPGo2ckoJpk8Z4rlmN2slJ32E9k8LEBLeFCzGuXRttQgJBg4eQduOmvsMSihFve29mNJuBhITfDT/WXFsD1l5k9dnE9hqL0HxwFnr7Qasp4N0dHKvrRo1lJEHoaTi3CnZN0q02/1MF+LEcLH8HdnwCp5dC0HFIjdN3NQU9yVMn6EfS09PZtGkTS5cu5dixY3Ts2JHt27djY2OT3/EJApJKhdOULzEoW5bI774jfssWNMHBuP76Cypra32HV+BkWSb6l1+4v2AhAJZduuD81XQkAwM9R/ZiSjNT3Bcv0iU/ly4R5OOD58oVGJYvr+/QhGKijWcbJtSdwKwzs/jh1A+4mrnyhvMbICnAygPsy0Gltx+fkKWB2LsQdRUir+r+jbqm25dyHwIO6bYnWbiCQ1VwqPL4X/tKoBajzUqyPCVAJ0+eZNmyZfj5+eHl5YWPjw8bNmwQiY9QKGz69cXA04PQ8RNIOX2agB49cV+4oMBmOi4K5IwMwr/8kvg/dZND2o0ejd2HYwp9mPvrUJqb4/H7EgJ9fEi/eo1AHx88V67EsEwZfYcmFBMDqw0kMDGQP27+waeHPmVJ6yXPPlip1iUv9pWgWtfH+zNSIOamLhmKuvLw32uQEPp4u73n8fGSAqzLPE6KHKvq/rUpq7uGUOzlKQFq1KgRHh4ejB07lrp16wJw+PDhXMd17tw5f6IThP8wa9YML791BI8chSY4mIBevXGd8zNmJXAJlqzERELGjiXl2HFQKnGePg2r99/Xd1ivRGlpiYevL0EDB5F+8yZBg3zwXLUSAw8PfYcmFAOSJPF5w88JSwrjaNhRxh0cR3dld9Iy01CrXzIZMTABl1q67UmpcRB9/XFLUdQ1iLwCqbEQe0e3Xf/78fFKA7Cr+DAxqgIO1XT/WrqD4pV7lQh6kOdbYEFBQXz99dfPfF7MAyQUNMPy5fHasJ6QMR+SevYswcNH4PTFZKx799Z3aPlGExFB8PARpN+8+XCSyDmYNWum77Bei8raGo9lSwkcOJCM23cIHDQIz5WrMHBz1XdoJUrG3XtYnD6N3KYNvGxyUAyoFWp+av4TA/4ZwO242yxgAQs2LMDO2A4XMxdcTV1xMXPR/Wym+9nZ1BkjldHzCza2Ao9Guu0RWdZ1ms5Oip5IjjTJuo7Xkf8Z2WhgBvaVc95Gc6wGpvZQjFpsS5M8JUDalxhamJKS8srBCMLLUtnY4LF8GRFfTiH+zz+JmP4V6Xfu4vjZp0iqV+raVmSk3bhB8PARZEZGorS3w2PRIoyqVtV3WPlCZWuL57JlBPYfQEZAAEGDBuG5aiXqJxZVFl5NZkwM0b/+Rtwff+CUlcUDRyccx3yg77DylbmBOfNbzefzw59zKfIS6aQTkxpDTGoMF6MvPvWcZyVIrmauOJs5Y6g0zH2SJIG5o24r1/Lxfq0W4oNyJ0XRNx53vA49nbMsE9sn+hc9TI7sK+sSL0Gv8u2TIj09nXnz5vHDDz8QERGRX8UKwjMpDAxwnjkDg7Jlif75Zx6sXk1GYCCus2ehNDfXd3ivJPnoUUI+HIs2ORmDcuXwWLwItWvJaiFR2dvjsWI5gf0HoAkKym4JErN9vxptaiqxy5dzf8nvaJ/4Aprwxx84jBqJpFTqMbr852zmzOJWi9m+fTtvtH6DqPQowpLCCE0KJSwpTPdzciihiaGkZKa8MEGyN7bP1XL0KFnKlSApFGDtpdue1vE68krO5Eh0vC7S8pQApaenM23aNPbs2YOBgQETJ06kS5cuLF26lC+++AKlUilWgxcKlSRJ2I0YjoGXF2GffkryoUME9umD24IFGLi56Tu8PInbspXwL7+EzExM6tfH7bdfUZbQiUXVjo54Lte1BGkCg7JbglR2dvoOrdiQs7KI3/on0XPnkhkVBYCRtzc2Y8YQPH48RESQdOgQ5i1a6DfQAiJJEpaGltiZ2VHVNncLqSzLJGQkEJoUmp0cPflvaFIoqZmpRKdGE50azYXoC0+9zpMJkpuZW45kydnUGQOlQc6O1096xY7XSvvKVEgwgbhqYC9GTBaUPCVAU6ZMYdGiRbRu3ZqjR4/SvXt3fHx8OH78OLNnz6Z79+4oS9i3DaF4sGjXFrWrKyGjR5N+6zYB3XvgNu83TOrU0XdoLyTLMjELFhDzy68AWHTsiPOM71AUg2Hur0Pt4pLdEpRx7x5BPj54rFiBSowqfaGkw0eI+vFH0m/cAEDt6or9hPFYvP02mVlZJNSpg/Xhw8Rt2FhiE6AXeZQgWRpaPjNBik+PJzT5iZajV0iQHIwdcrUg5UiQXqbjdeRVXYKU+gBi76CIvUNVgHkbwaMx1OgJ1bqAccmf9qMw5SkB2rhxIytXrqRz585cvnyZGjVqkJmZyYULF4rVsFyhZDKuXg2vjRsIGTWatKtXCRo4COdvv8GyCI9KlDUaIr76iriNfwBgO2wo9uPHI5WS0SQGbm7ZLUHpt24T5DMYj+XLSsX8Tq8i7cZNon78keSHo28VFhbYjRyJdb++jxPmrCziGzTA+vBhkg4cQBMRUaQnzNQXSZKwMrLCysiKarbVcj0vyzJx6XG5EqOw5MfJUmpmKlGpUUSlRnE++nzuayBltyC5mrviYvrEbTYrV5xc6+hakHQXzO54nRV+idjj67BLuoYUdAyCjsE/E6FiO6jRCyq0BVXJ/oJUGPKUAIWEhGQPf69evTqGhoaMHz9eJD9CkaF2dMRz9SrCPv2UxD17CZv4Kel372I/dmyRSyqykpIJHT+e5EOHQKHA6csvStRItpdl4OmJx/LlBA4YQPqNGwQPGYrH8mUoLSz0HVqRoYmMIvqXucRv2arriKtWY9OnN7YjRz41WcxwdMCobl3SzpwhbtMm7D8oWZ2hC4MkSVgbWWNtZE01uxcnSE/eagtLCiMsOezlEiQT+8ctRw8TJEfPOlyPMqFzowbY3NmL6tJGXWvRtb90m7G1bo6jGr3AvYEYZfaK8pQAZWVlYfBEs7xKpcLMzCzfgxKE16EbNj6X6Dlzub94MfcXLiLjXgAuM2egMC4aHQw1UVEEjxxJ+tVrSMbGuM6ahflbLV98YgllWLaMriVowEBd693QYXgs9UVZyv++ZCUlE7vUl/vLliOnpgJg3r49DhPGv3AOJcv339clQH9swm5kyesMrW8vkyA9SH+QuwXpidttaVlpRKVEEZUSxbmoc7nKmPuwe5C5lTmWdvWwyszAMjkWy4xUrO5uwfL2JiwNrbByb4RluTZY2VfJvu1nrjYXjRMvkKcESJZlBg0ahKGhrld8WloaI0eOxNTUNMdxmzdvzr8IBeEVSAqF7kOiTBnCp0whcdcuAkNDcZs3T++jjdJv3yZo+HAyw8JR2tjgvmghxt7eeo2pKDAsXx6PZUsJGjCQtIsXCR42HI/fl6D4z9+X0kDOzCRu02aif/2VrJgYAIxr18Zh4ieY1K79UmWYtmmNcuZMMsPDS3Rn6KJKkiRsjGywMbKhul31XM/LskxsWmz2qLUnE6PQxFDCE8JJIw2ARE0iiZpEQgAMFGDw5P8TMtw/ptueoJSUWBpaYmFggZWhFVaGVlgYPv75UaL06OdH/xopjUpN4pSnBGjgwIE5Hvfr1y9fgxGE/GbVtQsG7m6EjPmQtMuXCejRA/cF8/U2r07yyZOEjPkQbUICBl5euC9ZjIG7u15iKYqMKlXCfakvQT6DST13juARI3FfvAiFiYm+QysUsiyTdPAgUT/9RMbtOwCoPTxw+PhjzNu2ydMHk8LQEMsuXYhdsYK49RtEAlTESJKErbEttsa2eNvn/AKk0WjYsWMHbdu3JVVOJS49jvj0eOLT43P+nBpDXMx1EuICiMuIJ06hIEGhIFWhIEvOIjYtlti02DzFZaAw0CVDRpZYGljmSpaemkgZWKIuhsuD5CkBWrZsWUHFIQgFxqRePbw2rCd41Ggy7twhoG8/XH/8AfPWrQs1jvjt2wn/bBKyRoNx7dq4zZ8nOvs+hXG1anj4/k6Qz2BSTp8m+IMPcF+wAIXRC2b0LeZSr1wh6sefSDl+HNAtH2L3wQdY9+r5ygvfWvXsQeyKFSQdPCg6QxdDKoUKG7WuFemFEiPh8h9wwY/0yIvEKZTEKxTEG5kT59mQeNdaxJnaEp+RkDuRevhzppxJhjYju99SXpiqTbE0yNmq9LQWJktDS8wUZqRoU9DKL55cuSAV7ylzBeElGXh44OW3jtBx40k+coSQD8diP2E8tkOHFnhzryzLxPr6EvXTLADM27bF5YfvS/wH+usw9vbGffFigocOJeXYcULGfIjbvN9QGD5l1t5iThMeTvScOdkL3koGBtgM6I/t8OGv3RHcsGxZTOrVI+X0aeL+2IR9CZsZWniCuSM0/gAaf4Bh1DUcL67H8eJGSAiBy//oNksPqNEdavQD+4o5TpdlmZTMFOLS457d4vTfnzPiSUhPQEYmWZNMsiaZsOSwlw753tl7fN7o8/x+JV6aSICEUkNpbo77ooVEfjeDB2vXEj1rNhl37+E8fdorf8N+ETkri4hvviFunR8ANgMH4vDpxCI3Iq0oMqlTG/dFCwkaPoLkw4cJ/Wgcbr/MLbDfVWHLSkzk/uIlxK5ciZyeDoBFp044jPsoX2f/turZ82EC9Ad2JXBmaOEpHKpA62nw1hQIPAwX18OVP3XLeByapduca0HNXlD9fTCzR5IkTNWmmKpNcTV7+fdfljaLxIxE4jNyJ0jPS55SMlOwMrAqqFfgpYgESChVJJUKpylfYlC2LJHffUf8li1ogoNx/fWXfL8dpU1JIfTj/5G0fz9IEo6ffYrNf/rRCc9nUr8+7gvmEzxiJEkHDhD68ce4zp6NVIwX+ZQ1Gh5s2EDMb/PIevAA0NXTYeJEjL1zd5Z9XeZt26D81orMEj4ztPAUCgWUeVO3dfgJbuyAixvg9l4IP6/bdk2Gcm/pkqFKHcAgb/3tlApl9nxKnni+1DkajYZt27fRtkrbvNcpH4mvoUKpZNOvL+6LFqIwNSXl9GkCevQk/c6dfCs/8/59AgcOImn/fiRDQ1znzBHJzysybdQIt3nzkNRqEvfsJXTiROTMTH2HlWeyLJO4dy93O3Um8utvyHrwAIMyZXCbPw+PlSsKJPmBx52hAeLWbyiQawjFgNoYqr8HfdbDxzfg7R/ApQ7IWbrlODYNgZ8qwtbRcPcAaLMKNByVpMJIpd9uACIBEkots2bN8PJbh9rVFU1wMAG9epN05Mhrl5t+7x4BvXqTdukSSktLPJYtw6Kdfr/pFHdmbzTF9ddfQK0m8Z+dhH3+OXJWwf6Bzk+pFy8S2K8/IWM+JCMgAKWNDU5Tp1B225+Yv/VWgfdDs+rRHSC7M7RQypnaQcMRMHw/jDkNb34CVh6QkQjn18DKd+Hn6rBnim6B1xKqSCRA8+bNw8vLCyMjIxo2bMjJkyefeWyLFi2QJCnX1rFjx+xjpk2bRuXKlTE1NcXa2prWrVtz4sSJwqiKUMwYVqiA18YNGNepgzYxkeDhI3jg5/fK5aWcPUdg7z5ogoNRu7vj6bcOkzovN2+L8HzmLVrgNudnUKlI2PYX4V9OQdbqdxTJi2SEhBA64WMCevQk9cwZJENDbEeOoNzuXVj37l1ot/IMy5bFpH590GqJ+2NToVxTKCbsKsBbX8DYC+CzE+oOAiNLSAyDI3NhQRNY8AYc/RUSwvUdbb7SewK0fv16JkyYwNSpUzl79iw1a9akXbt2REU9fQje5s2bCQ8Pz94uX76MUqmke/fu2cdUrFiR3377jUuXLnH48GG8vLxo27Yt0dHRhVUtoRhR2djgsXwZlu92hqwsIqZNJ+Lb7/J8myVh126CfHzIiovDyNsbL791GJYpU0BRl07mrVrh+tNPoFQSv3kzEdOmI8uyvsPKJSs+nsjvf+Du2x1I2LEDJAnLLl0ot2snDuPG6WWGa6sePQCI++OPYtV6JhQShQI8G0OnufDxTeixCiq/Awo1RF6C3V/Az1VhZRe44AfpSfqO+LXpPQGaPXs2w4YNw8fHh6pVq7Jw4UJMTExYunTpU4+3sbHByckpe9uzZw8mJiY5EqA+ffrQunVrypYtS7Vq1Zg9ezYJCQlcvHixsKolFDMKAwOcZ87Eftw4AB6sWkXw6NFkJb3c/+SxK1YQOm4ccno6Zi1b4rliOSpb2wKMuPSyaN8Ol5kzQaEgbsOG/7d33+FNlW8Dx78n6V50AB1Q2jIsw7IRAQWUQgVFEdnIHsr4CSJTZcsUkCGCILJkiQwnSzaITIvsWShCyyyU7jQ57x+leQkt0ELatOn9ua5cFznjOfeThOTuOfd5Hq5/MS7PJEGGlBRuL1rE+UZh3Fm4EFWnw7l2LYLWrsFv4gSLjsPj2qghWvcHxdC7dlksDpEP2DpA+behzTIYeBbenAb+NUE1wMXtsO4DmFIG1vRIK6jW57+aPLDwXWApKSkcPnyYYcOGGZdpNBpCQ0PZt2/fE/b8fwsWLKBNmzYZpuN4+Bjz5s2jUKFCVKpUKdNtkpOTSX5wGypAbGwskFaprtPpstqdLElvz9zt5hX5vX+FunVF61+c6599Tvyu3Vxq0wbfWbOwLV4cyNg/1WDg1pdTuPfDDwC4tW5FkWHD0Gu16PPpa5Af3kOnN8IompzEjeEjiFm2DINGQ+FBA7NUS5MT/VNVlfjNW7g1fTqp//0HgF3pUnh98glOdeqgKEquvZ6P7Z9Gg+vbb3N3yRLurFqFwyuv5Eo8OSE/fEafR57qn60rVO6Y9oiJQHNsNZrjq1FiIuDYj3DsR1TnohgqNMcQ0gq8Q546OWtO9i87bSqqBf90unbtGsWKFeOvv/6iVq1axuWDBw9m586dT63bOXDgADVr1mT//v289NJLJut+++032rRpQ0JCAr6+vqxfv54aNWpk2s6oUaMYPXp0huXLly/HqYAMwS9M2f/3H8UWLcbm/n1SnZ251rEjSYGmt3gqOh0+K1fhevw4ADcbNyamXl2ZmTkXuR04gM+atLkH79Svx6033sj119/h0iWK/P4HjpGRAKS6unKrUUNiq1WDPDbmju3NmwRNmYqqKEQMHUKqu7ulQxL5karikXAB/zt78YvZj73+/8+UxzoU4z+P2lzxrE2SXe6fBU9ISKBdu3bcu3cPt6cMJJqvE6APPviAffv2ZXppKz4+nqioKG7dusX8+fPZtm0b+/fvp2jRjBNhZnYGyN/fn1u3bj31BcwunU7Hli1baNiwIbb5eCyTx7Gm/qVGRxP10UcknzoNtrZ4jxmDQ1gjtmzZwuvVq3Prk4EkhYenrftiLK5Nmlg6ZLPIb+/hvVWruPnFOAA8PvwArz5PHu3YXP1LiYzk9vTpxG/5EwDF0RGPLp1x79TJonOXPa1//3XpStKhQ3j27oVnr14WiPD55bfPaHblq/7pU1AubEVzbDXKuU0o+rTfUhUFNaA2hhdboZZ7G+xdjbvkZP9iY2MpXLhwlhIgi14CK1y4MFqtluvXr5ssv379Oj5PuVYeHx/PypUrGTNmTKbrnZ2dKV26NKVLl+bll1+mTJkyLFiwwORyWzp7e3vjDPcPs7W1zbEPX062nRdYQ/9s/f0JXLaMq4MHE/fnVq4PG4ZHRAS2hdy43rUbusuX0bi5UfzrWTg/cgbSGuSX97Dw+++jMRjSRvie+y029vYUzsIP+7P2LzUmhlvfzCFmxQpITQWNBvf33qPw//pim8kfWJbyuP55tmnDtUOHiF27jqK9e6PY5N/xcPPLZ/RZ5Yv+2dpChbfTHol34eTP8O8qlMt7US7vRXN5L2wakjbIYsXWULpB2j7kTP+y055FP/l2dnZUq1aNrVu30uzBQF0Gg4GtW7fSt2/fJ+67evVqkpOTszwjvcFgMDnLI0RWaJycKD5zJje/ms7t+fOJmTePQK0WnV6PjZ8vJebNw750aUuHWeB5duyIqkvlxpdfcnPGTBRbW7y6dzfrMQzJycQsXcqtb+dhuH8fAOd6dfEeOBD7MmXMeqycZFIMvXs3rq+9ZumQhLVwdIdqndIeMZfh2Oq0aThunYUTa9MeToXRlH8X93g/sPDNCxa/C2zAgAHMnz+fxYsXc+rUKXr16kV8fDxdunQBoGPHjpmetVmwYAHNmjXD65E7beLj4/n000/5+++/uXz5MocPH6Zr165cvXrV5E4xIbJK0Wgo+skAfMePBxsbFL0e+3JlCVyxUpKfPMSrW1fjXXw3pkzlzuLFZmlXNRi49+tvXGjcmBtTpmK4fx/7cuUo8f0CSnz7bb5KfiDtjsdC774LyMjQIgd5BEDdgdDnAPTcATV7gXMRSLiF9tB86p0djfa3jywaosXPfbZu3ZqbN28yYsQIoqOjqVy5Mhs3bsTb2xuAyMhINI9MHHnmzBn27NnD5s2bM7Sn1Wo5ffo0ixcv5tatW3h5eVGjRg12795NhQoVcqVPwjq5N38XbVAg/yxdSs0RI7CVAtI8p/CHH6DqdNyaPZvrEyaCjQ2e7ds/c3vx+w9wY/Jkkk6kjYZr4+NDkf79KPT22/l6Qlv3li25s3Ahcbt2oYuKwtbX19IhCWulKOBXJe3R6Au4uB1D+HIMJ3+DgDoWPQtj8QQIoG/fvo+95LVjx44My4KDgx877oeDgwNr1641Z3hCGDm8+CIx9eqhecywC8LyCvftg6rTcXvePK6P/QLF1haPB4MAZlXyhQvcmDI1bSJbQOPsjFfPnnh26ojGwbLzF5mDfckgnF56iYQDB7j70xqK/O/JJQdCmIXWBso0RB9Yn83aNTQqZ9kbR/LvnzBCCJEJRVEo8nF/PDt3BiB65Cjurl2XpX1Tb98mavRoLr79Tlryo9Xi0a4tpTZvovAHPa0i+UlnHBl6zZp8ObmsyN9StY5pE7RaUJ44AySEEOakKApFhwxG1emIWbaMqM8+Q7G1oVDTpplub0hM5M7ixdye/x2G+HgAXBo0oOgnA7AvWTI3Q881piND78b1dSmGFgWLnAESQlglRVHw/vwz3Fu3BlXl2pChxG7caLKNqtdzd+06LrzRmJvTZ2CIj8chJISApUvwn/211SY/8Egx9I9SDC0KHkmAhBBWS1EUfEaOoFDz5mAwcPWTgcRt3QpAwr6/iXivBVGffkrq9evY+vnhN2UKgatWps2cXgC4P7gzNr0YWoiCRC6BCSGsmqLR4Dt2DGqqjthffiV64CCKl/Dn2sUIADSurhT+8EM83m+PJpMBUa2ZFEOLgkzOAAkhrJ6i1eI3fjxuTRpDaipOFyPSbpHv1JFSmzfh1a1rgUt+0hmLoX/6SYqhRYEiZ4CEEAWCYmOD36RJKJ5eRJ4+Rcjo0TiXKmXpsCzOWAx9/boUQ4sCRc4ACSEKDMXWliJDBnO9ZUvsSpSwdDh5ghRDi4JKEiAhhCjg3FtJMbQoeCQBEkKIAs4+KK0YGoOBuz+tsXQ4QuQKSYCEEELg3lqKoUXBIgmQEEIIXBs2ROvhYSyGFsLaSQIkhBDCtBh61SoLRyNEzpMESAghBADuLVsAELd7N7pr1ywcjRA5SxIgIYQQwINi6Jo1pRhaFAiSAAkhhDBKvyX+7po1UgwtrJokQEIIIYxMi6F3WTocIXKMJEBCCCGMTIuhZWRoYb0kARJCCGFCiqFFQSAJkBBCCBNSDC0KAkmAhBBCZOAhI0MLKycJkBBCiAxcQkPTiqFv3JBiaGGVJAESQgiRgcbOjkLN04qhY2RkaGGFJAESQgiRKfcWacXQ8bukGFpYH0mAhBBCZMpYDK2q3P3pJ0uHI4RZSQIkhBDisf6/GFpGhhbWRRIgIYQQj2VSDL1zp6XDEcJsJAESQgjxWCbF0D/KyNDCekgCJIQQ4ok8WqZNkCrF0MKaSAIkhBDiiewCA3F6+WUphhZWRRIgIYQQT+XRKu0skBRDC2shCZAQQoincg0NRevpKcXQwmpIAiSEEOKpFDs7Cr3bDJBiaGEdJAESQgiRJSbF0FevWjgaIZ6PJEBCCCGyxKQYes0aS4cjxHORBEgIIUSWycjQwlpIAiSEECLLXBs0kGJoYRUkARJCCJFlip0d7ukjQ69aZeFohHh2kgAJIYTIFvf0Yujde6QYWuRbkgAJIYTIFruAAJxqpRVDx8jI0CKfyhMJ0OzZswkMDMTBwYGaNWty4MCBx25bv359FEXJ8HjzzTcB0Ol0DBkyhJCQEJydnfHz86Njx45ck/lrhBDCbDxapRVD35NiaJFPWTwBWrVqFQMGDGDkyJEcOXKESpUqERYWxo0bNzLdfu3atURFRRkfx48fR6vV0vLBKdmEhASOHDnC8OHDOXLkCGvXruXMmTO8/fbbudktIYSwasZi6Js3iduxw9LhCJFtFk+Apk2bRo8ePejSpQvly5dn7ty5ODk58f3332e6vaenJz4+PsbHli1bcHJyMiZAhQoVYsuWLbRq1Yrg4GBefvllvv76aw4fPkxkZGRudk0IIayWSTG0jAwt8iEbSx48JSWFw4cPM2zYMOMyjUZDaGgo+/bty1IbCxYsoE2bNjg7Oz92m3v37qEoCu7u7pmuT05OJjk52fg8NjYWSLucptPpshRHVqW3Z+528wrpX/5n7X2U/pmP87vvcvu7BcTv3kPCpUvYFiuW48cEeQ/zu5zsX3baVFRVVc0eQRZdu3aNYsWK8ddff1GrVi3j8sGDB7Nz507279//xP0PHDhAzZo12b9/Py+99FKm2yQlJVGnTh3Kli3LsmXLMt1m1KhRjB49OsPy5cuX4+TklI0eCSFEwVJs/nc4nz/P7ddf53ZYI0uHIwq4hIQE2rVrx71793Bzc3vithY9A/S8FixYQEhIyGOTH51OR6tWrVBVlTlz5jy2nWHDhjFgwADj89jYWPz9/WnUqNFTX8Ds0ul0bNmyhYYNG2Jra2vWtvMC6V/+Z+19lP6ZV5zWhuiBAyl67BgvTfkSJReOKe9h/paT/Uu/gpMVFk2AChcujFar5fr16ybLr1+/jo+PzxP3jY+PZ+XKlYwZMybT9enJz+XLl9m2bdsTExl7e3vs7e0zLLe1tc2xD19Otp0XSP/yP2vvo/TPPNwbNeSmpyf6mzdJ/usvXENDc/yY6eQ9zN9yon/Zac+iRdB2dnZUq1aNrVu3GpcZDAa2bt1qckksM6tXryY5OZn3338/w7r05OfcuXP8+eefeHl5mT12IYQQj44MLcXQIv+w+F1gAwYMYP78+SxevJhTp07Rq1cv4uPj6dKlCwAdO3Y0KZJOt2DBApo1a5YhudHpdLRo0YJDhw6xbNky9Ho90dHRREdHk5KSkit9EkKIgsQ4MvSePaT8JyNDi/zB4jVArVu35ubNm4wYMYLo6GgqV67Mxo0b8fb2BiAyMhKNxjRPO3PmDHv27GHz5s0Z2rt69Sq//PILAJUrVzZZt337durXr58j/RBCiIIqfWTohH1/c3fNTxTt18/SIQnxVBZPgAD69u1L3759M123I5MBtoKDg3nczWuBgYGPXSeEECJneLRuTcK+v7n30xqK9O6dK8XQQjyPPJEA5UeqqpKamoper8/WfjqdDhsbG5KSkrK9b34g/cv/rL2P0r+cYVu7NpQrR8rdu9zZvRvn2rXN0q5Wq8XGxgZFUczSnhDpJAF6BikpKURFRZGQkJDtfVVVxcfHhytXrljlf2jpX/5n7X2U/uUc/dAhGOLiuGZnh01EhNnadXJywtfXFzs7O7O1KYQkQNlkMBiIiIhAq9Xi5+eHnZ1dtr5kDAYDcXFxuLi4ZKhtsgbSv/zP2vso/cvBY6ekkHLpEgB2xYqhec6ERVVVUlJSuHnzJhEREZQpU8Yq3zNhGZIAZVNKSgoGgwF/f/9nGiXaYDCQkpKCg4ODVf5Hlv7lf9beR+lfDnJwQOvmhiEuDpvERGzNMJCso6Mjtra2XL582dgvIczB+v735xJr/OIUQojnZePhAYA+JgbVYDBLm/J9K3KCfKqEEEKYjcbVFcXGBjU1FUNcnKXDEeKxJAESQghhNopGg9bdHYDUO3csG4wQTyAJUAFSv359+vfvb+kwnmjUqFEZBrDMKXnp9chLseSGS5cuoSgK4eHhuXZMRVFYv369WduMjo6mYcOGODs74/7gR1+A9sFlMENcHIZMRuAPDAxk+vTpuRyVEKYkARJ52vz583n11Vfx8PDAw8OD0NBQDhw4YJa2165dy9ixY83SliiYvvrqK6KioggPD+fs2bOWDifP0Njbo3FxAdJqgYTIiyQBEnnajh07aNu2Ldu3b2ffvn34+/vTqFEjrl59/vmGPD09cXV1NUOU+Z/Mk/dsLly4QLVq1ShTpgxFixa1dDg5SqfTZWv7nCiGFsKcJAEyA1VVSUhJzfIjMUWfre0f93ieKT+WLl1K9erVcXV1xcfHh3bt2nHjxg3j+piYGNq3b0+RIkVwdHSkTJkyLFy4EEj7sezbty++vr44ODgQEBDAhAkTjPteuXKFZs2a4eLigpubG61ateL69evPFOeyZcvo3bs3lStXpmzZsnz33XcYDAa2bt2apf2/+eYbypQpg4ODA97e3rRo0cK47tHLTlFRUbz55ps4OjoSFBTE8uXLM5yq12q1LFmyhObNm+Pk5ESZMmWMc8+lO378OI0bN8bFxQVvb286dOjArVu3jOvj4+Pp2LEjLi4u+Pr6MnXq1Gy9JoGBgYwfP56uXbvi6upKiRIlmDdvnsk2x44d4/XXX8fR0REvLy969uxJ3EMFqZ07d6ZZs2aMGzcOPz8/goODjZelfvzxRxo3boyzszM1atTg7NmzHDx4kOrVq+Pi4kLjxo25efOmyfG+++47ypUrh4ODA2XLluWbb74xWX/gwAGqVKmCg4MD1atX559//slyf3fs2IGiKGzdupXq1avj5ORE7dq1OXPmjMl2c+bMoVSpUtjZ2REcHMzSpUuf2O6VK1do1aoV7u7ueHp68s4773DpwRg2AAcPHqRhw4YULlyYQoUKUa9ePY4cOWJcHxgYyJo1a1iyZAmKotC5c2cApk2bRkhICM7Ozvj7+9O7d2/jax8bG4ujoyMbNmwwiWXdunW4uroaB1f966+/qFy5svH1Wr9+fZYvGe7YsQMPD4/nfr0URWHOnDm8/fbbODs7M27cOPR6Pd26dSMoKAhHR0eCg4OZMWOGyX7pn61p8+ZR8rXXKPbyy/T54IMnJlDfffcd7u7uWf5/LYQ5yDhAZpCo01N+xKZcP+7JMWE42T3bW6jT6Rg7dizBwcHcuHGDAQMG0LlzZ/744w8Ahg8fzsmTJ9mwYQOFCxfm/PnzJCYmAjBz5kx++eUXfvzxR0qUKMGVK1e4cuUKkDYGSfv27SlUqBA7d+4kNTWVPn360Lp160zndcuuhIQEdDodnp6eT9320KFDfPTRRyxdupTatWtz584ddu/e/djtO3bsyK1bt9ixYwe2trYMGDDAJClMN2nSJCZPnsyUKVOYNWsW7du35/Lly3h6enL37l1ef/11unfvzldffUViYiJDhgyhVatWbNu2DYBBgwaxc+dOfv75Z4oWLcqnn37KkSNHslX7NHXqVMaOHcunn37KTz/9RK9evahXrx7BwcHEx8cTFhZGrVq1OHjwIDdu3KB79+707duXRYsWGdvYunUrbm5ubNmyxaTt0aNH88UXX1C2bFm6d+9Ou3btcHV1ZcaMGTg5OdGqVStGjBjBnDlzgLQkdcSIEXz99ddUqVKFf/75hx49euDs7EynTp2Ii4vjrbfeomHDhvzwww9ERETQ7xkmy/zss8+YOnUqRYoU4cMPP6Rr167s3bsXSEsg+vXrx/Tp0wkNDeW3336jS5cuFC9enNdeey1DWzqdjsaNG1OrVi12796NjY0NX3zxBW+88Qb//vsvdnZ23L9/n06dOjFr1ixUVWXq1Kk0adKEc+fO4erqysGDB+nYsSNubm7MmDEDR0dHIO2W7ZkzZxIUFMTFixfp3bs3gwcP5ptvvsHNzY233nqL5cuX07hxY2M8y5Yto1mzZjg5OREbG0vTpk1p0qQJy5cv5/Lly89UHzZ8+PDnfr1GjRrFxIkTmT59OjY2NhgMBooXL87q1avx8vLir7/+omfPnvj6+tKqVSvjftu3b8fX15fNa9dy9t9/6ThoEFVeeokePXpkiHPy5MlMnjyZzZs389JLL2W7n0I8M1VkcO/ePRVQ7927l2FdYmKievLkSTUxMdG4LD5ZpwYM+S3XH/HJumz1q169emq/fv0yXXfw4EEVUO/fv6+qqqo2bdpU7dKlS6bb/u9//1Nff/111WAwZFi3ceNGVavVqpcuXTIuO3HihAqoBw4ceGqMI0eOVCtVqvTY9b169VJLlixp8vo/zpo1a1Q3Nzc1NjY20/UPvx6nTp1SAfXgwYPG9efOnVMB9auvvjIuA9SBAweqer1eVVVVjYuLUwF1w4YNqqqq6tixY9VGjRqZHOfKlSsqoJ45c0a9f/++amdnp/7444/G9bdv31YdHR0f+948KiAgQH3//feNzw0Gg1q0aFF1zpw5qqqq6rx581QPDw81Li7OuM3vv/+uajQaNTo6WlVVVe3UqZPq7e2tJicnG7eJiIhQAXXevHlqTEyMqtfr1RUrVqiAunXrVuN2EyZMUIODg43PS5UqpS5fvtwkxrFjx6q1atVSVVVVv/32W9XLy8vkPZszZ44KqP/8889T+7t9+3YVUP/880+T/gDGNmvXrq326NHDZL+WLVuqTZo0MT4H1HXr1ql6vV6dO3euGhwcbPIZTk5OVh0dHdVNmzZlGoder1ddXV3VX3/91bjsnXfeUTt16vTE+FevXq16eXkZn69bt051cXFR4+PjVVVN+75xcHAwfobmzJmT4fWaP39+ll+vrVu3qoC6efNm47Jnfb369+//1OP16dNHfe+994zPO3XqpAYEBKipqamqPilJTTh2TG3eqJHaqmVL4zYBAQHqV199pQ4ePFj19fVVjx8//sRjPPq9m5KSoq5fv15NSUl5anz5kfTv2T3p9/tRcgbIDBxttZwcE5albQ0GA/dj7+Pq5vrcg3s52mqfed/Dhw8zatQojh49SkxMDIYH1+gjIyMpX748vXr14r333uPIkSM0atSIZs2aUfvB5IadO3emYcOGBAcH88Ybb/DWW2/RqFEjAE6fPk2xYsXw9/c3Hqt8+fK4u7tz6tQpatSo8cwxT5w4kZUrV7Jjx44sjQbbsGFDAgICKFmyJG+88QZvvPEG7777bqYjeJ85cwYbGxuqVq1qXFa6dGk8HtQxPKxChQrGfzs7O+Pm5mY8U3T06FG2b9+Oy4MC0IdduHCBxMREUlJSqFmzpnG5p6cnwcHBT+3PwypWrGj8t6Io+Pj4GGM4deoUlSpVwtnZ2bhNnTp1MBgMnDlzBm9vbwBCQkIynVvp4bYf3vbhZenHio+P58KFC3Tr1s3kr/vU1FQKFSpkjKdixYom71mtWrWy1d9H4/L19QXgxo0blChRglOnTtGzZ0+T7evUqZPh8ky648ePc/78+Qw1YElJSVy4cAGA69ev8/nnn7Njxw5u3LiBXq8nISGByMjIJ8b5559/MmHCBE6fPk1sbCypqakkJSWRkJCAk5MTTZo0wdbWll9++YU2bdqwZs0a3NzcCA0NBdI+i4++Xs9yZsQcr1f16tUztDt79my+//57IiMjjZ/nR89eVqhQAa1WC1otGhcXfIoU4eRDlxch7SxmfHw8hw4domTJktnunxDPS2qAzEBRFJzsbLL8cLTTZmv7xz2edaLD9Eskbm5uLFu2jIMHD7Ju3Trg/4thGzduzOXLl/n444+5du0aDRo0YODAgQBUrVqViIgIxo4dS2JiIq1atTKprckJU6ZMYeLEiWzevNnki/1JXF1dOXLkCCtWrMDX15cRI0ZQqVIl7t69+1yx2NramjxXFMWYQMbFxdG0aVPCw8NNHufOnaNu3brPddysxpBVDydIj2s7/TP26LKH+wtpd+s93N/jx4/z999/Zyuep8ksruz2OV18fDzVqlXL8D6dPXuWdu3aAdCpUyfCw8OZMWMGf/31F+Hh4Xh5eT2xYPzSpUu89dZbVKxYkTVr1nD48GFmz54N/P//LTs7O1q0aMHy5csBWL58Oa1bt8bGxrx/j5rj9Xr0M7Jy5UoGDhxIt27d2Lx5M+Hh4XTp0iXDa/LwsW08PFAUBX1Kikkx9Kuvvoper+fHH3/MVkxCmIskQAXQ6dOnuX37NhMnTuTVV1+lbNmymda6FClShE6dOvHDDz8wffp0k0JbNzc3Wrduzfz581m1ahVr1qzhzp07lC1blqtXrxprggBOnjzJ3bt3KV++/DPFO3nyZMaOHcvGjRsz/Yv0SWxsbAgNDWXy5Mn8+++/XLp0yViL87Dg4GBSU1NNinPPnz9PTDZv4a1atSonTpwgMDCQ0qVLmzycnZ0pVaoUtra27N+/37hPTEyMWW+hLleuHEePHiU+Pt64bO/evWg0mmyfaXoab29v/Pz8uHjxYob+BgUFGeP5999/SUpKMu5n7uSoXLlyxvqWdHv37n3sZ65SpUqcO3eOokWLZog7/czV3r17+eijj2jSpAkVKlTA3t7epJg9M4cPH8ZgMDB16lRefvllXnjhBa5du5Zhu/bt27Nx40ZOnDjBtm3baN++vXFdcHAwx44dIzk52bjs4MGDWX4tsiK7r9fD29SuXZvevXtTpUoVSpcubTxj9jgaV1dQFFBVDPfvG5e/9NJLbNiwgfHjxzNlypRn74wQz0gSoAKoRIkS2NnZMWvWLC5evMgvv/ySYTycESNG8PPPP3P+/HlOnDjBb7/9Rrly5YC0u1xWrFjB6dOnOXv2LKtXr8bHxwd3d3dCQ0MpX748HTp04MiRIxw4cICOHTtSr169bCcvkFZwPHz4cL7//nsCAwOJjo4mOjra5I6mx/ntt9+YOXMm4eHhXL58mSVLlmAwGDJNAsqWLUtoaCg9e/bkwIED/PPPP/Ts2RNHR8dsnWnr06cPd+7coW3bthw8eJALFy6wadMmunTpgl6vx8XFhW7dujFo0CC2bdvG8ePH6dy5s1nnOmrfvj0ODg506tSJ48ePs337dv73v//RoUMH4yUtcxo9ejQTJkxg5syZnD17lmPHjrFw4UKmTZsGQLt27VAUhR49enDy5En++OMPs//gDRo0iEWLFjFnzhzOnTvHtGnTWLt2rfGs5aNatmxJ4cKFeeedd9i9ezcRERHs2LGDjz76iP/++w+AMmXKsHTpUk6dOsX+/ftp3769sdD5cUqXLo1OpzP+31q6dClz587NsF3dunXx8fGhffv2BAUFmVwSbdeuHQaDgZ49e3Lq1Ck2bdpkfL2e9azvo7L7eqUrU6YMhw4dYtOmTZw9e5bhw4c/NTlTNBo09vYApD7yB0Xt2rX5448/GD16tAyMKHKdJEAFUJEiRVi0aBGrV6+mfPnyTJw4McMPkp2dHcOGDaNixYrUrVsXrVbLypUrgbRLS5MnT6Z69erUqFGDS5cu8ccff6DRaFAUhWXLluHu7k7dunUJDQ2lZMmSrFq16plinTNnDikpKbRo0QJfX1/jIys/oO7u7qxdu5bXX3+dcuXKMXfuXFasWGFSw/OwJUuW4O3tTd26dXn33Xfp0aMHrq6u2Zp92s/Pj71796LX62nUqBEhISH0798fd3d3Y5Lz5Zdf8uqrr9K0aVNCQ0N55ZVXqFatWpaP8TROTk5s2rSJO3fuUKNGDVq0aEGDBg34+uuvzXaMh3Xv3p3vvvuOhQsXEhISQr169Vi0aJHxDJCLiwu//vorx44do0qVKnz22WdMmjTJrDE0a9aMGTNmMGXKFCpUqMC3337LwoULqV+/fqbbOzk5sWPHDkqUKEHz5s0pV64c3bp1IykpCbcHM5gvWLCAmJgYqlatSocOHfjoo4+eOtZPpUqVmDZtGpMmTeLFF19k2bJlJkNEpFMUhbZt23L06FGTsz+Qdnb1119/JTw8nMqVK/PZZ58xYsQIALPNhJ7d1yvdBx98QPPmzWndujU1a9bk9u3b9O7d+6nHUx7EndnI0K+88gq///47n3/+ObNmzXrmPgmRXYqqPsdgMlYqNjaWQoUKce/ePeOXYbqkpCQiIiIICgp6pi8jg8FAbGwsbm5uVjnDsTX177///sPf358///yTBg0aANbVv8ex9j7mx/4tW7aMLl26cO/evaeehcqr/Uu+dAlDXBw2RYpgm80zkY9+7+p0Ov744w9jQbm1kf49uyf9fj9K7gIT4oFt27YRFxdHSEgIUVFRDB48mMDAQLMWLwuRFUuWLKFkyZIUK1aMo0ePGseSelryk5fZeHiQEheHPiYGmyJFUPJQciYKJvkEilxVoUIFXFxcMn0sW7YsW23t3r37sW1ldhv60+h0Oj799FMqVKjAu+++S5EiRYyDIuYWc/cpP/jwww8f298PP/zQ0uFZRHR0NO+//z7lypXj448/pmXLlsabEPLr66VxdUWxsUFNTTUphhbCUuQMkMhVf/zxx2OHxM9ugW716tXNOpt4WFgYYWFZG88pp5i7T/nBmDFjHlt8+7RT2NZq8ODBDB48ONN1+fX1UjQatO7upN66RWpMDNoHd9sJYSmSAIlcFRAQYLa2HB0dKV26tNnaywussU9PU7RoUaufSNScnvZ6PevYSLlB6+lJ6q1bxmJoTSYDcQqRW+QSmBBCiFyhsbND8+BSrj6bY2wVFIaEBG59OQWfFSvR34u1dDhWTRIgIYQQucbmwfQy+pgYk5GhBSQeO05E8/e4u2QJbuHhXO3ePcPYScJ8JAESQgiRa6QYOiNVr+fW/PlcatuWlEuX0BYtSqqLCymnTxPZsROpTxmBXDwbSYCEEELkmrRi6LSzQHJ2A3RRUUR26crNqdMgNRXXRo0osXYNVz7oibZIEZLPneNyh47orl+3dKhWRxIgIYQQuUrrmZYAZTYydEESu3EjF99pRsKBAyhOTviO+4JiM6ajLVQIXdGiFFu0EBtfX1IiItKSoEzmlRPPThIgQefOnWnWrJmlwxBCFBAmxdB3Ct5ZIH1cPNc+/Yyr/T/GEBuLQ0gIJdeuwf2990zme7MrUYKApUuxLV4cXWQkl9/vQMpDE02L5yMJkBBCiFxnLIa+W7CKoROPHiWieXPurV0LioLXhx8QuHwZdoGBmW5vV7wYAUuXYBcQgO7aNS6/34HkiIjcDdpKSQIkhBAi1xW0YmhVr+fWnDlcatceXWQkNr6+BCxZTNH+/VGeMtq8ra8vJZYuwa5UKVKvX+dyh44knzuXS5FbL0mAzEFVISU+6w9dQva2f9wjm/PY/vTTT4SEhODo6IiXlxehoaHEx8cb10+ZMgVfX1+8vLzo06ePyYjNS5cupXr16ri6uuLj40O7du24ceOGcf2OHTtQFIXff/+dOnXq4OTkxMsvv8zx48ef//UVQlidglQMrbt6lcsdO3FzxkzQ63Fr0piSP6/HqUaNLLdhW7QoAUsWYx8cjP7WLS537ETS6dM5GLX1k5GgzUGXAOP9srSpBnA313E/vQZ2zlnaNCoqirZt2zJ58mTeffdd7t+/z+7du1EfJFHbt2/H19eX7du3c/78eVq3bk3lypXp0aMHkDZP1tixYwkODubGjRsMGDCAzp0788cff5gcZ8iQIYwbN46SJUvy+eef07RpU86ePWuVMxoLIZ6P1tOD1Fs3rXpk6Hu//U70qFEY4uLQODvjM2I4bm+/bVLrk1U2Xl6UWLSQK917kHTiBJc7dabEd9/hGPJiDkRu/SQBKiCioqJITU2lefPmxukoQkJCjOs9PDz4+uuv0Wq1lC1bljfffJOtW7caE6CuXbsaty1ZsiQzZ86kRo0axMXFmUzSOXz4cF577TXc3NxYvHgxxYsXZ926dbRq1SqXeiqEyC/Si6ENcXHo78Sg8cnefIB5mf7+faLHjiX2l18BcKxcGb8vJ2Pn7/9c7dp4eFBi4fdc6dGTxKNHiezSBf/583CqUsUcYRcokgCZg61T2tmYLDAYDMTev4+bqysazXNegbR1yvKmlSpVokGDBoSEhBAWFkajRo1o0aIFHg8KEStUqIBWqzVu7+vry7Fjx4zPDx8+zKhRozh69CgxMTHG+YYiIyMpX768cbtatWoZ/+3p6UlwcDCnTp165i4KIaybjYcHKXFx6O/GYFO0CMrzfi/mAQlHjnBt0GB0V6+CRkPhXr0o3OtDFBvz/ORq3dzwX7CAKx9+QOKhw1zp1h3/b+dm65KakBog81CUtEtRWX3YOmVv+8c9snEKVavVsmXLFjZs2ED58uWZNWsWwcHBRDy4m+DRS1SKohiTnPj4eMLCwnBzc2PZsmUcPHiQdevWAZBSgMfwEEI8P2sqhlZTU7k5cxaX3++A7upVbIsVI+CHHyjyv75mS37SaV2cKTFvHk61XsaQkEBkj57E//WXWY9h7SQBKkAURaFOnTqMHj2af/75Bzs7O2Mi8ySnT5/m9u3bTJw4kVdffZWyZcuaFEA/7O+//zb+OyYmhrNnz1KuXDmz9UEIYV0UjQatR/4vhk65coXL7d/n1jffgMFAoXfeJmj9Opyq5tylKY2TE/5z5uD86quoSUlc+bAXcTt35tjxrI0kQAXE/v37GT9+PIcOHSIyMpK1a9dy8+bNLCUnJUqUwM7OjlmzZnHx4kV++eUXxo4dm+m2X3zxBTt37uT48eN07tyZwoULyyCLQognSk+A8uPI0Kqqcnf9eiKavUvi0aNoXF3xmzIFv0mT0Lq65vjxNQ4OFJ/9NS6vv46aksKVvv/j/tatOX5cayAJUAHh5ubGrl27aNKkCS+88AKff/45U6dOpXHjxk/dt0iRIixatIjVq1dTvnx5Jk6cyJQpUzLddvz48QwdOpQaNWoQHR3Nr7/+ip0V3tkhhDCf/DoytD42lmuffELU0GEY4uNxrFaNkuvXUeitN3M1Do2dHcVnTMf1jTdAp+O/fv2J3bAhV2PIj6QIuoAoV64cGzduzHTdokWLMiybPn26yfO2bdvStm1bk2VqJuMQvfLKK+zbtw83N7fnL/IWQhQY+a0YOuHgQa4OGULqtSjQainyv7549eiB8tDNJLlJsbWl2JQvuWZrS+yvv3L1k4GoOh2F3n7bIvHkBxb/hM2ePZvAwEAcHByoWbMmBw4ceOy29evXR1GUDI833/z/bHvt2rU0atQILy8vFEUhPDw8F3ohhBDieeSXYmhVp+PGV9O53LETqdeisC1RgsDlyyj84YcWS37SKTY2+E2cQKH3moPBwLUhQ7n7008WjSkvs2gCtGrVKgYMGMDIkSM5cuQIlSpVIiws7LEFtmvXriUqKsr4OH78OFqtlpYtWxq3iY+P55VXXmHSpEm51Q0hhBDPKT8UQ6dcusSldu25/e23oKoUat6coLVrcaxUydKhGSlaLb5jx+Letg2oKlGfD+fO8uWWDitPsuglsGnTptGjRw+6dOkCwNy5c/n999/5/vvvGTp0aIbtPT09TZ6vXLkSJycnkwSoQ4cOAFy6dCnnAhcZ1K9fH1VV08Y5io21dDhCiHxI6+FB6s28NzK0qqrcW7uW6HHjURMS0Li54TtmNG5vvGHp0DKlaDT4jBiBxs6OO4uXcH3MWNSUFLw6d7Z0aHmKxRKglJQUDh8+zLBhw4zLNBoNoaGh7Nu3L0ttLFiwgDZt2uDsnLXpIB4nOTmZ5ORk4/P0H3CdTmcyH1b6svQfesMzzGCcXjeT3oa1kf7lf9beR+lfHmZjYxwZOvXOHWyKFgXSBpBVVRWdTodWqzV+Lz/6/ZwT9PfucWP0aOK3/AmAY40aFB0/Dlsfnxw7vrn65/HJJxhsbLm7YAE3Jk5Cn5iER/du5gjxueTk+5edNi2WAN26dQu9Xo+3t+nQ597e3pzOwgRvBw4c4Pjx4yxYsOC5Y5kwYQKjR4/OsHzz5s04OZmOtmxjY4OPjw9xcXHPNQjg/Tx8jdscpH/5n7X3UfqXN2ns7bF9kAAl2NuDopCSkkJiYiK7du0iNTXVuO2WLVtyNBbH8+fxWfUjtrGxqBoNt8IaEVO3Lhw5kqPHTWeW/pUpjWdoKIX//JPbM2Zw9sRxboeGZmsg3ZySE+9fQkJClrfNt3eBLViwgJCQEF566aXnbmvYsGEMGDDA+Dw2NhZ/f38aNWqEm5ubybZJSUlcuXIFFxcXHBwcsn0sVVW5f/8+rq6uzzQZXl4n/cv/rL2P0r88ztWVlHv3IDUVF0VB4+ZGUlISjo6O1K1bFwcHB3Q6HVu2bKFhw4Y5MtGyqtNxe9bX3F20CFQV28AAvCdOokyF8k/d1xzM3r833+TOd99xZ8ZMvP7cSqmAQLz6fWSxz0dOvn/ZKcGwWAJUuHBhtFot169fN1l+/fp1fHx8nrhvfHw8K1euZMyYMWaJxd7eHnt7+wzLbW1tM7w5er0eRVHQaDTPdJt3+inp9DasjfQv/7P2Pkr/8r70WiD93bvYuLuj0WhQFCXDd3Jm39HPK/niRa4NHETSyZMAuLdqhffQIWicsj73ormYs3/evXph4+jIjYmTuLtgAZpUHUWHDrVokpwT71922rPY/w47OzuqVavG1odGrDQYDGzdutVkQs3MrF69muTkZN5///2cDlMIIUQuMxkZ+qH6zJykqioxK1cR0fw9kk6eROvuTvGvZ+E7ZrRFkp+c4NW5M94jhgNwZ/ESoseMQc1vdWJmZNE/DwYMGMD8+fNZvHgxp06dolevXsTHxxvvCuvYsaNJkXS6BQsW0KxZM7y8vDKsu3PnDuHh4Zx8kL2fOXOG8PBwoqOjc7Yz+UD9+vXp378/AIGBgRkGOxRCiLzAZGToXLglPjUmhv/6/o/oUaNQk5Jwrl2LoJ9/xjU0NMePnds827XD94uxoCjcXbGSqOHDUfV6S4dlERatAWrdujU3b95kxIgRREdHU7lyZTZu3GgsjI6MjMxwCvfMmTPs2bOHzZs3Z9rmL7/8YkygANq0aQPAyJEjGTVqVM50JB86ePDgc989J4QQOcXG0/PByNB3UR+pxTSnuD17uTZsKPqbt1BsbSkyYACenTrm+ZGon4d7ixYodnZcGzqMe2vWoqbo8Jsw3uwz1ud1Fu9t37596du3b6brduzYkWFZcHBwplMwpOvcuTOdZayDpypSpIilQxBCiMfSuLj8/8jQ8fFmb9+QksLNqdO4s3gxAHalSlFsypc4ZGGCaGtQ6O23UezsuDpwELG//oqq01Hsy8koOVBUnldZb4ornujRS2CKovDtt9/y1ltv4eTkRLly5di3bx/nz5+nfv36ODs7U7t2bS5cuGDSzs8//0zVqlVxcHCgZMmSjBkzxuQ2VSGEeBYPjwytN/PgqsnnznGpZStj8uPRri1BP60uMMlPOrc33qD4jOlga8v9jRv5r//HGJ5jeJf8RhIgM1BVlQRdQpYfiamJ2dr+cY8nnQl7FmPHjqVjx46Eh4dTtmxZ2rVrxwcffMCwYcM4dOgQqqqanK3bvXs3HTt2pF+/fpw8eZJvv/2WxYsXM3XqVLPGJYQomIzF0PHxqGb4w0pVVe4sW0ZEi5YknzmD1tOT4nO+SRs12dHxudvPj1wbNMB/9tcodnbEbd3Kf337YkhKsnRYucLil8CsQWJqIjWX18z14+5vtx8nW/PdndClSxdatWoFwJAhQ6hVqxbDhw8nLCwMgH79+pnUV40ePZqhQ4fSqVMnAEqWLMno0aMZMmQI48aNM1tcQoiCyVgMHRuLIRsD3GUm9fZtrn36KfE7dwHg/Oqr+I0fh42UA+BSty7+387lSq/exO/azZVevfCfPdtq7n57HDkDJIwqVqxo/Hd6IXpISIjJsqSkJONAU0ePHmXMmDG4uLgYHx988AHR0dHZGo1TCCEex+bBHJCGhATUZ7w8E7drFxfffof4nbtQ7Ozw/vRT/Od9K8nPQ5xr1aLE/HlonJxI2Pc3kT17oo8zf+1VXiJngMzA0caR/e32Z2lbg8FgHKX1eQcpc7Qx7ynbhweQSh8cK7Nl6QOtxcXFMXr0aJo3b27cxmAwEBcX90yjZAshxKPSi6ExGIjfvx/Hhg2zvK8hKYkbU6YS88MPANiXKYPflCk4BL+QU+Hma041auC/4Duu9OhJ4qHDXOnWDf/589Dm4F14liQJkBkoipLlS1EGg4FUm1ScbJ3y7Sit6apWrcqZM2coXbq0cVn6bPD5vW9CiLxB0WjQuLlBVBSxGzZQOIsJUNKZM1wbOJDkc+cB8OjYgaKffIImk1H/xf9zqlKFEgsXEtm9O4lHjxLZuQv+C77D5kE9ljWRXynxzEaMGMGSJUsYPXo0J06c4NSpU6xcuZIvvvjC0qEJIaxI+hmIxH/CSbl8+YnbqgYDd5Ys4VLLViSfO4+2cGH858/D59NPJfnJIseQFwlYvAithwdJJ08S2bkLqbdvWzoss5MESDyzsLAwfvvtNzZv3kyNGjV4+eWXmTFjBv7+/pYOTQhhRTR2digPkpe7P/302O10N25wpecHXB8/ATUlBZf69Sn583pcXn01t0K1Gg5lyxKwdAnaIoVJPnOGyx07obtxw9JhmZVcAitAHh5Y8tKlSybrHr2lPjAwMMOy+vXrZ1gWFhZmvEsM/v8SmBBCmJPmwcj1d9euw71Xrwzr72/bRtRnn6OPiUGxt8d76BDc27Sx6GSf+Z196dIELFlCZOcupFy4QGSHjpRYvAjbp0xYnl/IGSAhhBB5nsbeHq2nJ/rbt4nftt243JCYSNSoUfzXuw/6mBjsy5YlaO0aPNq2leTHDOyDggj4YSm2fn6kXL7M5fc7kPLfVUuHZRaSAAkhhMj7FAXXsEYA3HtwGSz51Cki3mvB3ZWrAPDs0oXAH1dhX6qUxcK0Rnb+/mlJUIkS6P77j8sdOjy1Fis/kARICCFEvuAWFgaKQuLff1P4jz+40q49KRcvYlOkCCW+X4D3kMFo7OwsHaZVsvXzI2DpEuyCgkiNiuLy+x1IvnjR0mE9F0mAhBBC5Au23t44v/oKAJ47d0FqKi6hDQj65Weca9e2cHTWz9bbm4ClS7AvU4bUmze53KEjSWfOWjqsZyYJkBBCiHzDo107AAy2thQZOYLis2ZZ5Rg1eZVN4cKUWLIY+3Ll0N++TWTHjiSeOGHpsJ6JJEBCCCHyDdf69Sm28HsuffIJhVq0kEJnC7Dx8CBg0UIcQkLQ37tHZJeuJB49aumwsk0SICGEEPmKY/XqpHq4WzqMAk1bqBAlFn6PY9WqGGJjiezajYTDhy0dVrZIAiSEEEKIbNO6uFBi/jycXnoJQ3w8kd17EP/335YOK8skARJCCCHEM9E4O+P/7Vyc69RBTUzkygcfErd7j6XDyhJJgESOUxSF9evXWzoMIYQQOUDj6Ejxb2bjUr8+anIy//Xuzf3t25++o4VJAiTynFGjRlG5cmVLhyGEECKLNPb2FJ85A9eGDVF1Ov7730fEbtps6bCeSBKgAiwlJcXSIQghhLASip0dxaZNxa1JE0hN5eqAAdz77XdLh/VYkgAVIPXr16dv377079+fwoULExYWxrRp0wgJCcHZ2Rl/f3969+5NXFwckDZBapEiRfjpodmXK1eujK+vr/H5nj17sLe3JyEhAYBz587RpEkTnJycKF++PFu2bMkQx5AhQ3jhhRdwcnKiZMmSDB8+HJ1OB8CiRYsYPXo0R48eRVEUFEVh0aJFAE+MVQghhOUptrb4fTmZQs2agV7PtUGDuLt2naXDypTMBm8GqqqiJiZmaVuDwYAhMRGDjQ1oni//VBwdsz0GxuLFi+nVqxd79+4FYMOGDcycOZOgoCAuXrxI7969GTx4MN988w2KolC3bl127NhBixYtiImJ4dSpUzg6OnL69GnKli3Lzp07qVGjBk5OThgMBlq0aIGXlxf79u3j/v379O/fP0MMrq6uLFq0CD8/P44dO0aPHj1wdXVl8ODBtG7dmuPHj7Nx40b+/PNPAAoVKgSARqN5bKxCCCHyBkWrxXf8OBRbW+6uXk3Up5+i6nR4tG5l6dBMSAJkBmpiImeqVsvWPtfNcNzgI4dRnJyytU+ZMmWYPHny/7cRHGz8d2BgIF988QUffvihMamoX78+3377LQC7du2iSpUq+Pj4sGPHDsqWLcuOHTuoV68eAH/++SenT5/m33//JTg4GI1Gw/jx42ncuLFJDJ9//rnJMQcOHMjKlSsZPHgwjo6OuLi4YGNjg4+Pj8l+DydTmcUqhBAib1A0GnzGjEaxtyfmhx+IHjkSNTkZz44dLB2akVwCK2CqVTNN1P78808aNGhAsWLFcHV1pUOHDty+fdt4SatevXqcPHmSmzdvsnPnTurXr0/9+vXZsWMHOp2Ov/76i/r16wNw6tQp/P39TS6R1apVK0MMq1atok6dOvj4+ODi4sLnn39OZGTkU2N/WqxCCCHyDkVR8P7sUzy7dgXg+vjx3F6wwMJR/T85A2QGiqMjwUeyNgKmwWAg9v593Fxd0ZjhElh2OTs7G/996dIl3nrrLXr16sW4cePw9PRkz549dOvWjZSUFJycnAgJCcHT05OdO3eyc+dOxo0bh4+PD5MmTeLgwYPodDpqZ2MSwn379tG+fXtGjx5NWFgYhQoVYuXKlUydOvWJ+2UlViGEEHmLoigUHTQQxd6O23PmcuPLKaQmJEIJf0uHJgmQOSiKkvVLUQYDmtRUNE5Oz50APa/Dhw9jMBiYOnWqMZYff/zRZBtFUXj11Vf5+eefOXHiBK+88gpOTk4kJyfz7bffUr16dWNSVa5cOa5cuUJ0dDRubm4A/P3IqKB//fUXAQEBfPbZZ8Zlly9fNtnGzs4OvV6f7ViFEELkPYqiULRfPzR2dtycMZM7s2fj9fprqI+UR+Q2uQRWgJUuXRqdTsesWbO4ePEiS5cuZe7cuRm2q1+/PitWrKBy5cq4uLig0WioW7cuy5YtM9b/AISGhvLCCy/Qu3dvjh49yu7du00SHUirQYqMjGTlypVcuHCBmTNnsm6d6R0CgYGBREREEB4ezq1bt0hOTs5yrEIIIfKmwr16UXTQIAC8tm3n9lfTLRqPJEAFWKVKlZg2bRqTJk3ixRdfZNmyZUyYMCHDdvXq1UOv1xtrfSAtKXp0mUajYc2aNSQmJvLyyy/TvXt3xo0bZ9LW22+/zccff0zfvn2pXLkyf/31F8OHDzfZ5r333uONN97gtddeo0iRIqxYsSLLsQohhMi7vLp1pfDQoaiKgl1QkEVjUVRVVS0aQR4UGxtLoUKFuHfvnvFSTrqkpCQiIiIICgrCwcEh220bDAZiY2Nxc3Oz+CWwnCD9y/+svY/Sv/zn0e9dnU7HH3/8QZMmTbC1tbV0eGZXEPr358JFhHbpbPb+Pen3+1HW8b9DCCGEEPlGindRS4cgCZAQQgghCh5JgIQQQghR4EgCJIQQQogCRxIgIYQQQhQ4kgA9I7l5Tgghcod834qcIAlQNqXfsifzTwkhRO5I/761xlvCheXIVBjZpNVqcXd358aNGwA4OTmhKEqW9zcYDKSkpJCUlGQ1Y3Q8TPqX/1l7H6V/+YeqqiQkJHDjxg3c3d3RarWWDklYEUmAnoGPjw+AMQnKDlVVSUxMxNHRMVuJU34h/cv/rL2P0r/8x93d3fi9K4S5SAL0DBRFwdfXl6JFi6LT6bK1r06nY9euXdStW9cqT+dK//I/a++j9C9/sbW1lTM/IkfkiQRo9uzZfPnll0RHR1OpUiVmzZrFSy+9lOm29evXZ+fOnRmWN2nShN9//x1I+wto5MiRzJ8/n7t371KnTh3mzJlDmTJlzBq3VqvN9n9MrVZLamoqDg4OVvHl9CjpX/5n7X2U/gkhIA8UQa9atYoBAwYwcuRIjhw5QqVKlQgLC3vs5aW1a9cSFRVlfBw/fhytVkvLli2N20yePJmZM2cyd+5c9u/fj7OzM2FhYSQlJeVWt4QQQgiRh1k8AZo2bRo9evSgS5culC9fnrlz5+Lk5MT333+f6faenp74+PgYH1u2bMHJycmYAKmqyvTp0/n888955513qFixIkuWLOHatWusX78+F3smhBBCiLzKopfAUlJSOHz4MMOGDTMu02g0hIaGsm/fviy1sWDBAtq0aYOzszMAERERREdHExoaatymUKFC1KxZk3379tGmTZsMbSQnJ5OcnGx8HhsbC6RdS89ujc/TpLdn7nbzCulf/mftfZT+5X/W3kfp3/O3nRUWTYBu3bqFXq/H29vbZLm3tzenT59+6v4HDhzg+PHjLFiwwLgsOjra2Majbaave9SECRMYPXp0huXr16/HycnpqXE8i59//jlH2s0rpH/5n7X3UfqX/1l7H6V/2Zc+ZlRWBs/ME0XQz2rBggWEhIQ8tmA6q4YNG8aAAQOMz69evUr58uXp3r3784YohBBCiFx2//59ChUq9MRtLJoAFS5cGK1Wy/Xr102WX79+/aljPsTHx7Ny5UrGjBljsjx9v+vXr+Pr62vSZuXKlTNty97eHnt7e+NzFxcXrly5gqurq9nH0YiNjcXf358rV67g5uZm1rbzAulf/mftfZT+5X/W3kfp37NTVZX79+/j5+f31G0tmgDZ2dlRrVo1tm7dSrNmzYC0UUy3bt1K3759n7jv6tWrSU5O5v333zdZHhQUhI+PD1u3bjUmPLGxsezfv59evXplKS6NRkPx4sWz3Z/scHNzs8oPdjrpX/5n7X2U/uV/1t5H6d+zedqZn3QWvwQ2YMAAOnXqRPXq1XnppZeYPn068fHxdOnSBYCOHTtSrFgxJkyYYLLfggULaNasGV5eXibLFUWhf//+fPHFF5QpU4agoCCGDx+On5+fMckSQgghRMFm8QSodevW3Lx5kxEjRhAdHU3lypXZuHGjsYg5MjIyw3w2Z86cYc+ePWzevDnTNgcPHkx8fDw9e/bk7t27vPLKK2zcuBEHB4cc748QQggh8j6LJ0AAffv2fewlrx07dmRYFhwc/MQKb0VRGDNmTIb6oLzA3t6ekSNHmtQcWRPpX/5n7X2U/uV/1t5H6V/uUNSs3CsmhBBCCGFFLD4StBBCCCFEbpMESAghhBAFjiRAQgghhChwJAESQgghRIEjCVAumDBhAjVq1MDV1ZWiRYvSrFkzzpw5Y+mwzGrOnDlUrFjROLBVrVq12LBhg6XDyjETJ040jjllDUaNGoWiKCaPsmXLWjoss7t69Srvv/8+Xl5eODo6EhISwqFDhywdllkEBgZmeA8VRaFPnz6WDs0s9Ho9w4cPJygoCEdHR0qVKsXYsWOzNOdTfnL//n369+9PQEAAjo6O1K5dm4MHD1o6rGeya9cumjZtip+fH4qisH79epP1qqoyYsQIfH19cXR0JDQ0lHPnzuVafJIA5YKdO3fSp08f/v77b7Zs2YJOp6NRo0bEx8dbOjSzKV68OBMnTuTw4cMcOnSI119/nXfeeYcTJ05YOjSzO3jwIN9++y0VK1a0dChmVaFCBaKiooyPPXv2WDoks4qJiaFOnTrY2tqyYcMGTp48ydSpU/Hw8LB0aGZx8OBBk/dvy5YtALRs2dLCkZnHpEmTmDNnDl9//TWnTp1i0qRJTJ48mVmzZlk6NLPq3r07W7ZsYenSpRw7doxGjRoRGhrK1atXLR1atsXHx1OpUiVmz56d6frJkyczc+ZM5s6dy/79+3F2diYsLIykpKTcCVAVue7GjRsqoO7cudPSoeQoDw8P9bvvvrN0GGZ1//59tUyZMuqWLVvUevXqqf369bN0SGYxcuRItVKlSpYOI0cNGTJEfeWVVywdRq7p16+fWqpUKdVgMFg6FLN488031a5du5osa968udq+fXsLRWR+CQkJqlarVX/77TeT5VWrVlU/++wzC0VlHoC6bt0643ODwaD6+PioX375pXHZ3bt3VXt7e3XFihW5EpOcAbKAe/fuAeDp6WnhSHKGXq9n5cqVxMfHU6tWLUuHY1Z9+vThzTffJDQ01NKhmN25c+fw8/OjZMmStG/fnsjISEuHZFa//PIL1atXp2XLlhQtWpQqVaowf/58S4eVI1JSUvjhhx/o2rWr2Sd0tpTatWuzdetWzp49C8DRo0fZs2cPjRs3tnBk5pOamoper88wa4Gjo6PVnZGNiIggOjra5Lu0UKFC1KxZk3379uVKDHliJOiCxGAw0L9/f+rUqcOLL75o6XDM6tixY9SqVYukpCRcXFxYt24d5cuXt3RYZrNy5UqOHDmSb6/HP0nNmjVZtGgRwcHBREVFMXr0aF599VWOHz+Oq6urpcMzi4sXLzJnzhwGDBjAp59+ysGDB/noo4+ws7OjU6dOlg7PrNavX8/du3fp3LmzpUMxm6FDhxIbG0vZsmXRarXo9XrGjRtH+/btLR2a2bi6ulKrVi3Gjh1LuXLl8Pb2ZsWKFezbt4/SpUtbOjyzio6OBjBOe5XO29vbuC6nSQKUy/r06cPx48etLpuHtClKwsPDuXfvHj/99BOdOnVi586dVpEEXblyhX79+rFlyxarnFPu4b+iK1asSM2aNQkICODHH3+kW7duFozMfAwGA9WrV2f8+PEAVKlShePHjzN37lyrS4AWLFhA48aN8fPzs3QoZvPjjz+ybNkyli9fToUKFQgPD6d///74+flZ1fu3dOlSunbtSrFixdBqtVStWpW2bdty+PBhS4dmdeQSWC7q27cvv/32G9u3b6d48eKWDsfs7OzsKF26NNWqVWPChAlUqlSJGTNmWDosszh8+DA3btygatWq2NjYYGNjw86dO5k5cyY2Njbo9XpLh2hW7u7uvPDCC5w/f97SoZiNr69vhmS8XLlyVnep7/Lly/z55590797d0qGY1aBBgxg6dCht2rQhJCSEDh068PHHHzNhwgRLh2ZWpUqVYufOncTFxXHlyhUOHDiATqejZMmSlg7NrHx8fAC4fv26yfLr168b1+U0SYBygaqq9O3bl3Xr1rFt2zaCgoIsHVKuMBgMJCcnWzoMs2jQoAHHjh0jPDzc+KhevTrt27cnPDwcrVZr6RDNKi4ujgsXLuDr62vpUMymTp06GYafOHv2LAEBARaKKGcsXLiQokWL8uabb1o6FLNKSEhAozH9ydJqtRgMBgtFlLOcnZ3x9fUlJiaGTZs28c4771g6JLMKCgrCx8eHrVu3GpfFxsayf//+XKsdlUtguaBPnz4sX76cn3/+GVdXV+P1zUKFCuHo6Gjh6Mxj2LBhNG7cmBIlSnD//n2WL1/Ojh072LRpk6VDMwtXV9cMNVvOzs54eXlZRS3XwIEDadq0KQEBAVy7do2RI0ei1Wpp27atpUMzm48//pjatWszfvx4WrVqxYEDB5g3bx7z5s2zdGhmYzAYWLhwIZ06dcLGxrq+3ps2bcq4ceMoUaIEFSpU4J9//mHatGl07drV0qGZ1aZNm1BVleDgYM6fP8+gQYMoW7YsXbp0sXRo2RYXF2dyFjkiIoLw8HA8PT0pUaIE/fv354svvqBMmTIEBQUxfPhw/Pz8aNasWe4EmCv3mhVwQKaPhQsXWjo0s+natasaEBCg2tnZqUWKFFEbNGigbt682dJh5Shrug2+devWqq+vr2pnZ6cWK1ZMbd26tXr+/HlLh2V2v/76q/riiy+q9vb2atmyZdV58+ZZOiSz2rRpkwqoZ86csXQoZhcbG6v269dPLVGihOrg4KCWLFlS/eyzz9Tk5GRLh2ZWq1atUkuWLKna2dmpPj4+ap8+fdS7d+9aOqxnsn379kx/+zp16qSqatqt8MOHD1e9vb1Ve3t7tUGDBrn62VVU1cqG0RRCCCGEeAqpARJCCCFEgSMJkBBCCCEKHEmAhBBCCFHgSAIkhBBCiAJHEiAhhBBCFDiSAAkhhBCiwJEESAghhBAFjiRAQohcc+nSJRRFITw83NKhGJ0+fZqXX34ZBwcHKleu/FxtKYrC+vXrzRKXECJnSQIkRAHSuXNnFEVh4sSJJsvXr1+PoigWisqyRo4cibOzM2fOnDGZl+hR0dHR/O9//6NkyZLY29vj7+9P06ZNn7jP89ixYweKonD37t0caV+Igk4SICEKGAcHByZNmkRMTIylQzGblJSUZ973woULvPLKKwQEBODl5ZXpNpcuXaJatWps27aNL7/8kmPHjrFx40Zee+01+vTp88zHzg2qqpKammrpMITIcyQBEqKACQ0NxcfHhwkTJjx2m1GjRmW4HDR9+nQCAwONzzt37kyzZs0YP3483t7euLu7M2bMGFJTUxk0aBCenp4UL16chQsXZmj/9OnT1K5dGwcHB1588UV27txpsv748eM0btwYFxcXvL296dChA7du3TKur1+/Pn379qV///4ULlyYsLCwTPthMBgYM2YMxYsXx97ensqVK7Nx40bjekVROHz4MGPGjEFRFEaNGpVpO71790ZRFA4cOMB7773HCy+8QIUKFRgwYAB///13pvtkdgYnPDwcRVG4dOkSAJcvX6Zp06Z4eHjg7OxMhQoV+OOPP7h06RKvvfYaAB4eHiiKQufOnY19mjBhAkFBQTg6OlKpUiV++umnDMfdsGED1apVw97enj179nD06FFee+01XF1dcXNzo1q1ahw6dCjT2IUoCCQBEqKA0Wq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nclustlasso_l2_signed_normed_noleafavg_norankshaplimerawdata
02.02.168181e-010.2284762.269150e-012.182501e-01
13.02.203815e-010.1761272.229092e-011.807376e-01
24.01.717935e-010.1683141.868456e-011.715644e-01
35.01.671121e-010.1581491.751256e-015.048289e+09
46.01.622066e-010.1352691.378946e-011.598046e-01
57.06.708413e+100.1265751.149422e-012.445028e+09
68.01.045041e+110.1098859.833184e-022.104611e+10
79.01.504094e+110.1081927.995167e+096.966726e+09
810.01.827444e-010.0908976.795563e-024.229521e+10
\n", + "
" + ], + "text/plain": [ + " nclust lasso_l2_signed_normed_noleafavg_norank shap lime \\\n", + "0 2.0 2.168181e-01 0.228476 2.269150e-01 \n", + "1 3.0 2.203815e-01 0.176127 2.229092e-01 \n", + "2 4.0 1.717935e-01 0.168314 1.868456e-01 \n", + "3 5.0 1.671121e-01 0.158149 1.751256e-01 \n", + "4 6.0 1.622066e-01 0.135269 1.378946e-01 \n", + "5 7.0 6.708413e+10 0.126575 1.149422e-01 \n", + "6 8.0 1.045041e+11 0.109885 9.833184e-02 \n", + "7 9.0 1.504094e+11 0.108192 7.995167e+09 \n", + "8 10.0 1.827444e-01 0.090897 6.795563e-02 \n", + "\n", + " rawdata \n", + "0 2.182501e-01 \n", + "1 1.807376e-01 \n", + "2 1.715644e-01 \n", + "3 5.048289e+09 \n", + "4 1.598046e-01 \n", + "5 2.445028e+09 \n", + "6 2.104611e+10 \n", + "7 6.966726e+09 \n", + "8 4.229521e+10 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_results[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the data, only shap and aloo_l2_unsigned_nonnormed_leafavg_rank\n", + "# i=0\n", + "# for data_result in data_results:\n", + "# data_result = data_result.set_index(\"nclust\")\n", + "# data_result[[\"shap\", \"nonloo_l2_signed_nonnormed_leafavg_rank\"]].plot(grid=True)\n", + "# # move legend to side\n", + "# plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "# # call aloo_l2_unsigned_nonnormed_leafavg_rank lmdi+\n", + "# plt.legend([\"SHAP\", \"LMDI+\"])\n", + "# # y axis label is RMSE\n", + "# plt.ylabel(\"RMSE\")\n", + "# # x axis label is number of clusters\n", + "# plt.xlabel(\"Number of Clusters\")\n", + "# # title is the dataid\n", + "# plt.title(\"DataID: \" + dataids[i])\n", + "# plt.show()\n", + "# i+=1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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3K/3Zt5UrV84xF+fqsVYQ7hrP2W6++WaHYJ7fvrLHTFRUVK7Ls2vYs2ePpD/fZHMTEhLiUOvVz6ns+p15w79ahQoVcvR36dKltWPHDvv1AwcOqEqVKg5vrpJ06623OtR1tYI89pI0a9YsjR8/Xj/++KND+I6JiXHqvjizfvHixXX//fdr7ty5ysjIkL+/vxYsWKBLly45BJkDBw4oNjY2R9/k9TxwRlGNxV27dumll17S6tWrlZaW5nDbla+HknOPf7bBgwfrwoUL2rZtm+Li4vK8n96KIOMhjzzyiPr27atjx47pnnvuyfMv8qysLIWFhemDDz7I9fZy5co5vU8fH59cl7t6tKQoPPTQQ/ryyy/13HPPqW7duipZsqSysrJ09913Kysry6FdixYttHDhQi1fvlyvv/66xo4dqwULFtiPOIwfP149e/bUv//9by1fvlwDBw5UUlKSNm7cqAoVKti35e7ToK+2YsUK9ejRQ/Hx8UpJSSnw+tlfYLVx40b997//1bJly9S7d2+NHz9eGzduVMmSJd1S57XGS3b/d+/eXYmJibm2rV27tltqyY27x/OVf+06uy9n+2jOnDkKDw/P0a4wT48vzOd7QR77999/Xz179lRCQoKee+45hYWFycfHR0lJSfr555+vua+CrN+1a1dNmzZNS5YsUUJCgj7++GNVr15dderUuc57nL+iGItnzpxRy5YtFRISon/+85+KjY1VQECAtm7dqueff97h9bCgNXXu3Fnz5s3TmDFjNHv27BzB1tsRZDykS5cu6tevnzZu3OhwKPRqsbGxWrlypZo1a5bvC610/W/AFStWlCTt3r3b4cjKxYsXtX//fvvpwdnt9uzZ4/CX5qVLl7R//363vWicPn1aq1at0siRI/Xyyy/bl2f/lXu1iIgIPfXUU3rqqad04sQJ1atXT6+++qrDRye1atVSrVq19NJLL+nLL79Us2bNlJKSotGjR6tixYrKysrSnj177H+RStLx48d15swZ+/2+Hl9//bW6dOmiBg0a6OOPP76uN7LGjRurcePGevXVVzV37lw9+uijmjdvnh5//PEcbcPCwhQQEKC9e/fmuC23Zc4oV66cgoODlZmZec1TxytWrKidO3fKGOMwTnfv3u3Svp3h7HguTNkfzYWFheW7vyufU1cr7D7asWOHsrKyHN68sj+myGvMF+Sx/+STT1SpUiUtWLDA4bF39rtiCrL+HXfcoYiICH300Udq3ry5Vq9erRdffNGhTcWKFfX999/nGIuuPg+c4Y6xuHbtWv3+++9asGCB7rjjDvvy/fv3X3d9CQkJateunXr27Kng4GBNnTr1urdZlKwVu/5CSpYsqalTp+qVV15Rx44d82z30EMPKTMzU6NGjcpx2+XLlx1O3StRokSup0k7q23btvLz89PEiRMdUvu//vUvpaamKj4+XpLUoEEDlStXTikpKbp48aK93cyZM3Pdv6unX2f/RXH1XxDJyckO1zMzM3McVg0LC1NkZKT99N60tDRdvnzZoU2tWrVUrFgxe5sOHTrkuv0JEyZIkv3+u+qHH35QfHy8oqOjtXjx4msG07ycPn06R59kf6ad1+nMPj4+atu2rRYtWuQwj2jv3r1asmSJS3X4+Pjo/vvv16effqqdO3fmuP3K0087dOigI0eOOHwV+vnz5/P8WMIdnB3Phal9+/YKCQnRa6+9lut8puw+ioiIUN26dTVr1iyHsbxixQp9//33hVZfhw4ddOzYMYc/pi5fvqxJkyapZMmSatmyZa7rFeSxz+15/PXXX+urr75yqsaCrF+sWDE98MAD+u9//6s5c+bo8uXLDh8rSX8+JocPH9Z//vMf+7ILFy7onXfecaoeV7hjLObWDxcvXtSUKVPcUmOPHj00ceJEpaSk6Pnnn3fLNosKR2Q8KK9Dsldq2bKl+vXrp6SkJG3fvl3t2rWTr6+v9uzZo/nz5+utt96yz6+pX7++pk6dqtGjR6ty5coKCwvL87P53JQrV07Dhw/XyJEjdffdd6tTp07avXu3pkyZottvv90+cczX11ejR49Wv379dOedd+rhhx/W/v37NWPGjFznyPTo0UPr1q0r8GHWkJAQ3XHHHRo3bpwuXbqkm2++WcuXL8/xF0h6eroqVKigBx54QHXq1FHJkiW1cuVKbdq0SePHj5f05zeGDhgwQA8++KCqVq2qy5cva86cOfYXZEmqU6eOEhMTNX36dPth3G+++UazZs1SQkKCWrduXaD6r66xffv2On36tJ577rkck/tiY2PVpEkTp7Y1a9YsTZkyRV26dFFsbKzS09P1zjvvKCQkxB7GcvPKK69o+fLlatasmZ588kllZmZq8uTJqlmzprZv3+7S/RozZozWrFmjRo0aqW/fvqpRo4ZOnTqlrVu3auXKlfbvVOnbt68mT56sHj16aMuWLYqIiNCcOXMK9Uu+nB3PhSkkJERTp07VY489pnr16qlr164qV66cfv31V3322Wdq1qyZJk+eLElKSkpSfHy8mjdvrt69e+vUqVOaNGmS4uLidPbs2UKp74knntC0adPUs2dPbdmyRdHR0frkk0+0YcMGJScnKzg4OM91nX3s7733Xi1YsEBdunRRfHy89u/fr5SUFNWoUcOp+1XQ9R9++GFNmjRJI0aMUK1atRyOrkpSv379NHnyZHXr1k2DBg1SRESEPvjgA/sXSBbGR8vuGItNmzZV6dKllZiYqIEDB8pms2nOnDlunRowYMAApaWl6cUXX1RoaKj9+2u8XtGdIHVju/L06/zkdvqhMX+e2ly/fn0TGBhogoODTa1atczQoUPNkSNH7G2OHTtm4uPjTXBwsMNp0HntO/vUu6tPN5w8ebKpXr268fX1NeXLlzdPPvlkrqcHT5kyxcTExBh/f3/ToEEDs379etOyZUu3nn596NAh06VLF1OqVCkTGhpqHnzwQXPkyBGHU0EzMjLMc889Z+rUqWOCg4NNiRIlTJ06dcyUKVPs29m3b5/p3bu3iY2NNQEBAaZMmTKmdevWZuXKlQ41XLp0yYwcOdLExMQYX19fExUVZYYPH+5waqMzrj79Ovu+5XVJTEx0ettbt2413bp1M7fccovx9/c3YWFh5t577zWbN292aHdlH2VbtWqVue2224yfn5+JjY017777rnnmmWdMQEBAjnVzO529YsWKOWo9fvy46d+/v4mKijK+vr4mPDzctGnTxkyfPt2h3YEDB0ynTp1MUFCQKVu2rBk0aJD9awRcOeXVneO5ZcuWJi4uLse+sh+3119/Pdd9XX06f361tW/f3oSGhpqAgAATGxtrevbsmeMx+/TTT82tt95q/P39TY0aNcyCBQtMYmKiS6df53Z/ctvW8ePHTa9evUzZsmWNn5+fqVWrlsNzMFtu48mZxz4rK8u89tprpmLFisbf39/cdtttZvHixbnWktvrX0HWz24fFRVlJJnRo0fn2j/79u0z8fHxJjAw0JQrV84888wz5tNPPzWSzMaNG3NdJzdFORaNMWbDhg2mcePGJjAw0ERGRpqhQ4faTzu/cl/OPv55jeOhQ4caSWby5MnX7gQvYDPGi2d6Aih0CQkJ2rVrV55zj4AbQXJysp5++mkdOnRIN998s6fLQQEwRwa4gfzxxx8O1/fs2aPPP/88x89ZAH9lVz8PLly4oGnTpqlKlSqEGAtijgxQAGfPnr3m5/rlypXL89THazl58mSO3025kp+fn8qUKePStiWpUqVK9t+uOnDggKZOnSo/Pz8NHTrU5W26yx9//JFj0vbVypQpk+d3vdwITp065TDB/mo+Pj4F+kqGG9V9992nW265RXXr1lVqaqref/99/fjjj/avuWAsWoynP9sCrCT791Lyu1zrZwnyU7FixXy3ndtPUBREz5497XMNQkJCTPv27c2WLVuua5vukj3fIL9LQebS/BVlzzfL61LQuTQ3qjfffNPExcWZEiVKmICAAFOvXj0zb948++2MRWthjgxQAPv27cv1K76v1Lx5c/sZEAW1YcOGHIe9r1S6dGnVr1/fpW17u6NHj+b6i9BXql+/vv1bU29EW7ZscfjV+6sFBgaqWbNmRVjRXxNj0VoIMgAAwLKY7AsAACzrLz/ZNysrS0eOHFFwcHCh/4YOAABwD2OM0tPTFRkZme/vP/3lg8yRI0dy/EItAACwhoMHDzr8sO/V/vJBJvsrtg8ePKiQkBAPVwMAAJyRlpamqKiofH8qQ7oBgkz2x0khISEEGQAALOZa00KY7AsAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACzLo0Fm/fr16tixoyIjI2Wz2bRo0SKH240xevnllxUREaHAwEC1bdtWe/bs8UyxAADA63g0yJw7d0516tTR22+/nevt48aN08SJE5WSkqKvv/5aJUqUUPv27XXhwoUirhQAAHgjj/5o5D333KN77rkn19uMMUpOTtZLL72kzp07S5Jmz56t8uXLa9GiReratWtRlgoAALyQ186R2b9/v44dO6a2bdval4WGhqpRo0b66quv8lwvIyNDaWlpDhcAAPDX5NEjMvk5duyYJKl8+fIOy8uXL2+/LTdJSUkaOXJkodaWLXrYZ0WyH3f7ZUy8p0sAAMAtvPaIjKuGDx+u1NRU++XgwYOeLgkAABQSrw0y4eHhkqTjx487LD9+/Lj9ttz4+/srJCTE4QIAAP6avDbIxMTEKDw8XKtWrbIvS0tL09dff60mTZp4sDIAAOAtPDpH5uzZs9q7d6/9+v79+7V9+3aVKVNGt9xyiwYPHqzRo0erSpUqiomJ0T/+8Q9FRkYqISHBc0UDAACv4dEgs3nzZrVu3dp+fciQIZKkxMREzZw5U0OHDtW5c+f0xBNP6MyZM2revLmWLl2qgIAAT5UMAAC8iM0YYzxdRGFKS0tTaGioUlNT3T5fhrOWAAAoHM6+f3vtHBkAAIBrIcgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLKu7pAoC/quhhn3m6hAL7ZUy8p0sAgALhiAwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsggwAALAsrw4ymZmZ+sc//qGYmBgFBgYqNjZWo0aNkjHG06UBAAAvUNzTBeRn7Nixmjp1qmbNmqW4uDht3rxZvXr1UmhoqAYOHOjp8gAAgId5dZD58ssv1blzZ8XHx0uSoqOj9eGHH+qbb77xcGUAAMAbePVHS02bNtWqVav0008/SZK+/fZbffHFF7rnnnvyXCcjI0NpaWkOFwAA8Nfk1Udkhg0bprS0NFWvXl0+Pj7KzMzUq6++qkcffTTPdZKSkjRy5MgirBIAAHiKVx+R+fjjj/XBBx9o7ty52rp1q2bNmqU33nhDs2bNynOd4cOHKzU11X45ePBgEVYMAACKklcfkXnuuec0bNgwde3aVZJUq1YtHThwQElJSUpMTMx1HX9/f/n7+xdlmQAAwEO8+ojM+fPnVayYY4k+Pj7KysryUEUAAMCbePURmY4dO+rVV1/VLbfcori4OG3btk0TJkxQ7969PV0aAADwAl4dZCZNmqR//OMfeuqpp3TixAlFRkaqX79+evnllz1dGgAA8AJeHWSCg4OVnJys5ORkT5cCAAC8kFfPkQEAAMgPQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFgWQQYAAFiWS0Fm37597q4DAACgwFwKMpUrV1br1q31/vvv68KFC+6uCQAAwCkuBZmtW7eqdu3aGjJkiMLDw9WvXz9988037q5NknT48GF1795dN910kwIDA1WrVi1t3ry5UPYFAACsxaUgU7duXb311ls6cuSI3nvvPR09elTNmzdXzZo1NWHCBJ08edItxZ0+fVrNmjWTr6+vlixZou+//17jx49X6dKl3bJ9AABgbdc12bd48eK67777NH/+fI0dO1Z79+7Vs88+q6ioKPXo0UNHjx69ruLGjh2rqKgozZgxQw0bNlRMTIzatWun2NjY69ouAAD4a7iuILN582Y99dRTioiI0IQJE/Tss8/q559/1ooVK3TkyBF17tz5uor7z3/+owYNGujBBx9UWFiYbrvtNr3zzjv5rpORkaG0tDSHCwAA+Gsq7spKEyZM0IwZM7R792516NBBs2fPVocOHVSs2J+5KCYmRjNnzlR0dPR1Fbdv3z5NnTpVQ4YM0QsvvKBNmzZp4MCB8vPzU2JiYq7rJCUlaeTIkde1XwAAPCF62GeeLqHAfhkT79H9uxRkpk6dqt69e6tnz56KiIjItU1YWJj+9a9/XVdxWVlZatCggV577TVJ0m233aadO3cqJSUlzyAzfPhwDRkyxH49LS1NUVFR11UHAADwTi4FmT179lyzTX5HTZwVERGhGjVqOCy79dZb9emnn+a5jr+/v/z9/a9rvwAAwBpcmiMzY8YMzZ8/P8fy+fPna9asWdddVLZmzZpp9+7dDst++uknVaxY0W37AAAA1uVSkElKSlLZsmVzLA8LC7N/DOQOTz/9tDZu3KjXXntNe/fu1dy5czV9+nT179/fbfsAAADW5VKQ+fXXXxUTE5NjecWKFfXrr79ed1HZbr/9di1cuFAffvihatasqVGjRik5OVmPPvqo2/YBAACsy6U5MmFhYdqxY0eOs5K+/fZb3XTTTe6oy+7ee+/Vvffe69ZtAgCAvwaXjsh069ZNAwcO1Jo1a5SZmanMzEytXr1agwYNUteuXd1dIwAAQK5cOiIzatQo/fLLL2rTpo2KF/9zE1lZWerRo4db58gAAADkx6Ug4+fnp48++kijRo3St99+a/8xR84mAgAARcmlIJOtatWqqlq1qrtqAQAAKBCXgkxmZqZmzpypVatW6cSJE8rKynK4ffXq1W4pDgAAID8uBZlBgwZp5syZio+PV82aNWWz2dxdFwAAwDW5FGTmzZunjz/+WB06dHB3PQAAAE5z6fRrPz8/Va5c2d21AAAAFIhLQeaZZ57RW2+9JWOMu+sBAABwmksfLX3xxRdas2aNlixZori4OPn6+jrcvmDBArcUBwAAkB+XgkypUqXUpUsXd9cCAABQIC4FmRkzZri7DgAAgAJzaY6MJF2+fFkrV67UtGnTlJ6eLkk6cuSIzp4967biAAAA8uPSEZkDBw7o7rvv1q+//qqMjAzdddddCg4O1tixY5WRkaGUlBR31wkAAJCDS0dkBg0apAYNGuj06dMKDAy0L+/SpYtWrVrltuIAAADy49IRmf/973/68ssv5efn57A8Ojpahw8fdkthAAAA1+LSEZmsrCxlZmbmWH7o0CEFBwdfd1EAAADOcCnItGvXTsnJyfbrNptNZ8+e1YgRI/jZAgAAUGRc+mhp/Pjxat++vWrUqKELFy7okUce0Z49e1S2bFl9+OGH7q4RAAAgVy4FmQoVKujbb7/VvHnztGPHDp09e1Z9+vTRo48+6jD5FwAAoDC5FGQkqXjx4urevbs7awEAACgQl4LM7Nmz8729R48eLhUDAABQEC4FmUGDBjlcv3Tpks6fPy8/Pz8FBQURZAAAQJFw6ayl06dPO1zOnj2r3bt3q3nz5kz2BQAARcbl31q6WpUqVTRmzJgcR2sAAAAKi9uCjPTnBOAjR464c5MAAAB5cmmOzH/+8x+H68YYHT16VJMnT1azZs3cUhgAAMC1uBRkEhISHK7bbDaVK1dOd955p8aPH++OugAAAK7JpSCTlZXl7joA4IYRPewzT5dQYL+Mifd0CUCu3DpHBgAAoCi5dERmyJAhTredMGGCK7sAAAC4JpeCzLZt27Rt2zZdunRJ1apVkyT99NNP8vHxUb169eztbDabe6oEAADIhUtBpmPHjgoODtasWbNUunRpSX9+SV6vXr3UokULPfPMM24tEgAAIDcuzZEZP368kpKS7CFGkkqXLq3Ro0dz1hIAACgyLgWZtLQ0nTx5MsfykydPKj09/bqLAgAAcIZLQaZLly7q1auXFixYoEOHDunQoUP69NNP1adPH913333urhEAACBXLs2RSUlJ0bPPPqtHHnlEly5d+nNDxYurT58+ev31191aIAAAQF5cCjJBQUGaMmWKXn/9df3888+SpNjYWJUoUcKtxQEAAOTnur4Q7+jRozp69KiqVKmiEiVKyBjjrroAAACuyaUg8/vvv6tNmzaqWrWqOnTooKNHj0qS+vTpw6nXAACgyLgUZJ5++mn5+vrq119/VVBQkH35ww8/rKVLl7qtOAAAgPy4NEdm+fLlWrZsmSpUqOCwvEqVKjpw4IBbCgMAALgWl47InDt3zuFITLZTp07J39//uosCAABwhktBpkWLFpo9e7b9us1mU1ZWlsaNG6fWrVu7rTgAAID8uPTR0rhx49SmTRtt3rxZFy9e1NChQ7Vr1y6dOnVKGzZscHeNAAAAuXLpiEzNmjX1008/qXnz5urcubPOnTun++67T9u2bVNsbKy7awQAAMhVgY/IXLp0SXfffbdSUlL04osvFkZNAAAATinwERlfX1/t2LGjMGoBAAAoEJc+Wurevbv+9a9/ubsWAACAAnFpsu/ly5f13nvvaeXKlapfv36O31iaMGGCW4oDAADIT4GCzL59+xQdHa2dO3eqXr16kqSffvrJoY3NZnNfdQAAAPkoUJCpUqWKjh49qjVr1kj68ycJJk6cqPLlyxdKcQAAAPkp0ByZq3/desmSJTp37pxbCwIAAHCWS5N9s10dbAAAAIpSgYKMzWbLMQeGOTEAAMBTCjRHxhijnj172n8Y8sKFC/rb3/6W46ylBQsWuK9CAACAPBQoyCQmJjpc7969u1uLAQAAKIgCBZkZM2YUVh0AAAAFdl2TfQEAADyJIAMAACzLUkFmzJgxstlsGjx4sKdLAQAAXsAyQWbTpk2aNm2aateu7elSAACAl7BEkDl79qweffRRvfPOOypdunS+bTMyMpSWluZwAQAAf02WCDL9+/dXfHy82rZte822SUlJCg0NtV+ioqKKoEIAAOAJXh9k5s2bp61btyopKcmp9sOHD1dqaqr9cvDgwUKuEAAAeEqBvkemqB08eFCDBg3SihUrFBAQ4NQ6/v7+9m8eBgAAf21eHWS2bNmiEydOqF69evZlmZmZWr9+vSZPnqyMjAz5+Ph4sEIAAOBJXh1k2rRpo++++85hWa9evVS9enU9//zzhBgAAG5wXh1kgoODVbNmTYdlJUqU0E033ZRjOQAAuPF4/WRfAACAvHj1EZncrF271tMlAAAAL8ERGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFleHWSSkpJ0++23Kzg4WGFhYUpISNDu3bs9XRYAAPASXh1k1q1bp/79+2vjxo1asWKFLl26pHbt2uncuXOeLg0AAHiB4p4uID9Lly51uD5z5kyFhYVpy5YtuuOOOzxUFQAA8BZeHWSulpqaKkkqU6ZMnm0yMjKUkZFhv56WllbodQEAAM/w6o+WrpSVlaXBgwerWbNmqlmzZp7tkpKSFBoaar9ERUUVYZUAAKAoWSbI9O/fXzt37tS8efPybTd8+HClpqbaLwcPHiyiCgEAQFGzxEdLAwYM0OLFi7V+/XpVqFAh37b+/v7y9/cvosoAAIAneXWQMcbo73//uxYuXKi1a9cqJibG0yUBAAAv4tVBpn///po7d67+/e9/Kzg4WMeOHZMkhYaGKjAw0MPVAQAAT/PqOTJTp05VamqqWrVqpYiICPvlo48+8nRpAADAC3j1ERljjKdLAAAAXsyrj8gAAADkhyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsiyADAAAsyxJB5u2331Z0dLQCAgLUqFEjffPNN54uCQAAeAGvDzIfffSRhgwZohEjRmjr1q2qU6eO2rdvrxMnTni6NAAA4GFeH2QmTJigvn37qlevXqpRo4ZSUlIUFBSk9957z9OlAQAADyvu6QLyc/HiRW3ZskXDhw+3LytWrJjatm2rr776Ktd1MjIylJGRYb+empoqSUpLS3N7fVkZ592+zaJQGH2BnKw4PhgbRYOxgbwwNnJu1xiTbzuvDjK//fabMjMzVb58eYfl5cuX148//pjrOklJSRo5cmSO5VFRUYVSoxWFJnu6AngrxgbywthAXgp7bKSnpys0NDTP2706yLhi+PDhGjJkiP16VlaWTp06pZtuukk2m81t+0lLS1NUVJQOHjyokJAQt233r4r+ch595Tz6ynn0lfPoK+cVZl8ZY5Senq7IyMh823l1kClbtqx8fHx0/Phxh+XHjx9XeHh4ruv4+/vL39/fYVmpUqUKq0SFhIQw0AuA/nIefeU8+sp59JXz6CvnFVZf5XckJptXT/b18/NT/fr1tWrVKvuyrKwsrVq1Sk2aNPFgZQAAwBt49REZSRoyZIgSExPVoEEDNWzYUMnJyTp37px69erl6dIAAICHeX2Qefjhh3Xy5Em9/PLLOnbsmOrWraulS5fmmABc1Pz9/TVixIgcH2Mhd/SX8+gr59FXzqOvnEdfOc8b+spmrnVeEwAAgJfy6jkyAAAA+SHIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLI5GH9+vXq2LGjIiMjZbPZtGjRomuus3btWtWrV0/+/v6qXLmyZs6cWeh1eoOC9tXatWtls9lyXI4dO1Y0BXtQUlKSbr/9dgUHByssLEwJCQnavXv3NdebP3++qlevroCAANWqVUuff/55EVTrWa701cyZM3OMq4CAgCKq2HOmTp2q2rVr279dtUmTJlqyZEm+69yIY0oqeF/dqGMqN2PGjJHNZtPgwYPzbVfUY4sgk4dz586pTp06evvtt51qv3//fsXHx6t169bavn27Bg8erMcff1zLli0r5Eo9r6B9lW337t06evSo/RIWFlZIFXqPdevWqX///tq4caNWrFihS5cuqV27djp37lye63z55Zfq1q2b+vTpo23btikhIUEJCQnauXNnEVZe9FzpK+nPr0q/clwdOHCgiCr2nAoVKmjMmDHasmWLNm/erDvvvFOdO3fWrl27cm1/o44pqeB9Jd2YY+pqmzZt0rRp01S7du1823lkbBlckySzcOHCfNsMHTrUxMXFOSx7+OGHTfv27QuxMu/jTF+tWbPGSDKnT58ukpq82YkTJ4wks27dujzbPPTQQyY+Pt5hWaNGjUy/fv0Kuzyv4kxfzZgxw4SGhhZdUV6sdOnS5t133831NsaUo/z6ijFlTHp6uqlSpYpZsWKFadmypRk0aFCebT0xtjgi4yZfffWV2rZt67Csffv2+uqrrzxUkferW7euIiIidNddd2nDhg2eLscjUlNTJUllypTJsw1j60/O9JUknT17VhUrVlRUVNQ1/9L+K8rMzNS8efN07ty5PH+TjjH1J2f6SmJM9e/fX/Hx8TnGTG48Mba8/icKrOLYsWM5fjahfPnySktL0x9//KHAwEAPVeZ9IiIilJKSogYNGigjI0PvvvuuWrVqpa+//lr16tXzdHlFJisrS4MHD1azZs1Us2bNPNvlNbZuhDlF2Zztq2rVqum9995T7dq1lZqaqjfeeENNmzbVrl27VKFChSKsuOh99913atKkiS5cuKCSJUtq4cKFqlGjRq5tb/QxVZC+upHHlCTNmzdPW7du1aZNm5xq74mxRZBBkatWrZqqVatmv960aVP9/PPPevPNNzVnzhwPVla0+vfvr507d+qLL77wdClez9m+atKkicNf1k2bNtWtt96qadOmadSoUYVdpkdVq1ZN27dvV2pqqj755BMlJiZq3bp1eb5B38gK0lc38pg6ePCgBg0apBUrVnj1BGeCjJuEh4fr+PHjDsuOHz+ukJAQjsY4oWHDhjfUG/qAAQO0ePFirV+//pp/1eU1tsLDwwuzRK9RkL66mq+vr2677Tbt3bu3kKrzHn5+fqpcubIkqX79+tq0aZPeeustTZs2LUfbG31MFaSvrnYjjaktW7boxIkTDkfKMzMztX79ek2ePFkZGRny8fFxWMcTY4s5Mm7SpEkTrVq1ymHZihUr8v3cFf9v+/btioiI8HQZhc4YowEDBmjhwoVavXq1YmJirrnOjTq2XOmrq2VmZuq77767IcbW1bKyspSRkZHrbTfqmMpLfn11tRtpTLVp00bfffedtm/fbr80aNBAjz76qLZv354jxEgeGluFNo3Y4tLT0822bdvMtm3bjCQzYcIEs23bNnPgwAFjjDHDhg0zjz32mL39vn37TFBQkHnuuefMDz/8YN5++23j4+Njli5d6qm7UGQK2ldvvvmmWbRokdmzZ4/57rvvzKBBg0yxYsXMypUrPXUXisyTTz5pQkNDzdq1a83Ro0ftl/Pnz9vbPPbYY2bYsGH26xs2bDDFixc3b7zxhvnhhx/MiBEjjK+vr/nuu+88cReKjCt9NXLkSLNs2TLz888/my1btpiuXbuagIAAs2vXLk/chSIzbNgws27dOrN//36zY8cOM2zYMGOz2czy5cuNMYypKxW0r27UMZWXq89a8oaxRZDJQ/YpwldfEhMTjTHGJCYmmpYtW+ZYp27dusbPz89UqlTJzJgxo8jr9oSC9tXYsWNNbGysCQgIMGXKlDGtWrUyq1ev9kzxRSy3fpLkMFZatmxp77tsH3/8salatarx8/MzcXFx5rPPPivawj3Alb4aPHiwueWWW4yfn58pX7686dChg9m6dWvRF1/EevfubSpWrGj8/PxMuXLlTJs2bexvzMYwpq5U0L66UcdUXq4OMt4wtmzGGFN4x3sAAAAKD3NkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZf0fZJnPldOKFGYAAAAASUVORK5CYII=", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# for each row in auroc_ranks, plot a histogram of the ranks\n", + "for i in range(auroc_ranks.shape[0]):\n", + " auroc_ranks.iloc[i].plot(kind=\"hist\")\n", + " plt.title(\"Method: \" + auroc_ranks.index[i])\n", + " plt.show()\n", + "\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/current/subgroup-class.ipynb b/feature_importance/subgroup/current/subgroup-class.ipynb new file mode 100644 index 0000000..ae40711 --- /dev/null +++ b/feature_importance/subgroup/current/subgroup-class.ipynb @@ -0,0 +1,1033 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from os.path import join as oj" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['dataid361272',\n", + " 'dataid7592',\n", + " 'dataid361237',\n", + " 'dataid9971',\n", + " 'dataid3903',\n", + " 'dataid14954',\n", + " 'dataid361261',\n", + " 'dataid361259',\n", + " 'dataid361253',\n", + " 'dataid3021',\n", + " 'dataid125920',\n", + " 'dataid361619',\n", + " 'dataid361254',\n", + " 'dataid9957',\n", + " 'dataid361621',\n", + " 'dataid3904',\n", + " 'dataid361266',\n", + " 'dataid9976',\n", + " 'dataid361247',\n", + " 'dataid3917',\n", + " 'dataid3913',\n", + " 'dataid9978',\n", + " 'dataid361249',\n", + " 'dataid361243',\n", + " 'dataid361234',\n", + " 'dataid10101',\n", + " 'dataid361617',\n", + " 'dataid14965',\n", + " 'dataid361250',\n", + " 'dataid146820',\n", + " 'dataid361257',\n", + " 'dataid361622',\n", + " 'dataid31',\n", + " 'dataid361244',\n", + " 'dataid167141',\n", + " 'dataid9977',\n", + " 'dataid361255',\n", + " 'dataid361618',\n", + " 'dataid361267',\n", + " 'dataid14952',\n", + " 'dataid3902',\n", + " 'dataid361260',\n", + " 'dataid49',\n", + " 'dataid43',\n", + " 'dataid219',\n", + " 'dataid361252',\n", + " 'dataid361258',\n", + " 'dataid167120',\n", + " 'dataid15',\n", + " 'dataid361241',\n", + " 'dataid361236',\n", + " 'dataid29',\n", + " 'dataid9946',\n", + " 'dataid361256',\n", + " 'dataid361623',\n", + " 'dataid361264',\n", + " 'dataid10093',\n", + " 'dataid9952',\n", + " 'dataid361269',\n", + " 'dataid37',\n", + " 'dataid146819',\n", + " 'dataid3',\n", + " 'dataid361251',\n", + " 'dataid361616',\n", + " 'dataid3918',\n", + " 'dataid361235',\n", + " 'dataid361242']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.listdir('results/nonl2unsignedunnormed')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['7592',\n", + " '9971',\n", + " '3903',\n", + " '14954',\n", + " '3021',\n", + " '125920',\n", + " '9957',\n", + " '3904',\n", + " '9976',\n", + " '3917',\n", + " '3913',\n", + " '9978',\n", + " '10101',\n", + " '14965',\n", + " '146820',\n", + " '31',\n", + " '167141',\n", + " '9977',\n", + " '14952',\n", + " '3902',\n", + " '49',\n", + " '43',\n", + " '219',\n", + " '167120',\n", + " '15',\n", + " '29',\n", + " '9946',\n", + " '10093',\n", + " '9952',\n", + " '37',\n", + " '146819',\n", + " '3',\n", + " '3918']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get all of the dataids in results/pipeline2\n", + "dataids = []\n", + "binary_class_ids = {31, 10101, 3913, 3, 3917, 9957, 9946, 3918, 3903, 37,\n", + " 9971, 9952, 3902, 49, 43, 9978, 10093, 219, 9976, 14965,\n", + " 9977, 15, 29, 14952, 125920, 3904, 9910, 3021, 7592,\n", + " 146820, 146819, 14954, 167141, 167120, 167125}\n", + "for filename in os.listdir('results/nonl2unsignedunnormed'):\n", + " # get digits of filename\n", + " id = filename[6:]\n", + " # print(id)\n", + " if int(id) in binary_class_ids:\n", + " dataids.append(id)\n", + "dataids" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# set the path we want to look at\n", + "# dataids = [\"361242\", \"361251\", \"361253\", \"361259\", \"361260\"]\n", + "# dataids = [\"361242\", \"361251\", \"361253\", \"361254\", \"361256\", \"361259\", \"361260\", \"361622\"]\n", + "seed = \"1\"\n", + "metric = \"accuracy\"\n", + "pipeline = 2\n", + "clustertype = \"hierarchical\"\n", + "paths = []\n", + "for dataid in dataids:\n", + " # paths.append(oj(\"results\", f\"pipeline{pipeline}\", f\"dataid{dataid}\", f\"seed{seed}\", f\"metric{metric}\", str(clustertype)))\n", + " paths.append(oj(\"results\", \"nonl2unsignedunnormed\", f\"dataid{dataid}\", f\"seed{seed}\", f\"metric{metric}\", str(clustertype)))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "data_results = []\n", + "for path in paths:\n", + " # if path exists\n", + " if not os.path.exists(path):\n", + " continue\n", + " files = os.listdir(path)\n", + " method_results = []\n", + " for file in files:\n", + " method_result = pd.read_csv(oj(path, file))\n", + " method_result = method_result.rename(columns={\"accuracy\": file[:-4]})\n", + " method_results.append(method_result)\n", + " data_result = pd.concat(method_results, axis=1)\n", + " data_result = data_result.loc[:, ~data_result.columns.str.contains('^Unnamed')]\n", + " data_result = data_result.loc[:, ~data_result.columns.duplicated()]\n", + " data_results.append(data_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# in each data_result, keep only nclust, lmdi_lasso, lmdi_ridge, shap, lime, rawdata\n", + "for i in range(len(data_results)):\n", + " data_results[i] = data_results[i][[\"nclust\", \"lmdi_lasso\", \"shap\", \"lime\", \"rawdata\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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YtGkTt2/fZu3atfj4+ADw119/8fXXX7Ns2TLu3r3Ltm3bqFmzZmbbw4cP58KFC+zYsYPTp08jSRJdu3Yt9FImYglMEHLp6bZt6OLiUNvaorK0RP3kCQ+HDsNhxAicx32I3MzM2CEKgvCMZLWW6tP3G+XeN7/ohIVp/v/EdujQgdGjRyOXy5k+fTo//fQTjRo1on///gBMmjSJZs2aERYWhpubG9988w2dO3dm4sSJAFSuXJlTp06xb98+g/Snb9++WT5fsWIFzs7O3Lx5k5o1a/Lo0SMqVapEy5YtkclklC9fPvPaR48e4ebmRvv27VEqlZQrV46GDRsSFxfH3bt32bFjBydPnqR58+YArF27Fm9vb7Zt25bZ38IgZoAEIRcknY6Y3/8AIKZNa8r99Se2ffuAJBG9YgUP+vUj5eZNI0cpCEJpUaNGjcyPXV1dAahVq9YLj2WUhfD396dJkyZZ2mjWrJnB4rl79y6DBg3Cz88PGxubzNmdR48eAfpZnCtXrlClShU+/PBDDhw4kPnc/v37k5ycjJ+fH6NGjWLr1q1o0vdS+vv7Y2JikiV2R0dHqlSpgr+/v8Hiz46YARKEXEg4epS0hw+RW1sT27AhcisrPObMwbpdO0KmTSf17j3uD3wT5w8+wPHdd5CZiP+1BMHYVEoFN7/oZLR7F4RSqcz8OCPxX3aPZSxBFbbu3btTvnx5li9fjoeHBzqdjpo1a5KWlgZA/fr1uX//Pnv37uXQoUMMGDCA9u3b8+eff+Lt7c3t27c5dOgQBw8e5P3338fX15ft27cXSew5Eb+lBSEXotNnf2z69kF6ZqnL+vXXUdWtS+iMGcQfPETE0qUk/PMPHvO/wjT9HZIgCMYhk8kKtAxVklSrVo2zZ89meezMmTMGaTsqKorbt2+zfPlyWrVqBcCJEydeuM7GxoaBAwcycOBA+vXrR+fOnYmOjsbBwQGVSkX37t3p3r07H3zwAVWrVuXmzZtUq1YNjUbD2bNnM5fAMu5XvXp1g8Sfk7LxkyEIBZBy6xZJZ86AQoHt4MFw+XKWr5s4OOD57bfE7dhB6OwvSb56lcDefXD59BPsBw0SafkFQSh0H374IS1atGDRokX07NmT/fv3G2z/j729PY6Ojvzyyy+4u7vz6NEjJk+enOWaJUuW4O7uTr169ZDL5WzevBk3Nzfs7OxYtWoVWq2WJk2aYGFhwZo1a1CpVHh7e+Pj40PPnj0ZNWoUy5Ytw9ramsmTJ+Pp6UnPnj0NEn9OxB4gQXiFjNkf644dULq7Z3uNTCbDtmdP/HZsx6JpU6TkZMK+mM3jd0ehDg0tynAFQSiDmjZtyvLly/nmm2+oU6cOBw4cYOrUqQZpWy6Xs2HDBi5evEjNmjUZP348CxcuzHKNtbU1CxYsoGHDhjRq1IgHDx6wZ88e5HI5dnZ2LF++nBYtWlC7dm0OHTrE9u3bcXBwAGDlypU0aNCAbt260axZMyRJYs+ePVmW/AqFVAx8//33Uvny5SUzMzOpcePG0tmzZ3O8Ni0tTZo1a5bk5+cnmZmZSbVr15b27t1boDafFxsbKwFSbGxsvvrzMmlpadK2bduktLQ0g7ddHJS2/qnDwyX/mrWkm1WqSkmXL+eqfzqtVor6Y7XkX7uOdLNKVelWo8bS0x07JZ1OV4SR519pew2fV9r7J0mlv4859S85OVm6efOmlJycbKTIDEOr1UoxMTGSVqs1diiFpiB9fNnrnJe/30afAdq4cSMTJkxgxowZXLp0iTp16tCpU6fMne3Pmzp1KsuWLeO7777j5s2bjBkzht69e3P5mWWJvLYpCDmJWb8BSa1GVacOqlwmEJPJ5TgMeRvfrVswr1ULXVwcwZ9+StD4CWhiYgo3YEEQBCFXjD4AWrJkCaNGjWLEiBFUr16dn3/+GQsLC1asWJHt9atXr+azzz6ja9eu+Pn58b///Y+uXbuyePHifLcpCNnRpaYSs2EDAA7Dh+X5+WZ+fvisX4fT/40FExPi9+0jsEcP4o8cMXCkgiAIeZNRFiO7f8ePHzd2eEXCqJug09LSuHjxIlOmTMl8TC6X0759e06fPp3tc1JTUzE3N8/ymEqlytyRnp82BSE7cTt3oo2OxsTDHesOHfLVhszEBOcPPsCqdRuCJ08mLSCAJ2P+h92AAbhOmojc0tLAUQuCILzalStXcvyap6dn0QViREYdAEVGRqLVajMTOmVwdXXl1q1b2T6nU6dOLFmyhNatW1OhQgUOHz7Mli1b0Gq1+W4zNTWV1NTUzM/j4uIAUKvVBk/FndFeYaf4NpbS0j9JkohatQoA20GD0EgSPPPzkNf+mVStgteG9UR9+x2xq1fzdNMmEk6dwnXOl6jq1zd0+AVSWl7DnJT2/kHp72NO/VOr1UiShE6nK7L8OIVBSi9vkdGXwuDn5/fSrxf2968gfdTpdEiShFqtRqHImm8pLz/zJe4Y/DfffMOoUaOoWrUqMpmMChUqMGLEiAItb82bN49Zs2a98PiBAwewsLAoSLg5OnjwYKG0W1yU9P5Z3LmD170AdKamnLW2RrdnT5av57t/NWugem8Ubps2w5MnPBk+gpjWrYnq2AGpmCVPLOmv4auU9v5B6e/j8/0zMTHBzc2NhISEzAR9JVl8fLyxQyh0+eljWloaycnJHDt2LDOjdIakpKRct2PU37hOTk4oFArCwsKyPJ5R2yQ7zs7ObNu2jZSUFKKiovDw8GDy5MmZo9n8tDllyhQmTJiQ+XlcXBze3t507NgRGxubgnTxBWq1moMHD9KhQ4fCP+JnBKWlf8E7dpIE2PfrR+dnauAYqn/a4cOJXLCQ+G3bcDh6FLegIFznzcWsalUDRF8wpeU1zElp7x+U/j7m1L+UlBQeP36MlZXVC1slShJJkoiPj8fa2rrU5hErSB9TUlJQqVS0bt36hdc5YwUnN4w6ADI1NaVBgwYcPnyYXr16AfqprcOHDzN27NiXPtfc3BxPT0/UajV//fUXAwYMyHebZmZmmGVTyFKpVBbaL4/CbLs4KMn9Sw0IIOnkSZDJcBo+LNt+FLR/SgcHvL6aR3yH9oRMm07avXs8HvwWzmPH4vjOyGJRSqMkv4a5Udr7B6W/j8/3T6vVIpPJkMvlyOVGP+OTbxlLQhl9KY0K0ke5XI5MJsv25zsvP+9G/85OmDCB5cuX8/vvv+Pv78///vc/EhMTGTFiBABDhw7NsqH57NmzbNmyhcDAQI4fP07nzp3R6XSZFXBz06YgvExG4kOrdq9jWq5cod7Lul07/HbuwKp9O1Crifj6ax6+PYS0hw8L9b6CIAhlndHfZg4cOJCIiAimT59OaGgodevWZd++fZmbmB89epRldJiSksLUqVMJDAzEysqKrl27snr1auzs7HLdpiDkRBMTQ2x6gT7HYXk/+p4fJo6OeH33HbHbthM2Zw7JV64Q2Ks3rpMmYjdwYKmdAhcEQTAmow+AAMaOHZvj8tSR53KmtGnThps3bxaoTUHIydONG5FSUzGvXh1Vw4ZFdl+ZTIZd715YNm5E8Gefk3T2LKEzZxF/6DDuc75EKQbvglAmDR8+nKdPn7Jt2zZjh1LqGH0JTBCKCyktjZi16wB94kNjzLwoPT0pt3IFrp9NQWZmRuKJEwR270Hsrt1FHosgCEJpJgZAgpAubu9eNBERmDg7Y9O5s9HikMnlOAwdiu+WvzCvWVNfSuOTTwiaIEppCIIgGIoYAAkC6YkPf/8dAPu33kJmamrkiMCsQgV9KY2xY0GhIG7PXu736EnCsWPGDk0QBAP7888/qVWrFiqVCmdnZ3r16kViYmLm1xctWoS7uzuOjo588MEHWRL+rV69moYNG2JtbY2bmxuDBw/OUvvyyJEjyGQydu/eTe3atTE3N6dp06Zcv369SPtY3IgBkCAASefPk3rTH5m5OXYDBxg7nEwypRLnsR/gs2EDpn5+aCIiePzeaEJmzET3zC9HQRCyIUmQlmicf+mZjnMjJCSEQYMGMXLkSPz9/fn777/p1q1bZrbkf/75h4CAAP755x9+//13Vq1axar0TPWgz4s0e/Zsrl69yrZt23jw4AHDhw9/4T6ffvopixcv5vz58zg7O9O9e/dSmy08N4rFJmhBMLaMo++2PXtiYm+f43Wy65t5/eYXyGo5gl/LogoPVa2a+G75i4ivvyb69z94unEjiadO4TH/KyyKWSkNQSg21Ekw18M49/4sGExzV+svJCQEjUZDnz59KF++PDqdjvLly2NlZQWAvb0933//PQqFgqpVq/LGG29w+PBhRo0aBcDIkSMz2/Lz8+Pbb7+lUaNGJCQkZLYBMGPGDDqk1zX8/fff8fLyYuvWrZl59MoaMQMklHlpDx+S8PffADgMG5rzhaHXUez6COvUEBQ7x4I6uYgi1JObm+M6ZQrlVq3ExN0d9ePHPHx7COGLF6MrBWn/BaGsqlOnDu3ataNWrVr079+f5cuX8/Tp08yv16hRI0vNK3d39yxLXBcvXqR79+6UK1cOa2tr2rRpA+jTyDyrWbNmmR87ODhQpUoV/P39C6lXxZ+YARLKvOjVa0CSsGzdCrOcCgSmJcKfI5Fp9UVzZTH34dgiaDetCCPVs2zaFL8d2wmbM5fYbduIWv4rCUeP4bFgPubFoJSGIBQbSgv9TIyx7p1LCoWCgwcPcurUKQ4cOMAPP/zA1KlTOXPmjL6p57Iby2SyzEzKiYmJdOrUiU6dOrF27VqcnZ159OgRnTp1KhX10AqTmAESyjRtXBxPt2wBwOFliQ/3TYbI20hWrlzxTs8ofvIbCDfOuyeFtTUeX83D6/vvUDg4kHrnDvf7DyDyl+VIWq1RYhKEYkcm0y9DGeNfHtNoyGQyWrRowaxZs7h48SKmpqa5yv1z69YtoqKi+Oqrr2jVqhVVq1bNMjv0rIwBFUBMTAx37tyhWrVqeYqzNBEDIKFMe7r5T6SkJMwqVcKyefPsL7r+F1z6A5Ch7fkTDx3boqvUGXRq2PkRpL8TMwbr9u31pTTapZfSWLJEX0rjualvQRCKr7NnzzJ37lwuXLjAo0eP2LJlC5GRkVTNxYxuuXLlMDU15bvvviMwMJAdO3Ywe/bsbK/94osvOHz4MNevX2f48OE4OTll1swsi8QASCizJI2G6LVrAP3en2wTH8Y80A9yAFpNQPJpDTIZ2k5fgdISHp+By38UWczZMXF0xOv773CfOxe5pSXJly8T2Ks3MRs2Zp4iEQSh+LKxseHYsWN07dqVypUrM336dGbPnk2XLl1e+VxnZ2dWrVrF5s2bqV69Ol999RWLFi3K9tqvvvqKcePG0aBBA0JDQ9m5cyemxSDlh7GIPUBCmRV/8CCa4BAUDg7YdO/+4gVaNfz5DqTGgVdjaDsFMiZ7bL3g9amwfwocnA5VuoKVS5HG/yyZTIZdn95YNmlM8JTPSDp3jtCZM4k/fBj3L79E6Wq82ARBeLlq1aqxb9++zM91Oh1xcXEAWY67Z1i6dGmWzwcNGsSgQYOyPJbdm5+WLVuW+dw/zxIzQEKZFb0qPfHhm28iNzN78YJ/5kDQBTCzhb6/giLrRkQavwfudSAlFvZNKYKIX03p6Um5VStxnTIZmakpicePE9ijB3F79hg7NEEQhGJFDICEMin5yhWSr15FplRiP3jQixcE/AMnluo/7vEt2Jd/8RqFCXT/BmRyuP4n3DtUqDHnlkwux2HYMH0pjRo10MXGEjThY4ImfIz2maO1giAIZZkYAAllUkbZC5tu3TBxcsr6xYQI2DoakKDBCKjRK+eGPOpBkzH6j3dNgLSkQok3P8wqVsRnw3qcPvggvZTGHgK79yDh+HFjhyYIQhFq27YtkiRhZ2dn7FCKFTEAEsocdXAw8QcOAtkkPtTpYNsYSAgD52rQed6rG3ztM7DxhKcP4diCQog4/2RKJc7/NxafDesx9fXVl9IY9R4hM2eiSyo+gzVBEISiJgZAQpkTvWYtaLVYNG36YuLAMz/ol7JMzKH/SlCqXt2gmTV0Xaj/+NR3EHbD8EEXkKpWLXy3/IX9kCEAPN2wkcBevUm6dNnIkQmCIBiHGAAVsdsxt0mVUo0dRpmlTUjk6ebNQDazP0GX4NAs/ced54FLHhKEVX0DqnYDncbouYFyIlepcPv8M8qtXIGJmxvqR494+PbbhC/5GklkjBUEoYwRA6AitO3eNobsH8KOpB3GDqXMit2yBV18PKY+Plil18sBICUO/hypT25YrYd+709edVkAplbw5BxcXGm4oA3Mslkz/HZsx7ZnT9DpiPrlF+4PGEjK7dvGDk0QBKHIiAFQESpvUx5JkriqvsquwF3GDqfMkbRaolevBsB+6BBk8vQff0mC3R9DzH2w9daf+spjGnsAbD3h9fTaYIdmQXyogSI3PIWNDR7zv8Lz229Q2NuTeusWD/r1J+rXX0UpDUEQygQxACpC9VzqMbrWaADmXZjHg9gHxg2ojEn45x/Ujx8jt7XF7tn071fXw7+bQKaAvr+Byj7/N2k8Sn8yLLX45AZ6GZuOHfWlNF57DUmtJnzRYoJGjMQkOtrYoQmCIBQqMQAqYiOqj8DXxJdkTTITj00kTSv2XhSVzMSHA/ojt0iv1Bx5D3Z/ov/4tSlQrknBbiJX/Jcb6MYWuHuwYO0VARMnJ7x+/AH3OXOQW1qScvkyXr/+hi4lxdihCUKZ0bZtWz766CMA/Pz8+Omnn4wbUBkgBkBFTCFX0N+iP3ZmdvhH+/P1xa+NHVKZkHzjBkkXLoCJCfZvvaV/UJMKf44AdSL4tIKWEwxzM/c60PR9/ce7JkBaomHaLUQymQy7vn3w3b4dE1dXTKOiiPnlF2OHJQhl0tmzZxk2bJixwyj1xADICGzkNsxsOhOANf5rOPbkmHEDKgOiMxIfdu6M0s1N/+DBGRB6DSwcoc9y/eyNobSdot9PFPsIjs43XLuFzNTLE6cp+qW7mJWrSL1718gRCULZ4+zsjEXGLLVQaMQAyEhae7bm7WpvAzD1xFTCk8KNHFHppQ4LJ27PXgAcMt5V3d4LZ9OnmHv9BDbuhr2pmRV0Ta/IfOp7CP3XsO0XIqt2r5NQvRpoNITMnIVUDI/0C0Jp9vwSmEwmY9myZXTr1g0LCwuqVavG6dOnuXfvHm3btsXS0pLmzZsTEBCQpZ3t27dTv359zM3N8fPzY9asWWg0mqLuTrElBkBGNL7BeKo5VCMmNYYpx6eg1YnTN4UhZt060GhQNWiAqlZNiAuGbelLVE3fh8qdCufGVTrrj9RL2vTcQCXn9Q3v0ROZSkXyxYvEbtli7HAEIV8kSSJJnWSUf9lVYy+I2bNnM3ToUK5cuULVqlUZPHgwo0ePZsqUKVy4cAFJkhg7dmzm9cePH2fo0KGMGzeOmzdvsmzZMlatWsWcOXMMGldJZmLsAMoyU4UpC1ovYMCuAZwLPcdv13/jvdrvGTusUkWXnMzTDRuA9MSHOi38NQqSo8GtNrSfWbgBdJmvL6wadAEurNCfEisBNPZ2OHzwAVGLFhG2cBFWr72GiaOjscMShDxJ1iTTZF0BDzbk09nBZ7FQGm4Za8SIEQwYMACASZMm0axZM6ZNm0anTvo3cOPGjWPEiP/yl82aNYvJkydn7iXy8/Nj9uzZTJw4kRkzZhgsrpJMzAAZmY+tD583+RyAH6/8yJXwK8YNqJSJ3b4DbWwsSi8vrNu1g+OL4eEJUFpCv5VgYla4Adh4QPv0XzaHv4C4kMK9nwHZvTUYs2rV0MXGEr6geNU4E4Sypnbt2pkfu7q6AlCrVq0sj6WkpBAXFwfA1atX+eKLL7Cyssr8N2rUKEJCQkgSdQABMQNULPSo0IPTIafZHbibiccmsrn7ZmzNbI0dVokn6XRE//EHAA5D3kb25BwcSS9u+sZicKpYNIE0HKnPNRR0EfZNggF/FM19C0hmYoL7rJk8GPgmsdt3YNurF5bNmhk7LEHINZWJirODzxrt3oakVCozP5alJ2rN7jFd+p69hIQEZs2aRZ8+fV5oy9zc3KCxlVRiAFQMyGQypjWdxrWIazyOf8ys07NY3GZx5g+0kD+JJ06QFhiI3NIS2y6vw5rOIOmg9kCoO6joAsnIDbSsDdzcDrf36fcHlQCq2rWxHzyYmLVrCZ05C98d25GbFfKsmSAYiEwmM+gyVElSv359bt++TcWKRfRGrwQSS2DFhKXSkoWtF2IiN+Hgw4NsvrPZ2CGVeBmJD+369UPx92SIewIOfvrZn6LmVguafaD/eM8nkJpQ9DHkk/NH4zBxdibt4UOiloncQIJQEkyfPp0//viDWbNmcePGDfz9/dmwYQNTp041dmjFhhgAFTFdQkKOlcJrONXgo/ofAbDg/ALuxogcLPmVcucOiadOgVyOfR0l3NoFciX0WwFm1sYJqu1ksC0HsY//W4orARTW1rh+rt+nFrV8OamBgUaOSBCEV+nUqRO7du3iwIEDNGrUiKZNm/L1119Tvnx5Y4dWbIglsCKUfPUqTyZMwL52HejWLdtrhlQfwumQ05wMOsmnRz9lfbf1Bl9LLgsy9v5Yt2qM6cX0RITtZ+rrdBXAuQfR/HRTTvUmiVRys8vbk00t9bNP6/rDmZ+g9gB91ugSwLpTRyzbtCbx6DFCZ8yk3B+/iyVaQTCgI0eOZH4cGBiYuZkZeOFIvY+PzwuPtW3b9oXHOnXqlHlKTHiRmAEqQim3b6MJCsZp/35S/s0+MZ5cJmdOizk4qZwIiA1gwXlx+iavNFFRxO3YCYCD01XQpkKljv+Vp8gnSZKYvfs2t2Ll/PBPPmdBKneEGr3TcwONKzG5gWQyGW7TpiMzNyfp/Hlit203dkiCIAgFIgZARciuf3+sOnZEptMROnES2oTs94E4qhyZ23IuMmT8eedP9j/YX8SRlmwxGzYgpaVh7m2DyiQArNz02Z7lBftxv/Yklluh8QAc8A8nKS2fGVU7fwVmNhB8Gc7/WqCYipKplyfOY/X7mMLnz0cTE2PkiARBEPJPDICKkEwmw3nGdNT2dmiePCF0xswcs4U282jGyJojAZh1ahZBCUFFGWqJpUtLI2Z9euJDr4f6ZZo+v4ClU4Hb3nD+UebHSWla9t8IzV9D1m7P5AaaDbEl57V1GDYMs8qV0T59SvjCRcYORxAEId/EAKiIKWxsCBk0CBQK4nbvJnbrthyv/aDeB9R2rk28Op5Jxyah1qmLLtASKm7XbrSRkZhY6LDxToZWE8CvTYHbTUjVsP1KMABVbPWb2LdcKsDApcFI8GoEafGwd2KB4ysqMqUSt1kzQSYjdssWEs+dM3ZIgiAI+SIGQEaQUr48Dh/o96OEzp5NauD9bK9TypXMbzUfK6UVVyOu8tOVn7K9TtCTJInoVasAsK+UgKxcY31VdgPYcSWYpDQtfk4WDPDTD4BO3oskLC4lfw3K5frcQHIT/Qm1W7sNEmdRsKhXD7uB+pT8oTNnoUtLM3JEgiAIeScGQEZiP3IkFk2aICUnE/Txxzn+EfGy9mJGc/1yya///srZEONkNS0Jks6eJfXOHWQKHfbVFND3V1AoX/3EXFh/Tr/8NbChF07m0KCcHToJtl8pwCyQaw1oll68cM+nkBpvgEiLhsuECSicnEgLDCTq15Kzj0kQBCGDGAAZiUyhwGPBAhR2dqT6+xO+KOf9FJ19OtO3Ul8kJKYcn0J0SnQRRlpyRP+4BAA732QU/b8Fe8Pku7geFMu/QbGYKuT0qusBkPnfAi2DAbSZBHblIS4I/ilBuYFsbHCdMhmAqJ+XkfbggXEDEgRByCMxADIipasL7vPmAhDzx2ri//knx2snNZ6En60fEckRTD0xFZ2UfTLFsir1xiUSzulTC9j36Qg1ehms7YzZn0413XCwNAWgS01XTBVyboXGczM47mVPfzlTC3hDP3Dj7E/6k2ElhE3Xrli2aIGUlkbIrFk5bugXBEEojow+APrhhx/w8fHB3NycJk2acO4VmyqXLl1KlSpVUKlUeHt7M378eFJS/tuHER8fz0cffUT58uVRqVQ0b96c8+fPF3Y38s36tdewHzoEgJDPPkcdFp7tdSoTFQvbLMRUbsrxoOOsubmmKMMs3nQ6Yubql5KsfEwwe/tbgzWd+Mzm50GNvTMft1UpaVfNBYCtl58U7CaV2kPNvvo6ZTvHgTafx+uLmEwmw23GdGRmZiSdPkPcrl3GDkkQBCHXjDoA2rhxIxMmTGDGjBlcunSJOnXq0KlTJ8LDsx8ErFu3jsmTJzNjxgz8/f357bff2LhxI5999lnmNe+++y4HDx5k9erV/Pvvv3Ts2JH27dsTFFR8jxq7fPIJZtWqoY2JIXjiRCRt9snxKttXZmIj/Ymhry99zY2oG0UZZrGlPbSYp1eiAHAYN1U/q2Igu64Fk5CqwcfRgmZ+jlm+1qe+FwDbrgSj0RZwRq7TPDC3hZCrcH55wdoqQqblyuH0v/8BEDbvK7RPnxo3IEEQ8kQmk7Ft2zZjh2EURh0ALVmyhFGjRjFixAiqV6/Ozz//jIWFBStWrMj2+lOnTtGiRQsGDx6Mj48PHTt2ZNCgQZmzRsnJyfz1118sWLCA1q1bU7FiRWbOnEnFihX56afie4JKbmqK5+LFyCwsSDp7lqjlOW8qHVBlAO3KtUOj0zDx6EQS1YlFGGkxFHSJmN++RdLKMfN2xqLzAIM2v/7cYwDebFzuhdIPbSo7Y2+hJCI+lZMBUQW7kbUrtJ+l//jvLyG2gLNKRchx5AhMK1ZAGx1N+OIlxg5HEIRCNHPmTOrWrWvsMAzCaLXA0tLSuHjxIlOm/HdMWS6X0759e06fPp3tc5o3b86aNWs4d+4cjRs3JjAwkD179jBkiH4JSaPRoNVqMTc3z/I8lUrFiRMncowlNTWV1NTUzM8zarCo1WrUasPm3klITiFJwwvtyr29cJ4yhfBp04j47jtMG9RHlcMP2dRGU7kReYNH8Y+YfWo2s5vPNmiMBZHRL0N/37KVGo9i4whi7uhrpdm+939oNIZbPvIPiefK46coFTJ61XbN8vOgVqtRKuGNWm6sOfuYvy48prmvXcFuWHswiivrkD85h273J2j7ry54J/Ihz6+hTIbztGkEDRvO082bsez2Bqr69QsxwoIp0p9RIyntfcypf2q1GkmS0Ol06HIoOl0SSJJEWlpaZl8KW16+Xxl7/QoaV0Y7+emjTqdDkiTUajUKhSLL1/LyM2+0AVBkZCRarRZXV9csj7u6unLr1q1snzN48GAiIyNp2bIlkiSh0WgYM2ZM5hKYtbU1zZo1Y/bs2VSrVg1XV1fWr1/P6dOnqVixYo6xzJs3j1mzZr3w+IEDB7CwMNxyysVIGVvuy2ngJMfi4MEXL1Ca4Fa3DjZXrvLgww95OG4cOlX2hVC7ybvxG7+x+8FuzMPNqWdasCKfhnYwu/4ZkiRR/+EybK6FoUm2R2NlyUm5HGnPHoPd4s9AOSCnhp2Ws8cOZ/laRv+cEwFM2Hc9mBbmjzFXvNBMnlhb9aItF5Hf2cv5dbMJtWtQsAYLIK+voWujRtieP0/gxEk8/PD/wKR411ou9J/RYqC09/H5/pmYmODm5kZCQgJpJSw/Vbdu3ahWrRomJiZs2rSJ6tWr07lzZ9auXcvDhw+xs7Ojc+fOzJo1CysrKyRJolKlSixevJiePXsC0KpVKyIiIjL/hp4+fZpevXpx//59LCwsCAgI4P/+7/+4dOkSPj4+zJunP3manJyc+cZ/xowZ7N69m+DgYFxcXOjfvz8TJ05EqVSybt06vvjiC4DMgccPP/zA4MGD+eGHH3KM9WXi4/Oe/iMtLY3k5GSOHTv2wpvepKSkXLdTvH9DPefIkSPMnTuXH3/8kSZNmnDv3j3GjRvH7NmzmTZtGgCrV69m5MiReHp6olAoqF+/PoMGDeLixYs5tjtlyhQmTJiQ+XlcXBze3t507NgRGxsbg8Vvcy+KP+5e5EwELBz2GvZWLw5udG3a8KhffwgKou6p07guWphj1W3lv0p++vcn9qTtYcjrQyhnU85gseaXWq3m4MGDdOjQAaXSMDl4siO7tgHF5VM8uOMMgMuw4VTt0cNg7Senafl8wVFAw0fdG9Gign7/z/P9kySJbSEnuR+VBF516FrPs8D3lv6JgFNf0zhyE5q+H4GZdYHbzIv8vobaFi141KMHZmFhNA0Px+Hddwsxyvwrqp9RYyrtfcypfykpKTx+/BgrKyvMzc2RJAkpOdkoMcpUqhx/d2fHxMSEDRs2MGbMGI4fP05iYiLHjx/nu+++w9fXl8DAQMaOHcucOXP44YcfAGjdujXnzp1jyJAhxMTEcOfOHVQqFcHBwVStWpWLFy/SqFEj3Nzc0Ol0DB8+HFdXV06fPk1sbGzm3z2VSpX5t87JyYlVq1bh4eHBv//+y+jRo3FycuLTTz9l2LBhBAQEsH//fg4cOACAra0tKpUKCwuLl8b6PEmSiI+Px9raOk/fJ9C/ziqVitatW7+w4pMxkMvV9zxPdzUgJycnFAoFYWFhWR4PCwvDzc0t2+dMmzaNIUOG8G76L9ZatWqRmJjIe++9x+eff45cLqdChQocPXqUxMRE4uLicHd3Z+DAgfj5+eUYi5mZGWZmZi88rlQqDfrLo21VVyo6W3IvIpFt18J5r002s1L29nh9vYQHg98i4cABrLa3xL5//2zbG113NBciLnA+9DxTTk1hTdc1mCpMDRZvQRj6e5dF5D3YN4mkSFNSopXITE1xfGswJga837aroSSkaijnYEHryq7I5Vn/B322f33qe7H44B12XAtlYGOfgt/8tUngvxVZzAOUx+dDl/kFbzMf8voaKp2ccJ08meCJk4j5eRn23bph6u396icaSaH+jBYTpb2Pz/dPq9Uik8mQy+XI5XJ0SUncadjIKLFVuXQReR5XECpVqsTChQvR6XTExcXRoEED5OlFnP38/Pjyyy8ZM2ZM5p7W1157jWXLliGXyzlx4gT16tXDzc2NY8eOUb16dY4ePUqbNm2Qy+UcOnSIW7dusX//fjw89HnM5s6dS5cuXTK/X0DmZELGPe/evcuGDRuYNGkSlpaWWFtbY2JiktlGhvHjx2d53vOxPi9j2Svj9coLuVyOTCbL9uc7Lz/vRtsEbWpqSoMGDTh8+L+lBZ1Ox+HDh2nWrFm2z0lKSnrhG5UxDfd8DhJLS0vc3d2JiYlh//79mVOExiSTyRjeXJ+c748zj9Dqss+boqpdG5ePxgEQNmcuqffuZXudQq5gXst52JnZ4R/tz9JLSwsl7mJFkwp/jgB1ItGP9d9L2549MHFwMOhtNpzXb34e2Mj7hcHP83qlz/qcCogiJNYA7zaVKuj2tf7js8sgKOfZy+LGpnt3LJo1RUpNJXTWFyI3kCDkQYMGWZe8Dx06RLt27fD09MTa2pohQ4YQFRWVuczTpk0bbt68SUREBEePHqVt27a0bduWI0eOoFarOXXqFG3btgXA398fb2/vLAOX7P7Wbty4kRYtWuDm5oaVlRVTp07l0aNHL1z3vFfFWhwZdQlswoQJDBs2jIYNG9K4cWOWLl1KYmIiI0aMAGDo0KF4enpmrlN2796dJUuWUK9evcwlsGnTptG9e/fMgdD+/fuRJIkqVapw7949Pv30U6pWrZrZprH1rOPOvN03eBKTzCH/MDrVyH62y2HkSBJPnSbx1CmCJnyMz+ZNyLOZpXK1dGV2i9n839//x+qbq2nq3pTWXq0LuxvGc3AGhF4jTeNI/D39YMNh6FCD3uJ2aDwXH8ZgIpfRv6HXK6/3drCgsa8D5+5Hs+1yMP9rW6HgQVR4HWoNgH836XMDjToCiuK/Yi2TyXCbPp37PXuReOIE8Xv3YtO1q7HDEsoomUpFlUvGeQMhy2H/5stYWlpmfvzo0SN69OjB//73P+bMmYODgwMnTpzgnXfeIS0tDQsLC2rVqoWDgwNHjx7l6NGjzJkzBzc3N+bPn8/58+dRq9U0b9481/c/ffo0b731FrNmzaJTp07Y2tqyYcMGFi9e/NLnPXjwgG7dur001uLIqMfgBw4cyKJFi5g+fTp169blypUr7Nu3L3Nj9KNHjwgJCcm8furUqXz88cdMnTqV6tWr884779CpUyeWLVuWeU1sbCwffPABVatWZejQobRs2ZL9+/cXm2lgc6WC5q76d8UrTmRfBBVAJpfjMf8rFA4OpN65Q/j8BTle29a7LW9VewuAqSemEp6UfR6lEu/2Pn22ZCAmtQPodFi2aIFZpUoGvU1G5uf21VxxsTZ/xdV6fdJngbZefmK4WY9Oc8HcDkL/hbM/G6bNImDm64vj6PcACJ07D20e1uQFwZBkMhlyCwuj/MvrvpbnXblyBZ1Ox+LFi2natCmVK1cmODj4hf61atWK7du3c+PGDVq2bEnt2rVJTU1l2bJlNGzYMHNQVa1aNR4/fpzlb+qZM2eytHfq1CnKly/P559/TsOGDalUqRIPHz7Mco2pqSna53LVXbx48ZWxFkdGzwQ9duxYHj58SGpqKmfPnqVJkyaZXzty5Air0qt7g36T2IwZM7h37x7Jyck8evSIH374ATs7u8xrBgwYQEBAAKmpqYSEhPD9999ja2tbhD16tZauOkzkMs7ej+Z6UGyO15k4O+Mx/ysAYtatI/7QoRyvndBgAlUdqhKTGsNnxz9Dq8s+mWKJFRcM2/QJ97R13uXp3/p3dQ7Dhxn0NilqLVsu6XPwDGqS+03lXWq5Y2oi505YAjcKUhrjWVbO0EF/4oJ/5sDTV09DFxeOo0Zh6uuLNjKS8CUiN5Ag5JWvry9qtZrvvvuOwMBAVq9ezc8/v/hGqG3btqxfv566detiZWWFXC6ndevWrF27ljZt2mRe1759eypXrsywYcO4evUqx48f5/PPP8/SVqVKlXj06BEbNmwgICCAb7/9lq1bt2a5xsfHh/v373PlyhUiIyNJTU2lYsWKuYq1uDH6AKgssjPT15ICWHnywUuvtWrVCof05bvgz6eifmb0/ixThSkLWi9AZaLibOhZfrv+m0FjNiqdFra8B8nR4FabpzHV0CUmYlqhApYtWxr0VnuvhxCXosHTTkWrik65fp6tSkmHavrXdOtlA2YdrzcEyjUHdZK+YnwJ2VMjNzXFbeZMAJ5u3ETS5ZJT40wQioNatWqxePFi5s+fT82aNVm7dm3mdpBntWnTBq1Wm7nXB/SDoucfk8vlbN26leTkZBo3bsy7777LnDlzsrTVo0cPxo8fz9ixY6lbty6nTp3KsikaoG/fvnTu3JnXXnsNZ2dn1q9fT506dViyZMkrYy12JOEFsbGxEiDFxsYavO20tDRp27Zt0vnACKn8pF1Spc/2SGFxyS99ji41VQrs01e6WaWq9OCttyWdRpPjtVvvbpVqrqop1fm9jnQ57LKBo3+1jP6lpaUZrtEjCyRpho0kfeku6UJvSXdfbyfdrFJVit6w0XD3SNf/p1NS+Um7pG8P3cn26y/r36GboVL5SbukBrMPSmqN1nBBhflL0ixH/ffgxjbDtZsDQ76GQZOnSDerVJUCuveQdIb8mSiAQvkZLWZKex9z6l9ycrJ08+ZNKTn55b9TizutVivFxMRIWq0Bf48UMwXp48te57z8/RYzQEZSx8uWBuXtSdPqWHvm5UsbMlNTPJcsRm5hQdKFC0S+ZGqxZ4WedPXtilbSMvHYRGJTc15iKxEenYEj6e8k3lhM/JWHqIOCUNjZYdvTcHl/AO6Fx3PuQTQKuYz+DfN+fLt1ZWccLU2JTEjl+L1IwwXmUhVafqT/eM9ESCk5r6nLxE9R2NmReucO0X/8YexwBEEQMokBkBGNbOELwNqzD0lRv3zPjmn58rjNnAFA5A8/knThQrbXyWQypjWdhre1NyGJIcw6PavkHkVOjoG/3gVJC7UHQt1BRP/+OwB2bw5Ebp67Dcq5lVH36/WqLrjZ5r1tpUJO9zr6I6ZbLxm4+G6rT8DBDxJC4XDxKX3yKib29rhM1Bfwjfjue9KeFN+ixIIglC1iAGREnWq44mFrTmRCGjuvvnrHvG2PHvpZD52OoE8n5lh528rUigWtF2AiM+Hgw4NsvrPZwJEXAUmCHf8HsY/1f/jfWEzyv/+SfOkSKJXYDx5s0Ntl2fzcOP/J+/rU158G238jlPgUA9ZhUpr/lxvo/K/wJPsBcHFk27sXFo0aIaWkEDpb5AYSBKF4EAMgIzJRyBna3AeAFScf5OoPg+u06SjLl0MTEkLItGk5PqemU03G1dcnU1xwfgF3Y+4aLO4icWEF+O8EuRL6rQAza6JX6Wd/bLt2QeniYtDb7b8RSkySGndbc9pUzn/btTxtqeBsSapGx97roQaMEPBrC7XfBCR9biBtySh0KZPJcJs1E5RKEo8eI37/AWOHJAiCIAZAxvZmI29USgX+IXGcCYx+5fUKK0s8Fy8BpZL4g4d4umFDjtcOrTGUFh4tSNWmMvHYRJI1xqmJk2dhN2G/vsAt7WeCRz3UoaHE7d8PgMMwwx59h/9y/wxs5I3iFZmfX0Ymk9Gnvj55osGXwQA6zQGVPYRdhzM/Gr79QmLm54fTKH0Jm7A5c9DmowCiIOSWmGUs3Qz1+ooBkJHZWZjSt4F+2WTFyZwTIz5LVbMGLulF7MLmfUXK7TvZXieXyfmy5Zc4mjty7+k9Fp5faJigC1Nakr7UhSYFKnaApu8DELN2LWg0WDRqhHn16ga9ZWBEAmcCo5HLYEA+Nj8/r2dd/T6gM/ejCHpq4EGnpRN0/FL/8T/zIObhy68vRhxHj9bPXkZEELH0G2OHI5RCGQlvi3P5BaHgMl7fgiY4Lv659cuA4c19WXPmEYf8w3gUlUQ5x1enDXcYNpTE06dIPHacoI8n4Lt5M/JsUq87qZyY22ouow+OZvOdzTTzaEaH8h0KoxuGsX8KRNwCK1fo9ROkFzSM2aTfx2ToxIcAG9PrfrWt4oKHXd7T1z/Py96Cpn4OnAmMZtvlID54LZuitwVR9y24sh4enoA9n8DgTVDArLNFQW5mhvuMGTwa+Q4x69Zh26snqlq1jB2WUIooFArs7OwID9dnw7cwQEZmY9DpdKSlpZGSkpLnQqElRX76KEkSSUlJhIeHY2dnl1kCK7/EAKgYqOhiRdsqzhy5HcGqUw+Y3v3VMxwyuRyPefMI7NWLtHsBhH01H/dZM7O9trlHc0bWHMmK6yuYcWoGNRxr4GHlke21RnVjK1xcBcigzy/6TMjA023b0MXGoixXDqtnEnsZQqpGy+aLGZufc5/5+VX61PPiTGA0Wy8H8X7bCob9JSyT6TdE/9wC7h7Qf99q9jFc+4XIsnlzbHp0J27HTkKmz8B38yZkJuLXkGA4bm76+ooZg6CSSJIkkpOTUalUJXIAlxsF6aOdnV3m61wQ4jdPMTGyhS9Hbkew6cJjxneohLX5q6f2TBwd8Zw/n0fvvMvTjRv1f1w6dcz22rH1xnIh9ALXIq8x6dgkVnZeiYm8GL38MQ9hh37TNi3H6zf8ApJOR8zv+vwxDkOGICvgiP95B2+GEZ2YhquNGa9VcTZYu11quTFt+3XuhSdwPSiOWl4GLsfiXBlaToCjX8G+yfriqSo7w96jkLhOmkTC0WOk+vsTvWYNjsOHGzskoRSRyWS4u7vj4uKCWl0yDgo8T61Wc+zYMVq3bl1s6lgaWn77qFQqCzzzk6EY/QUs21pVcqKiixX3whPYfOEJI1v65up5ls2b4/juO0Qt/5WQadNQ1ayB0tPzheuUciXzW8+n/87+XIm4wo9XfuTD+h8auhv5o1XDX+9Aaix4NYLXPsv8UsLRo6Q9fIjc2hq7Pr0NfuvMzc8NvTFRGG6q2dpcSccabuy8Gsxfl54YfgAE+oHi9T8h6h4c/gK6lYyaWyaOjrh88jGh06YT8e132HTsiNKjGM5ICiWaQqEw2B/KoqZQKNBoNJibm5faAVBx6GPpXFwsgWQyWWZixFWnHqDV5X6Xu/OHH2Jeuza6uDiCPp2IpNFke52XtRczmumTKf7676+cDTlb8MAN4Z+58OQ8mNlC399A8d//DNHpsz92/fsjT69qbCgPoxI5eS8KmQwGNCr45ufnZVSI33k1GLVWZ/D29bmBluo/vrACHp8z/D0KiV3fvqjq10dKSiJ0zlxjhyMIQhkkBkDFSO96nthZKHkUncRh/7BcP0+mVOK5eBFyKyuSL10i4ocfcry2s29n+lbqi4TElONTiE559dH7QhV4BE6kJ/jr8Q3Yl8/8UsqtWySdOQMKBQ5vv2XwW29I3/zcupIzXvav3nieV60qOeFkZUpUYhrH70YYvH0AfFvpN0WXtNxAcrl+z5qJCQmHDxN/6JCxQxIEoYwRA6BiRGWqYHD6RtzcHonPYOrtrU82B0T9vIzEsznPBkxqPAk/Wz8ikiOYdjLnZIqFLiFCX+UdCeoPgxpZl7gyEh9ad+xg8CWSNI2OzRf0AyBDbn5+lskzpTG2FEZOoAwdZoPKAcJvwunvC+8+BmZWqRKOI0cCEPrlHLQJiUaOSBCEskQMgIqZIc3KYyKXcSYwmhvBeSt6afvGG9j27QOSRPCnn6KJicn2OpWJioVtFmIqN+XYk2Os8V9jiNDzRqeDbf+DhDBwrgqdv8ryZU1EBHG7dwPgWAiJDw/7hxGZkIaztRntqhk2q/Sz+qYnRTxwM4w4Q5bGeJalI3RKX0Y6Mh+i8zZ4Nian/41B6e2NJjSUyO++NXY4giCUIWIAVMy426roWssdgJUnH+T5+W6ff46pry+a8HBCPvs8x9mdyvaV+bTRpwAsubiEm1E38x1zvpz5Ee4dBBNzfakL06xLUDHr1yOp1ajq1EFVt67Bb78uffNz/wZeKA24+fl5NTxsqORiRZpGx95/QwrtPtR5E3xagSYZdn+sr6VWAshVKtymTwcgevUakm/cMHJEgiCUFWIAVAxlnADbcSWYiPjUPD1XbmGB55LFyJRKEv75h5g1a3O8dmCVgbQr1w6NTsPEYxNJVBfREkTwZTg0U/9xpzngWiPLl3UpKcSs15f4cBgx3OC3fxydxIl7kQC82ahwlr8yyGQyeqcXSC3UZTCZTL8hWmEKAYfh+l+Fdy8Ds2rVEpuuXUGnI3TGTCSt1tghCYJQBogBUDFU19uO+uXsSNPqWHs276UOzKtVw+VT/exO+IIFpNy6le11MpmMWc1n4WbpxsO4h8w9WwSncVLj4c+RoFND1W7Q8J0XLonduRNtTAxKDw+s27c3eAgbzz9GkvSblHOTdbugetX1RCaDs/ejeRxdiCn6nSpCq0/0H++bDMnZL4EWR65TJiO3tibl+nVi1q03djiCIJQBYgBUTGXMAq0585BUTd7fEdsPeRurtm2R1GqCxk9Al0NtHFszW75q9RVymZwdATvYGbCzQHG/0u6PIToQbL2h5/cvlHCQJImYP/RH3+3fftvgWYLVWh2bCnnz8/M87FQ083MEYPuVQpwFAmj5EThVhsSI/2bZSgATZ2dcPtbXt4tYuhR1WO5PQQqCIOSHGAAVU51ruOFha05kQho7r+Z974hMJsN93lxMXFxIu3+f0Dlzcry2gWsDxtQZA8CXZ77kYVwhFdi8sh6ubQSZAvr+qq9q/pzEk6dIvXsPuYUFdv37GTyEv2+FEx6fiqOlKe2ruRq8/Zz0Ts8JtOVyUOGeujMx+y830MVV8OhM4d3LwOwGDEBVpw66xETCRG4gQRAKmRgAFVMmCjlDmvkAsOLE/Xz90TSxt8djwQKQyYj9awux6aeqsvNerfdo6NqQJE0Snx79lDRtWn5Dz17kPf3sD0DbKVCuabaXRf+uP/pu27cvCmtrw8YAbEjf/NyvoRemJkX349+lljvmSjmBEYlce5K303155tMC6r2t/3jnONAY+LUsJDK5HLcvZoFCQfyBA8T/84+xQxKKKW1sHLISWuZCKD7EAKgYG9TYG3OlnJshcZy9n7+EhZZNm+A4ZjQAoTNmkvbkSbbXKeQK5rWah62ZLf7R/iy9tDS/Yb9Ikwp/jgB1ov6kUqsJ2V6WGhBA4vHjIJPhMORtw90/XdDTZI7c0SckLOzNz8+zMjOhUw198b4tl7J/DQyqw2ywcIKIW3Cq5BwvN69SBcf0je+hs2fnuHQrlE0pt+8QPGkS99u2xWfhIpLOnzd2SEIJJgZAxZidhWlmHpkVJ/Kf28X5gw9Q1auHLiGBoI8/RsrhnZObpRuzm88GYPXN1Rx7cizf98zi0EwIvaZP1tfnF5BnX58no+yFVbvXMS1n+AFKxubn5hUc8XUybFmN3MhYBtt5LaRwSmM8y8Lhv9xAxxZCVEDh3s+AnN5/H6WHB5rgECK+zzmruVA2SJJE4pmzPHrvPe737Ens9h2g0aCMjSX4nXcJ/3ppjr/TBOFlxAComBvRwgeAg/5hPIrK37thmYkJnosW6k/ZXL1GxLff5Xjta+VeY3DVwQBMPTGV8KTwfN0z0539+pw/AL1+ApvsMzprYmKI3b4dKJzEhxqtjk3ni3bz8/NaVnTCycqM6MQ0jt4upNIYz6o9AHzbgCYFdk8oObmBLCxwm5GeG+j333M8xSiUbpJWS9y+fTzoP4BHw4eTeOw4yOVYd+mM56qVxDZqCJJE1LJlPHjrbdIePTJ2yEIJIwZAxVxFF2vaVHZGkuD30w/y3Y7S0xP32frZnahffyXx1Kkcr53QcAJV7KsQkxrDZ8c/Q6vLZ16WuBB9tmeAJv+DKp1zvPTpxo1IqamYV6+OqmHD/N3vJY7eiSA0LgV7CyUdaxTd5udnmSjk9KybXhrjchEsg8lk0O1rUJjpa679u7nw72kgVm3aYN2pE2i1hMyYIXIDlSG65GSi160joHMXgj4aT8r168jMzLAfPIgK+/bi9fXXqBo0IKxfP9wWLUJuY0PKtWvc79Wbp9u2Ga+0j1DiiAFQCZBxJH7j+cfEF6Ccgk3nTtgNGACSRNCkSWiiorK9zkxhxsI2C1GZqDgbepYV11fk/WY6LWwZBUlR4FYbOszK8VIpLY2YtesAcBg+DNlzR+MNYX3G5ucGXpiZZL8EVxT6pCdFPOQfTmxyEUzbO1aANvqcUOybAklGLn6bB66ffYbc0pKUq9d4ummTscMRCpkmJoaI73/g3uvtCPtiNurHj1HY2eH0wQdU/Odv3KZPf2Fp3KpTR/y2bcWiYUN0SUmETJ5C8Cefoo2PN1IvhJJEDIBKgNaVnKjoYkVCqoY/LxZs5sB1ymRMK1ZAGxFJ8JQpSLrs96L42voypfEUAH648gNXwq/k6T7yU9/Ag+OgtIR+K/XHs3MQt3cvmogITJydsemc8yxRfoXEJvP3Lf1S3ptGWv7KUN3dhiqu1qRpdOwpzNIYz2o+Tl9vLSkSDk4vmnsagNLVBefx4wEIX7wEdXgBl2OFYintyRNCZ3/JvddeJ/L77/VJUL28cJ06lYp/H8b5/8Zi4uCQ4/OVHh6U+30Vzh+NA4WCuN27ud+rN0mXLhdhL4SSSAyASgCZTJa5F2jVqQdodfmf4pWrVHguXoLMzIzEY8eJTk86mJ1eFXvRxbcLWknLpGOTiEuLy9U9HBLuID82X//JG4v0GYpzIEkSUelH3+3feguZqWnuO5NLm84/QSdBE18HKjhbGbz9vMhaGqMIlsEATEz/yw10eTU8OFk09zUA+0FvYl6rFrqEBMK/+urVTxBKjOTrNwiaMIGAjp2IWbsWKSUF8+rV8VyymAr79uLw9lvILXKXqV2mUOA0Zgw+a9eg9PJCHRTEw7ffJuKHH5A0mkLuiVBSiQFQCdGnnhe2KiUPo5IyZzPyy7xKZVwnTwL076yTr2dfgFImkzG96XS8rLwITgxm5qmZr15fT35Kgwc/IZO0UGsA1Bn00suTzp8n9aY/MnNz7AYOyFd/Xkark9h4Xr/8ZazNz8/LKI1x/kFM4ZbGeFb5ZlA/fXP5ro/0qQlKAJlCgfusmSCXE7dnLwnHjxs7JKEAJEki4fgJHo4YwYN+/Yjbsxd0OixbtqTcqpX4/PUnNl275jsDvKpuXXy3bcWmR3fQ6Yj87nseDh2GOqiQM7ALJZIYAJUQKlMFg5vo/4AX5Eh8Brs338S6Q3tQqwn6eALahOwLoVqZWrGg9QJMZCYcfHiQP+/++eJFmlR4fA5OfYdifX8s1FFI9r7QbckLpS6el3H03bZnT0zsX8wMXVDH7kYQHJuCrUpJ55puBm8/P9xszWlRwQmArZeL8Bdzh1lg6QyRd+DkN0V33wIyr14dh6FDAQid9QW65GQjRyTklaRWE7tzJ/d79+HxqFEknT4DCgU2Pbrju20r5X5djmXTpgbZ/6ewssJzwQI8Fi5AbmlJ8qVLBPbqTdyePQboiVCaiAFQCTK0WXkUchmnA6O4GZy75aicyGQy3GfPxsTNDfXDR4SlnxDLTi3nWnxY/0MA5p+bz73gc3BrNxyYBr91gnne8FsHODAVechltDITtL1+AbOXZ3JOe/iQhL//BsBh2NAC9Scn68/qZ3/61vfCXGm8zc/Py8gJtLWwS2M8S2UPndOXkY4t0mfnLiGc/28sJu7uqJ88IfLHn4wdjpBLusREon//nXudOhH86URSb91CZmGBw7ChVDywH88FCzCvWrVQ7m3bXT+4UtWpgy4+nqAJHxM85bMc3+wJZY8YAJUg7rYqutZyB2DlyYLPAins7PBctBDkcmK3byd2x44XL9LpINyfYckSLRR2pGpT+XT3EFI2vqXPMPz4DGhTwcIRqryB9vXpHKk6B8mj3ivvH716DUgSlq1bYebnV+D+PC8sLoXD6cuFgxp7G7z9guhc0w2VUsH9yEQuP35adDeu2RcqvK5/zXaPLzm5gSwtcZs2FYColStJuXPHyBEJL6OJjCT866Xcfb0dYfO+QhMcgsLREeePPqLS34dxnTIFpadnocdh6u1N+TWrcXr/f/rfc1u3cr9vH5L//bfQ7y0Uf2IAVMKMTN8Mvf1KMJEJBd/HYdGwIU7vvw9A6MxZpN31h8CjcHQhrOkL833gx6bId4/ny8DrOGq03DM1ZaGnr35PSc8f4f8uwacBMGgdumYfkmDu/sr7auPieLplCwAOhZD4EGDzhcdodRINy9tTydXwdcUKwtLMJHNJbuulIlwGk8ngjcVgYg73j8HVDUV37wKyfv11rNq3A42G0BkzczzBKBhP6v37hEyfwb3X2xG1bBm62FhMy5fHbdYsKv59GKcxo1HY2RVpTDKlEucPP6T876v0s4gPH/Fg0GAif1ku8kuVcWIAVMLUK2dPvXJ2pGl1rD1jgMynTx/j1NIJlZ8DuqQkgoZ1R1rZA/75Eu4dgtRYUFqATyucWkxgbm39YGmTUsPBOt2h3lv6XDN5XLt/uvlPpKQkzCpVwrJ584L34zk6ncQGI2d+fpX/SmMEk6Ypwj/mDn7QRr8Jnv2fQWL2+aCKI7fPP0duYUHy5cs83ZzNfjTBKJKvXOHJ//0fgV3f4OmmTUhpaajq1MHzu2/x27Mb+4EDkJvlnAqjKFg0aoTftq1Yd+4MGg0RS5bwaOQ7qMPCjBpXdtI0OuJKRg3jEk0MgEqgkS30iRFXn3lIqiYP72C0agi6BGd+hs3DYUl1WFoT2bb38KxxE7mpjpRoJeG3PfRLJV0WwHtHYPJjGL4L2k2jeaOxjKw5EoAZp2YQnBCc5/gljYbotWsA/d6fwkh8eOJeJE9ikrExN+GN2q+ekTKGFhWdcLE242mSmn9uF3GOm+b/By7VITm6ZOUGcnfHeZx+P1r44sVoIiONHFHZJel0xP/9Dw/eepsHbw4i/uAhkCSsXnuN8mvXUH7Demw6dECmKD577xS2tnh+vQT3OXOQWViQdPYs93v0JO7gQWOHlkmnk3h39SVmXFLgHyISOhYmMQAqgTrXdMPd1pzIhFR2XX1JMr2kaLhzAA5/Aau6wVflYPlrsG8S3NgKcUEgU4BHPZSvv4fHR/oK7NHXIMF1BDQZDR71QJH1SOrYemOp7VSb+LR4Jh+fjEaXtzwb8QcP6vcEODhg0717nvufGxmZn/sUs83Pz1LIZfTK2AxdlMtgAArlf7mBrqyB+yXneLn9W29hXr06urg4wr6ab+xwyhxdWhpP//qLwO49ePL++yRfvAhKJbZ9++C3ayfeP/2IRYMGhfLGxhBkMhl2ffvg+9efmNeogTY2lqD/+5CQGTOLxQnDDecfczowGp0kY8e1IkqWWkaJAVAJpFTIGdrMB4AVJ+/rTxFJEkTehctrYMf/wfeNYYEvrOsPxxfrszKrk8DcFip1hNenwrBdMOWxfpany3ysR07DfrA+b0/w5CloIrIv2KmUK5nfej5WSisuh1/mp6t5O5UTtWoVAPZvvlko0+IR8akcvKmf1n6zmG1+fl7GMtjft8KJTSriitblmkBD/Wweu8aXnNxAJia4zZqlzw20axcJJ0tOYseSTBsfT9SvvxLQrj0hn08lLSAAuZUVju++Q8VDB/GYMwezijknPS1uzHx98Vm/Dsd33wH09Qjv9+1Hir+/0WKKiE/lq73/3f+Qf7iobVaIxACohBpUz4kWytu0ClvD09/6wgI/+L4hbP8ALv0Bkbf1FzpWhLpvQfdv4f2zMPEBvLUZWn8Kvq3A1DJLuy4TJ2JWuTLaqCiCJ03OcaOpl7UXM5rNAGD5teWcCzmXq7iTLl8m5eo1ZEpl5mDL0P68+ASNTqJeOTuqutkUyj0MpZq7DVXdrEnT6tj1b96XEwus3QywdIGou3Di66K/fz6patXE/q23gPTcQCkpRo6o9FKHhhK2YCH32r5G+KLF+rI1Li64fPopFf/5G5dPPkHpapwCwwUlMzXF5ZNPKLfiN0ycnUkLDOTBgIFErVpllE32c3bfJC5FQ1VXKxQyiQdRSQREJBR5HGWF0QdAP/zwAz4+Ppibm9OkSRPOnXv5H9KlS5dSpUoVVCoV3t7ejB8/npRnfvlptVqmTZuGr68vKpWKChUqMHv27JI/io4PhZvbYd9nsLwddt9UYK1iFpOVG7B/cli/l0NhBuWaQYuP4M31+pNZ/3cRev0IDYaBS1WQv/wll5ub47lkMTJzcxJPnSJ65cocr+3s25k+lfogITHl+BRiUmJe2Y2MxIc23bph4uSUp29Bbug3PxevzM+v0iezNIYRstWq7KBLem6g44v1s4glhPO4DzFxcUH96BGRy5YZO5xSJ/XuXYKnfMa9Dh2JXrECXWIiphUr4D53LhUPHcTxnZEorIvX6cr8smzeHN8d27F6/XUktZrwr+bz+L3ROc6CF4YTdyPZdiUYmQzm9KpBZVv936wDN4vfJu3SwqgDoI0bNzJhwgRmzJjBpUuXqFOnDp06dSI8h6KH69atY/LkycyYMQN/f39+++03Nm7cyGeffZZ5zfz58/npp5/4/vvv8ff3Z/78+SxYsIDvvvuuqLpVcDothP4L55bDX6NgaW1YXAU2DYUzP0DQBdCp0Vi4sEfbmC81bxHafxdMeQIj9+kz/lbtCpb5G2CYVayI62f6QqjhXy8l+dq1HK+d1GgSvra+hCeHM+3ktJcONNVBQcQfOADoq74XhtOBUTyMSsLazIRuxXTz8/N61vVELoOLD2N4GGWEJG01+kDF9qBN0y+FlZA3CworK1ynfg5A1K+/kRoQYOSISj5Jkkg6f57Ho8cQ2L0HsVu3glqNRcOGeP38E347dmDXp3eh1OwzNhN7e7x++B63mTP0tRJPnCCwZy8Sjh4t9HunqLVM234dgKFNy1Pby5ZaDukDoBtiAFRYjDoAWrJkCaNGjWLEiBFUr16dn3/+GQsLC1asWJHt9adOnaJFixYMHjwYHx8fOnbsyKBBg7LMGp06dYqePXvyxhtv4OPjQ79+/ejYseMrZ5aMKiUO7h2Gf+bBH73gq/Lwc0vY8wn8uwmePgRk4FoLGr4DvX+BcVcx+fQOG3zn8KvmDZbfd9QXvTQQu/79M4+LBn38CdqE7KdhLZQWLGy9EFO5KUefHGX97fU5thm9Zi3odFg0a4p5lSoGi/VZ69I3P/es54GFaf7qCRU1VxtzWlQ0QmmMDJm5gVTw4DiyayUoN1CHDli1bQtqNSEzZojcQPkkabXE7T/Ag4Fv8nDIUP0ffZkM644d8dm4gfJrVmPdti2yV8wgl3QymQz7N9/E968/MatSBW10NI9HjyH0yznoUgtvj9yPRwK4H5mIi7UZH3fS/26saa8fAF15/JSwOLHEWxiM9hciLS2NixcvMmXKlMzH5HI57du35/Tp09k+p3nz5qxZs4Zz587RuHFjAgMD2bNnD0OGDMlyzS+//MKdO3eoXLkyV69e5cSJEyxZsiTHWFJTU0l95oc7Lk5fZkKtVqNWG3ZjqjotDYvUcHRX1qMNuYT8yTkIv4mMrO+6JVMrJM+GSF6N9f88G7xYWkKjYVhTb47diWDj+ceMbeuLlZnhXlKnaVNJvnYN9ePHBE+fgetX87I92eFn7cf4+uOZf2E+31z5hlEWo174vukSE3m6eTMAtm+/bfDvK0BUYhoHboQC0L++R6HcI6NNQ7fds7Ybx+9GsuXSE95v7VP0J2isPJG3/hTF31+gODwd04pfFsr3rzA4TplM4tkzJF+4SPSff2LTu/dLry+s17A4yW0fdSkpxO/YwdPf/0D9SP/mQWZqinXPntgNG4pp+fK5aqeoFfZrKC9fHs+1a4haupTYNWuJWbOGxLNncV0w3+AbvQMjEvnpiL4szdSuVVAp9P2yNYXanjZcC4pj//VgBjUq3gc68qqwXsO8tGe0AVBkZCRarRbX5zbPubq6cuvWrWyfM3jwYCIjI2nZsiWSJKHRaBgzZkyWJbDJkycTFxdH1apVUSgUaLVa5syZw1vpGyazM2/ePGbNmvXC4wcOHMDCwiKfPXyRZ/Qpagatp4MmFm5m/VqiqTPRlpUy/8WpvEAmhwTgVhLcyv6Ysk4CV5WCsGQNX645SGt3wy5fmPfsifeyZSTs2cM9SwviGjbM9joryYpqymr4q/3ZlLQJlwMumMj++/GyO3kSl4QE0pydORoXB4VQmPDvYBlqrYJylhIPLp/gwWWD3yLTQQPnDdFpwVSu4FF0Mj9u2ouvEbZWyCQf2ph7Y5v8mJpP1nLwYMnZ32H/2us479lDyFfzOa1Wo7WyeuVzDP0aFkc59VGelITd6dPYnTyFSaJ+2VWrUvG0WTOeNm+G1toabtzQ/yvGCv01rFULixEjcNu8Ge7e5WH/AUR0e4PYpk3znPw1O5IE39+Uo9bKqWanQ/fwEnueyW9bThHDNRSsP3YD24jSWb7D0K9hUlJSrq8tGWsE6Y4cOcLcuXP58ccfadKkCffu3WPcuHHMnj2badOmAbBp0ybWrl3LunXrqFGjBleuXOGjjz7Cw8ODYTmUXJgyZQoTJkzI/DwuLg5vb286duyIjY3hThHJ7ikx2fgzOpkCya0OlGuSPrvTCFNrN9yA/NQrj3N+zIyd/pyPs2LuiJbI5YadPYg2URD93fe479pN4yFDMPX1zfa6lqkt6bOrD5GpkZhWN6Wzb2dAP73+8Lvv0QCeo9+jerduBo0P9HsXvv7mJJDE6PY16NrQy+D3AP27i4MHD9KhQweUSqVB2z6d9i9br4QQpvLhg67VDdp2bsnqeiL93gXvmFO4tByKvHY/o8SRV1KHDjwOCCDt9m3qXr2K65w5OV5bmK9hcZFTH9VBQTxdvZq4LVuQkvXLKibu7tgNG4pN797IDfiGrzAV6WvYtSuaoUMJnzqVpJMncd22Hb+nsbh8MQuFvX2Bmt56OZh7Z65jrpTzw8iWeNvrv/8Z/RvTrRm7fjzHvXgFrV5vj7V5ifqT/VKF9RpmrODkhtG+m05OTigUCsKeS0MeFhaGm1v2w4Bp06YxZMgQ3n33XQBq1apFYmIi7733Hp9//jlyuZxPP/2UyZMn8+abb2Ze8/DhQ+bNm5fjAMjMzAyzbPLRKJVKw/7P5dcazdBd7L0aSuduvQzWdv9G5Vhy6B6PopM5HhBD++qGPZLqMmYMKefOk3T2LGGTJ+OzYQPybDZBOimd6F2xNyturGDH/R10r6xPchh/9CiaJ0+Q29ri0KcP8kL4hXU6IIoHUUlYmiroVd8bpbJwf7QN/rMB9G1Qjq1XQthzPYyZPWtiZmKEBI4+TdE2H4/i5GJMD0xEVqEF2JWA03RKJR5fzNJnJN6xE/s+fbFs2uQVTzH8a1jcZPQx5eZNon5bQdy+fZBe/8qsWjUcR47EpnMnZCX0+1BUr6HS3Y1yy38hZs0awhcuIvHIER737YfH/K/yXconJjGNr/bri/qOa1cZPxfbF66p4m6Hn5MlgZGJnLofQ7faHgXqR3Fk6NcwL20ZbUebqakpDRo04PDhw5mP6XQ6Dh8+TLNmzbJ9TlJSEvLnNuEp0tOsZ5w+yukaXXHYHGlmheTdFJ3csCcoLExNMo98rzBAlfjnyRQKPBbMR2FnR+pNfyIWL87x2l4VegFwNvQsQQn6Db3Rq34HwH7AgEJ7h5mR+blHXU8sDbgPqig1q+CIq40Zsclq/rlVxKUxnqFr9QnRFhWQpcbBltH69bkSQFWnDvaD9G98QmfORJdWxospSRJJp07zaOQ73O/Tl7jdu0GrxbJ5M7x/+xXfLX9h271biR38FDWZXI7D0KH4bN6EaYUKaCIieDTyHcIWLkTKx8/avL3+RCemUcXVmndbZT+rDtChhv4N7UFxHN7gjLqlf8KECSxfvpzff/8df39//ve//5GYmMiIESMAGDp0aJZN0t27d+enn35iw4YN3L9/n4MHDzJt2jS6d++eORDq3r07c+bMYffu3Tx48ICtW7eyZMkSer9iY2RJN7RZeRRyGacCovAPyf0UYG4pXV1xnzcX0OfyiT9yJNvrvKy88DPxQ0Ji692tJN+4QdKFC2Bigv3bOe/DKoiYxDT2Xddvfh5cQnL/ZOfZ0hhGyQmUGYiSiz5jkEwt4dEpOJ7zAYLixnn8eBTOTqQ9eEDUL8uNHY5RaJ8+JX73bsp9+y3Bo0eTeOoUyOXYvPEGvlv+otyKFVi1aFFsS1UUd+ZVq+L752bs3hwIQPRvK3jw5iBS7+f+zee5+9FsuvAEgLl9aqJU5PynuGP6jP7ft8JRa4vBG/lSxKgDoIEDB7Jo0SKmT59O3bp1uXLlCvv27cvcGP3o0SNCQv6rhTJ16lQ+/vhjpk6dSvXq1XnnnXfo1KkTy55Jgvbdd9/Rr18/3n//fapVq8Ynn3zC6NGjmT17dpH3ryh52KnoUlO/dLiyEGaBAKxfew379BN3IVM+Qx2W/SxFI9NGAGy9tzWz7IVN586Fli32r0tPSNPqqOlpQy2vF6eRS5I+9fR7l/65HU5MovFmMJLMXNF2WqD/5Mg8eHzeaLHkhcLaGrf0QxFRy5aRGlg4/y8UF9rYWBJPnybq11958tF47rXvwJ2mzQibPAXz4BBkKnPs336bCgf247l4EebVjbO3rLSRq1S4z5yJ1/ffobC1JeXmTe736cvTP/98ZdLdNI2Oz7bqNzQPauxNg/IOL72+rrc9TlZmxKdoOBsYbbA+CMVgE/TYsWMZO3Zstl878twsg4mJCTNmzGDGjBk5tmdtbc3SpUtZunSpAaMsGUa29GXXtRC2XQlmYueqOFkZvs6Wy6efkHThAqn+/gRPmkS53359odpzNWU17MzsUIeFEbdnDzLAIYf9VwUlSVLm8ldJyfz8MlXcrKnubsPNkDh2XQtmSHrNN2OQag2AwMNwYwtseRdGHwfz4l1aBMC6c2cst2wl8fhxQmfNotyqlaVitkMbH0/KjZuk3LhByo3rJF+/kXl0/XlKb29Cqlal0fRpmDs7F3GkZYd1+/aY16pF8KTJJJ05Q8jUaSQcP4H7F7NQ2Gb/Zmz58UDuhSfgaGnKpM5VX3kPhVxG+2oubDj/mAM3Q2lZyfAZ9Muq0p3VqoypX86eut52pGl0rDub/S/GgpKbmuK5eDEylYqkM2eIWv7rC9eYyEx4w+cNOl3SIdPqUDVogKpWzUKJ5/yDGAIiElEpFfSoUzo2CGaWxjBGUsRnyWTQ7Wuw9YaYB7B3knHjySWZTIbbjOnIzM1JOnuW2O3bjR1SnmkTEkg8d46oFSsJ+vgTAjp15k6jxjwaPpzwhQuJ27M3c/Cj9PLCunNnnD+eQLmVK6h89gzl9+wmun07FHZ2xu1IGaB0daXcit9w+eRjMDEhfv9+Anv1Jun8i7OmD6MS+fawvtzM1G7VsLPI3X7Qjs/sAyrxZZ2KEaPPAAmGNbKlLx+uv8zqMw8Z3cavUE4Smfn54jZ1KiGff07Ed99h0aQxFvXqZbmml1dX4i7pNz+bvNnL4DFk2JCx+bmOB9bmpWMzZ4+6Hszd48/lR0+5H5mIr5Plq59UWFR20OcXWPUGXF0HldpDzb7GiyeXTL28cHr/fSKWLCF8/gKs2rTBpIBHlguLLjGRFH9/kq9f18/wXL9O2oMH2ZYkUXp4YF6zJuY1amBeswbm1atn2y9dMUtcWNrJ5HIc330XiyZNCfrkY9QPH/Fw6DAcx4zG+f33kSmVSJLEtO03SNXoaFHRkV51PXPdfvMKTliYKgiJTeF6UFyJX+ovLsQAqJTpUtMNNxtzQuNS2H0thD71Cycfjm2f3iSeOkXc7t0Ef/wJvtu2ongmZ5LT0etIKRBmBye9o3m3EGJ4mpTGrn/1e8QGNSn5y18ZXKzNaVXJmaN3Ith6OYgJHSobN6DyzaHVx3BsIewcD16NSsTReMcRw4nbuZPUu3cJX7QIj5fkBioquqQkUm7dIuX6dVJu3CD5+g3SAgOzHeyYuLujqllDP9ipURPzmjWK7SBO0FPVqonfli2EzplL7JYtRP30M4mnTuG5aBH7oxUcuxOBqULO7J4187Qsa65U0KayM3uvh3LgZqgYABmIWAIrZZQKOUOb69PX/3bifqFNl8pkMtxmzkDp5YU6OJiQ6TP+u5dOx9M1awDY20DOXwFb0UmGP72w9XIQaRod1dxtqFPKfiFkLINtvfykeEx5t5kEng0hNbbEHI2XKZW4pWd4j/1rS7ZLEoVJl5xM0uXLRK9eQ/DkKQR2787tho14OPgtwubOI3b7DtICAkCSMHF1xapdO5w+/D+8f1lGpZMnqPTP33h99x1OY8Zg1aqlGPyUEHJLSzzmzsHz6yXIra1JuXqNwF69OfStfkb8/dcq4Of86kzlz+tQXRyHNzQxA1QKDWpUjm8P3+VGcBznH8TQ2PflpwzyS2FtjefiRTx4623i9+3jaYvmWPXqhcWdu6jv30dmacm5hiZEJjzhXOg5mro3Ndi9s25+9i4Vm1yf1bG6G5amCh5HJ3PhYQyNfArnNcw1hRL6LoefW/13NL7Np8aNKRcs6tfDbsAAnm7aRMjMWfht3WKQEgbP06WkkHr7dpZlrNSAgMykg88ycXbOsoylqlEDE7FRudSx6dIFVe3aBE2cRPLFi4w5+Qf1KzRmwKRv8tXe61VdUMhl3AqN51FUEuUcS0bW7uJMzACVQvaWpvROP0694kThHgNW1amD87gPAQibM5e0wEDsT5zQx9GvH+2q6ctebLmzxaD3vfToKXfCEjBXyumZh7X0kkJlqqBLLXfAyDmBnuXgB10X6T8uQUfjXT6egMLRkbSAAKJWrChwe7q0NJL//ZeY9esJnjqVwF69ud2gIQ8GvknY7C+J3bKF1Dt3QKtF4eSEVZs2OL3/Pl4//kjFo0epdPwY3j/9iPPYD7Bu21YMfkoxpacnMV9+zepqndDK5DQOOEdQ//4kXc57oUI7C1OapL+ZPXAz1NChlkliBqiUGtnCh/XnHnHgZiiPo5Pwdii8dwuO77xD0ukzJJ46RfD7H2AZFARyOfZDhtBHFcvG2xs59OgQMSkx2JsbZho/Y/anW20PbFWlY/Pz8/rU8+TPi0/YdS2YGd2rY640QmmM59V5E+4dhOt/lZij8QpbW1wnTyb400+J/PEnVB065Pq5urQ0Uu/c/W/Pzo3rpN65CxrNi/dxcNBvTK5RA1X6DI+Jq2upm50Uck+j1fHZjlv4V+mATYvm9Nv7C+onT3j49hCcPngfp9GjX0gj8jIdqrtyKiCKAzfDeLeVXyFGXjaIAVApVcnVmlaVnDh+N5LfTz1garfCS4Amk8vxmP8VgT17oQnSz1ZYtnsdUy9PquNJNYdq+Ef7szNgJ0NrDC3w/WKT1ey6FgyUjtw/OWnq54i7rTkhsSn8fSucrukzQkYlk8EbS/SzPzEPYO9E6P2zsaN6JZtubxC7dQuJp04T8eUc6NnjhWuktDRS793LsoyVcucOZHOiSmFn9+Iylru7GOwIWaw8+QD/kDjsLJSMGtMTu9FdCJ31BXG7dhH57Xf6DdILFqD0yF0Kjw7VXZm18yYXHkQTnZiGg6VhyyqVNWIJrBQb2VJfX2bj+cckpL74jtWQTJyd8Zj/VebndukZowH6VdZXFN9yd4tBNvRuvxJEilpHZVcr6pezK3B7xZW8uJTGeF7G0XiZHK6uh3//NHZEr6TPDTQDmakpyadPY3PpEqm3b/P0zz8JmTWL+/0HcLtBQ+736Uvo9Bk83biRlBs3QK1GbmuLZfPmOI4ahec331Dx8CEqnT5FuV+X4zL+I2w6dEDp4SEGP0IWQU+TWXJQX+x0SpeqOFqZ6fdNLlqIx4L5yC0sSL5wkcBevYnbuzdXbXrZW1Dd3QadBIf9xWboghIDoFKsTSVn/JwtiU/V8OeFx4V+P6tWrXBduICwPn1QPZMXqItvF1QmKgJiA7gacbVA95AkKTPJ46DG5Ur9H50+6QOgI7fDiUpINXI0zyjfDFp9ov941wR4WjiJNw3JtHx5nP43BgC3TZt53K8/IVOn8XT9BlL+/RdJrUZubY1Fs6Y4vvsOnku/psKhg1Q+c1qf6O7jCdh06ojS07PU/9wVa6kJKNb3p/ajlVAIp0sNZcb2GySrtTTysad/A+8sX7Pt0QPfbVsxr1MbXVwcQeMnEPz55+gSE1/ZbkdRHNVgxACoFJPLZYxooZ8FWnXqATpd4R+ntu7cmdgmjbM+ZmpNx/IdAfjzTsFmC64+ieVWaDxmJnJ61yt9m5+fV8nVmpqeNmh0Eruuhbz6CUWpzcRnjsa/B9rCnWU0BId33sG0ShUA5FZWWDRpgsPIkXguWUyFA/upfO4s5VeuxOWTT7Dp3BlTLy8x2CluTn+PPPAffKP+Qf73F8aOJlv7b4RyyD8ME7mMOb1rIZe/+DNkWq4cPmvW4DhmNMhkxP61hft9+pL87/WXtp1xHP7Y3QiS04p/OoriTAyASrm+9T2xMTfhQVQS/9zOvnhpUchYBjvw8ADxafH5bmd9+uzPG7Xcc51GvqTLKJBq9NIYz8s4Gm9qBY9Ow4mvjR3RK8lNTfH643cCJ03E9+QJyv++CteJn2LTtSum5Ur/jGKJFx8GJ7/N/FRx5nu4+LsRA3pRQqqGmTtuAPBeaz8qu1rneK1MqcTlo48o9/sqTNzcSHv4kAeDBhGzYgXosp/dqu5ug6edihS1jhP3IgulD2WFGACVchamJplZklcUUpX43KjjXAc/Wz+SNcnsvZ+79e7nxaeo2XFVv/n5zVK8+fl5Pep6oJDLuPr4KQERCcYOJ6sSeDRebmGBxsEBmVz8+itxjn4F6kR0Hg245dZL/9juCRB4xJhRZfH1wTuExKbg7aDi/16vlKvnWDZujN+2rVh37AgaDVFfL8XztxXZLonJZLLMWaADN8Rx+IIQvwHKgKHNfFDIZZy8F8Wt0DijxCCTyehbSV9DKr/LYNuvBJOs1lLB2ZJGPmUnK66TlRmt0ytAby1Om6Ez1HlTXx9M0uqPxqcY52dMKOUi72bO9ujazeC2W290NfqATgMbh0LEHSMHCNeDYlmZ/kZzds+aqExzf8RdYWeH5zdLcf9yNjKVOZb37hExd26212bsAzp8KxxtEWxtKK3EAKgM8LRT0bmmGwArTzwwWhzdK3RHKVfiH+3PzaibeX7+hvNlZ/Pz83qn13TbejmoSPZy5UnG0Xjbcv8djRcEQzs0Uz/IrtwFqVxzkMnQdvsWvJvo96Gt6w+JUUYLT6uT+Hzrv+gk6FbbnbZVXPLchkwmw65fPzx+/hlJJiN+x05id+564brGPg7YqpREJ6Zx8WGMIcIvk8QAqIwYmb4ZeuuVIKOdJrI3t6dduXaA/kh8Xvz7JJbrQXGYKuT0LaQCr8VZx+quWJuZEPQ0mfMPoo0dzotK4NF4oQR5dBZu7dL/fLWf+d/jJuYwcK2+OG/MA9j4FmiM8/tt7dmHXH0Si7WZCdMLmHdNVb8+Ue30vytDZ84k7XHWU7wmCjntquoHWGIZLP/EAKiMqF/OjjredqRpdJnHyI2hb2X9MtjuwN0kqZNy/bx16ZmfO9d0w74MJv8yVyroUks/i1escgI9qwQejRdKAEmCg9P0H9cbAi5Vs37dyhkGbwYzG/1m/B3/p39OEQqLS2HhvtsATOxcBRcb8wK3Gf36a5jXr4cuMZGgTz5Bei4hZ+ZxeP+w4lEwuQQSA6AyQiaTMbKFDwB/nHlImsY4+TMauzXGy8qLBHUCBx8ezNVzElM17Lii/6NfmjM/v0pGfbc9/4aQoi6mx1/bTAKvRiXqaLxQzN3aDY/PgokK2k7J/hqXqjDgd5Ap4NpGOLaoSEP8YudN4lM11PG2Y3CT8oZpVKHA9auvkNvYkHL1GhHf/5Dly60qOWNqIudhVBJ3worZ4YgSQgyAypCutdxxtTEjIj6V3f8GGyUGuUxOn0p9APjr7l+5es7Oq8EkpmnxdbKkqZ+Rq6IbURNfBzztVMSnajhUXLPAKkygz3IwtU4/Gr/E2BEJJZlWDYdm6D9uPhZsXlIOpsLr8Eb6wOefL/X16orAP7fD2f1vCAq5jLm9a6LIJudPfind3XH/Qp/rKOqXX0g8cybza5ZmJrSqqD8ccVAUR80XMQAqQ5QKOUOb+QDw24n7Rps27VWxFwqZgsvhlwl4GvDK6zMKnw5q7F3mNj8/S18aQ18zqFieBsvg4PvfH6IjX8Hjc8aNRyi5Lv0BUffAwhGaf/jq6xuOhKYf6D/e+r9CT8uQnKZl2jZ94sIRzX2o4WFr8HvYdO6EXf/+IEkET5yEJua/Tc+Zx+FFVuh8EQOgMmZw43KYmci5HhTHBSOdHnC2cKa1V2vg1ZuhrwfFcvVJLEqFrExufn5exjLYkTsRRBan0hjPqz3wv6Pxf4mj8UI+pCboB9AAbSaDuU3untdxNlTuAtpU2DAIYh4WWojf/n2XJzHJeNiaM75D5UK7j+uUyZj6+aEJDyfk86mZb17bVXNFJoNrT2IJiU0utPuXVmIAVMbYW5rSp76+hMSKE8ZLjJiRE2hHwA7StGk5Xpdx9L1jDTccrcyKJLbirKKLFbW9bNHqJHZeNc4yZq48ezT+6UPY86mxIxJKmtPfQ2I42PtCg+G5f55cAX1/BbdakBgB6wZCSqzBw7sdGs/yY4EAzOxRA0szE4PfI4PcwgLPJYuRKZUk/P03MevXA+BsbUb9cvqcaIfELFCeiQFQGZRRH2z/jVAeR+f+JJYhtfBsgYuFC09Tn/L3o7+zvSYpTcP2y/o/8oPL8Obn52UUSN1a3EpjPO/Zo/HXNoij8ULuJYT/V/Ki/QwwyePJTzMrGLQRrNwgwh82jzDohnxdes4fjU6iQ3VXOtZwM1jbOTGvWhWXT/WnLMPnLyDljj7xY0exDJZvYgBUBlV2taZVJSd0Evxx+oFRYjCRm9C7Ym8g583Qu66FEJ+qobyjBc38HIsyvGKtex0PTOQyrj2J5V54/uuqFQlxNF7IjyP6khd4NoDqvfLXhq0nDN6gPz0WcBj2TTZYeJsuPObCwxgsTBXM6lHDYO2+iv2QIVi2aY2Umkrwxx+jS0nJ3Ad0OiCK2GT1K1oQniUGQGVURmLEDecfk5BqnKPKvSv1RoaMMyFneBz/+IWvZ2x+frNRuWyrKZdVjlZmtKnsDBTjnEDPEkfjhbyIvAsXV+k/7vCFfjk1vzzq6Qv2IoPzy+HssoKHl5DKvL23AJjQoTIedqoCt5lbMpkMj7lzUTg5kXr3HuELFuDnbEVFFys0OokjRix4XRKJAVAZ1aayM35OlsSnaPjr4hOjxOBp5Ukzj2YAbL27NcvXboXGcfnRU0zkMvo1EJufn9cnfUP49ivBxa80xvPE0XghLw7Pyix5gU/LgrdXrft/2aP3TYY7+wvU3Nzd/sQmq6nubsPw5j4FDi+vTBwd8Ziv3xwes2498YcOZS6DHRTLYHkiBkBllFwuY0R6YsRVpx4Y7Y9oxmbobfe2odH9NzOw4Zx+RqhDdVecrcXm5+e1q+aCtbm+NMbZ+8WwNMbzxNF4ITcenwP/nS+WvCioFuP0WaQlHfw5EkKv56uZU/ci2XI5CJkM5vaphYnCOH9CrVq0wOGdkQCEfD6VDs76WbIjtyNI1RTTJKnFkBgAlWF96nthY27C/chEjtwxztTpa96v4WDuQERyBMefHAf0uTW2XNLPSpXlzM8vY65U8EYtfVK4jO9VsVd7INTsJ47GC9mTJDiQUfLi7RdLXhRExqlEn1aQlqA/GRaft+SBKWotn6fn/BnStDx1ve0MF18+uIwbh3nNmmhjY7H/Zi6ulkoSUjWcCSwBb4iKCTEAKsMszUwyBxgrjFQlXqlQ0qNCD+C/zdB7/g0hLkWDl72KlumZToUX9U4/Dbb3eijJaSXgXZ9MBt3E0XghB7d2w+Mz6SUvPjN8+yamMHA1OFaEuCewfhCk5f4U7E9HArgfmYiLtRmfdKpi+PjySGZqiueihcgsLEg+d45xEacBURw1L8QAqIwb2twHhVzGiXuR3A41zomijNIYx4OOE5YY9szmZ2+x+fklGvk44GWvIiFVw8HiWhrjeea2+k2p4mi88CytBg7N1H/c7IOXl7woCJU9DN6k/2/wJdg2BnSvrosYEJHAT0f0Weund6+OjbmycOLLI1MfH9ym6WfN6h7aTNXohxzyDyv++wKLCTEAKuM87VR0Ts9hsfKkcRIj+tr6Ut+lPjpJx29XN3LhYQwKuYz+Db2NEk9JIZfLMmeBSswyGEC5ptA6ffZn1/hCzdQrlBCX/4Cou/qSFy3GFe69HCvAwLUgV8LN7fD37JdeLkkS07ZdJ02ro01l58yl5+LCtldPbN54A5lOy+SL64iPesq1IMMnfiyNxABIYGRLHwC2XA4iykjlFfpV7gfA9oBtgI52VV1wtTE3SiwlScYA6PjdSCLii3FpjOe1nph+ND5OHI0v67KUvJiU+5IXBeHTAnp8p//4xBK4vDbHS7deDuJUQBRmJnJm96xZ7OoRymQy3GbOQOnpiWtiFGOvbuHgjRBjh1UiiAGQQP1y9tTxsiVNo8tcfipqHcp3wEppRZIuAoVlAIOaiM3PueHnbEVdbzu0Ookdxbk0xvOePRr/+AwcX2zsiARjOf0DJISll7wYUXT3rTvovySdO8fBgxMvXPI0KY05u/0B+LBdJco5WhRdfHmgsLbGc/EiJLmc155c5um27cYOqUTI0wAoPPzlJ4U0Gg3nzonjrSWNTCZjZEt9YsQ/Tj8kTfPqNXFDMzcxp4bNawDYOF+kdSXnIo+hpMqo7bb1cglaBoOsR+OPzhdH48uihHA4lV7yot30vJe8KKjXPtdnmtapYePbEBWQ5ctf7b1FVGIalVysGNXKr2hjyyNV3brYvP8BAH2Pryfgsr+RIyr+8jQAcnd3zzIIqlWrFo8f/5fBNyoqimbNmhkuOqHIdKnpjquNGeHxqez51zjTp+HBdQHQqa4Tm2acSvUlUbfa+tIY14PiuBNWzEtjPE8cjS/bjs7XH0v3qA81ehf9/eVy6P0zeDaE5BhY2x+S9MfIzz+IZsN5/d+3uX1qYWpS/BdMPP83mgfeVVFp04iaPBEpLedC08aUrEkmKMH4Wezz9IpKUtad5Q8ePECtVr/0GqFkMDWRM7SZDwArTt4v8tcxICKBa4GWaJO90KFhZ8DOIr1/SeZgaUrbKi5ACSmN8SxxNL7sirwHF1bqP+44u2AlLwpCqYJB68HWG6IDYNNQ0lJT+Hzrv4D+NGojHwfjxJZHMoWCp+M/J05pgfXDe4R/842xQ8rWsSfH6L6jO2sTc957VRQMPqQtbhvEhNwb1LgcZiZyrj2J5eLDop2B2ZC+96ii6nUA/rzzpxhM50Hf9GWw7VeC0Ja0I7DiaHzZlFnyorNhSl4UhJULDN6o35P24DgBK0dxJyweR0tTJncxYELGItCmeQ2+rj8AgOjfVpBw8qSRI3rR34/+BsBRbtwi18V/Tk8oMg6Wppn7SVYU4ZH4VI2WP9Prkb1Xvx8qExUP4h5wKfxSkcVQ0r1ezQUbcxNCYlM4Exhl7HDyThyNL1senwP/HYYveVEQrjWg/0okmZxqoTsYo9jJ529Uw86iiPclFZCHnYrEhi3Y5avfjhI8eTKa6OKTHVqtU2dm/a+urG7UWPI0AJLJZMTHxxMXF0dsbCwymYyEhATi4uIy/wkl2/Dm+s3Q+66H8iQm91lSC2L/jTBiktS42ZjTuXp5uvh2AWDL3S1Fcv/SwMxEwRu1PYASuAyWofVE8GosjsaXdpIEB6frP677FrhUM248z5AqtmeN3f8AmKzcQG+zC0aOKH86Vndlec0eRDp5oo2IJHjKlGIzo34h9ALx6ngczR3xUhi30HWe9wBVrlwZe3t7HBwcSEhIoF69etjb22Nvb0+VKvlLD/7DDz/g4+ODubk5TZo0eeVJsqVLl1KlShVUKhXe3t6MHz+elJSUzK/7+Pggk8le+PfBBx/kK76ypIqbNS0rOqGT9CfCikLG8teARt6YKOSZmaEPPDhAXJoYVOdWxuzdvushJKWVwMGDwkS/FCaOxpdut/fAo9P6khevFULJiwLY/W8I00JasFrbCQDZ1jEQVPJmojvUcCVNoWR2vUFgakri0WPErF5t7LCA/5a/Wnu2Ri4z7iKUSV4u/ueffwwewMaNG5kwYQI///wzTZo0YenSpXTq1Inbt2/j4uLywvXr1q1j8uTJrFixgubNm3Pnzh2GDx+OTCZjyZIlAJw/fx6t9r/aSNevX6dDhw7079/f4PGXRiNb+nDiXiTrzz1iXLtKWJrl6cckT+5HJnIqIAqZDAY20md+ru1Um4p2Fbn39B67A3czqOqgQrt/adKwvD3eDioeRydz4EYYvdKTJJYo9j7wxmLY+p7+hFCF18C7sbGjEgwlS8mL98HGw6jhPCsuRc2snTcBiGo1E8J1cO8grH8TRv0NtsadrciLKq7WlHOw4A5uRA0Zg+Nv3xK+cBEWjRphXs14M26SJPHPY/04oq1XW+KjjHtqNU/DrzZt2uTqX14sWbKEUaNGMWLECKpXr87PP/+MhYUFK1asyPb6U6dO0aJFCwYPHoyPjw8dO3Zk0KBBWWaNnJ2dcXNzy/y3a9cuKlSokOfYyqq2lV3wdbIkPkXDX4VcYmHDef3sT5vKznjaqQD9UmtGZui/7vxVbKZuizuZTEbvevpf0lsul9BlMIA6A6FWf3E0vjS6vBoi7xRNyYs8WrT/NhHxqfg6WTLmtSrQbwW4VNcnaVw3EFJLTooJmUxGh+quAGwp1xSr119HUqsJ+vgTdElFs7UhOzejbxKWFIbKREVjN+O/scnTW3uNRoNWq8XMzCzzsbCwMH7++WcSExPp0aMHLVvmfjd/WloaFy9eZMqUKZmPyeVy2rdvz+nTp7N9TvPmzVmzZg3nzp2jcePGBAYGsmfPHoYMGZLjPdasWcOECRNyPKGWmppKaup/ZQQy9jKp1eoXjvkXVEZ7hm7X0IY29WbWrlusOHGfgfU9cl2UNC/9S9Po2HxBn2djQH3PLM/p5N2JJReWcDvmNtfCrlHd0bib5TIU99evey0Xvj18lxN3IwiKTsDF2uzVT3pOsehjx/mYPDqD7OlDdLsmoO35k8GaLhb9K2TFso9piZj8MxcZoG35MTqFBeQzPkP37+qTWFaf0S/5z+peFQU61AoVDFiLycpOyMKuo9s8Am3/NSBXGOSeL2OI/r1exZHfTtzn71sRfDFtGsnXr5MWGEjInLm4zJxhqFDz5ND9QwC0cG+BXKeffymsv7G5IZPy8PZ6xIgRmJqasmzZMgDi4+OpUaMGKSkpuLu7c/PmTbZv307Xrl1z1V5wcDCenp6cOnUqSwLFiRMncvToUc6ePZvt87799ls++eQTJElCo9EwZswYfvop+1+QmzZtYvDgwTx69AgPj+ynW2fOnMmsWbNeeHzdunVYWBTP1OeFLVULMy4qSNbKeK+qlhr2hp+FuRIlY+UdBTZKiZn1tSiem4/clLiJa+prNDRtSC+LXga/f2n19b8KHiTI6FVey2seJXf2zCHhDi3vzkGGxIXyYwhyaG7skIQCqBy6jWohW0g0deFwta+Q5IW3tJ4XWgkWX1MQlCSjoZOOIZWyZsK3Swyg5d25KCQ1Ac6duO71lpEizRutBNMuKEjUyBhbXUvtyHt4Lf8VmSQR/NZbJNSuVeQxfRf3HWG6MPpZ9KOuad1CuUdSUhKDBw8mNjYWG5uX15XL00/gyZMn+f777zM//+OPP9Bqtdy9exdbW1smTZrEwoULcz0Ayo8jR44wd+5cfvzxR5o0acK9e/cYN24cs2fPZtq0aS9c/9tvv9GlS5ccBz8AU6ZMYcKECZmfx8XF4e3tTceOHV/5DcwrtVrNwYMH6dChA0ql0qBtG9pt5W1+O/mQG2oXPu3aIFfPyUv/Nq26CETxVnM/urev9MLXXcJceO/we9zU3WRph6VYKI0/GC0Jr1+M02Nm7vTnVqodC7vmPTN78eljV3RHU1CcWESD0LXU6fYe2BW8Rlzx6V/hKXZ9TIzA5Ef96SqzN+bQpXqPAjVnyP6tPPWQoKTb2KpM+P6dFjhavThrKvmXgy3vUCFiPz4N2qFrMLJA93wVQ/XvROp1tlwOJt7Wl9cGdSFKpyPm19/w2rED72FDUboXXWX7x/GPCdsZhkKm+P/27jq8yvp94Pj7ObEO1sU2ukeO7hZxKqAgIB0GKGF8QUXCAAxE/aGEAhYC0ko30jC6txELWMG6d87z++Ow4WDABufsOds+r+vadZ2d88T9WZ17n7oZ++xYrCVrk/yMFmc1erESoKioKKpXv/dGtXPnTvr06YOjoyMAQ4YMYcmSJUW+nqurK2q1mpiYmALPx8TE4OnpWeg5U6ZMYdCgQYwcORIwlONIS0tj9OjRfPjhh6hU97oRbty4wY4dO1iz5tHLqS0tLQsM6+XRarUm++Nhymsby7A2VVhy8AYHwm5z7U4mNTzsi3zu49oXfjudA2GG/WoGNK9U6LEtfFrgZ+9HeEo4u6J20au6AlvlP4Q5f/9eaFiRzzZd4lJ0CmG3M6jl+WRJvFm0seNkuL4PKfIo2g1vwtCNhtViRmAW7TMxs2njgTmQnQbejdEEvGQoQWEET9u+m4kZzN0ZCsCkHrXxdLIr/MD6L0Hiddj1Ceqtk1G7VoVqXZ74vkX1tO3rXs+LNSdvsvNSHNOer4fHuHFkHDtG5ukzxH7wAf5LlyJpSqYn7t9bhr1/Aj0DcbF1yR+qMvbPaHGuVayfQisrKzIyMvI/P3z4MM2bNy/wempqapGvZ2FhQZMmTdi5c2f+c3q9np07dz60plh6enqBJAdArTaMyd4/mrdkyRLc3d3p2bNnkWMS7qnoZMMz9QyJ6BIjb4yYN/m5bXVXfJ0L79mRJCl/SfyqELE7cFE52VrQ8W5pjLWldU+gPGJpfOl3OwyC7/5j3HWG0ZIfY5i24Tzp2ToC/Z3oF+j76IPbvgMNBhgm5/81DGLNv9hou+puWGlVRCZkcPFWCpJWi89XX6GytSXjeDDx8xeUWCx5y987+XYqsXs+TrF+Ehs2bMhvd/cS+Pfff4mJiaFTp3uNCQsLe+RQU2EmTpzIokWL+OWXX7h48SJvvPEGaWlpDBs2DIDBgwcXmCQdFBTEjz/+yPLly7l27Rrbt29nypQpBAUF5SdCYEiklixZwpAhQ9CUUIZbFg1vbdgYcc2JKO6kGaewXo5Oz8rjhtVlA5o9ekjjhWovoJE0nIk7Q0hCiFHuXx70bmxYDbauNJbGuF/e0ngQVeNLo53TQZ8L1btD5bZKR5Nv2/lotl2IQaOS+Lx3wOMXekgSBM0Fv1aGzTqX9YXUuBKJ9UlZW6hpW90NgO0XDCMtFr6+eN6dBB3/ww+kBwebPI7bGbc5FXcKgI6+HU1+v6IqVgL08ccf8+2331K1alW6d+/O0KFD8frPGOLatWtp3bp1sQLo168fX331FR9//DENGzbk1KlTbNmyBQ8PwxK+8PBwbt26V538o48+4p133uGjjz6iTp06jBgxgu7du+dPzM6zY8cOwsPDGT7ctGO1ZV0TfyfqV3QkK1fPn3c3LHxaOy/GEp+ahaudJV3uLtV8GFdrV9r7GrYvEDtDF13HWm44WmuJSc7iYFi80uE8PbE0vnSKOAYX1ptXyQsgLSuXaRvOAzCqXZWiD+9rLOGVP8C5CiSGw/L+kJPx+PMUlLccftuF6PznHIOCcHzhedDriXrvPXRJSSaNYV/kPvSyntrOtfGyK7l5R49T7H2AgoODefvtt1myZAmLFi0q8HrDhg2ZMGFCsYMYO3YsN27cICsriyNHjhQYVtuzZw9Lly7N/1yj0TB16lRCQ0PJyMggPDycefPmUaFChQLX7NatW/7O1cKTkyQpvxfo10PXyc7VP+aMx8tLpF5qUhHt/Uu/CtGneh8A/r76N1m6rMccLYChNMZz9Q1/aEr9MFienl8bJkEn3oBN7yodjfA4BUpeDAAP89jKAuCb7Ve4mZSJr7M1b3d6cAHGI9k4w4CVhiK+kcdg/RhDW81U51ruqCQ4fzOZqMR7yZrHlI/R+vuRe/MWt6ZOM+l+a7si7g5/+ZnP8Bc8QTHU2rVrM27cOPr16/fAXJzRo0fTsGFDY8UmmIlnA7xwt7ckJjmLzeduPf6ER4i4k86+EEO38StNHzPmflcr71Z42nqSlJXEzhs7H3+CANwbBttyPpq0rFJYGuN+Vo7QO69q/Ao485fSEQmPcnkzhB80lLzoYD4lL87fTGLJwesAzHihHtYWT7Cvj2t16Pc7qDRwbjXsmWncII3Ixc6SQH9nALafv9cLpLazxeerr0CjIWXLFpJWrzbJ/dNz0jl007CvX6lOgPbt21ekD6FssdCoGNzSH4Cf9197qv8UVh6PQJahdTUXKrnaFukctUpNr2qGFWCrQ0zzS1oWNfarQCUXG9KzdWz9zx++Us2vhaFoKsDGiZBwXdFwhIfQ5cKOu5vttXgDHM2jLItOL/PB2nPo9DI9A7zyFws8kcrt4Lm5hsd7Z8OZlUaJ0RS61TUMg22/WHDFtXVAAO7jDTtyR3/2OVlXrxr93oduHiJLl4WPnQ/VKxSzt83EipUAdejQgY4dO9KxY0c6dOhQ6EfHjuYzwUkwnv7N/LDUqDgTmcSJ8IQnukauTs/Kuzs/93/M5Of79arWCwmJo9FHCU82zlyksk6SpPx6YGtLc2mM+7V7T1SNN3enfjeUvLB2hjbjlY4m37IjNzgdkYi9pYaPg4wwJNd40L2SHuvHwI3CKxgoLW8e0OGrd0hKL7hTsvPw4di2aomckWEolZFtnMUuef47/PWwagxKKVYC5OTkhK+vL1OmTCEkJISEhIQHPu7cuWOqWAUFudhZ0uvum+ni/def6Bq7L8cRk5yFi60F3eoUvs/Tw3jZedHaxzDBXkyGLrq879mB0HiikzIVjsZICiyNPwL/fqV0RMJ/ZafB7s8Nj9v/zzB0aQZikzP5YstlAN7tXhMPByvjXLjzNKgdBLpsWDEQ7hh3yxBj8HexpaaHPTq9zO7LsQVek1QqvGbNQu3kRNbFi8R9bbytJnL1ueyN3AuY1/L3PMVKgG7dusXs2bM5dOgQAQEBjBgxgoMHD+Lg4ICjo2P+h1A2DW1dCYDN524RmVD8gnp5k5/7NKmIhab4e4HkTYZeF7qOHL0Z1TgyY/4utgT6O6GXYf2pMtQLdP/S+PDCy+YICjj0g6GAqFMlCDSfVbgz/rlASlYu9Ss68moLf+NdWKWCXgvBqyGk3zYsj89INN71jaSw1WB5tO7ueH3+GQB3fvmVVCNNZTkZe5KkrCScLJ1o6N7QKNc0pmK9C1lYWNCvXz+2bt3KpUuXqF+/PmPHjsXX15cPP/yQ3FzRFV2W1fJ0oHU1F/Qy/HboRrHOvZmYwZ67/3kUdfLz/dr7tsfFyoXbmbfZFyHmmhVVr8ZlcBgM/rM0Xg9rRoml8eYgNQ4OzDU87vwxaCwUDSfP3itx/HPmFioJPu8VgLqIxZ2LzMIG+i8HBx/D0N/KwaAzr3/S8uYB7bkcR2aO7oHX7Tt2xOnVVwG4OWkyuXFPv8dR3uaH7Sq2Q2Mmtd/+64m35PTz8+Pjjz9mx44d1KhRg1mzZhWrBodQOuUtif/zaHixVhatPB6BXoYWVZyp4vaQ7eYfQ6vS8kK1FwAxGbo4ngvwxkKt4lJ0ChdulrHfUbE03rzs+wKyU8G7EdQxj9I1mTk6pqw7B8Cw1pWp52OiUQoHL0MSpLWFa3sNP49mtDw+wMcRTwcr0rN1HLpbhuh+7u+9i2XNmuju3OHmpMnI+iff9kSW5Xu7P5vZ6q88T5QAZWVlsWzZMrp06UK9evVwdXVl48aNODs7Gzs+wcx0rOlOJRcbkjNzWXMiskjn6PQyK4492eTn++WVxjhw8wDRaWVkZZOJOdpo6Vz7bmmMk0X7npUaYmm8+bgdBscXGx6bUcmL73eFEH4nHS9HKyZ2NfG+cF714aWfAQmCl8Kheaa9XzFIkvTIYTAAlaUlPl9/hWRlRdqBA9xZ+ssT3+9KwhVupt3ESm1FS+/iF2UuCcX6CT169ChvvPEGnp6efPnllzz//PNERESwcuVKnnnmGVPFKJgRlUpi2N1eoCUHrqMvQpmFvVdiuZWUSQUbLd3rFm/y8/38Hfxp6tkUvaxnbcjap7pWeZI3GXrdqZvk6p5+M0uzIpbGm4f8khfdDEvEzcCVmBQW7jMs7Z72fF1sLUtgGKZmD+h+dxL4to/g0kbT37OI8pfDX4h96N9uy2rV8Lhbfir2m2/IOHf+ie6V1/vTyrsV1hrrJ7qGqRUrAWrRogWbN2/m7bffZvr06VSqVIn9+/ezYcOGAh9C2fZSk4rYW2m4Gp/G3iuPHydedsTQ+9OncUWstE+w6dh98iZDrw1di07/4Fi28KAONd1xstESl5LFgYd0f5dq7d4D3+ZiabxSIo//p+TFdKWjAUCvl/lw7VlydDJdans89T9fxdLijbsTwGVD6ZZbp0vu3o/QvLIL9pYa4lOzOBmR+NDjKvR9GfuuXSEnh5vvvIM+La3Y98pb/t7Rz3y3xil2H2V4eDiffPIJL774YqEfvXqZx7ivYDq2lpr8icyLH1MlPjopM3/ZZf9mTzb5+X5d/LvgYOHArbRbHLplnvtumBsLjYrn6hsKFa8t4tBlqaLWQO+FYOkglsaXNFmGbVMMj82o5MVfwREcu56AjYWa6S/ULdmbSxL0+AKqdIScdFj2CiTfLNkYCmGhUdGxlmE4PK84amEkScLrkxloPD3JvnGD6E8/K9Z9olKjuHTnEipJRfuK7Z8qZlMqVgKk1+sf+5GSkmKqWAUzMrhlJVQS/BsSz5WYh3/P/zoegU4v06ySM9Xci1hw8DEs1ZYEVQ0CYPUVMRm6qHrfXQ225Xw0qWWhNMb9xNJ4ZVzZcrfkhZXZlLy4nZrFzM2XAJjQpQY+FRQYglFr4eWl4FYLUm7Csn6GPZIU9rh5QHnUFSrg8+UXoFKRtHYtSRuLPpS3J2IPAI3dG+Nk5fSkoZqc0WapZWVlMWfOHKpUqWKsSwpmzNfZJr9LecmB64Ueo9PLLL87+fkVI/X+5MmbDL0nYg/xGWWg2nkJaOhbgcqutmTm6NlyroxOIK/fFwL63l0aPxIyTVvlutzT5cL2vJIXb5pNyYvPNl0kMT2H2l4ODLu7f5kirCvAgBVg4wrRZ2D1KFB42L5DTTe0aomrcWmExqY+8libpk1xff01AKKnTiM7smi9x+a++itPsRKgrKwsJk+eTGBgIK1atWLdunUALF68mMqVK/PNN988UTV4oXQa3sYwGXrNiUgS0h7cPv3fkDiiEjNwsNLwbICXUe9dw6kG9V3rkyvnsiFMzDsrCkmS8idDl7nVYP/V86u7S+PDYaNYGm9Sp/6A+MtmVfLiYFg8a05EIUnwea96aNQKr0ZzqgSvLAO1JVzeeK9GmkLsrbS0rOoKPHoYLI/rm29i3agR+tRUbr77HvJj9vtLzEwkOCYYgI6+5jv/B4qZAH388cf8+OOPVKpUievXr/Pyyy8zevRo5s6dy5w5c7h+/Tr/+9//TBWrYGYC/Z0I8HEkK1fPsqMP1ufK2/m5t5EmP9+vTw3DZOg1IWueqkBreZKXAB0Mu82tpAyFozGR/y6NP7vSrItUlmoFSl68bxYlL7JydXy01rDnz8DmfjTyM5PhF7/m8OIPhscHv4fgJ19ebgzd6uStBnt8T7Ck0eD95Zeo7O3JOHWKuHmPXtq/L2ofOllHDacaVLSvaJR4TaVYCdBff/3Fr7/+yqpVq9i2bRs6nY7c3FxOnz7NK6+8glpt/Dc5wXxJksTwNpUA+PXQdXL+s7w6NiWLnRfzJj8/3d4/D/NMpWew0dhwI/kGx2OOm+QeZY2vsw3NKjkjy7DupPKTMk2mwNL4d8TSeFM4/AOkRkMFf7MpeTF/z1WuxqfhZm/Je91rKR1OQQEvQQfD8nI2ToSrexQLJW8e0MmIRGKTH18j0KKiD17TpwFwe/4C0o4cfeixu8N3A+Y//AXFTIAiIyNp0qQJAPXq1cPS0pIJEyaYXYVXoeT0DPDGzd6SmOQsNp29lf/8mhNR5OplGvtVoKancSY/389Ga0OPyj0AsTN0cdwrjRFZtnvOxNJ400mNg/3fGh53/hg0lsrGA1yLT2PenlAApjxXB0drrcIRFaL9/wzlW/S5sGIwxF1RJAwPBysa+FZAlmHHxdjHnwA4PPssjn16gyxz8/33yU1IeOCYzNxMDtw8AJhn8dP7FSsB0ul0WFjcq+2i0Wiws3uysgZC2WChUTH4bmHBxfuvIcsyehlWBBvqTpmq9yfPSzVeAmD79e0kZYkJr0XxbIAXFhoVV2JSOV/WSmP8l1gabzr7voDsFEMB0Lq9lY4GWZb5aN1ZsnP1tKvhRlB94845NBpJguf/725ingTLXoY0ZfblKs4wWB7PDz/EolIlcmNiuDVlygP/QB2+dZiM3Ay8bL2o5WxmPXCFKFYCJMsyQ4cOpXfv3vTu3ZvMzExef/31/M/zPoTyZUBzPyw0Kk5HJnEqIokrSRKRCRnYW2ny954xlboudanpVJNsfTb/XP3HpPcqKxyttXStbfjjt+ZEGSuQej+xNN74/lvyotsnZlHyYv2pmxwIvY2lRsWnL9Qz71EJrZVhUnQFf8PQ7IqBkJtV4mHkJUAHQm8XeVsMlY0N3l9/BVotqTt2krhiRYHX81Z/dfTtaN7fg7uK9ZM7ZMgQ3N3dcXR0xNHRkVdffRVvb+/8z/M+hPLFxc6SXg0NwypLD93gUKzhB79XIx+sLUw7L0ySpPwl8atDVpftIR0jypsMveF0GSyNcb8HlsaX4V6vkrBzhlmVvEhMz+aTfy4A8Hbn6vi52CgcURHYusKAlWDpCOGHYMNbJV44tZq7HZVdbcnW6dlXhB3981jXrYv7xIkAxMycRVZICAA6vY69kXuB0jH/B6BYhVGWLFliqjiEUm5Ym0qsOB7B1guxyLIhAXqlqWmHv/L0rNKTOcFzCEkI4Wz8Weq71S+R+5Zm7Wu64WxrQXxqFv+GxtOxprvSIZlWz68g4jAkhqPe+j5on1c6otIp8jhcWAdI0GWawsEYzN5yidtp2VR3t2NU21K0D517Lej7C/zex1DI16U6tH+vxG6fVxx14b6rbDsfXaytSpyHDCbtwAHS9u8n6p13qfTXSk4nnudO5h0cLBxo7NHYhJEbj/J9l0KZUMvTgVZVXdDpZfSyRP2KDtTxdiiReztaOtLNvxtgWBIvPJ5WreL5BobhyTI/DAZ3l8b/BJIa1blVVLxzUOmISh9Zhu0fGx43HAgeJVxeohDBNxL486hhs9XPegVgoSllb2lVOxqSc4Ddn8K5kl3MkTcMtutSbIFVvI8jqVR4z5qJ2sWFrCtXiP3iy/zhr3YV26FVmeEE9EKUsp8WwZwNv1slHuCVwJLd/yFvGGzTtU2k5Si/3XxpkDcMtu18NCmZOQpHUwL8mhv2qwHqRyxFurxJ4YBKmStb4cYBQ8mLjsqXvNDp4eMNFwHoG1iRZpWdFY7oCQUOh5ZjDY/XvgERx0rs1o38nHCxtSA5M5ej1+4U61yNqyves2YCkPDHH0RvM8zBLC3DXyASIMGIOtVyp1klJzytZZ6tV4KVl4EmHk2o5FCJjNwMNl/bXKL3Lq3qV3SkipstWbl6NpfV0hj3a/suev82aPWZaFYNhn8mQna60lGZP13uvR2MW7xhFiUvdt+SuBKbirOtBZN71FY6nKfTdQbUfBZ0WbC8v2EX8xKgVkl0qZ23Guzxu0Lfz65tW5yHDgXg5VUxeKRqaO3d2pghmpRIgASjUakk/hjRlMkNddhaFmt62VOTJIk+1e/tDC08niRJ9M4rjVEehsEA1Bp0r6wg1N2wfxTHf4ZFHSH6nLJxmbtTf0DcJbB2gtbjlY6GiIR0tkQa3r4+eLY2TrYWjznDzKnUht3LPQMgLQ7NygFodCWTmOcXRz0f/USLSNwmTiC1sjsOGfC/rdZYq5TfE6qoRAIklBlBVYPQqDScjT/L5TuXlQ6nVHjxbgJ06OptohLLaGmM+2ksOe/Tn9z+f4Gtu+GNfVEnODy/xFfilArZabDHMNRBu/cNBT4VJMsy0/+5RI5eonllJ/o0Vr43yigs7aD/CrD3Qoq7ROC1eYbVdibWpror1lo1N5Myn2hfMJWFBUtfdiJTCxWvJHD758UmiNI0RAIklBku1i75xffEztBFU9HJhuZ3506sO1lOeoHukqt0hDcOQvXuhqGHLf+DZX0NuxwL9xz+AVJuGfataTpC6Wj4YU8Ye6/Eo5ZkpgfVKRX7zRSZow/0/xNZa4NHyllUJbB5p5VWTbsahuKo255gGCw6LZp96jCWdDVseRL33XdknDlj1BhNRSRAQpnyUnXDztD/XP2HzNzH17gRoHd+aYyo8rePkp0bDFgBPb40VOsO2QY/toLQHUpHZh7S4s2q5MX6U1F8udXQu9urkp6qbraKxmMS3o3Q9fwGANWBOXD9gMlv2a2OYc7mtvPFnwu4J2IPAIldGmHf4xnIzSXqnXfRpaYaMULTEAmQUKa08G6Bt603KdkpbL+xXelwSoUeAV5YalSExqZyNqoclhORJGg+GkbvBrfakBZr2Jtl64eK7NBrVvaaT8mLI1dv895fhp6FEa39aetZdpN1uW4fwp3bIsl6WDMK0ou3Qqu4OtVyR62SuBSdQsSd4s09ylv+3smvM17Tp6P19iYnIoLoGTNMEapRiQRIKFNUkope1XsBYhisqBystPkTIcvFnkAP41HXkAQ1HWX4/ND/wU+dFStYqbjbYYZJ4mBYpaRgyYvQ2FRG/xZMtk5Pj3qevN+thmKxlJQzFQchO1eB5CiT7xTtZGtB00pOQPGGwZKzkzkWbVi239GvI2oHB7y/+hJUKpI3/E3S+vUmiddYRAIklDkvVnsRlaQiOCaY60nXlQ6nVMgbBvv79M1ibYhW5mitDRvTvfInWDtD9FlY0A6Cl5a/CdK7PjFMwq3WFaq0VyyMuJQshi45SlJGDo39KvBNv4aoVGVo3s9D6NRW5L64CFRauPQPBJu2EkPeMFhxiqP+G/kvuXIuVR2r4u9gKIpt07gxrmPHABA9fQbZN24YP1gjEQmQUOZ42nrSxqcNIJbEF1Xb6m642FpwOy2b/aHKVKc2K7WeNUyQrtwecjPg73GwcpDJhyLMRmQwnF+L0iUvMrJ1jPz1OJEJGfi72LBocCBWWtPWFzQrXg3uff23TIbYiya7VV4v8NFrd0hIyy7SObsjdgMPbn7o+tpr2AQGok9PJ+rd95Czi3a9kiYSIKFMytsTaH3YenJ05WCX46ekVat4vqGhNMa6UzcVjsZMOHjBoHV3h380cPFv+LE1XPtX6chMq0DJiwHgWU+RMHR6mbeXn+R0RCIVbLQsGdoUF7vSs8eM0bR4E6p1gdxMWDUcckyzXYWvsw21vRzQy4bSGI+Trcvm30jD78L9CZCkVuP95ReoHB3JPHuWuO+/N0nMT0skQEKZ1K5iO9ys3biTeYc9kXuUDqdU6N3IUL5kx6U4Mky//UjpoFJB63EwYjs4V4WUm/BLkKEiellNrK9shRv7FS958ck/F9h+IQYLjYqfBgdSxc1OsVgUpVLBiz8a9qyKvQDbppjsVvmbIhZhGOzIrSOk56bjbu1OHZc6D7yu9fLC6xPDROjbP/1M2qFDxg3WCEQCJJRJGpWGF6q9AMDqK2IydFHU83Ggmrsd2bl6Tt0u+3MsisWnMby2Dxq9Csjw79ewuDvcuap0ZMb135IXzV8Hx5Kt6Zdn8f5rLD14HYBv+jYksFIprfNlLHbu0OtHw+Nji+DSRpPcJq846r4r8WTm6B557K4Iw+qvjn4dUUmFpxIO3bpRoW9fkGVuvv8/cu+Y1xCySICEMqt3NcOy3YM3DxKVWo5XNxWRJEn5BVJXX1MxZMlx5u0O5VREIjp9OZsAXBhLO3hhHry81FBdPioY5reF08uVjsx4Ti+7V/KizQRFQthyLppPNl4AYHKPWvSs76VIHGanWpd7RVPXj4Fk4w9V1/V2wKeCNRk5OvaHxD/0OL2sz9//p5Pvo4ufekyehEXVquTGxXHrw4/Maq8xkQAJZZavgy/NvZojI7MudJ3S4ZQKfQN98XO2JkeWOHj1Dl9uvcyL8w7QcMY2Rv16nKUHrhESk2JWf8RKXN1e8PoB8GsF2amw9jVYPQoyS/keStnpsPtzw+N27ylS8uJkeALjlp9ElmFgcz9Gt6tS4jGYtc5TDXsyZSTAmtGgf3QvTXFJkpQ/DPao4qhn488SnxGPndaOpp5NH3lNlbU1Pl9/haTVkrp7Nwl/LDNqzE9DJEBCmZY3GXptyFp0Rv5jURa52VuyY3wbJjfIZUrPWnSt44G9lYaUzFy2X4hh2t8X6PrNPpp9vpNxy0+y8lgEkQnlsJp6BV8Y+g90/BAkNZxdaegNijiqdGRPLr/khR80HVnitw+/nc7IX46TlaunUy13pj9ft2yVuTAGjQW8tBi0tnD9X9g/x+i3yEuAdlyMeWjPb97mh2192qJVax97TatatXB/7z0AYr/4gszL5lGrUSRAQpnW2a8zFSwrEJMew4Gbpt9SviyQJAlPGxjcwo9FgwM5OaUr68e05v1natKmmiuWGhVxKVmsP3WT91efoc3s3bT/cjeT15zh79M3uZ1aTnZPVqmh/fswbLMhaUi8AYufgb1fGv0/c5NLi4f9cw2PO5V8yYvE9GyGLj3K7bRs6vk48H3/RmjU4u2pUC5VDXtVAeyeafSku1llZxysNNxOy+ZEeEKhx9zb/fnRw1//5TToVezat0fOziZq4jvoM5Qvvqz4T9i8efOoVKkSVlZWNG/enKNHH/3NnDt3LjVr1sTa2hpfX18mTJhAZmbBmk9RUVG8+uqruLi4YG1tTUBAAMePHzdlMwQzZaG2IKhqECAmQz8pjVpFA98KvNmhGr+PbM7pqd1YNqo5YztWo7FfBdQqiRu30/nzaARv/XmSJp/u4Jm5+/jknwvsuhRDalYZX1Lm1xxe3w/1XgJZB7s/haXPQWKE0pEV3b4v75a8aAD1+pTorTNzdIz+NZircWl4O1qxeEhTbC01JRpDqdOgPwS8bPh5Wz3CqMOvWrWKzrUfPgx2Nekq15Ovo1Fp8vdbKwpJkvCa+TkaNzeyw8KI/9L0hV4fR9EEaMWKFUycOJGpU6dy4sQJGjRoQPfu3YmNLXwPgmXLljFp0iSmTp3KxYsX+fnnn1mxYgUffHBvqWZCQgKtW7dGq9WyefNmLly4wNdff42Tk1NJNUswM3nDYHsj9xKXLip9Py0rrZpWVV15t3tN1rzZmlMfd+XnIYEMb12ZWp72AFyKTuHn/dcYvvQ4DaZvo/cPB/h622UOhd0mK7eU9Y4UhZUj9PkJei0ACzsIPwjzW8P5dUpH9nh3rsKxvJIXn5RoyQu9Xua9VWc4ev0O9pYalgxrhruDVYndv9SSJOg5Byr4Q2I4/D3eqDuV5y+HPx/9wHy/3eGGzQ+bezXHzqJ4WxNonJ3xnj0LJInkv/7C7tw54wT8hBRNs+fMmcOoUaMYNmwYAPPnz2fjxo0sXryYSZMmPXD8wYMHad26NQMGDACgUqVK9O/fnyNHjuQfM3v2bHx9fVmy5N624ZUrVzZxSwRzVrVCVRq6NeRU3CnWh61nZEDJz28oy+yttHSu7ZH/X2N8ahaHwm5zMCyeA6G3Cb+TzonwRE6EJ/L9rlCstCqaVnKmZVUXWld1pZ6PI+qyUNpAkqDBK+DbDFaPNKwS+2sIhA6CZ2YZVpGZo52fgD7HsMqohEtefLXtMn+fvolGJTF/UBNq3k2ghSKwcjDMB1rcHc6vgWqd727T8PTa1XDDQqPi+u10QmNTqe5x7/uSt/z9cau/Hsa2VStcRgzn9k8/47xzF/K77xol5iehWA9QdnY2wcHBdOnS5V4wKhVdunTh0EM2TGrVqhXBwcH5w2RXr15l06ZNPPvss/nHbNiwgcDAQF5++WXc3d1p1KgRixYtMm1jBLPXp4ahF2hNyBr0cjmudVUCXO0sCWrgzcze9dn3fkf+fb8js/sE8HwDb1ztLMnM0fNvSDxfbLnMC/MO0GjGNkb/epxfDl4nNLYMrDBzrgLDt0LbdwAJTv4GC9vDzZNKR/agqGDDmycSdJleordediScH/aEATCrT31aV3Mt0fuXCRUDDRPxATa9B/EhRrmsnaWG1lVdgILFUePS4zgTdwaADr4dnvj6bm+/jdOoUUSOHoWkYJFdxXqA4uPj0el0eHh4FHjew8ODS5cuFXrOgAEDiI+Pp02bNsiyTG5uLq+//nqBIbCrV6/y448/MnHiRD744AOOHTvG22+/jYWFBUOGDCn0ullZWWRl3Zu4mZycDEBOTg45Ocbd7TXvesa+rrkw1/Z18unEbO1sIlIiOBR5iGaezZ7oOubaPmMydhs97bX0buhF74ZeyLJMaGwaB6/e5tDVOxy5lkByZi7bLsTk/6F1t7ekRWVnWlZ1plUVZ7wrWBsljjwl9j1sNxnJvy3q9W8g3Q5F/qkr+o4fom/+Jjxk4zhjKVIbZRn11o9QAfr6/dC51IQS+rneeyWOKesNwx9vd6zKC/U9ivX9KOu/h8VqX/MxqMN2obr+L/Jfw8gdusUok9g713Jj9+U4tp6/xeg2hkKnO67vAKCeSz2ctE5P/vWXJBzeeB399u0me48tUhiyQv9u3bx5Ex8fHw4ePEjLli3zn3///ffZu3dvgWGtPHv27OGVV17h008/pXnz5oSGhjJu3DhGjRrFlCmG7cEtLCwIDAzk4MGD+ee9/fbbHDt27KE9S9OmTWP69Af/+1m2bBk2NjZP21TBTGxI38DR7KMEaAPoZ9tP6XAEQCdDZCpcSZa4kiRxLVkiRy44HOZqJVPDQaaGo0x1Rxm7x6+6NSva3BQahi/GOykYgFj7upzwf40sbQVF4/JIOkWLq3PQSVp21PmCTAuXErlvZBp8d05Nll6imZueAVX1iNXuT8cqJ4EOFz/EUpdKqFt3zlcc+NTXTM6Gj4PVyEjMaJKLowX8mvorV3Kv0NWqK+2tSna4tKjS09MZMGAASUlJODg4PPJYxRKg7OxsbGxsWLVqFS+++GL+80OGDCExMZH169c/cE7btm1p0aIFX375Zf5zv//+O6NHjyY1NRWVSoW/vz9du3blp59+yj/mxx9/5NNPPyUqqvDdgAvrAfL19SU+Pv6xX8DiysnJYfv27XTt2hWttpT9JS8Cc27fxTsXGbhlIFqVlq29tlLBskKxr2HO7TMWJduYlaPjREQih8LucPDqHc5GJXH/ViS1PO1pVcXQQxTo74RdMVcMKdI+WUY6+Svq7R8h5WYg27ig6/ktco1nTHK7x7ZRr0PzU3ukuEvoWr6FvtNUk8Rxv1tJmby84AgxKVm0rOLMT4MaY6Epfm9YWf89fJL2SSFb0aw0JD65/ZYjV+vymDMer+/CI5yMSGJ6UG2eb+RE59WdydHnsKrnKqo4Pt0mlab6HiYnJ+Pq6lqkBEixITALCwuaNGnCzp078xMgvV7Pzp07GTt2bKHnpKeno7pvvFCtVgPkzxto3bo1l+/bZOnKlSv4+/s/NBZLS0ssLR/sMtRqtSb75TLltc2BObavvkd9ajvX5uKdi2wJ38KgOoOe+Frm2D5jU6KNWq2WdjU9aVfTE4DkzByOXr3DgbB4Dobe5nJMCpeiDR+LD95Ao5Jo4FuB1lVdaFXNlUZ+FbDUqIt8rxJtX/ORUKUdrB6OFH0WzV+vQtNR0O0T0Bp3mC/PQ9t4Ynl+yQt1u3dRl8DXITkzh9G/nyQmJYsaHnbMHxSIrfXT3bes/x4Wq311noNmr8HRBWj+HgtvHAR7j8ef9wjd6npxMiKJnZfj8fC5Qo4+h0oOlajhUsNom1Qa+3tYnGspugps4sSJDBkyhMDAQJo1a8bcuXNJS0vLXxU2ePBgfHx8mDlzJgBBQUHMmTOHRo0a5Q+BTZkyhaCgoPxEaMKECbRq1YrPP/+cvn37cvToURYuXMjChQsVa6dgPvpU78OnRz5l9ZXVvFr7VbHTrJlzsNLSpY4HXe4uy41LyeLQ1dscDI3nQFg8EXcyCL6RQPCNBL77zwqzVlVdaV3NhbreZrbCzK0GjNxpqCZ/6P8MhS2v74eXfgaPuiUTQ3Y67P7M8LiESl7k6PSM+eMEl6JTcLO3ZPHQpjg+ZfIjFKLrDLhxAGLOGUq0vLrmqbY16FbXg9lbLnEoLB7Xyobl7x39OpaZv5uKJkD9+vUjLi6Ojz/+mOjoaBo2bMiWLVvyJ0aHh4cX6PH56KOPkCSJjz76iKioKNzc3AgKCuKzzz7LP6Zp06asXbuWyZMnM2PGDCpXrszcuXMZOPDpx0SF0u/ZKs/y1fGvCEsK43TcaRq6N1Q6JKEY3Owteb6BN8838AYg4k46B0LjORB2m0Nh8cSnZvNvSDz/3i3k6GitpUUVZ1pXc6VVVRequpnBUnSNJXT/DKp2hLVvQNxFWNgRun0KzUZh8gkxR34s0ZIXsizzwZqz/BsSj42FmiVDm1LRScytNAmtlWFp/IL2cHW3Iclu/fYTX66qmx1V3Gy5GpfEv5H/Ak++/N0cKb7d5tixYx865LVnz54Cn2s0GqZOncrUqY8er37uued47rnnjBWiUIbYW9jTrVI3NoRtYHXIapEAlXK+zja80syPV5r5IcsyV2JSORAaz8GweI5cvUNSRg5bz8ew9bxhhZmHgyUBlTJQZ17mGb1p5t8UWbUuhmGK9W9CyDbY/B6E7TRUnLc10ZLwtNslXvLi/3aF8ldwJCoJ5g1oTD0fx6e6nizLHIk+Qrzu4dXKyzW3mtBjFvw9DnZOh0qtwafJE1+uWx1PFh0/RaY+DRcrF+q71TdisMpSvBSGIJS0l2q8BMDW61tJzU5VOBrBWCRJoqanPcPbVOanIU05+XFX1rzZive616RVVRcsNCri9Sc5nPUxB9W/0enPoVyOj1Q2aDs3GLASnpkNagu4sgV+bAVhu0xzv31fQlZyiZW8WHsykq+3XwFgxgv16FjL/amuF58Rz9hdY3lj1xssTF1Iao74/S1U4yFQ5wXQ58KqEZCV8sSX6lbXA43deQDaVeyAysRbOJSkstMSQSiihm4NqeJYhYzcDDZd26R0OIKJaNQqGvs5MaZjNZaNasFngzKw8/0dSWWoTZYsneOlv3sz99AKZQOVJGjxOozaDW61IDUGfusF2z6C3Gzj3efONTh2d3Vs1xkmL3lxMCye91cZNs17rV0VXm3x8IUoRbHt+jZ6re/Fvsh9AKTL6awKWfXUcZZJkgRB34KjLyRcM2yS+IQa+Dhi4XARAE/Nk/ckmSORAAnljiRJ9K7eG4DVIaJAanmw9NxSZhyeih49QVWC6Kt5C02OH6gy+PnKpzy3/DVi0+4oG6RnPUMSFDjC8PnB7+HnLkbb3Zddd0teVO0MVToY55oPERKTwmu/BZOjk+lZ34v/PVPria+VlJXEpH8n8c7ed0jMSqSWcy1G1jXMXfrt4m9k5CpfVdwsWTtB70WGTTdP/wlnVj7RZS4lXARNErLOgsibPkYOUlkiARLKpeerPo9WpeXC7QtcvH1R6XAEE5FlmTnBc/g6+GsAhtYdyrTm06hv58HOfiuoYfESsqziRtZBuq18nlUXtisbsIUNPDcHXllmeAO7dRoWtIMTvz5dscuoYDi3GpCgq2lLXsSmZDJ0yTFSMnMJ9Hfi65cboHrClXgHbx6k94bebLy6EZWkYlTAKJY9u4zRAaNxUjmRkJXA6ivin5iH8m8J7f9nePzPREPh22LaGb4TgNy0muy6mFD6S9X8h0iAhHLJycqJzn6dAdELVFbl6nP5+ODHLDlnKIw8sclE3gl8J38Jr72VJav7T2Vc7e8g2wOdKonpxyYydMP/SMtOUzJ0qNXTMEG6cjvISYcNbxkKq2YkFP9asgzb7y4cafAKeAYYN9b/SM/OZcTS40QlZlDZ1ZaFgwOx0hZtX6YC18lJ57PDn/Ha9teITY/F38GfX3v8ytuN30ar1qJRaWhn2Q6AJeeWkK0z4lBhWdP2XfBrBdkphvlAxRxW3R1hWP6uzqhHdHImZ6OSTBGlIkQCJJRbecNgm65uEt3oZUxmbiYT90xkXeg6VJKKGa1mMKzesEKPHdW8PRt6/4VzrmHn3OCETXT483kORBwryZAf5OANg9YbipSqNHBhPfzYBq4fKN51QrbD9X9BbXmvcKYJ6PQyb/95krNRSTjbWrBkaFOcbS2KfZ3Tcafp+09fll9eDsArNV9h5XMraeDWoMBxjSwa4WHjQWxGLOtC1xmjCWWTWgO9F4JVBbh54t4eUEUQnhxOaGIoGklDK+82AGw7H/OYs0oPkQAJ5VZzr+b42PmQkpPCtuvblA5HMJKU7BRe3/E6uyN2Y6Gy4JsO39Creq9HnlPZxYldw+bwnOt09DkVyCSW13eOYNLumcr2LqhU0GY8jNhmqDKfHAm/PAe7PgVdEYo+6nWw427vT4vXoYKvScKUZZnpf59nx8VYLDUqFg0OpJKrbbGukaPL4fuT3zN482BuJN/A3cadBV0W8GGLD7HRPrhvkEbSMKS2ocD14nOLydGXzcKoRlHBF57/3vD4wLcQtrtIp+X1/gR6BtKzblUAtl8QCZAglHoqSUWf6oalwGtC1igcjWAM8RnxDN86nOCYYOy0dizouoBOfkXbuE2tkpjZszfzO/6BNr0ZSDIbw5fRdUVvLsRfMnHkj+HTBF77Fxq+CrLesJx9SQ/Dyq5HkM6ugNgLhv/+20wwWXg/77/Gr4duIEkwt19Dmvg7Fev80IRQBm4ayMIzC9HLenpW6cma59fQyqfVI897seqLOFs5E5UaxaarYkXnI9V5HpoMA2TDLtFpj99HaVe4YTuGjr4d6VjTHbVK4nJMCtfjFR4iNhKRAAnl2gvVXkAtqTkRe4KricWfICiYj4iUCAZvHsylO5dwsXJhyTNLCPQMLPZ12lb1Y8+w+dTVjEOfa8udnBu88s8rfHNsPjq9zgSRF5GlHbw4z7DTr6UjRB6D+W3hzF+FHq7WZ6HeaygjZCh5UbykpKg2n73FZ5sMCwk+fLY2PQK8inyuTq/jl/O/0O+ffly8cxFHS0e+av8Vs9rOwtHy8RsmWmmsGFp3KAA/nf1J2e9PadD983tbLax785ET629n3OZk7EkAOvl1wtHGsKs6lJ1eIJEACeWau407bSu2BcRk6NLs8p3LDN48mIiUCHzsfPitx2/Ucn7ypdcOVlr+HDCC9+ouQk6tiyzpWHxhHi+uGUBEcoQRI38C9frAG/vBt4VhYuuakbDmNchMLnBYldhtSCm3wNHPUGLDBIJvJDB+xSlkGQa39GdEm8pFPjcqNYoR20bw1fGvyNZn09anLWufX0v3St2LFUPfmn1xtHTkevJ1tt9QeBWfubOwgT4/G+aDhWyFIwseeujeyL3IyNRxqYOnraE4cdfahjJVIgEShDLipeqGnaH/DvtbrCYphYJjghm2ZRjxGfHUcKrBbz1+w9fh6ee6SJLE0BYBrH95Ea4Zg5F1llxPu0DQ2l78fn65ssuBK/jB0I3Q4QPDPi9nlsOCthB53PB6+m2qx/xjeNx5iklKXlyPT2PUr8fJytXTpbY7U4PqFqlIpizLrAlZQ+/1vQmOCcZGY8PUllOZ13kebjZuxY7DVmvLq7VfBWDBmQXoZX2xr1GueNYz1KID2D4Fbp0p9LDd4Yb5P/+t/dW1riEROn7jDrdTs0wbZwkQCZBQ7rX2aY27tTsJWQnsijBRCQLBJPZE7OG17a+RkpNCY/fGLHlmyRO9iT5KVXd7to58hxfd5pCbVgUdWcw+/hmDN40mNj3WqPcqFrUGOvwPhm029PIkXIefu8G+r1D9+xVafQayRwDUe8not76Tls2wpce4k5ZNgI8j3/VvhLoIe/3EZ8Tz1q63mHpwKum56TR2b8yq51fxUo2XnqrC+IDaA7DT2hGaGMqeiD1PfJ1yo+lIqPks6LJh9Qi4b9uH9Jx0Dt48CBiqv+fxqWBNPR8H9DLsvKTgz76RiARIKPc0Kg0vVn8RQGyqVoqsD13P+N3jydJl0b5ie+Z3nY+DhYNJ7mWhUfFpUDvmd1mIJvFFZL2GU/GH6bn6BTZf3WySexaZXwt4/V/D0Jisg12foD6+CABd52lGL3mRmaNj9K/HuRafhk8Fa34eGoiNxePram+/sZ1e63uxN3IvWpWWiU0msrj7Ynztn763zsHCgf61+gOw8MzCMrVZn0lIEjz/f2DvBfFXYMukAi8fvHmQbH02Fe0qUr1C9QKvda1t6AUqC8vhRQIkCBj2BJKQOHzrMJEpChfIFB7rl/O/8NGBj9DJOp6v+jzfdPwGa421ye/bvoYH20d8RIA0DV2GD5n6VN7/933G7XyHpCwFN4izrmCY2/HifLCwAyDGPgC5cnuj3kavl3ln5WmO30jA3krD0mFNcbe3euQ5ydnJfPDvB0zcM5HErERqOtVk+XPLGVZvGGpV8TdJfJhX67yKtcaa87fP5/deCI9g62LYHwjJsNP4+bX5L+Wt/urk1+mBnrludQ3zgPaHxpGRXbonnYsESBAAHzsfWni1AMSSeHMmyzLfBH/DV8e/AmBInSF80voTtCpticXgamfJsiHPM7He9+Te7oIsq9gVuY2eq19gf9T+EovjAZIEDfvDa/vQdfiIE/6vGf0Ws7deYuPZW2jVEgsGNaG6h/0jjz908xC91/fm76t/55ey+LPnn9RwqmH02JytnHm5xsuAYS6Q6AUqgsrtoO1Ew+MN4yAxnFx9Lnsj9wIUuoVELU97KjpZk5mjZ19IXElGa3QiARKEu/rUMOwJtD50Pbn6XIWjEe6Xq89l2qFpLD63GIAJTSbwbtN3UUkl/2dMkiRGtqnO6lem45L0DrosN5JybvPGjjeYdnAG6TnpJR5TPpeq6FuPJ1tr3OHA3w/fYMFew1YRX7xUn1ZVXR96bEZuBjOPzGT09tHEpMfgZ+/HL8/8kl/KwlSG1B2ChcqCk7EnOR5z3GT3KVM6TIaKTSErCVaP5MStYyRnJ+Nk6URDt4YPHC5JEt3qGIbBSvtqMJEACcJdnXw74WTpRGxGrLL/yQsPyNJl8c6ed1gTsia/tMXwesOVDos63g5seWMgz7l8Sfbt1gCsDvmLF9f14VTsKWWDM6Jdl2L4eP05ACZ2rUGvRhUfeuzZuLP0/bsvyy4tA6BfzX78FfQXDd0bmjxOdxv3/F2/F5x5+BJv4T/UWujzE1g6QMQRdh029K62923/0CHKrnUMw2A7L8aQqyu9q+5EAiQId2nVWp6v+jwgJkObk9TsVN7Y8Qa7InZhobJgToc5jy1tUZKsLdR80acJ33abihT9OvocR26lRzJ48xDmBs8t9VsrnItKYuyyk+hl6BtYkbc6VSv0uBx9Dv938v8YtHkQ15Ov427tzvwu8/moxUeFlrIwleH1hqORNBy5daRMJaEm5VQJnvsGGdiVeAEouPz9fk0rOVHBRktCeg7BN56gQK+ZEAmQIPxH7xqGAqn7ovYRk1a6u3fLgrzSFseij2GrtWV+1/l09uusdFiFeqaeF1teG04t3XRyEhsjo+fncz/T7+9XuHznstLhPZGoxAyGLT1GeraOttVd+axXQKHL1cMSwxi4cSALzixAJ+t4tvKzrHlhDa19Wpd4zN523jxfzfCPzKKzi0r8/qVWwEtcDniRWxoN1rJMS6eHbySqUavoVMsdgG2leBhMJECC8B9VHKvQ2L0xelnP+rD1SodTrkWmRDJk8xAu3rmIs5UzS7ovoalnU6XDeiTvCtasHNWJMfU+IitqEPpcW0KTQnjln/4sPre4VJVqSMrIYdiSo8SlZFHL0555AxujVRd8y9DLen49/yt9/+6bX8riy/ZfMrvd7CKVsjCVEfVGoJJU7Ivcx8XbFxWLo7TZ5d8IgJbpGVhtfPeRpTLy5gFtuxBdaieciwRIEO6TNxl6TcgasausQq4kXGHw5sGEp4TjY+fDrz1+pbZLbaXDKhK1SuKtztX5c+AoHO9MIielNrlyDt8Ef8PQLcOUL6VRBNm5et74PZgrMal4OFiyeGhTHKwKTl6OSo1ixNYRfHn8S7L12bTxacOa59fwTKVnFIr6Hj8HP3pU7gGIXqDi2HXTMPexU0Y2XPoHji9+6LHtarhiqVERcSeDyzEpJRWiUYkESBDu09W/K/Zae6JSozh867DS4ZQ7J2JOMHTLUOIy4qjuVJ1fe/yKv4O/0mEVWxN/J7a81ZOuzpPIuPkSss6SU3En6b2hD39d+cts/2uWZZnJa85yMOw2thZqFg9tincF6wKvrw1ZS58NfTgecxxrjTVTW07lh84/4G7jrmDkBY0KMNQ/235jO6EJoQpHY/4iUyK5nHAZlaSifbNxhie3fgAxFwo93sZCQ9vqhpWApXVTRJEACcJ9rDXW9KzSExB7ApW0fZH7GL19NCnZKTRyb8SS7kvM6k21uBystHzXvxGzuo1AHzmR3LTKZOoymHFoBm/ufJO4dPPbR+XbnSGsPhGJWiUxb2Bj6nrfG8qKz4jn7d1v8/HBj0nLSaOReyNWB61+6lIWplC1QlW6+ncF4KdzPykcjfnLKyHSxKMJFVpPhGpdIDfTUCojJ6PQc0r7cniRAAlCIfKGwXaG7+RO5h2Foykf/g77m7d3vU2WLot2FduxoOsCReeRGIskSbzUpCIb33yBqrp3yYx+DlmvYX/Ufl5c34st17coHWK+VcGRzN0RAsAnL9SjQ817yefOGzvpvb43eyL2oFVpmdBkAku6LzFK4VlTyesF2nxtM+HJ4QpHY97y6iB29O1oKJ/y4o9g6w6xF2DbR4We06m2O5IEZ6OSuJlYeJJkzkQCJAiFqOVci7oudcnV5/J32N9Kh1Pm/Xr+Vz7Y/0F+aYu5HeeWSGmLklTZ1ZY1b7RheMBg0q+9hS7Dh+TsJN7b+x7v73tf2VIawIHQeCatNlQGf6NDVQY09wMgJTuFD/d/yPg940nISqCGUw3+7Pknw+sNN2opC1Oo7VKbdhXboZcNK/KEwiVmJhIcEwzcTYAA7Nyh13zD42M/waWND5znamdJoL8TADsulr5eIJEACcJD5PUCrQ5ZbbbzNUo7WZb59sS3fHn8SwAG1xlc4qUtSpKFRsXkZ2vzy6AgbG5PICuuM7KsYvO1zfRe35uDUcrUsLocncLrvwWTq5cJauDNe91qAnD41mF6b+jNhrANqCQVIwNG8mfPP6npXFOROJ9EXi/QhtAN3Eq9pXA05mlv5F70sp6aTjWpaP+fTS6rdYZWbxkerx8DSVEPnJu3KWJpnAckEiBBeIgelXpgrbHmWtI1TsaeVDqcMidXn8v0Q9P56axhfsa4xuN4N1CZ0hYlrW11N7aO60Ab14GkX38DfZYrsRmxvLbjNT49/GmJltKITc5k+NJjpGTl0qySM1+9XJ9sfRazj85m1LZRRKdF42vvyy/P/MK4xuOwUFuUWGzG0NC9Ic29mpMr5+aXUREK2h2xGyi89hedPgavhpCRAGtGw31bOXS9Ow/o8NXbJGXkmDpUoyr7f2kE4QnZWdjlL+ldHSJ2hjamLF0W7+59l9Uhq1FJKqa1nMbIgJFmN5HWlFzsLPl5SCAfd+1OTsR4su+0AmDF5RW8/PfLJbKLcVpWLsN/OUZUYgZVXG1ZMKgJIYkX6ftPX36/+DtgKGWxKmhViZSyMJXX6hsKw64JWWOWE8+VlJGbwYGoA8B/hr/+S2MBLy0GrS3c2A/75xR4ubKrLdXd7cjVy+y5HFsSIRuNSIAE4RHyhsG2Xd9GcnaywtGUDanZqby54012hu80lLZoPyf/61zeSJLE0NaVWf9mR3zlAaTfGIk+x5HwlHCGbBnCdye+I0dnmv+qc3V6xi47wbmoZFxsLfhpSGOWhSzi1U2vci3pGm7WbvzY5ccSL2VhCoEegTRyb0S2Pptfzv+idDhm5fDNw2TqMvGy9aKW80N2f3apCj2/NjzePRPCjxR4uVvdu8NgpWw1mEiABOER6rvWp1qFamTqMtl0dZPS4ZR6tzNuM3zrcI5GH71X2sLfPEtblKTaXg78PbYN/QI6kXZ1PDmJht3IF51dRP+N/bmScMWo95NlmakbzrP7chxWWhXTX3Jl8pFRzD89H52so0elHqx9YS1tfNoY9b5KkSSJ0fVHA7DyykoSMktv/Spjy1v91cmv06N7YBu8AgEvg6yD1SMhIzH/pbxhsD2XYsnKLT27nYsESBAeQZIk+lQXk6GNISo1isGbB+eXtljcfbHZl7YoSdYWaj7vFcD8Aa2xTBxIRuSryDpbLidc5pV/XmHJuSVGK6WxcN9V/jgSjiTp6dPhGlOPj+TC7Qs4WDjwRbsv+KL9F2ViC4L/au3dmjoudcjIzeC3C78pHY5Z0Ol17I3YCzy6+CkAkgQ950AFf0gKh38m5JfKqO/jiIeDJWnZOg6G3TZ12EYjEiBBeIygqkFYqCy4dOcSFxNEXaEnEZIQwqBNgwqUtqjjUkfpsMzSM/W82DK+LU1c25EWNp7clNrk6HOYEzyH4VuHE5HydKU0/jlzk5mbLyFpEqjV8E82RP6YX8pi7Qtr80tIlDX/7QX689KfYkgbOBV3ioSsBBwsHGjs0fjxJ1g5GOYDqTRwfg2cNMwTU6mk/NVgpWlTRJEACcJjOFo65g/TrA1dq3A0pc+p2FMM2TKEuIw4qlWoVmpLW5QkL0drlo1qwTudm5B9cwgZN/uA3pITsSfos6EPq66seqLeyOPX7zBx5Sk0jsepUP07IjPPYq2xZkqLKWZXysIUOvp2pFqFaqTmpLLs4jKlw1HcrnDD8Ff7iu3RqDRFO6liIHT80PB48/sQb9g4s+t/doXW60tHT7lIgAShCF6q/hIAW65vIUvOUjia0mNf5D5GbRtFSnYKDd0asvSZpWX+TdZY1CqJsZ2qs/K1Vniq2pF6dRy69Mpk5GYw/dB0xuwcU6wVTdfi0xjx+25Unr9g7b2KXDJo6NaQVUGr6Fuzb7lYgaeSVPm9QL9f/J20nDSFI1KOLMv5CVChy98fpfV4qNwectJh1TDIzaJlFRfsLTXEpWRxOjLR6PGagkiABKEImno2xc/ej7TcNM5ln1M6nFIhr7RFpi6Ttj5tWdhtYZmbV1ISmvg7sWlcW4Lq1CP9xigyY3oiyRr+jfqXXht6sfX61sde43ZqFgP++Ilcry/R2l9Ao9IwvvF4lj6zFD8HvxJohfno5t8Nfwd/krKSWHl5pdLhKCY0MZTI1Egs1Za08m5VvJNVKui1AKydIfos7JiGhUZF+5puQOlZDSYSIEEoAkmS6FW9FwDHs48rHI35++3Cb/mlLZ6r8hzfdvq2zJW2KEkOVlq+faUhX7/cCIvUDqRefQuyfEjKSuLdve8y6d9JDy2lEZ+WSNDysaRW+AmVJo0qjtVZ3nM5IwJGmH0pC1NQq9SMDBgJwNLzS8nMzVQ4ImXk9f608GrxZNscOHgZ6oUBHP4BrmyjW93SVRxVJECCUEQvVnsRjaQhQhfBurB1YiltIWRZ5rsT3/HFsS8AeLX2q3zW5rMyW9qiJEmSRJ8mFdn4dlvqudcg5eobZMV1QkLFxqsb6b2hNwdvFiylcfTWUZ5Z9SIp2sMgS/SuMoi/gpaXqlIWptCzSk987Hy4k3mn3G5y+t/l70+s5jPQ/HXD43Vv0NFHh1YtERqbytW4VCNEaVoiARKEInK1dqVdxXYAzDgyg3Yr2vHy3y/z9fGv2R+1v0TLF5gjnV7HjMMzWHR2EWAobfF+0/fLRWmLklTJ1ZZVr7fitfY1yI7vRuq111Hr3IhNj+W17a/x2eHPSMxKZFPGJl7f/TpZ3EbOduZ/Db5jetv3S10pC1PQqrQMrzccgCXnlpCty1Y4opIVnRbNhdsXkJBoX7H9012sy3TwCID0eOw3vUXLyobiqKWhF0j8ZRKEYni/yfu0smxFtQrVALh05xJLzy/ljR1v0Hp5a4ZuGcqPp3/kVOwpcvSlqy7O08jWZfPevvdYdWUVKknF1JZTy11pi5JkoVExuUdtfh/RHBdtdRJD3kKXaJjHsfzycrqt6cbBLENvUHZCcz5u/DOvNuqgXMBm6MVqL+Ju7U5MegwbwjYoHU6Jyqv91ci9ES7WLk93Ma0VvPQzaKzh6m7G2RjmpJWGeUAiARKEYnC3cedZ62dZ+exKdvfdzey2s+ldvTfett7k6nMJjgnmh1M/MGjzINr82YaxO8fy24XfuJJwpcxuopiWk8abO95k+43taFVavm7/NS/VeEnpsMqFNtVd2TKuLZ1rViT91vOkh49AKzuRK+eiz7EnPXwYbzeYRN8m1ZQO1exYqC0YVm8YAD+d/Ylcfa7CEZWcvPk/hdb+ehJuNaHHLAAah35PfSmME+EJxKWY94pZs0iA5s2bR6VKlbCysqJ58+YcPXr0kcfPnTuXmjVrYm1tja+vLxMmTCAz895EtmnTpiFJUoGPWrUeUuNEEJ6Qq7Urz1Z5lumtprOlzxY29drElBZT6ObfjQqWFUjPTWdv5F6+OPYFfTb0oePKjry/733WhqzlZupNpcM3irzSFkeij2CjseHHLj/Sxb+L0mGVKy52lvw0JJDpz9dFnVWTO1feJiPqFdKuTqBPrU682aGq0iGarT41+uBs5UxUahSbr21WOpwSkZSVxPFow0KOjn5GSoAAGg+BOi8g6XNZYP0DNnIGOy+ady9QEXc+Mp0VK1YwceJE5s+fT/PmzZk7dy7du3fn8uXLuLs/uF/IsmXLmDRpEosXL6ZVq1ZcuXKFoUOHIkkSc+bcq1Jbt25dduzYkf+5RqN4U4UyTJIkfB188XXwpW/NvuhlPZfvXObIrSMcvnWYE7EnuJ15m83XNuf/ofWz96O5V3PDh2dzKlhVULYRxRSVGsVr21/jRvINnK2c+aHLD9R1qat0WOWSJEkMaVWJZpWdefvPk4TEWlPLUc+0oNpiGPIRrDXWDK4zmLkn5rLo7CKerfxsmV8Z92/Uv+TKuVSrUM24G5JKEgR9C1En8EqKYIZ2CRsv+PNKM/PdZkHxrGDOnDmMGjWKYcMMXZHz589n48aNLF68mEmTJj1w/MGDB2ndujUDBgwAoFKlSvTv358jRwpWp9VoNHh6epq+AYJQCJWkorZLbWq71GZovaFk67I5HXc6PyE6F3+O8JRwwlPC+evKX0hI1HKuRQuvFjT3ak5jj8ZmvWw8JCGE17e/TmxGLN623izouoBKjpWUDqvcq+3lwN9vteFwWBy3Lx5BqzaLTn6z1q9mPxafW8y1pGvsCN9B90rdlQ7JpHaHG+b/GG3467+snaDPT8hLetBHvZ9DYWtJy2qEraXiqUahFI0qOzub4OBgJk+enP+cSqWiS5cuHDp0qNBzWrVqxe+//87Ro0dp1qwZV69eZdOmTQwaNKjAcSEhIXh7e2NlZUXLli2ZOXMmfn6FZ6JZWVlkZd0bq0xONtSIycnJISfHuBNZ865n7OuaC9G+wklINHRpSEOXhrxW7zVSc1I5EXuCI9FHOBZ9jNCkUC7eucjFOxdZcn4JWpWW+q71ae7ZnKYeTanrUrfoW9U/pce18XTcacbtHUdydjJVHasyr+M83G3cS833vKz/jKqBZn4ObL9cdttozO+hpWRJ/5r9WXB2AQtOL6Cjd0fFe81M9TOapctif9R+ANp5tzPNz4dXE6Q276H5dzbTVD9z+PhztGv2YNFjU7WxONeTZAVnZt68eRMfHx8OHjxIy5Yt859///332bt37wO9Onm+++473n33XWRZJjc3l9dff50ff/wx//XNmzeTmppKzZo1uXXrFtOnTycqKopz585hb2//wPWmTZvG9OnTH3h+2bJl2Ng8wQZRglBMKfoUruZeJSw3jLCcMJLkgpvaWWJJZU1lqmirUFVTFXeVuyJ/pK/kXOHPtD/JIQc/tR+v2r6KjUr8jgilW7o+na+TvyaLLF61fZVa2rI5Z/RyzmV+S/sNB8mBdx3eNd0WFbKemudmUSv3EqGqKlwI+Ai5hP6BS09PZ8CAASQlJeHg4PDIY82zX+oR9uzZw+eff84PP/xA8+bNCQ0NZdy4cXzyySdMmTIFgB497lUzrl+/Ps2bN8ff35+VK1cyYsSIB645efJkJk6cmP95cnIyvr6+dOvW7bFfwOLKyclh+/btdO3aFa227G0OJ9r39GRZJiI1gqPRRzkafZRjMcdIyk7iUu4lLuVeAsDVypWmnk1p5tGMZp7N8LL1Mtr9H9bGTdc2sezwMnLJpbVXa75o+4VZD9M9TFn/GYWy30ZTtO/WqVssubCEU1anmNBtgqK9QKb6/gUfCYYw6F6tO881fc5o1y3M6UpVSFzbg2r6q/hbn4TOUwu8bqo25o3gFIWiCZCrqytqtZqYmIIzxWNiYh46f2fKlCkMGjSIkSMNW5kHBASQlpbG6NGj+fDDD1GpHsxoK1SoQI0aNQgNDS30mpaWllhaWj7wvFarNdkfD1Ne2xyI9j2dqs5Vqepclf51+qOX9Vy6c4nDtw5z5NYRTsScID4zns3XN7P5umFCtb+DP809m9PCuwXNPJsZpebWf9v4x8U/mHXUsMy1Z5WefNL6k1K/u3NZ/xmFst9GY7ZvSL0h/Hn5T87dPsfx+OPFr49lAsZsn17Wsy9qHwBdKnUx+c9Fo/r1+d+GN/hK/xXaw99D9U5Q9cFdp439M1qcayk6Q87CwoImTZqwc+fO/Of0ej07d+4sMCT2X+np6Q8kOWq1Ydb+w0bzUlNTCQsLw8vLeP8lC0JJUUkq6rjUYXi94SzouoAD/Q+wuPtiRtcfTX23+qglNTeSb7Dyykom7plI2+Vt6fdPP+YEz+Fg1EEycjOe+N6yLPP9ye/zk59Xa7/K520+L/XJjyDcz8XaJX//qoVnFiocjfGdiTvD7czb2GntaOrx4JwcY1OrJKTaz/NHbmfDE2tfh7R4k9+3OBQfAps4cSJDhgwhMDCQZs2aMXfuXNLS0vJXhQ0ePBgfHx9mzpwJQFBQEHPmzKFRo0b5Q2BTpkwhKCgoPxF69913CQoKwt/fn5s3bzJ16lTUajX9+/dXrJ2CYCwWaguaejalqWdT3mr0FinZKQTHBOf3EIUmhnLh9gUu3L7AknOGCdUN3RvmrzAr6oRqnV7HzMMz+evKXwC81egtRgWMUnyCqCCYytC6Q1lxeQXBMcEcjz5OoGeg0iEZTV7tr7YV26JVl8w/MN3qevJW8Ku00l6hcmoErHsDBqw0LJk3A4onQP369SMuLo6PP/6Y6OhoGjZsyJYtW/Dw8AAgPDy8QI/PRx99hCRJfPTRR0RFReHm5kZQUBCfffZZ/jGRkZH079+f27dv4+bmRps2bTh8+DBubm4l3j5BMDV7C3s6+Hagg28HAOLS4zgSfSR/yX10WjTHoo9xLPoY35/8HjutHYGegbTwakELrxZUcazyQFKTK+cy+cBkdkTsQELioxYf0bdmXwVaJwglx8PWg17VerHyykoWnllYphKgvOXvT1X8tJjaVHMFrTWvZ45hs81UVCHb4Mh8aPFGicXwKIonQABjx45l7Nixhb62Z8+eAp9rNBqmTp3K1KlTCz0eYPny5cYMTxBKFTcbN56r8hzPVXkOWZYJTwnn8M3D+UlRcnYyeyL2sCdij+F4a7f8DRlbeLXASrLi17RfuZp0Fa1Ky6y2s+hWqZuSTRKEEjM8YDirQ1Zz6NYhzsSdob5bfaVDempXk65yPfk6WpWWNt5tSuy+1hZq2lV3Y9sFPXv836bT1S9h+8fg3xpca5dYHA9jFgmQIAimIUkS/g7++Dv4069WP3R6HZcSLhl6h24adqiOy4jjn6v/8M/VfwCw1diSlpuGjcaG7zp9R3Ov5gq3QhBKjo+dD89VeY71YetZdGYR33f+XumQnlpe7a9mXs2ws7Ar0Xt3rePBtgsxfHWnHZ1qnofLm2DVcBi+4/Enm5hIgAShHFGr1NR1qUtdl7oMrzecLF0Wp2NPG+YPRR/hXPw5Q/Ij2bCw80IaeDZQOmRBKHEjA0ayIWwDeyL3cOnOJWo5l+59gfKHv3xLbvgrT+faHqgkuBCdQtTLX+Bz8yTcDkG97QNQKbvrttgnXRDKMUu1Jc28mvF247f549k/2P/Kfv6vw/8x1n4sdVzqKB2eICiikmMlnqn0DACLzixSOJqnE5sey5n4M4CJyl88hrOtBU0rOQOw7Xou9F4ISKhO/4F3QuGbHZcUkQAJgpDP3sKeVt6tcFAZdwNQQShtRtUfBcD2G9u5mnhV4WieXN5cv/pu9XGzUWYhUNc6hkVN287HQOV20PYdAKrHbARZr0hMIBIgQRAEQXhAdafqdPbrjIzMT2d/UjqcJ5a3/F2J3p883eoYNjY+ev0OienZ0GESunaTOFB9MpiqHEcRiARIEARBEAqR1wu06domIpIjFI6m+FKzUzlyyzDMVJLL3+/n52JDLU97dHqZXZdiQa1F3/ZdctXKltIRCZAgCIIgFKKuS13a+LRBJ+v4+dzPSodTbPuj9pOrz6WSQyWqOFZRNJZud4fBtl+IecyRJUckQIIgCILwEK/Vfw2A9WHriU6LVjia4slb/q5k70+erneHwfZeiSMzR6dwNAYiARIEQRCEh2jo3pBmns3I1eey5NwSpcMpshxdDv9G/QsoO/8nTz0fB7wcrUjP1nEwzDxqgokESBAEQRAeYXT90QCsDllNfIZ5vHk/zrHoY6TmpOJi5WIWu1lLklRwNZgZEAmQIAiCIDxCM89mNHBrQJYui1/P/6p0OEWSv/rLryMqBVda/VfearAdF2PQ6WWFoxEJkCAIgiA8kiRJ+b1Ayy8vJzEzUdmAHkMv6xXd/flhmldxxt5KQ3xqNqcjk5QORyRAgiAIgvA4bX3aUtu5Nhm5Gfx+8Xelw3mkC7cvEJsRi43Gxqxq+WnVKjrVcgdg+8VYhaMRCZAgCIIgPNZ/e4GWXVxGSnaKwhE9XN7qrzY+bbBQWygcTUH3hsFikRUeBRMJkCAIgiAUQSe/TlR1rEpKTgp/XvpT6XAeypyWv9+vfU03LNQqrt9OJyZD2VhEAiQIgiAIRaCSVPm7Q/924TfSc9IVjuhBN5JvEJYUhkbS0LZiW6XDeYCdpYZW1VwAOJsgKRqLSIAEQRAEoYi6V+qOn70fiVmJ/HXlL6XDeUDe5OdAz0AcLMyzqHHecvjzCcqmICIBEgRBEIQi0qg0jAwYCcDS80vJzM1UOKKC8pa/m+PwV57udT35+qUARtdSdkdokQAJgiAIQjE8V+U5vGy9iM+IZ23oWqXDyRefEc+p2FOAeez+/DCudpY838ALG42ycYgESBAEQRCKQavWMrzecAAWn1tMji5H4YgM9kbsRUamrktdPG09lQ7H7IkESBAEQRCKqVf1XrhauxKdFs3fV/9WOhwAdkcY5v+Yc++POREJkCAIgiAUk6XakqF1hwLw09mfyNXnKhpPek46h24eAsx7/o85EQmQIAiCIDyBl2u8jJOlExEpEWy5vkXRWA7cPEC2Phtfe1+qVaimaCylhUiABEEQBOEJ2GhtGFx3MACLzixCL+sViyV/80PfTkiSsvvrlBYiARIEQRCEJ/RKzVewt7DnatJVdobvVCSGHH0O+yL3AYbq70LRiARIEARBEJ6QnYUdA2sPBGDhmYXIChS4OhFzguTsZJwsnWjo1rDE719aiQRIEARBEJ7CwFoDsdHYcOnOJf6N+rfE7583/NXBtwNqlbrE719aiQRIEARBEJ5CBasK9KvVD4AFpxeUaC+QLMulYvdncyQSIEEQBEF4SoPrDMZSbcmZ+DMciT5SYve9dOcS0WnRWGusaeHVosTuWxaIBEgQBEEQnpKrtSsv1XgJMMwFKil5vT+tvFthpbEqsfuWBSIBEgRBEAQjGFp3KBqVhmPRxzgRc6JE7pm//F0MfxWbSIAEQRAEwQg8bT15sdqLQMn0AkWmRHIl4QpqSU07n3Ymv19ZIxIgQRAEQTCS4fWGo5bUHLh5gHPx50x6r7zaX409GlPBqoJJ71UWiQRIEARBEIzE196XnlV6AqbvBfrv7s9C8YkESBAEQRCMaETACCQkdkfs5vKdyya5R0JmAidiDfOMxO7PT0YkQIIgCIJgRFUcq9CtUjfAUCneFPZG7kUv66nlXAsfOx+T3KOsEwmQIAiCIBjZqIBRAGy9vpVrSdeMfv3d4Yb5Px19Re/PkxIJkCAIgiAYWU3nmnTw7YCMbPReoIzcDA7ePAiI5e9PQyRAgiAIgmACowNGA7Dx6kYiUyKNdt1DNw+RqcvE29abmk41jXbd8sYsEqB58+ZRqVIlrKysaN68OUePHn3k8XPnzqVmzZpYW1vj6+vLhAkTyMzMLPTYWbNmIUkS48ePN0HkgiAIglC4ALcAWnm3QifrWHxusdGu+9/NDyVJMtp1yxvFE6AVK1YwceJEpk6dyokTJ2jQoAHdu3cnNja20OOXLVvGpEmTmDp1KhcvXuTnn39mxYoVfPDBBw8ce+zYMRYsWED9+vVN3QxBEARBeMBr9V8DYF3oOqLTop/6ern6XPZG7gXE/J+npXgCNGfOHEaNGsWwYcOoU6cO8+fPx8bGhsWLC8+WDx48SOvWrRkwYACVKlWiW7du9O/f/4Feo9TUVAYOHMiiRYtwcnIqiaYIgiAIQgGNPRoT6BFIjj6HX87/8tTXOxV7isSsRBwsHGjs0dgIEZZfGiVvnp2dTXBwMJMnT85/TqVS0aVLFw4dOlToOa1ateL333/n6NGjNGvWjKtXr7Jp0yYGDRpU4LgxY8bQs2dPunTpwqeffvrIOLKyssjKysr/PDk5GYCcnBxycnKetHmFyruesa9rLkT7Sr+y3say3j4o+20sbe0bUXcEx2OOs+rKKobUGoKLtcsjj39U+3Zc3wFAO592yDqZHF3p+Brcz1Tfw+JcT9EEKD4+Hp1Oh4eHR4HnPTw8uHTpUqHnDBgwgPj4eNq0aYMsy+Tm5vL6668XGAJbvnw5J06c4NixY0WKY+bMmUyfPv2B57dt24aNjU0xWlR027dvN8l1zYVoX+lX1ttY1tsHZb+NpaV9sixTUV2RSF0kMzbNoLt19yKdd3/7ZFlmU8omABxiHNi0aZPRYy1pxv4epqenF/lYRROgJ7Fnzx4+//xzfvjhB5o3b05oaCjjxo3jk08+YcqUKURERDBu3Di2b9+OlZVVka45efJkJk6cmP95cnIyvr6+dOvWDQcHB6PGn5OTw/bt2+natStardao1zYHon2lX1lvY1lvH5T9NpbG9tlH2TN+73iCdcHM6DwDR0vHhx77sPaFJIaQsCkBS7UlY54bg7XGuiRCNwlTfQ/zRnCKQtEEyNXVFbVaTUxMTIHnY2Ji8PT0LPScKVOmMGjQIEaOHAlAQEAAaWlpjB49mg8//JDg4GBiY2Np3Pje2KhOp2Pfvn383//9H1lZWajV6gLXtLS0xNLS8oF7abVak/1ymfLa5kC0r/Qr620s6+2Dst/G0tS+Tv6dqOlUk8sJl1kZupI3G7752HPub9++m/sAaOnVEgdr4/5zrhRjfw+Lcy1FJ0FbWFjQpEkTdu7cmf+cXq9n586dtGzZstBz0tPTUakKhp2X0MiyTOfOnTl79iynTp3K/wgMDGTgwIGcOnXqgeRHEARBEExNkiRG1zfsC/T7xd9JzU4t9jX+u/xdeHqKD4FNnDiRIUOGEBgYSLNmzZg7dy5paWkMGzYMgMGDB+Pj48PMmTMBCAoKYs6cOTRq1Ch/CGzKlCkEBQWhVquxt7enXr16Be5ha2uLi4vLA88LgiAIQknp4t+FKo5VuJp0leWXlzMyYGSRz72VeouLdy6iklS0921vwijLD8UToH79+hEXF8fHH39MdHQ0DRs2ZMuWLfkTo8PDwwv0+Hz00UdIksRHH31EVFQUbm5uBAUF8dlnnynVBEEQBEF4LJWkYmTASD7Y/wG/nv+VAbUGYKMt2kKb3RGG2l8N3RribOVsyjDLDcUTIICxY8cyduzYQl/bs2dPgc81Gg1Tp05l6tSpRb7+/dcQBEEQBCX0qNyDH079QGRqJKuurGJw3cFFOm9XhBj+MjbFN0IUBEEQhPJCo9LkD30tPb+ULF3WY86ApKwkjkcfB8Tuz8YkEiBBEARBKEHPV30eDxsP4jLiWBey7rHH74vch07WUa1CNfwc/EwfYDkhEiBBEARBKEFatZbh9YYD8PO5n8nRP3r34rz5P6L3x7hEAiQIgiAIJax39d64WLlwK+0W/4T989DjsnRZ7I/aD0Bnv84lFV65IBIgQRAEQShhVhorhtYdCsBPZ39Cp9cVetyRW0fIyM3A3cadOi51SjDCsk8kQIIgCIKggL41++Jo6Uh4Sjhbr28t9Jj8zQ99OyFJUkmGV+aJBEgQBEEQFGCjtWFQ7UEALDq7CL2sL/C6Tq+7N//HT8z/MTaRAAmCIAiCQvrX7o+d1o7QxFB2h+8u8Nq52+e4k3kHe609TT2aKhRh2SUSIEEQBEFQiIOFAwNqDwBgwZkFyLKc/9ruSENC1LZiW7Tq0lH0tTQRCZAgCIIgKOjV2q9irbHm4p2L+Su+ZFlmT+QeQOz+bCoiARIEQRAEBTlZOdGvZj/gXi9QnD6O8JRwtCotbXzaKBxh2SQSIEEQBEFQ2JC6Q7BQWXA67jTHY49zMeciAM29mmOrtVU4urJJJECCIAiCoDBXa1f61OgDwE/nfspPgMTwl+mIBEgQBEEQzMCwusPQqDQcizlGpC4SCUmUvzAhkQAJgiAIghnwsvPihaov5H9ez6UertauCkZUtokESBAEQRDMxIh6I1BJhrdm0ftjWiIBEgRBEAQz4evgy4i6I/BQedCzck+lwynTNEoHIAiCIAjCPW/UfwP/SH/crN2UDqVMEz1AgiAIgiCUOyIBEgRBEASh3BEJkCAIgiAI5Y5IgARBEARBKHdEAiQIgiAIQrkjEiBBEARBEModkQAJgiAIglDuiARIEARBEIRyRyRAgiAIgiCUOyIBEgRBEASh3BEJkCAIgiAI5Y5IgARBEARBKHdEAiQIgiAIQrkjEiBBEARBEModjdIBmCNZlgFITk42+rVzcnJIT08nOTkZrVZr9OsrTbSv9CvrbSzr7YOy30bRvtLPVG3Me9/Oex9/FJEAFSIlJQUAX19fhSMRBEEQBKG4UlJScHR0fOQxklyUNKmc0ev13Lx5E3t7eyRJMuq1k5OT8fX1JSIiAgcHB6Ne2xyI9pV+Zb2NZb19UPbbKNpX+pmqjbIsk5KSgre3NyrVo2f5iB6gQqhUKipWrGjSezg4OJTZH2wQ7SsLynoby3r7oOy3UbSv9DNFGx/X85NHTIIWBEEQBKHcEQmQIAiCIAjljkiASpilpSVTp07F0tJS6VBMQrSv9CvrbSzr7YOy30bRvtLPHNooJkELgiAIglDuiB4gQRAEQRDKHZEACYIgCIJQ7ogESBAEQRCEckckQIIgCIIglDsiASoBM2fOpGnTptjb2+Pu7s6LL77I5cuXlQ7LqH788Ufq16+fv6lVy5Yt2bx5s9JhmcysWbOQJInx48crHYpRTJs2DUmSCnzUqlVL6bCMLioqildffRUXFxesra0JCAjg+PHjSodlFJUqVXrgeyhJEmPGjFE6NKPQ6XRMmTKFypUrY21tTdWqVfnkk0+KVPOpNElJSWH8+PH4+/tjbW1Nq1atOHbsmNJhPZF9+/YRFBSEt7c3kiSxbt26Aq/LsszHH3+Ml5cX1tbWdOnShZCQkBKLTyRAJWDv3r2MGTOGw4cPs337dnJycujWrRtpaWlKh2Y0FStWZNasWQQHB3P8+HE6derECy+8wPnz55UOzeiOHTvGggULqF+/vtKhGFXdunW5detW/sf+/fuVDsmoEhISaN26NVqtls2bN3PhwgW+/vprnJyclA7NKI4dO1bg+7d9+3YAXn75ZYUjM47Zs2fz448/8n//939cvHiR2bNn88UXX/D9998rHZpRjRw5ku3bt/Pbb79x9uxZunXrRpcuXYiKilI6tGJLS0ujQYMGzJs3r9DXv/jiC7777jvmz5/PkSNHsLW1pXv37mRmZpZMgLJQ4mJjY2VA3rt3r9KhmJSTk5P8008/KR2GUaWkpMjVq1eXt2/fLrdv314eN26c0iEZxdSpU+UGDRooHYZJ/e9//5PbtGmjdBglZty4cXLVqlVlvV6vdChG0bNnT3n48OEFnuvdu7c8cOBAhSIyvvT0dFmtVsv//PNPgecbN24sf/jhhwpFZRyAvHbt2vzP9Xq97OnpKX/55Zf5zyUmJsqWlpbyn3/+WSIxiR4gBSQlJQHg7OyscCSmodPpWL58OWlpabRs2VLpcIxqzJgx9OzZky5duigditGFhITg7e1NlSpVGDhwIOHh4UqHZFQbNmwgMDCQl19+GXd3dxo1asSiRYuUDssksrOz+f333xk+fLjRCzorpVWrVuzcuZMrV64AcPr0afbv30+PHj0Ujsx4cnNz0el0WFlZFXje2tq6zPXIXrt2jejo6AJ/Sx0dHWnevDmHDh0qkRhEMdQSptfrGT9+PK1bt6ZevXpKh2NUZ8+epWXLlmRmZmJnZ8fatWupU6eO0mEZzfLlyzlx4kSpHY9/lObNm7N06VJq1qzJrVu3mD59Om3btuXcuXPY29srHZ5RXL16lR9//JGJEyfywQcfcOzYMd5++20sLCwYMmSI0uEZ1bp160hMTGTo0KFKh2I0kyZNIjk5mVq1aqFWq9HpdHz22WcMHDhQ6dCMxt7enpYtW/LJJ59Qu3ZtPDw8+PPPPzl06BDVqlVTOjyjio6OBsDDw6PA8x4eHvmvmZpIgErYmDFjOHfuXJnL5gFq1qzJqVOnSEpKYtWqVQwZMoS9e/eWiSQoIiKCcePGsX379gf+OysL/vtfdP369WnevDn+/v6sXLmSESNGKBiZ8ej1egIDA/n8888BaNSoEefOnWP+/PllLgH6+eef6dGjB97e3kqHYjQrV67kjz/+YNmyZdStW5dTp04xfvx4vL29y9T377fffmP48OH4+PigVqtp3Lgx/fv3Jzg4WOnQyhwxBFaCxo4dyz///MPu3bupWLGi0uEYnYWFBdWqVaNJkybMnDmTBg0a8O233yodllEEBwcTGxtL48aN0Wg0aDQa9u7dy3fffYdGo0Gn0ykdolFVqFCBGjVqEBoaqnQoRuPl5fVAMl67du0yN9R348YNduzYwciRI5UOxajee+89Jk2axCuvvEJAQACDBg1iwoQJzJw5U+nQjKpq1ars3buX1NRUIiIiOHr0KDk5OVSpUkXp0IzK09MTgJiYmALPx8TE5L9maiIBKgGyLDN27FjWrl3Lrl27qFy5stIhlQi9Xk9WVpbSYRhF586dOXv2LKdOncr/CAwMZODAgZw6dQq1Wq10iEaVmppKWFgYXl5eSodiNK1bt35g+4krV67g7++vUESmsWTJEtzd3enZs6fSoRhVeno6KlXBtyy1Wo1er1coItOytbXFy8uLhIQEtm7dygsvvKB0SEZVuXJlPD092blzZ/5zycnJHDlypMTmjoohsBIwZswYli1bxvr167G3t88f33R0dMTa2lrh6Ixj8uTJ9OjRAz8/P1JSUli2bBl79uxh69atSodmFPb29g/M2bK1tcXFxaVMzOV69913CQoKwt/fn5s3bzJ16lTUajX9+/dXOjSjmTBhAq1ateLzzz+nb9++HD16lIULF7Jw4UKlQzMavV7PkiVLGDJkCBpN2frzHhQUxGeffYafnx9169bl5MmTzJkzh+HDhysdmlFt3boVWZapWbMmoaGhvPfee9SqVYthw4YpHVqxpaamFuhFvnbtGqdOncLZ2Rk/Pz/Gjx/Pp59+SvXq1alcuTJTpkzB29ubF198sWQCLJG1ZuUcUOjHkiVLlA7NaIYPHy77+/vLFhYWspubm9y5c2d527ZtSodlUmVpGXy/fv1kLy8v2cLCQvbx8ZH79esnh4aGKh2W0f39999yvXr1ZEtLS7lWrVrywoULlQ7JqLZu3SoD8uXLl5UOxeiSk5PlcePGyX5+frKVlZVcpUoV+cMPP5SzsrKUDs2oVqxYIVepUkW2sLCQPT095TFjxsiJiYlKh/VEdu/eXeh735AhQ2RZNiyFnzJliuzh4SFbWlrKnTt3LtGfXUmWy9g2moIgCIIgCI8h5gAJgiAIglDuiARIEARBEIRyRyRAgiAIgiCUOyIBEgRBEASh3BEJkCAIgiAI5Y5IgARBEARBKHdEAiQIgiAIQrkjEiBBEErM9evXkSSJU6dOKR1KvkuXLtGiRQusrKxo2LDhU11LkiTWrVtnlLgEQTAtkQAJQjkydOhQJEli1qxZBZ5ft24dkiQpFJWypk6diq2tLZcvXy5Ql+h+0dHRvPXWW1SpUgVLS0t8fX0JCgp65DlPY8+ePUiSRGJiokmuLwjlnUiABKGcsbKyYvbs2SQkJCgditFkZ2c/8blhYWG0adMGf39/XFxcCj3m+vXrNGnShF27dvHll19y9uxZtmzZQseOHRkzZswT37skyLJMbm6u0mEIgtkRCZAglDNdunTB09OTmTNnPvSYadOmPTAcNHfuXCpVqpT/+dChQ3nxxRf5/PPP8fDwoEKFCsyYMYPc3Fzee+89nJ2dqVixIkuWLHng+pcuXaJVq1ZYWVlRr1499u7dW+D1c+fO0aNHD+zs7PDw8GDQoEHEx8fnv96hQwfGjh3L+PHjcXV1pXv37oW2Q6/XM2PGDCpWrIilpSUNGzZky5Yt+a9LkkRwcDAzZsxAkiSmTZtW6HXefPNNJEni6NGj9OnThxo1alC3bl0mTpzI4cOHCz2nsB6cU6dOIUkS169fB+DGjRsEBQXh5OSEra0tdevWZdOmTVy/fp2OHTsC4OTkhCRJDB06NL9NM2fOpHLlylhbW9OgQQNWrVr1wH03b95MkyZNsLS0ZP/+/Zw+fZqOHTtib2+Pg4MDTZo04fjx44XGLgjlgUiABKGcUavVfP7553z//fdERkY+1bV27drFzZs32bdvH3PmzGHq1Kk899xzODk5ceTIEV5//XVee+21B+7z3nvv8c4773Dy5ElatmxJUFAQt2/fBiAxMZFOnTrRqFEjjh8/zpYtW4iJiaFv374FrvHLL79gYWHBgQMHmD9/fqHxffvtt3z99dd89dVXnDlzhu7du/P8888TEhICwK1bt6hbty7vvPMOt27d4t13333gGnfu3GHLli2MGTMGW1vbB16vUKHCk3zpABgzZgxZWVns27ePs2fPMnv2bOzs7PD19WX16tUAXL58mVu3bvHtt98CMHPmTH799Vfmz5/P+fPnmTBhAq+++uoDSeSkSZOYNWsWFy9epH79+gwcOJCKFSty7NgxgoODmTRpElqt9oljF4RSr8TKrgqCoLghQ4bIL7zwgizLstyiRQt5+PDhsizL8tq1a+X//jmYOnWq3KBBgwLnfvPNN7K/v3+Ba/n7+8s6nS7/uZo1a8pt27bN/zw3N1e2tbWV//zzT1mWZfnatWsyIM+aNSv/mJycHLlixYry7NmzZVmW5U8++UTu1q1bgXtHREQUqHLevn17uVGjRo9tr7e3t/zZZ58VeK5p06bym2++mf95gwYN5KlTpz70GkeOHJEBec2aNY+9HyCvXbtWluV7lbATEhLyXz958qQMyNeuXZNlWZYDAgLkadOmFXqtws7PzMyUbWxs5IMHDxY4dsSIEXL//v0LnLdu3boCx9jb28tLly59bBsEobzQKJZ5CYKgqNmzZ9OpU6dCez2Kqm7duqhU9zqSPTw8qFevXv7narUaFxcXYmNjC5zXsmXL/McajYbAwEAuXrwIwOnTp9m9ezd2dnYP3C8sLIwaNWoA0KRJk0fGlpyczM2bN2ndunWB51u3bs3p06eL2ELDHBpTefvtt3njjTfYtm0bXbp0oU+fPtSvX/+hx4eGhpKenk7Xrl0LPJ+dnU2jRo0KPBcYGFjg84kTJzJy5Eh+++03unTpwssvv0zVqlWN1xhBKGXEEJgglFPt2rWje/fuTJ48+YHXVCrVA2/8OTk5Dxx3/xCKJEmFPqfX64scV2pqKkFBQZw6darAR0hICO3atcs/rrDhKFOoXr06kiRx6dKlYp2Xlxj+9+t4/9dw5MiRXL16lUGDBnH27FkCAwP5/vvvH3rN1NRUADZu3Fjga3PhwoUC84Dgwa/PtGnTOH/+PD179mTXrl3UqVOHtWvXFqtNglCWiARIEMqxWbNm8ffff3Po0KECz7u5uREdHV3gzduYe/f8d+Jwbm4uwcHB1K5dG4DGjRtz/vx5KlWqRLVq1Qp8FCfpcXBwwNvbmwMHDhR4/sCBA9SpU6fI13F2dqZ79+7MmzePtLS0B15/2DJ1Nzc3wDDPKE9hX0NfX19ef/111qxZwzvvvMOiRYsAsLCwAECn0+UfW6dOHSwtLQkPD3/ga+Pr6/vYttSoUYMJEyawbds2evfuXegEdUEoL0QCJAjlWEBAAAMHDuS7774r8HyHDh2Ii4vjiy++ICwsjHnz5rF582aj3XfevHmsXbuWS5cuMWbMGBISEhg+fDhgmBh8584d+vfvz7FjxwgLC2Pr1q0MGzasQDJQFO+99x6zZ89mxYoVXL58mUmTJnHq1CnGjRtX7Hh1Oh3NmjVj9erVhISEcPHiRb777rsCw3n/lZeUTJs2jZCQEDZu3MjXX39d4Jjx48ezdetWrl27xokTJ9i9e3d+Iujv748kSfzzzz/ExcWRmpqKvb097777LhMmTOCXX34hLCyMEydO8P333/PLL788NP6MjAzGjh3Lnj17uHHjBgcOHODYsWP59xKE8kgkQIJQzs2YMeOBIaratWvzww8/MG/ePBo0aMDRo0efaq7Q/WbNmsWsWbNo0KAB+/fvZ8OGDbi6ugLk99rodDq6detGQEAA48ePp0KFCgXmGxXF22+/zcSJE3nnnXcICAhgy5YtbNiwgerVqxfrOlWqVOHEiRN07NiRd955h3r16tG1a1d27tzJjz/+WOg5Wq2WP//8k0uXLlG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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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HBQYGMnbsWMaOHcutt97Ktddey/nz5wkNDcXHx4cbb7yRG2+8kWnTphEfH09ycjLt2rXDZDKxbds2W4rNer327ds7xP7KqP+/FQKBwGHsSFX0Ry0b+XE8q5DDZwtcbJFAIKgOjzzyCP369ePtt9/m5ptvZsWKFQ7TH4WEhBAWFsbnn39OVFQUaWlpPP3002WOmT17NlFRUXTt2hWVSsVPP/1EZGQkwcHBLFiwALPZTO/evfH19eXrr7/Gx8eH6OhoYmNjufnmm5k6dSpz5swhICCAp59+mqZNm3LzzTc7xP7KEBokgUBQLc7l60nJKkSS4I6ezQA4kikcJIHAE+jTpw9z587l/fffp3PnzqxcuZJZs2Y5ZG2VSsX3339PYmIiCQkJzJgxg7feeqvMMQEBAbz55pv06NGDnj17kpqayp9//olKpSI4OJi5c+fSr18/OnXqxOrVq1m6dCmhoaEAzJ8/n+7du3PDDTfQt29fZFnmzz//LJNSdAYigiQQCKqFNb0W1ziA7s2DATgkHCSBoE5Yu3at7XZqaioWi4W8vDzbY5e3DIiNjS332OTJk5k8eXKZx/7zn//Ybr/44ou8+OKL1bJnwYIFZe4PHTqU5OTkMo9dev2pU6cyderUCtcaNWpUubEll+4vJCSEr776qlp2ORIRQRIIBNXCml7rGRtK6wg/JGSyCw22qjaBQCCoTwgHSSAQVIsdpRGkni1C8dV6EeatPH44I9+FVgkEAmdgHTtS0c+GDRtcbV6dIFJsAoGgSgr0JvafzgWgZ2wIAFG+Mll6iUOZ+VzVupErzRMIBA5mz549lT7XtGnTujPEhQgHSSAQVMnutBwsMjQL8SEqyAej0UiUL+zLgcOZIoIkENQ3Wrdu7WoTXI5IsQkEgiq5VH9kJcpXEWAeFCk2gUBQDxEOkkAgqJIdKaX6owocpMMZ+bUauikQCATuiHCQBALBFTGaLew+aY0ghdgej9CBRi1RaDCTfqHYVeYJBAKBUxAOkkAguCJJ6bmUGC2E+GpoHeFve1ytUjpqAxwSaTaBQFDPEA6SQCC4IjtL9UfdY0JtAzCttG2sOEyHhFBbIBDUM4SDJBAIrsj20v5HvVqElHuubWlESfRCEghcx8SJE8t1ohbUHuEgCQSCSpFl2TZipMclAm0rbUojSKKSTSAQ1DeEgyQQCCrl2LkCcoqM6DQqEpoElXs+rnEAAMfPFWI0W+raPIFAIHAawkESCASVYu1/1CU6GK1X+Y+LJkE6/LRqDGYLqVmFdW2eQNCgWLx4MR07dsTHx4fw8HBGjRpFYeHF993bb79NVFQUYWFhTJs2DaPRaHtu0aJF9OjRg4CAACIjI7nrrrs4e/as7fm1a9ciSRJ//PEHnTp1QqfT0adPH5KSkup0j+6EcJAEAkGlWPsf9aogvQagUkm0KY0iCaG2wCORZTAUuubHjv5hZ86c4c4772Ty5MkcOHCAv//+mxtuuMHWg+yff/7h2LFj/PPPPyxcuJAFCxawYMEC2/lGo5FXXnmFvXv38uuvv5KamsrEiRPLXeeJJ57gnXfeYceOHYSHh3PjjTeWcbQaEmLUiEAgqJQdJyrXH1mJjwxgz8kLilC7U11ZJhA4CGMR/K+Ja6797GnQ+lXr0DNnzmAymbjllluIiYnBYrEQExODv7+iAwwJCeGjjz5CrVYTHx/P9ddfz5o1a5g6dSoAkydPtq3VsmVLPvjgA3r27ElBQYFtDYAXXniBYcOGAbBw4UKaNWvGL7/8wu233+6oXXsMIoIkEAgqJCO3hJPni1FJ0C2mfAWblbalESQh1BYInEfnzp255ppr6NixI7fddhtz587lwoULtuc7dOiAWq223Y+KiiqTQktMTOTGG2+kefPmBAQEMHDgQADS0tLKXKdv376226GhocTFxXHgwAEn7cq9EREkgUBQIdby/vZNAvH3rvyjIj5ScZDE0FqBR6LxVSI5rrp2NVGr1axatYrNmzezcuVKPv74Y2bNmsXWrVuVpTSaMsdLkoTFohROFBYWMmLECEaMGME333xDeHg4aWlpjBgxAoPB4Lj91DOEgyQQCCrEWt7f8wrpNYC2pQ7SifNFFBvM+GjVVzxeIHArJKnaaS5XI0kS/fr1o1+/fsyaNYvY2Fh+/fXXKs87ePAg2dnZvP7660RHRwOwc+fOCo/dunUrzZs3ByAnJ4fDhw/Trl07h+3BkxApNoFAUCHbKxhQWxGN/L0J89Miy3DkrIgiCQTOYNu2bfzvf/9j586dpKWlsWTJErKysoiPj6/y3ObNm6PVavnwww85fvw4y5Yt45VXXqnw2Jdffpk1a9aQlJTExIkTadSoUYNtQikcJIFAUI7cYqOtKq1HbOX6IytxpVEkMZNNIHAOgYGBrF+/npEjR9K2bVuef/55XnnlFa677roqzw0PD2fBggX89NNPtG/fntdff5233367wmNff/11Hn30Ubp3705GRga//fYbWq3W0dvxCESKTSAQlGPXiRxkGWLDfIkI0FV5fNvGAWw+li0cJIHASbRr147ly5fb7lssFvLy8gDKlPNbee+998rcv/POO7nzzjvLPCZX0Gbg6quvbtC9jy5FRJAEAkE5dlRTf2TFFkESQm2BQFBPEA6SQCAoR00dJFHJJhAI6gsixSYQCMpQYjSz92QuAD1bVM9BsvZCyszTc6HIQLBvw9QsCASeyqBBgypMuTVkRARJIBCUYV96LgazhUb+3sSGVa9Pi7+3F81CfAAh1BYIBLXHlJGBNiMDS26uy2xwCwfp448/JjY2Fp1OR+/evdm+fXulxxqNRl5++WVatWqFTqejc+fOZYRrAJ9++imdOnUiMDCQwMBA+vbty19//VXmmJKSEqZNm0ZYWBj+/v6MGTOGzMxMp+xPIPAkLqbXQpAkqdrnxTUWaTaBQOAY5OISJJPJpTa43EH64YcfmDlzJi+88AK7du2ic+fOjBgxokyL9EuZNWsWc+bM4cMPPyQ5OZkHHniA0aNHs3v3btsxzZo14/XXXycxMZGdO3cyZMgQbr75Zvbv3287ZsaMGfz222/89NNPrFu3jtOnT3PLLbc4fb8Cgbuzo5r9jy7H2jBSjBwRCAS1QZZlLPoSACQfH5fZ4XIHafbs2UydOpVJkybRvn17PvvsM3x9fZk3b16Fxy9atIhnn32WkSNH0rJlSx588EFGjhzJO++8YzvmxhtvZOTIkbRp04a2bdvy6quv4u/vb2vJnpuby5dffsns2bMZMmQI3bt3Z/78+WzevNl2jEDQELFYZHaeyAHsd5DEyBGBQOAIZL0eLBaQJCQX9mByqUjbYDCQmJjIM888Y3tMpVIxdOhQtmzZUuE5er0ena5sXxYfHx82btxY4fFms5mffvqJwsJC2xC+xMREjEYjQ4cOtR0XHx9P8+bN2bJlC3369Knwunq93nbf2n/CaDRiNBqrueOqsa7lyDXdjfq+R0/e38GMfPJLTPhp1bRupKt0DxXtsWWYj20Ng8FgV3rO3fDk17A61Pf9QeV7NBqNSoTCYrHNKvNErIJq617qE5aiIuVfjabG+7NYLMiyjNFoLDPEF6r/e+9SBykrKwuz2Uzjxo3LPN64cWMOHjxY4TkjRoxg9uzZDBgwgFatWrFmzRqWLFmC2Wwuc9y+ffvo27cvJSUl+Pv788svv9C+fXsAMjIy0Gq1BAcHl7tuRkZGhdd97bXXeOmll8o9vnLlSnx9qz9wsLqsWrXK4Wu6G/V9j564vw0ZEqCmmY+RlSuWV3n8pXs0WUCFmvwSE9/9+hfB3k40tI7wxNfQHur7/qD8Hr28vIiMjKSgoKBeDGrNz69/EVuvvDzUgKzV1nh/BoOB4uJi1q9fj+kyLVNRqQNWpR01urILef/995k6dSrx8fFIkkSrVq2YNGlSuZRcXFwce/bsITc3l8WLFzNhwgTWrVtnc5Ls5ZlnnmHmzJm2+3l5eURHRzN8+HACAwNrtadLMRqNrFq1imHDhpWbzlxfqO979OT9rfzxXyCDa7u3YeTgVpUeV9kePz2+iaPnCmmW0IsBbRrVgcXOwZNfw+pQ3/cHle+xpKSEkydP4u/vXy4b4c4MGTKEzp078+6779KyZUseeeQRJk+eTEBAgEdHayvCmH0eC2DRaGu8v5KSEnx8fBgwYEC519maAaoKlzpIjRo1Qq1Wl6sey8zMJDIyssJzwsPD+fXXXykpKSE7O5smTZrw9NNP07JlyzLHabVaWrduDUD37t3ZsWMH77//PnPmzCEyMhKDwcCFCxfKRJGudF1vb2+8vct/JdZoNE75gHHWuu5Efd+jp+1PlmUST1wAoHerRtWy/fI9xkUFcvRcIUfPFXFNe8/Ze2V42mtoL/V9f1B+j2azGUmSUKlUqFQul+HahdXuHTt24OPjg8lksj1WX7hUoC1rNTXen0qlQpKkCn/Hq/s779L/Va1WS/fu3VmzZo3tMYvFwpo1a2x6ocrQ6XQ0bdoUk8nEzz//zM0333zF4y0Wi01D1L17dzQaTZnrHjp0iLS0tCqvKxDUV07lFJORV4KXSqJrdNUDaisivrEYOSIQOJvw8HCnSDvcAZtAW6VC9nJtksvlbufMmTOZO3cuCxcu5MCBAzz44IMUFhYyadIkAMaPH19GxL1t2zaWLFnC8ePH2bBhA9deey0Wi4Unn3zSdswzzzzD+vXrSU1NZd++fTzzzDOsXbuWu+++G4CgoCCmTJnCzJkz+eeff0hMTGTSpEn07du3QoF2XVK0fTsRPy+hJDnZpXYIGh47Tyjl/QlNg/DRqqs4umLaiko2gcDpxMbG8v7779vuS5LEnDlzuOGGG/D19aVdu3Zs2bKFo0ePMmjQIPz8/Ljqqqs4duxYmXWWLl1Kt27d0Ol0tGzZkpdeeqmcXqeusRQXA6DS6cDFqUOXa5DGjh3LuXPneP7558nIyKBLly4sX77cJtxOS0srE14rKSlh1qxZHD9+HH9/f0aOHMmiRYvKpMrOnj3L+PHjOXPmDEFBQXTq1IkVK1YwbNgw2zHvvvsuKpWKMWPGoNfrGTFiBJ988kmd7bsy8hYvJnj7dgqWLyegc2dXmyNoQGxPUcr7e1VzvEhFWJtFHskswGyRUavqlzZCUP+QZZliU7FLru3j5eMw/dArr7zC7NmzmT17Nk899RR33XUXLVu25JlnnqF58+ZMnjyZ6dOn25omb9iwgfHjx/PBBx/Qv39/jh07xn333QfACy+84BCbaoJcXNr/SOe6/kdWXO4gAUyfPp3p06dX+NzatWvL3B84cCDJVURXvvzyyyqvqdPp+Pjjj/n444+rbWdd4D90KAV/Ladg1WrkJ5+sd+I7gfuys7SDdo+YmqXXAJqH+qLTqCgxWjiRXUjLcH9HmScQOIViUzG9v+3tkmtvu2sbvhrHpMomTZrE7bffDsBTTz1F3759ee655xgxYgQAjz76qC0zA/DSSy/x9NNPM2HCBABatmzJK6+8wpNPPulSB8lSUhpB8nG9gN7lKTZBWXyvvhqLRoPp1Cn0Bw642hxBAyGn0MCRswUA9LCzQeSlqFSSbXCtmMkmENQdnTp1st22ZmA6duxY5rGSkhJbBdfevXt5+eWX8ff3t/1MnTqVM2fOVLsM3tHIsoylxPUdtK24RQRJcBGVry+FcW0JSNpP3sqV6GrYlkAgsAdr9+zWEf6E+tWuc23bxgH8eyqXQ5n5XNcxyhHmCQROw8fLh213bXPZtR3FpZVZ1sxDRY9Zmy4WFBTw0ksvVThiy1XtD6wCbUmlUjpolzpLrkI4SG5IQUJHApL2k79iJeGPPirSbAKnc3FAbc2jR1bEyBGBJyFJksPSXJ5Et27dOHTokK0djjtgFWhLbtKfSjhIbkhhu3jQaDCkpGA4ehTvNm1cbZKgnnPRQaq5/siKNcUmhtYKBO7L888/zw033EDz5s259dZbUalU7N27l6SkJP7v//7PJTbJpREjlRuk10BokNwSi06Hb2k/pryVK11sjaC+U2wws+9ULuDYCFJqViElRnMVRwsEAlcwYsQIfv/9d1auXEnPnj3p06cP7777LjExMS6zyRZBchMHSUSQ3BT/YcMoWr+e/JWrCJ82zdXmCOoxe05ewGSRiQzU0Syk9h9M4QHeBPtquFBk5Ni5Ajo0CXKAlQJBw+bSiu7U1FQsFotNcG0dXGslNja23GODBg0q99iIESNsVW6u5lKBtspNUmwiguSm+A0eBF5e6A8dwpCa6mJrBPUZW3qtRahD9G6SJCrZBAKBfVzaQVuqYKyXKxAOkpuiDgrCr1cvAPJW1v+J2wLX4Uj9kRVrmk2MHBEIBNXh0g7a7lKYJBwkNyZg+HAA8oUOSeAkTGYLu0pL/B2hP7IiIkgCgcAe3E2gDcJBcmsChl4DKhUlSUkY09NdbY6gHnIwI59Cg5kAnZfNqXEEcdZSf+EgCQSCauBuAm0QDpJb49WoEb7duwMizSZwDttTLo4XceTcNKuzdTq3hLwSo8PWFQgE9Q93FGiDcJDcHpFmEziTnSdKHSQHptcAgnw0RAUpH3QiiiQQCK6EOwq0QThIbk/A8GEAFO/ejTHzrIutEdQnZFlme4qiP+rVwrEOElxMswmhtkAguBLuKNAG4SC5PZrGjfHp0gWA/NUizSZwHCeyi8gq0KNVq+jY1PG9iuIaCx2SQCCoGncUaINwkDwCW5pthUizCRzH9tLy/s7RQeg0aoevL0aOCASC6uCOAm0QDpJHYE2zFe3cien8eRdbI6gv7Ehxjv7IStwlQ2sv7+ArEAg8A0mS+PXXX522vrsKtEE4SB6BtlkzdB06gMVC/urVrjZHUE/YWdr/qJeTHKTWEf6oJMgpMnIuX++UawgEAvfixRdfpEupLKQ6uKtAG4SD5DFcrGYTOiRB7TmbX0JKViGSBN1iHNdB+1J0GjWxYX6AEGoLBI7GYDC42gSH4K4CbRAOksdgTbMVbt2KOTfXxdYIPJ3EVCV6FNc4gCAfjdOuY6tkEzokgaBWDBo0iOnTp/PYY4/RqFEjrrvuOj7++GM6d+6Mn58f0dHRPPTQQxQUFABK6io8PJzFixfb1ujSpQtRUVG2+xs3bsTb25uioiIAjhw5woABA9DpdLRv355Vq8p/IX/qqado27Ytvr6+tGzZkueeew6jUel1tmDBAl566SX27t2LJElIksSCBQsAmD17Nh07dixnq7sKtEE4SB6Dd4sWeLdpAyYT+X//42pzBB7Odtv8Neek16yIkSMCd0eWZSxFRS75sVebt3DhQrRaLZs2beKTTz5BpVLx3nvvsX//fhYuXMjff//Nk08+CSjaoQEDBrB27VoAcnJyOHDgAMXFxRw8eBCAdevW0bNnT3x9fbFYLNxyyy1otVq2bdvGZ599xlNPPVXOhoCAABYsWEBycjLvv/8+c+fO5d133wVg7Nix/Oc//6FDhw6cOXOGM2fOMHbsWABUKhUffPBBOVvdVaAN4OVqAwTVJ2DECPRHjpC/ciXBo0e52hyBB7OzNILU0wn9jy7lUqG2QOCOyMXFHOrW3SXXjtuViOTrW+3j27Rpw5tvvgmAxWLhwQcfJDAwEJVKRWxsLP/3f//HAw88wCeffAIoUac5c+YAsH79erp27UpkZCRr164lPj6etWvXMnDgQABWr17NwYMHWbFiBU2aNAHgf//7H9ddd10ZG2bNmmW7HRsby+OPP87333/Pk08+iY+PD/7+/nh5eREZGVnmvMcee6zMeVZb35k+HXA/gTaICJJHYUuzbdyIuTSMKhDYS4HexP7TSpq2Z6xz9EdWLjpIBVgsopJNIKgN3buXdeTWrl3LsGHDaNq0KQEBAYwbN47s7GxbymzgwIEkJydz7tw51q1bx6BBgxg0aBBr167FaDSyefNmBg0aBMCBAweIjo62OUcAffv2LWfDDz/8QL9+/YiMjMTf359Zs2aRlpZWpe2rV6/mmmuuKW9rYaFbCrRBRJA8Cu82bdDGxmJITaVg7TqCbrje1SYJPJDdaTlYZGgW4kNUkHPD2jGhvmi9VBQbzZzMKSKmVLQtELgLko8PcbsSXXZte/Dzu/j+SU1N5Y477uCBBx7g1VdfJTQ0lI0bNzJlyhQMBgO+vr507NiR0NBQ1q1bx7p163j11VeJjIzkjTfeYMeOHRiNRq666qpqX3/Lli3cfffdvPTSS4wYMYKgoCC+//573nnnnSuel5qayg033MCDDz5Y3lajEX83FGiDcJA8CkmSCBgxguw5c8hfuVI4SIIaYe1/5Kzy/kvxUqtoE+HP/tN5HMrIFw6SwO2QJMmuNJe7kJiYiMVi4e2338bLS/lT/uOPP5Y5RpIk+vfvz9KlS9m/fz9XX301vr6+6PV65syZQ48ePWxOV7t27Th58iRnzpyxCbm3bt1aZr3NmzcTExPDf//7X9tjJ06cKHOMVqvFbDZXaOs777yDSqUqZ6s7CrRBpNg8DmuarWDDBiylYVSBwB52lOqPnNUg8nLihFBbIHA4rVu3xmg08tFHH3H8+HEWLVrEZ599Vu64QYMG8d1339GlSxf8/f1RqVQMGDCAb775xqY/Ahg6dCht27ZlwoQJ7N27lw0bNpRxhEDRQKWlpfH9999z7NgxPvjgA3755Zcyx8TGxpKSksKePXvIyspCr9fbbP3www8rtNUdBdogHCSPQ9e+PZqmTZGLiynYsNHV5gg8DIPJwu6T1gG1ztUfWWkrhtYKBA6nc+fOvPrqq7z55pskJCTwzTff8Nprr5U7buDAgZjNZpvWCBSn6fLHVCoVv/zyC8XFxfTq1Yt7772XV199tcxaN910EzNmzGD69Ol06dKFzZs389xzz5U5ZsyYMVx77bUMHjyY8PBwvvvuOzp37szs2bN54403bLb+73//u3htNxRog0ixeRySJBEwfDjn588nf+VKAkcMd7VJAg9i/+lcSowWQnw1tAr3r5Nriko2gaD2WMv1L+Whhx7i6aeftqWtAMaNG1fmmC5dupRrJ/DYY4+VqSqz0rZtWzZs2FDmscvPffPNN22VdJeuZ8Xb27tM7yUrM2bMYMaMGbb7lpISbuvWzW0F2iAiSB6J1SkqWLsWSz3ppiqoG3akXpy/VleiSGuK7fi5QgwmS51cUyAQuDeXzl9zR4E2CAfJI9F16oRX48ZYCgsp3LTJ1eYIPAir/sjZ5f2XEhWkI0DnhckiczxLtKcQCARK/ylwX4E2CAfJI5FUKgKGKWLt/BUrXWyNwFOwWGR21lEH7UuRJEkItQUCQRncuYO2FeEgeSjWarb8f/5BLp2DIxBcieNZBeQUGdFpVHRoElSn124rZrIJBIJSZFkuk2JzV4SD5KH4du+OOiwMS24uhdu2u9ocgQewPUVJr3WNDkHrVbdv/Xgh1Ba4GfbOQRM4DlmvB4vFqQJtR7y+wkHyUCS1moChQwHIX7HCxdYIPIGL6bW60x9ZsQ6tPSgiSAIXo1arATCIAheXURcCbeu4FY1GU+M1RJm/BxMwfBgXfviB/DVriHzxBaTSN75AUBHbrQ6SkwfUVoRVg3Qqp5gCvQl/b/HRI3ANXl5e+Pr6cu7cOTQaTZkSeU/CYrFgMBgoKSnxuD0Y8/IwWyyovbyQS52ly6np/mRZpqioiLNnzxIcHGxziGuC+JTyYPx69UIdFIT5/HmKdibi17uXq00SuClncos5lVOMSoKuzes+ghTipyUiwJuz+XqOZOa7xAaBAJSigaioKFJSUsqNyfAkZFmmuLgYHx8fty2TrwxTVhaywYDaaERVWFjhMbXdX3BwMJGRkbWyUzhIHoyk0eB/zTXkLllC/sqVwkESVIq1vL9DkyCXRW/iIgM4m6/nUIZwkASuRavV0qZNG49OsxmNRtavX8+AAQNqlUaqa2SzmZT/PI5cXEz0Z5+ijYmp8Lja7E+j0dQqcmRFOEgeTsDwYTYHqfF/n0XysFCroG7YaWsQ6TrHpG3jADYcyRIjRwRugUqlQufGFVRVoVarMZlM6HQ6j3KQ9EePIh0/jsrXl4DWrSuVhrjD/sRfUw/H76qrUPn7Yzp3juI9e11tjsBN2Z6iOEi96rD/0eWIkSMCgaBk/34AdPHxbq+bFQ6Sh6PSavEfPBiA/JWiaaSgPLnFRlvUpocrHSTRLFIgaPAUWx2kDh1cbEnVCAepHmBrGrlypejtISjHrhM5yDK0aORHeIDrhkK2aeyPJEFWgYGsAr3L7BAIBK6jZH8yALoO7V1sSdUIB8ndMBuJurATinOqfYr/1Vcj+fhgPH2akqT9TjRO4InYBtTGuFYY7av1onmoLwCHRRRJIGhwyGYzJQcOAOCTkOBia6pGOEhuhvrHu+mV8gGqfT9U+xyVjw/+AwYAIs0mKM8OF/Y/uhxrw0gh1BYIGh6G1FTkoiIkX1+0LVq42pwqEQ6SmyG3vRYA1e6vwI50WeCI4QDkrVwh0mwCGyVGM3tP5gJ1O6C2MsTIEUFdYMrOJvvDj9BmZrraFMEleJJAG4SD5HZYEm7DpNIiZR2GtC3VPs9vwEAkrRbjiTT0hw870UKBJ7EvPReD2UIjf29iw3xdbY4YOSJwOsbMTE7cM46czz8n/I8/XW2O4BKKk5IAzxBog3CQ3A/vANJD+iq3ExdU+zS1vx9+V18NQP4KkWYTKOy4ZP6aO3TbtZX6Z+SLSKfA4RhOpXPinnEYUlIA8ElJQTYaXWyVwIonCbRBOEhuSWqYUrbP/l+h6Hy1z7Om2fJXCQdJoLAjxeoguT69BkolnUYtUWgwk36h2NXmCOoRhtRUTowbh/HkSTTR0agCAlAZDOhLRcEC11JGoC0iSIKacsG3BXLjjmDWw97vq32e/+DBoNGgP3IU/fHjTrRQ4AlYLDI7TyjVkO7iIGnUKlqF+wOiH5LAceiPHiV13DhMZ86gbdGCmK8X4dO9OwDFO3e62DoBXCLQ9vFB27Klq82pFsJBckckCUvX8crtxPnVFmurAwPx69MHENVsAqVSLL/EhJ9WTbuoAFebY8OaZhOVbAJHUHLgACfGjcd8Lgvvtm2JWfQVmsaN8enZE4DinYkutlAAlwi027XzCIE2CAfJbbEk3AoaP7BTrG1tGpknHKQGj3X+WreYELzU7vNWtwq1RS8kQW0p/vdfTkyYiDknB12HDjRfuACvRo0A0PUojSDt2oVsMrnSTAGXOEgekl4D4SC5L94B0HGMcnvn/GqfFjB0KKjV6JMPYDh50knGCTyB7anulV6zEicq2QQOoGjnTtImTcaSl4dP1640XzAfr5CLzVC94+Iw63TIhYWUHDjoQksFAMVJVgfJMwTaIBwk96b7ROXf5KXVFmt7hYTgWxpaFmm2hossyzaBdo9Y13bQvhxriu34uUKMZouLrRF4IoWbN5M29T4shYX49upF8y/mog4om0aW1GqKW8QCULRjhwusFFjxRIE2uImD9PHHHxMbG4tOp6N3795s37690mONRiMvv/wyrVq1QqfT0blzZ5YvX17mmNdee42ePXsSEBBAREQEo0aN4tChQ2WOGTRoEJIklfl54IEHnLK/GtOkG0R2KhVrf1ft00SaTXAqp5iMvBK8VBJdo93LQWoa7IOfVo3BbCE1q9DV5gg8jPy1azn5wIPIxcX49e9P9OdzUPn5VXhscQtFDFx0hb8pAufjiQJtcAMH6YcffmDmzJm88MIL7Nq1i86dOzNixAjOnj1b4fGzZs1izpw5fPjhhyQnJ/PAAw8wevRodu/ebTtm3bp1TJs2ja1bt7Jq1SqMRiPDhw+nsLDsh/HUqVM5c+aM7efNN9906l7tRpIuRpESF1RbrB0wdChIEiV7/8WYkeE08wQVU7xjJ2ErV1GwahXG06dd0u/H2v8ooWkQPlr3EkSqVBJtxMgRlyKd3EaLc6vA4lnanLwVKzn18CPIBgP+11xDs48/QqXTVXp8UatSBykxEdlsriszBZfhaR20rXi52oDZs2czdepUJk2aBMBnn33GH3/8wbx583j66afLHb9o0SL++9//MnLkSAAefPBBVq9ezTvvvMPXX38NUC6itGDBAiIiIkhMTGRA6cwyAF9fXyIjI521NcfQ8TZY+Zwi1j6xGWL7VXmKJiICn27dKE5MJH/lKkLHj6sDQwUA5vx8zjz6KGH5+WSsWQOAOiwMn4QEdB074tNR+dcr1Lm6oB2l+qNebjB/rSLiIwPYc/KCItTu5GprlJSkMT2dkuRkSg4coOTgIcLUKuRhw0CjcbV5jsNigQ1vo/7nf3RCxpzYDq6a5mqrqkXub79x+ulnwGwmcOR1NHnjDaQqXht9VBSSnx+W/Hz0hw6ha+85+pf6hM1B8oABtZfiUgfJYDCQmJjIM888Y3tMpVIxdOhQtmypuHJLr9eju+wbg4+PDxs3bqz0Orm5yiyq0Mv+KH3zzTd8/fXXREZGcuONN/Lcc8/h61vxOAa9Xo9er7fdz8vLA5SUn9GBnVqta9nWVPug7jAa1Z6vseych7lpr2qt43fNEIoTE8ldvpyAO+9wmH2OoNwe6xHnv/4aS34+poAAfJo0wXj0KObsbArWraNg3TrbcV5NmuDdoQO6hA54JySga98elb+/w+zYnpINQNdmgU75f67ta9gqXHmfHTiTV+e/B7LZjDE1FX3yAfQHD6I/eAD9gYNY8stGs8KAk+MnEDX7HTRRUXVqo1MoOo962UOojq3G2lNd2vIRxm4TQa11pWVVkrdkCWdffAlkmYCbbyb8pRcxAVzhd8doNIJajXeXLpRs2kT+lq2o27SpM5udjSd9jhbtU0aMaOLjqm2vM/dX3TVd6iBlZWVhNptp3LhxmccbN27MwYMVVx2MGDGC2bNnM2DAAFq1asWaNWtYsmQJ5krCpxaLhccee4x+/fqRcIn3etdddxETE0OTJk34999/eeqppzh06BBLliypcJ3XXnuNl156qdzjK1eurNSpqg2rVq2y3Q4ubs1AQN6/lFXSYIxeVfe08VKpaQkU797Nih9+wBzgPn1wrFy6x/qAZDDQ4osv8QLOjbyO/G7dkIxGvE+fRnfqFLqTp9CdOoX23DlMp09jOn2awtL/A1mSMDRqhD66GSXNmlHSLBp9kyjkGkQvCoxw7Jzy1j5/eCd/pjhyl2Wp6Wt4IVcC1OxOyeTPP503L0syGtFmZqJLP4336XS800/jnZGBqoIPSFmtRt+4MfomTTCGhhCyYSOGpCSOjxrNmTvvoKhtW6fZ6WyCC4/TM+VDNMZszJKGfc3uIf7ML+jyT7Pn2xc4Gdbf1SZWSvDmzUQsXQbAhT59ONynN6xYUe3zTwYGEg6k/vEHpxuFOclK1+H2n6MWC62TklAB27OyMdj5fnfG/oqKiqp1nMtTbPby/vvvM3XqVOLj45EkiVatWjFp0iTmzZtX4fHTpk0jKSmpXITpvvvus93u2LEjUVFRXHPNNRw7doxWrVqVW+eZZ55h5syZtvt5eXlER0czfPhwAgMDHbQ7xbNdtWoVw4YNQ2P94yjLyF/+jDpzHyMan8fSe2y11jr5x+/o9yXRR5IIKk1JugMV7rEecOGbb8kqLETdpAn5nTtXuj9zfj765GT0Sfsp2Z+EPmk/pjNn8D53Du9z5wjcVaqn8/LCu01rvDsk4J3QAV1CAtpWrZC8rvy2XX3gLOzcQ6twP26/ueqUbE2o7WvYu0DPx8nryNZLDB46wiE6KUtBQWlE6CD6A8q/huPHoYIeOJKPD95xcXi3a4d3u3i84+PRtm5tS9kYjUb++eEH4pcuw3DwIM3mzSf0wQcIuf9+JJXLpZvVR5ZR7ZqPatX/kMwG5JAWWMbMp21oHMe+LqLD6R/oWriWjve8CpL77Stn/nyyS52joHHjaPXE49WeKWj9He14x1gy/vqLwFOn6HzttZ71+l0BT/kcNRw/TprBgOSj45oJ46utQXLm/qwZoKpwqYPUqFEj1Go1mZmZZR7PzMysVBsUHh7Or7/+SklJCdnZ2TRp0oSnn36alhUo46dPn87vv//O+vXradas2RVt6d27NwBHjx6t0EHy9vbG29u73OMajcYpv5zl1u05GX6fgXrPItT9HlYE3FUQNGIEZ/clUbRmDY3uvtvhNtYWZ/3fuQLZYODCwoUAhE6eBGp1pfvThIaiu/pqKB0uDGDKzqZ43z5K9iVRkpREcVIS5uxs5Q/9gYOweDEAkk6Hrl07dB0TFF1TQke0sTFlPvR3n1Le/L1ahDn9/7emr2FkiIYwPy3ZhQZSc0ro1CzYrvNN2dmUJB9Q9ELJyZQcSMZ4Iq3CY9XBwejat8O7XTt07duja9cebUzzKj+oTaGhNPt6EefffIsLP/7I+U8+Rf/vPpq89WaZfjtui6EQfnsM9v2o3I+/AWnUJ2h0QWA0ktpoCO3PL0fKPoLm6Apof5NLzb0UWZbJ+vgTsj/6CICwB+4n/NFHazRw2a9jRyRfXyx5eVhSUtDFxzvaXJfi7p+jRaUV5Lr4dmivIKivDGfsr7rrudRB0mq1dO/enTVr1jBq1ChASYmtWbOG6dOnX/FcnU5H06ZNMRqN/Pzzz9x+++2252RZ5uGHH+aXX35h7dq1tGjRokpb9uzZA0CUu2oNEm6FFbPsEmsHDB/O2bffoXDbdkw5OZ7xoe6h5P7+B6YzZ1A3akTAqFFQKtCuLl5hYQQMGkTAoEGA8jtsOnOG4n1JlCTtK/03CUtBAcW7d1O8ezc5peeqAgLQdeigCMATOnLo3xKQVfR0s/5HlxMXGcDmY9kcysiv1EGSZRnT6dMUJyejP3BAcYqSkzFVUuXqFRWlOJDt2qFrrzhEXpGRNfrDCqDy9ibq5Zfw6daVjBdfonDjRlJuGUOz99/Dp5MbqMsr49xh+HEcnDsIkhqGvghXlf1iZVL7YOl+L+pN78DG2dDuxmp98XI2sixzbvZssud+AUD4Y4/SqBYtWCSNBt9u3SjcuJGi7TvqnYPk7nhiB20rLk+xzZw5kwkTJtCjRw969erFe++9R2Fhoa2qbfz48TRt2pTXXnsNgG3btpGenk6XLl1IT0/nxRdfxGKx8OSTT9rWnDZtGt9++y1Lly4lICCAjNJS96CgIHx8fDh27BjffvstI0eOJCwsjH///ZcZM2YwYMAAOrnrh54uEDreCrsWKiX/1XCQtM2b4x0fj/7gQQr+/pvgMWOcb2cDRDabyZ47F4CwSRNRVRBptBdJktA0aYKmSRMCRwxXrmOxYEg9cdFh2rePkgMHsOTnU7R1K0VbtwLwNHC/tz+NznXhXLcudVY5Zy9tG190kED5fzSkptqcoJIDSoTIUlpkUQZJQhsTo0SELokOOetLQPCoUejatSP9kUcxnDhB6t330PiZpwm5884aO19OI2kJLHsYDAXg3xhunV/p54Wl51TU2z6B07vh+FpoNbhubb0M2WIh83+vkVNakRzx9FOETZxY63V9e/ZUHKQdO0RVbx1TbKtgEw6S3YwdO5Zz587x/PPPk5GRQZcuXVi+fLlNuJ2WlobqkvRBSUkJs2bN4vjx4/j7+zNy5EgWLVpEcHCw7ZhPP/0UUJpBXsr8+fOZOHEiWq2W1atX25yx6OhoxowZw6xZs5y+31rRfaLiICUvheveAN+q/+AFjhjOuYMHyVu5UjhITiJ/1WoMKSmoAgMJHnsHzuoNLalUeLdsgXfLFgTdpKRDZKMR/dGjtvRcVuIepJRjhOgLMG/eSNbmi9o7TZMmF1sNJHREl9ABtQMr5+zBYjDQpTiDEanbiE39k9Svz1Ny6BBycXH5gzUavFu3Lo0KKQ6RLi6u0uaAzkIXF0fs4p848+x/yV+1isyXX6F4126iXnqxzm2pEJMBVj0H2z5T7sf2hzFfQkDjys/xawTdJyjnbJztUgdJNpvJePFFLvykpJMjX3yBkDscU4FrnS5QtGMHssVSb3RI7o5ssaBP9rwO2lZc7iCBohWqLKW2du3aMvcHDhxIcnLyFderqjFfdHQ06y4pufYYmnaDqM5wZq/SWbtv1f1LAoYP59z7H1C4eQvmvDzUDhSUC0q1Ep/PASD0nntQ+/thqcOyW0mjsaWUuP12flpzhI+W72d8uJ6HIg0UJymOkyElBePp0xhPnybfWgEkSWhbtLA5TD4dE/Bu184hEbBLMRcUoj90sExkSH/0KHEmE3Glx1jdIsnHB118vC1F5t2uHd5t2qDSukcZujoggKYfvM/5BQs5+/bb5P3+OyUHD9Dsgw/wdmWH4Nx0+GkinCrtGH31DBg8C9TV+IjvOx12fAEp6+FUIjTr7lRTK0I2mTj97LPkLfsNVCqiXn2V4NGjHLa+T0IHJB8fzBcuoD96FJ0HVyR6EobUVCwe2EHbils4SAI76D4Rfp+hDLDt81CVmgHvVq3QtmqF4dgxCtautUUeBI6hcONG9MkHkHx9CRl3j6vNYUfqeQxqDTH9uxDaN9b2uDk/n5L9+22RpuKkfZhOn8Fw/DiG48fJLa0UwssL77Zt8EnoqAjBO3bEu3XrKivnrJjOny8VT5dqhvYnY0hLq7ALvCooiERtBEeDmjJ18rWEdumINibG7TvtSpJE2KSJ+HTqSPpjMzAcPUbqrbcR9er/EXjddXVv0LG/4ed7oSgbvINg9GcQb0fVanA0dBoLe75Rokh3fOM8WytANhhIf/wJZXakWk3Tt94k0MFVt5JWi2/XLhRu3kLRjh3CQaojSpKU/kee1kHbinCQPI2Otyli7ewjdoi1h5H96THyVq4UDpKDyZqjRI9Cbr/d5SJ4k9nCrhOKdLtHbNn0qzogAL8+ffDr0+fi8ZdUzlkjTebz55UGiskH4Eel+qlM5VzHjugSEpCaNMEr5wIFf/+D6fBhW2TIVMloG6/IyIvi6Q7t0bVrh1dUFFPe/IdTOcWM6NKHqJae1aPGt3t3WvyyhPT/PE7Rtm2kz5hJ0a7dNH7icaS6iHiVdsXmn/8BMkR2hNu/gtAafFPv9xjs+RYO/g5nD0JE3QiZLXo96Y8+RsHatUgaDU3fe5eAa65xyrV8e/ZUHKTtOwh1w6re+ognC7RBOEieh3fAJWLtysWXlxI4YgTZn35G4YaNWAoL3UMvUQ8o2rmT4p2JSBoNoZMmutocDpzJp9BgJkDnRVzjajQTrahy7vRpRQC+P+mKlXN4edHSZKIid0gbE4OuQ3tFON1O0QxVJhCPaxzAqZxiDmfm09vDHCQAr0aNaP7lF5z74EOyP/+cnEWLKNm3j6bvvYvGmWOMis7DkqlwdLVyv9t4uO5N0PjUbL3wttDuBjjwG2x6T4lCORlLURGnpk+ncPMWJG9vmn30Ef79r676xBri20uZQlC0cyeyLLufuL4eUiwcJEGd02PSJWLtN6sUa3vHxaFp3hxjWhoF69e7Jg1QD8n6/HMAgkaPRtP4CkLYOsI6oLZHTAgqlf0f/pIkoWnaFE3TpgReOwKovHJO1uuRVSq827TBp337i9VkcfGo/avvgLeNDGDNwbMczPDcobWSlxcRM2fg06ULp596iuI9e0gZfQtN33kbv6uucvwF0xPhxwmQexK8dHD9bOjqgIjI1TMVB+nfH2HQMxASU/s1K8FcUMjJB+5XvmD4+hL96af49a7eGKWaouvYEcnbG3N2Nobjx/GuoN+dwHFcKtDWdfDMGXhCyu+JNOmqiLXNBiUsXgWSJBE4fBgAeStXOtu6BkFJcjKF6zeASkXYvVNcbQ5w0UHq6cABtdbKuaCbbiLyv88S+/13xO3cQcyff3D0lZdpvvgnmrz2P0LH3YNv9+52OUegDK0FOJzpuQ6SlYAhg2mx5Ge827fDnJND2pR7OffJJ8gWB9U1yrIipp53reIchbaEe1c7xjkCpQik5SCQzbD5Q8esWQHm3FzSpkymeGciKn9/mn/5hdOdIwCVVotPly4AFG3f7vTrNXQuFWi7tIChFggHyVPprvSJInFBhQLYywkYoUQECtatx1JS4kTDGgZZnyt9jwJHjkTbvLmLrVHSYztSlQRYz1jn9juSNBo00dHI1RRuX4m2panAgxn5VVafegLa6Ghiv/uO4NtuBVkm64MPOfnAA5hycqo++UoYCmHJffDHf5QvRvE3wH1rFd2RI7m6dJzS7kVQUHEzztpgOn+eExMnUbL3X9RBQTRfsADfrl0dfp3K8O11sdxf4Fxs+qP4+GoXebgbwkHyVDreClr/UrH2pioP1yUk4NUkCrmoiMLL5tIJ7EN/PMVWKh82daqLrVFIzS4iq0CP1ktFp2ZBrjan2rQM90OtksgvMZGRVz8cd5W3N1GvvELU//6H5O1N4foNpIwZQ/G+fTVb8NxhmDtEGRkiqWH4/8HYr0HnhNe5xQBo2h1MJbD1U4cubTx7lhPjx6M/cAB1WBjNv/oKnzpuHmjth1S4Y0e9cMjdGVsFm4fqj0A4SJ6LVawNShSpCiRJInCYSLM5guwvvgBZxn/IEHRx7lEubE2vdW4WhLeX55TTenupadlIScsd8mAdUkUE3zKa2B++R9O8OabTZzhx193kfPedfX+Yk5bA3MHKyBD/xjDht3IjQxyKJF2MIu34Akoq6GJeA4xnzpA2bjyGo8fwatyYmEWLXPLe8encGUmrxXwuC0NKap1fvyHh6QJtEA6SZ9N9ovJv8lIozK7y8IDhysiKgn/WIhsMTjSs/mI8fZrcZUrPoEb3uUf0CGBHSqn+yMnpNWfQtlSHVN8cJFDSCy1+Xoz/0GuQjUYyXnqZ0089haWo6Monmgzw11OweJIyMiS2P9y/oVpVq7UmbiSEx4M+D3Z8WevlDCdPcuKecRhOnEDTtCkxXy/Cu2XV8zGdgcrbG5/OnQGRZnMm9UGgDcJB8myadIWoLoomYe93VR7u07UrXuHhWPLzKSyd2yWwj+x588FkwrdPH5vg0x3YeaJu9EfOIL5Uh3SoHgi1K0IdEECzDz8k4oknQK0mb9lvpI4di/54SsUn5KbDgusvjgy5egaM+/XKI0MciUql9EUC2PoJGCsY/1JN9MdTOHHPOIzp6WhimhPz9SK00dGOsbOGXDp2ROAcbAJtnc5jBdogHCTPxxpFqoZYW1KpCBg2FIA867gJQbUxZWdz4aefAGh0/30utuYiZ/NLSMkqRJKgW4xrm1XWhLb1qJKtMiRJImzKZGIWzEcd3gj9kaOk3norecsvex8e+xvm9FdGhngHwR3fwdAXqzcyxJF0vBWCmkPhOdj9dY2WKDl0mBPjxmHKzETbuhUxixahiYpysKH2YxNqb98udEhOwibQbtfOYwXaIBwkz8dOsbYtzbbmb2STydnW1SvOL/wKWa9H16kTvpd0pHY1iaXVa3GNAwjy0bjYGvuxNrU8klmA2VK//2D59uxJyyVL8O3RA0tREemPPUbma68h6/Ww7k1YdIsyMiSyE9y/zr6RIY5ErVG0TgCbPwCzfZ8Vxfv3kzZ+PObsbLzbtSPmq6/QREQ4wVD78encGUmjwXT2LMa0NFebUy8pSfJ8/REIB8nzuVSsvXN+lYf79uiBOjgY84ULIsRsB+a8PHK+VXpONbr/Prfqwru9VKDdy4H9j+qS5qG+6DQq9CYLJ7ILXW2O0/EKD6f5gvm2/lnnF37Fiev7YvzjNUBWumJPWQmhrtHp2Oh6D/g2ggtpkPRztU8r2r2btImTMOfmouvUiZgF8yvtpO4KVD4+6Dp1AkSazVl4+ogRK8JBqg9YeyIdWFalWFvy8rqYZhPVbNUm59vvsBQU4N2mNf6DB7vanDLsTK14/pqnoFJJtn5I9VGoXRGSlxcRjz9Os5dmoNJC8aliUlZEUNjqCbjpw5qPDHEkWl/o86Bye+O7yuy3Kijctp20Kfdiyc/Hp0d3ms/7EnWQ+7Wd8O3ZA4BC0TDS4cgWCyXJyYBnC7RBOEj1gyZdLhFrV91Z25pmy1+1Gtlsdq5t9QBLcTHnFy4ElL5Hksp93jYFehP7Tyul2D1jPU9/ZKVtPRdql0OWYftcAg49S4thmXiHSZj1KtJe+46szz5zXPft2tLzXtAGwLkDcHj5FQ8t2LCRk/fdh1xUhN9VfWn++eeo/f3ryFD78LPOZduxU+iQHEx9EWiDcJDqDz2q31nbr3dvVAEBmLOyKN692/m2eTgXflqMOSdHmVE20kWakErYnZaDRYZmIT5EBblB1KGG1KeRI1Vi7Yr95+NgMaLteR2xf/xD0JhbwGLh3Hvvc+rBhzBfuOBqS8EnGHqWjtLZOLvSz5b8v//m1EMPIev1+A8cSLNPP0Xl61t3dtqJT5cu4OWF6cwZjOnprjanXlEfOmhbEQ5SfSFhTKlY+yikXrlTtqTVEjBESROJNNuVkQ0GsufNAyBs6r1u94a39j/q5aHpNSuXjhyp11TSFVsV3Jgmr75K1Kv/h+TtTcG6daSMuZXiUrGrS+k7TRmKe2pHhZ8teX/9xalHHkU2GgkYPpxmH36AytvbBYZWH5WvLz4JCQAUbRNpNkdiE2iX/v96MsJBqi94B0DH25Tb1eisbZ3Nlr9ylfuE892Q3N9+x5SRgTq8EUGjR7vanHLs8HD9kRVrBCk1q5ASYz1N+5bpih0JE38v1xU7eMwYYr//Dk10NMb0dE7ceSc5P/zo2jSQf4Qi2AYlinQJF379lfT/PA4mE4E33kjT2e8gabUuMNJ+fG1pNiHUdiT1RaANwkGqX1h7IlVDrO3Xrx8qX19MGRmU1HRGVD1HNpvJnqsMpQ2bOMntvhUbTBZ2n1QcpF4tPFd/BBAe4E2wrwaLDMfOFbjaHMdSYVfs9RBzVYWH69q1U7pvDxmidN9+4QXOPP0MluKaN2ysNVc9okS8jv0Np5W0fM73P3Dm6WfAYiH4tltp8vprbhdhvRKiYaTjqU8CbRAOUv3CDrG2ytsb/0EDAZFmq4z8VaswpKaiCgoieOxYV5tTjv2ncykxWgjx1dAq3D3FsNVFkuppJVtuOiwYaXdXbHVgIM0+/oiIx/8DKhW5S5eSOvYO9CmVdN92NiExF9uJbHyX8wsXkvHii8pT99xD5EsvIak9ZwYgKJMFUKsxpqcLHZKDqE8CbRAOUv3DDrF2wPBL0myikqMMsiyTNedzAELvuQe1v5+LLSqPdUBtj9hQt+rLVFOsabZ6U8lm64q9o0ZdsSVJIuzee2k+fz7qRo3QHz5M6q23kbfCRV9orp4BQNbiNWS+9joAYfdOofF/n3Wrys7qovb3Q5egpIEKRRTJIdQngTYIB6n+YYdY239AfySdDuPJk+gPHKgjAz2Dwg0b0B84gOTrS8g9d7vanArZnmKdv+bZ6TUr9SaCZLE4tCu2X+9etFjyMz49umMpLCT90UfJfP0NZKPRwYZfGTk8nrPpXTn3byAAjaZPJ/w///Fo59xPpNkcSn3poG1FOEj1jTJi7St31lb5+uLf/2pApNkuxxo9Chk7Fq8Q93NALBaZxBNKBMkTB9RWRJy11N+THaSi8/DtbfDPqyhdsSfAlFW17oqtiYggZv58QidPBuD8ggWcmDgJY+ZZBxhdNbIsc/bNt8jekAlARJcCwseN8mjnCC7RIW0XDpIjqE8CbRAOUv3EJtb+DQqzrniorWnkipUizVZK0c6dFCcmImk0hE6c6GpzKuTYuQJyiozoNCo6NHG/TsU1wRpBOp1bQl5J3UZHHMKpRJgzAI6uVsrib/4EbvoANDqHLC9pNDR+8gmafvgBKn9/ihMTSbnlFgq3brN7LYPZQJ4lr1rHyhYLma+8wvn5yheuxsMaERafB1s+svu67oZP9+6gUmE8eRJjRoarzfFoZIuFktJMhDV16ekIB6k+0qQLNOmqiLX3XFms7T9oEJJGgyElBcPRo3Vjn5tjjR4F3XILmsbuMWDzcqzl/V2jQ9B61Y+3cZCPhqggxZnwqChSaVds5o2A3JMQ2hLuXQ1dnZOaDRw2jBaLf8I7Lg5zdjZpkyeTNedzu9p1PLXxKd7Oe5tdZ3dd8TjZbObMrOfI+fY7kCQiX3mZ0EdfVJ5MXFBltay7o/b3R9deqbYSabbaYUg9gaWwsN4ItEE4SPUXaxSpCrG2OiAAv6uUcmORZlOmkBdu2AAqlW2YqDtiFWjXF/2RFY8bOXJZV2zib4D71kJkR6deVhsbS+z33ym9uSwWzr37LqcemoY5N7fKc5OykliXvg4LFj7f93mlx8lGI6efeJLcJUtArabJm28Qcttt0GoIRHUGYxFsn+PIbbmEi2k20TCyNtQ3gTYIB6n+knCrItY+fwxSN1zxUFuabeWqurDMrcn+XOl7FHj99Wijo11sTeXYHKQW9UN/ZCXek3RIlXTFRlc3KU+Vjw9R/3uVyFdeRtJqKVi7Vum+vf/K3be/2PeF7fb2zO0kZSWVO8ZiMJA+cyZ5f/4JXl40nT2boBtvVJ6UJLh6pnJ72xzQe8BrdQV8ewkdkiMoSVJ+j+qL/giEg1R/8favdmdt/yGDQa1Gf+gQhtRUp5vmruiPHye/NIoWNvVeF1tTOWdyizmVU4xKgq7N62cEye1HjlSjK3ZdIEkSIbfdRsx336Jp1gzjqVOcuPMucn76qUJN4fELx1mTtgaAWHUsUNZhArCUlHBq+nTyV61G0mpp9tGHBI4YXnahdjdCWGsouVCtzv3ujG/37iBJGE6cqDPRe32kvgm0QThI9RtrT6TkZVcUa3uFhODXuzcAeQ04ipQ99wuQZfyvuQZd27auNqdSrPqjDk2C8PeuH6FsK3GXDK11y6IBO7ti1xU+HToo3bcHD0Y2GMh47nnOPPvfct23v0z6EoDBzQZzk+9NAKxJW8PxC8cBsBQWcvL+ByhcvwFJpyP6s08JGDSo/AVVauj3mHJ7y8dg0jtra05HHRiIrl07QOiQakoZgbZwkAQeQVRnRaxtMVYp1r6YZmuYOiRjejq5v/0GQKP7prrYmitjHVDbo57pjwBaR/ijkiCnyMi5fDf7o5t7qkZdsesKdVAQzT7+iPCZM5Xu27/8Quodd2I4cQKAMwVn+PP4nwBMaj+JCHUEg5spQ6u/TPoSc34+aVPvo2jbNlS+vjT/Yq5Nn1ghncZCYFPIPwN7v3P6/pyJGDtSO8oItFvVD4E2CAep/tO9ep21A4ZeA5JESVJSg2y7nz1vPphM+Pbtg0/nzq4254pY9Ue96kn/o0vRadTEhildy91KqH3sb6WEv4ZdsesKSaWi0X1TaT5vHuqwMPSHDpEy5lbyVq1iwf4FmGQTvSN7k9BImbQ+qb3y+bBu/x8cmzCO4l27UAUG0nz+PHx79Ljyxby00He6cnvT+2Dx3CHDF3VIQqhdE+qjQBuEg1T/SRgD2oAqxdpejRrZPhDzVjWsNJspK4sLixcD0Oj++11szZXJLTbaHIce9dBBgotpNrfoqC07tit2XeHXpzctlizBp1s3LAUFpD/8CNpPv0NlkZnS8WJ1ZkKjBAb5deW/3+gxJx9CHRJCzMIF1f+S0H0C+ITC+eOQ/KtzNlMH2HRIKSmYzp1ztTkeR33UH4FwkOo/3v7QqVSsvfPKnbUvbRrZkDi/8CtkvR5d5074lmqx3JVdJ3KQZWjRyI/wAG9Xm+MU3GXkiMaUj/r7Ox3eFbuu0DSOIGbhAluz05Fbjbzxo44e6la2Y0xnz3L/56eIPQsX/CWC535g0+NUC60f9H5Aub3h3SrnP7or6uBgvOPiAKVRrMA+6mMFGwgHqWFQzc7aAcOHAVC8e3eDqeYw5+WR862iz2p0//0OGZ1QbCom3+KcP+7b62n/o0u5VKjtKqT0XQw6+Dyq42uUrtijPnVoV+y6QtJo8Js5jU9u86NICzEphaSMGUPxjp145eSQPnES6rQz5AZreP5uFT8Yt9h/kV5TlZYimfuULuIeilWHVOghabYLRUaKTK62ov4KtEE4SA2DqM7QpFupWPubSg/TNG5sC63nr24Yabacb7/FUliId5s2+FdUrWMnsiwz7Z9pvJP3DgfOO34A8M5Uq0C7fqbX4FIHqQCLpY4jEkXn4c8nUS+8Dl9jNnJIC7h3DXS5q27tcCA/Hf6Jta31fPxwDNq2bTFnZZF+7700/+hjjCdPomnWjOL3/0tGqMR3B7+jwFBg3wV8Qy9+Cdsw2+H21xU2HZIHCLVzCg1c9+EmXt+jpkDvWi+pvgq0QThIDYdqdtYOGDECaBhpNktREecXfgVA2H33Ialq/3bYe24ve87twYSJtxLfcmipeonRzN6TSqfk+ijQthIT6ovWS0Wx0czJnKK6uajZpIwL+bAbbJ+DJJtJD+6FafIaiEyoGxucgN6s56tk5Xf8psEP0OKH7wm6+WawWPAqKEATG0vMN18zsNdttAhqQb4hn58O/2T/hfpOB7UW0jZD2lYH76JusGowDUePYcp27xEqn60/RlaBgVyjxC+7T7vUFpv+KC6uXgm0QThIDQebWPs4pKyv9DBrmq1o505M58/XlXUu4cLixZhzctBERxN43bUOWfPSPy57zu1heepyh6wLsC89F4PZQiN/b2LCfB22rrvhpVbRJsIfqCMd0rF/4LOrlXEhxTkQ0QHT3UvY2WI66AKdf30nsvToUrKKs4j0i+T6Ftcr3bdff42IV/+PC7160XT+fDSNG6OSVExOmAzAV8lfoTfb2WIhMAo636nc9tAokldICN5t2gBQtMN9dUhn80pYuDnVdv+rrWl1H2m9BJuDlOC5XyQqQzhIDYVLxdpX6HyrbdZMGd5osZC/2nP1BFUhGwxkfzkPgLApUxzyzSdXn8uK1BUAtPNShK7v7HyHYlPxlU6rNttL+x/1ahHiEK2UOxNXF0Lt7GPw3V2waBScO6BUY13/Dty/Hjl2gPOuW0eYLCbmJymFGRM7TESj1gBK9+3Am27i7Jhb8GoUZjv++hbXE+kXSVZxFkuPLrX/gv0eBUkFR1ZARvnxJZ6Ab69egHun2T7+5yglRgsdmwaiU8ukZhex7rDrKu/qawUbCAepYWHtiXTgNyio/A3VEGaz5S5bhikzE6/wcIJGj3LImr8f/x29WU/r4Nbc7nc7UX5RZBZl2v5I1Rab/iim/qbXrLSNdOLQWn0+rHoBPukDh/5Q5qj1fhAe2QU973W73kY1ZWXqSk4VnCLYO5jRrUdXebxGrWFC+wkAzE+aj8lip7YlrBW0H6Xc3viunda6B+7eMPJUThHfbk8D4MnhbekToUSO5m1KcYk9ssVCSXIyIBwkgacT1emiWHtv5Z21A0rnLhVu3Vqt6eCehmw2K2NFgNBJk1B5175cXpZlfjqkpNfGtB6DRtIwo+sMAOYlzeNMwZlarW+2yOw8oYwY6VXPBtRWhFMq2SwW2P01fNANNr0HZgO0ugYe3AzXvQ4+9acyUJZl21iRu9vdja+meinZW9rcQrB3MKcKTrEytQY6xKuV33n2L1HS+R6Gb09Fh6Q/fBhTTo6LrSnPB2uOYDTL9GsdRp+WofSPtCBJsOFIFkfP1n3VZ30WaINwkBoePS7prG2xVHiId4sWSi7eZCL/73/qzrY6In/lSgwnTqAKCiJk7O0OWXP32d0cyz2Gj5cPI2OVJoLXRF9Dj8Y90Jv1zE6snS7jcGY++SUm/LRq28T7+ow1xXb8XCEGU8W/p3aRtlUZLrt0GhSehdBWcNePcM/PEBFf+/XdjA3pGziccxhfL1/ujL+z2uf5any5u93dgDJ+xO4ig6hO0HqY0mBz0wf2nesGeIWFoW2t9Ilyt35Ix84V8PMuZcrB48OVnk2NdHBNXDgA8zel1rlN9VmgDcJBanh0uOWiWPsKnbXr62w2WZbJmvM5AKHjxqHy83PIulZx9nUtriNAq/xxlySJp3o9hUpSsTx1OYmZiTVe3zpepFtMCF7q+v+2jQrSEaDzwmSROZ5lZ9n5peSegsVTYN4IOLMHvANh+P/BQ1uh7Qiop1quL/YpEdLb424nyDvIrnPvjL8TXy9fDuccZkN65Z8RldJ/pvLvnm8gP8P+812Mu6bZ3l11GLNFZmi7CLo2vxjtnNA3BoAlu9LJLTLWqU31WX8EwkFqeHj7Q6fSqEli5doYW5pt0ybMBbX4A+VmFK5fj/7gQSRfX0Lvudsha14ouWBLR9zW9rYyz8WHxjOmzRgA3tj+BuYazqvakaqE+3vW4/L+S5EkqXZCbUMRrH0dPuwBSYsBSemE/fAuuOphZY5YPSUxM5HdZ3ejUWkY136c3ecHeQdxe5zyGWF1tOwi5iqI7qOkMLd8bP/5LsbP6iBtdx8HKfl0Hr//q6TpZw6LK/Nc7xYhxEcGUGw088POtDq1qz5XsIFwkBomts7av1cq1vZu0wZtbCyywUDB2nV1Z5sTkWWZrM/mABByxx2og4Mdsu6yY8swWAy0C21Hh7Dy36Smd51OgCaAA+cP8OvRX+1eX5ZldqRYO2g3DAcJLhFq2+MgyTIk/Qwf9YS1r4GpGGL6KfPTbvoA/MOdZK37YHVqbm59MxG+ETVaY1z7cWhUGnaf3V2zyKc1irRzntI6wYOwRpD0hw65jQZz9qpDANzYuQntm5RtPSFJEpP6xQKwcPMJTGYHpKSrQX0XaINwkBomUZ2gafcrdtaWJKnepdmKd+6kePduJI2G0IkTHLKmLMu29NqtbW+tsPw+VBfKA52VeVUf7P6AfIN9EZFTOcVk5JWgUUt0iQ6utc2eQry9Qu3Tu2H+dbB4MuSdgqBouG0BTPxD6SbfADh0/hAb0zcqfY06TK7xOhG+Edzc+maghlGkNsOhcQIYCpQGnB6EV3g42hYtQJYpSqx5WtxR7ErLYfWBs6hVEjOGtqnwmJu7NCXEV0P6hWJWH8isE7sMJ+q3QBuEg9RwsUaRdi2sVKxtdZAKNmzAUlRHHY2diFV7FDTmFjQRNftmfTk7M3eSmpeKr5cv17e8vtLj7oy/k9jAWM6XnOfzfz+36xpW/VFC0yB8tOpa2etJWIfWHqwqglRwFpZOh88HQ9oW0PjC4FkwfQd0GF1vdUYV8eU+pXJtRMwIogOja7XW5A6TUUkqNqZv5OD5g/adLEkXK9q2fgqGwlrZUtfYdEjbXD+X7Z2VSvRoTLemtAz3r/AYnUbNXb2bAzCvjsTaJUn1W6ANdjpIZ89eeYCpyWRiu4cM+mvwXNpZO7Xiztq6Du3RNG2KXFxMwYaNdWygYylO2k/hxo2gVhM2ZYrD1rWW9o9sORI/TeWCb41aw5M9nwTg6wNfk5qbWu1rNDT9kRWrBulUTnHF86ZMetj0vlK2v3sRIEPH22H6Thj4BGh86tZgF5OWl8aKE0qj0ikda/87Hh0YzYgYZfTQvH3z7F+g/SgIiYXi87Drq1rbU5e4S8PIzcey2HQ0G41a4pFrKo4eWRnXJxYvlcT2lPPsP+381GB9F2iDnQ5SVFRUGSepY8eOnDx50nY/Ozubvn37Os46gfPQ+l0i1l5Q4SH1Kc2W/bkStQm8fiTa6Np9s7ZyvuQ8q9KUZpqXi7Mron+z/vRv2h+TxcTbO9+u9nWsEaSG5iCF+GmJCFB6VB25NM0my3DoL6XR46rnwZCv9PeasgrGzIWgpi6y2LXM3z8fi2yhf9P+xIXGVX1CNbA6WitOrCAtz04BsNpL6a4NsPlDMBkcYlNdYI0glRw8iDkvzyU2yLLM2yuU6NFdvZrTLOTKvawig3Rc1zEKqJuSf+EgXcblPTFSU1MxGo1XPEbgxlh7Il1BrG2dzVawdi0Wg+d8wF2K/tgx8lcpjkyjqVMdtu7So0sxWUx0COtA+7D21TrniZ5P4CV5se7UOjamVx2VO19o4OhZpYqwR0z9aWRYXeIuF2qfPQCLRsN3dyjRT//GMOpTuHcNRPdyoaWu5WzRWdt4kHs73uuwdeNC4+jftD8W2cL8/TXoCN/5LuU1ykuHfT86zC5no2kcgSamOVgsLtMh/XPoLLvSLqDTqJg2pHW1zrGKtZftOU1WgZ3z9OygjEC7nlawgRM0SPV9RlS9IrJjlWJtn86d8YqIwFJYSOGmTXVsoGPInvsFyDL+Q6+xDaOsLRbZwuLDi4HqRY+stAhqwV3t7gLgzR1vYrRcuW+JdbxImwh/Qvzqb2l6ZVh1SCfS0+HPJ+DTfnD8H2Vy/NUz4eFE6HIXqBq2nHJR8iKMFiPdIrrRrXE3h65tdbiWHl3K2aIryyzKodFB32nK7Y3vQQ3bXLgCP1uare4bRlosMm+tOAzAhKtiiQjQVeu8rtHBdG4WhMFs4dttziv5bwgCbRAibYF1PlslYm1JpbqYZlvheWk2w6l0cn/7DYBG993nsHW3Z2wnLT8Nf40/17W4zq5z7+98P6G6UFJyU/jh4A9XPNY6XqRHA0uvWYmP8GGceiUP7bsNtn8Oshnib4Bp22HoC+Bd/7uKV0WuPpcfDynRGUdojy6nW+NudI3oitFiZFHyIvsX6DEZdEGQfQQO/u5w+5yFKxtG/pl0hgNn8gjw9uKBAa2qfZ5S8t8CgEVbTzimC30FlOwvjR7VY4E22OkgSZJEfn4+eXl55ObmIkkSBQUF5OXl2X5qwscff0xsbCw6nY7evXtfUehtNBp5+eWXadWqFTqdjs6dO7N8+fIyx7z22mv07NmTgIAAIiIiGDVqFIcOHSpzTElJCdOmTSMsLAx/f3/GjBlDZmbdlEe6FQm3KN2FryDWtqbZ8v/5B9lYt51aa8v5efPAbMbvqr74dOrksHWt4uzrW15f7TlXVgK1gTzc9WEAPtnzCedLzld67PbS/ke9WjS89BrH/uGGzbfzimYBAZZ8iOgA45fBHd9AaAtXW+c2fHfwO4pMRbQNaUv/pv2dcg1rFOnHQz+Sq7dTAOwdAL1Kv5xsmK1oyDwAmw5p//46bZZrMluYvUqJHt3bv6XdkeORHaOICPDmXL6eP/fVbgZkZZQkJQH1W38ENdAgtW3blpCQEEJDQykoKKBr166EhIQQEhJCXJz9wsAffviBmTNn8sILL7Br1y46d+7MiBEjKq2YmzVrFnPmzOHDDz8kOTmZBx54gNGjR7N7927bMevWrWPatGls3bqVVatWYTQaGT58OIWFF0tNZ8yYwW+//cZPP/3EunXrOH36NLfccovd9ns81RBr+3bvjjosDEtuLoVuUPZaXUznznFhsZIGC7vvfoetm1Wcxd9pfwP2pdcuZXTr0cSHxpNvzOfj3RV3Gy42mElKV/4Y9YhpQBGk7GPw3V2waBQ+Fw5zXvZnlnESWfesgpYDXW2dW1FkLOKbA0p6fErCFKdJHPo37U/bkLYUmYr47uB39i/Q+wHw8lHGvRz3jPmOmqgoNNHRYLFQvGtXnV33l93pHD9XSIivhslXx9p9vtZLxT19lPEj8zelOEUX3BAE2mCng/TPP//w999/234qu28Ps2fPZurUqUyaNIn27dvz2Wef4evry7x5FZeVLlq0iGeffZaRI0fSsmVLHnzwQUaOHMk777xjO2b58uVMnDiRDh060LlzZxYsWEBaWhqJpWK73NxcvvzyS2bPns2QIUPo3r078+fPZ/PmzWzdutUu++sFVXTWltRqAoYOBSB/xYo6NKx2nP/qK2SDAZ/OnfHt7TgB769Hf8Ukm+gU3qnG1UJqlZqnej4FwOIjizl0/lC5Y3afzMFkkYkK0tEspAGUrJfkKVVpn/SBQ3+ApIbeDzLO7zO+Ng/j8NliV1vodiw5soQL+gs082/G8NjhTruOJElMSVDSd98c+IYio5190fwaQffS5qwbaje4uS6p6zSbwWTh/TVHAHhwUCsCdJoarXNX7+Zo1Sr2nsplV9oFB1p4uUBbOEg2Bg4cWK2f6mIwGEhMTGRo6R9fAJVKxdChQ9myZUuF5+j1enS6soI1Hx8fNm6svCIot7RdfGio8i08MTERo9FY5rrx8fE0b9680uvWayI7QtMeVxRr29Jsa9Ygm91faGnOzSXnW+Wbbtj99zvsm3VNxdkV0SOyByNiR2CRLbyx441y3/R2pl7UH9Xr4geLBXYtgg+7K32NzAZodQ08uBmue50mkUrp8qHqdtRuIBjNRhbsXwDApIRJeKmcqwUZHjucZv7NuKC/wJIjS+xfoO90UHkpQ7JP1b3wuSZYHaTCOurv98OONE7lFBMR4M34vrE1XqeRvzc3dWkCKFEkR2ITaHt7492q+vooT8Sud5TJZMJsNuPt7W17LDMzk88++4zCwkJuuukmrr766mqvl5WVhdlspnHjxmUeb9y4MQcPVty5dcSIEcyePZsBAwbQqlUr1qxZw5IlSzBX8kfbYrHw2GOP0a9fPxJKyxEzMjLQarUEXzaLq3HjxmRkVDx9Wq/Xo9dfLJu06q2MRmO5Vge1wbqWI9esDlKXcXil70ROXICp14MglfWdtV27ogoMxHz+PHnbttk+OGpCXezx/KKvsRQWom3TBu9+VznsWptPbya9IB1/jT9Dmg6pcF179vdw54dZe3ItOzJ2sOL4Cq5pfo3tuW3HswHoHh1Y578PVeGo11A6uQ3VymdRZewFQA5tiXno/yG3HqZ0YzYaaRPuxyrg4JncOvt/cNX70B6WHltKZlEmjXSNGBkz0i5ba7q/8e3G878d/2PB/gXc0vIWNGo7Ihx+kagTbkP173dY1r+N+bYaCL7twBGvobZrF0DpGq3PzUXla5/e0B6KDWY+/PsoAA8NbIEaC0Zj5SLrqvY3rnczFiee4q+kDNKy8okKql4lXFUU/vsvANq4OEyyDE56jzjzPVjdNe1ykKZOnYpWq2XOHGXgZ35+Pj179qSkpISoqCjeffddli5dysiRI+23uJq8//77TJ06lfj4eCRJolWrVkyaNKnSlNy0adNISkq6YoSpOrz22mu89NJL5R5fuXIlvk5406wq7dtTV6jNvoxQ+aDJSWH7j++QFVA+dNq4TRuCEhPZ/8UXnDtXcd8ke3DWHiWDgZbz5qEGTvToTtJlIv7a8G3htwAkSAn8s/LKWorq7q+vV1/Wmtfy6uZXKdhXgEbSYJZhZ4oakChKS+LP7KTamu4Uavoa6gzZdDj9A81ylJS2UeXDoahRHG80DPmICY78ZTu2MEsC1Gw7eIo/NSccYXa1qev3YXWxyBY+zle0a93pzpoVa2q0jr3708ga/CV/MosyeX3Z63T37m7X+f7Gzgzhe1SH/2Ltz5+T79PMrvNrQm1fwxYhwWhyLrD+888patvWQVaV5+/TEmfz1YR6ywScS+LPP6v3nr/S/loFqDmWDy9/t5Ybmzumoq3R738QCmT6+bHvzz8dsuaVcMZ7sKiao7PscpA2bdrERx99ZLv/1VdfYTabOXLkCEFBQTz11FO89dZb1XaQGjVqhFqtLlc9lpmZSWRkZIXnhIeH8+uvv1JSUkJ2djZNmjTh6aefpmXL8r0Ypk+fzu+//8769etp1uziGzEyMhKDwcCFCxfKRJGudN1nnnmGmTNn2u7n5eURHR3N8OHDCQwMrPCcmmA0Glm1ahXDhg1Do6lZ/rmmqDRbIHEefTUHMY98otzzhf7+nElMpNGRo/S49lqkGvaecfYeLyxaRFZREZroaK5+8kmHlaGeKzrHC0tfAOA/Q/9Dq+CKw8v27m+waTCjfxvN2eKzZMVmMaXDFJLS89Bv3UqAzospY4ahUrlXiq3Gr6GxCNWWj1Bt+RDJVIyMhNzlHhj0LHF+4VSk6GqTWcDCI5s5Z/DiuuuG10m60ZXvw+qw5uQasjZkEaAJYNaNs6445qYiarO/C8kXeH/P++zW7ObZa59FrbJvPqC8eCPSod8ZpNmDeaTjWm9cjqNew8xNm8hf9hsdJYkwJ335zy8x8cLsDYCRJ0cmcFO3qrvBV2d/6phMpn+/l8Qcb96dMgCdpvazHNMX/0wx0Pa66+jhxGCIM9+D1a24t+svR3p6Om0uabS3Zs0axowZQ1BQEAATJkxg/vzqd1vVarV0796dNWvWMGrUKEBJia1Zs4bp06df8VydTkfTpk0xGo38/PPP3H777bbnZFnm4Ycf5pdffmHt2rW0aFG2JLh79+5oNBqb/QCHDh0iLS2t0lEp3t7eZVKLVjQajVM+QJ217hXpORkS56E69AcqfQ74lx3oGti/P5n+/pjPncO0Pxnfbl1rdTln7NFiMHBhoTL3KWzqvWh9HCdu/i31N8yymW4R3YgPj6/y+OruT6PRMLPHTJ7e8DTz9s9jdJvR7DqlfMPpEROCt7f7Nois9msoy5D0M6x6AfJOKY/F9EO69jWkqM5XFEO2iQpCo5YoNJg5W2iqcuSCI3HJ+7AKZFlmQfICAO5sdyfBvsE1Xqsm+7uz/Z3MS55Hal4qGzI2MCxmmH0XHfAfOPQ7qqSfUQ2ZBSEx9p1vJ7V9Df179yF/2W+UJO5y2u/CV+tSuFBspGW4H7f2aI6XuvpfPq+0v+s6NaXp8sOkXyjmz/1nGduzea3slC0W9KUCbb/OnerkveGM92B117MrBKDT6SguvlhJsnXrVnr37l3m+QI7+0XMnDmTuXPnsnDhQg4cOMCDDz5IYWEhkyYpDQzHjx/PM888Yzt+27ZtLFmyhOPHj7NhwwauvfZaLBYLTz75pO2YadOm8fXXX/Ptt98SEBBARkYGGRkZNtuDgoKYMmUKM2fO5J9//iExMZFJkybRt29f+vTpY5f99QqbWNtUoVhbpdXiP2gQ4L6z2XKXLsWUmYlXRARBpU63IzBbzPx85GcAbm17q8PWtTKyxUi6hHeh2FTM+7vet3XQ7tmiHpT3n94N866Fn6cozlFQNNy2ACb+AVGdqzxdo1bRqnSKuW3kSANm65mt7M/ej06t4+52d9f59f00ftwZfycAX+z7wv4y8qbdoOUgpenn5g8db6CD8e2l6C2Lk5KwVDM1Yw85hQa+2KAIqWcOa2uXc1QVapXEhKusJf+ptS75b0gCbbDTQerSpQuLFinCug0bNpCZmcmQIUNszx87dowmTZrYZcDYsWN5++23ef755+nSpQt79uxh+fLlNuF2WloaZ85cbHZVUlLCrFmzaN++PaNHj6Zp06Zs3LixTKrs008/JTc3l0GDBhEVFWX7+eGHi12L3333XW644QbGjBnDgAEDiIyMZMmSGlRm1Des89kSK+6sbatmW7nS7ebuyWYz2V98AUDopEmotI6LvGw6vYkzhWcI8g5ySjm1JEk83etpAH47/hvbTit9Vzx6QG1+JiydBp8PhpNbQeMLg2fB9B3QYbQiwq4mtplsopKNL/d9CcCYtmMI1bnm9+PudnejU+tIzk5m65katEa5ulSusHsRFNg5vqSO0TRrhldkJBiNFO/Z4/D1P1t/jAK9ifZRgYxMiHL4+mN7NMdHo+ZgRj5bSgs/aoq1g7Z3fP3uoG3FLgfp+eef5/3336dVq1aMGDGCiRMnEhV18QX95Zdf6Nevn91GTJ8+nRMnTqDX69m2bVuZqNTatWtZsGCB7f7AgQNJTk6mpKSErKwsvvrqq3JOmSzLFf5MnDjRdoxOp+Pjjz/m/PnzFBYWsmTJkkr1Rw2KDqOVzto5KZCyrtzT/v37I/n4YDx9mpKk/S4wsHLyV6zAeCINdVAQIbfXrgT/cqyds29qdRPe6vKpVkfQoVEHRrUeBUBJ4C9ovaBTsyCnXMupmPRKuf6H3WH314AMHW+H6Tth4BOgsT/taZ3JdriBR5D2ndvHtoxteEleTGg/wWV2hOpCGdNWkSdYHTa7aDFAmQNpKoGtnzrYOsciSZItilTo4H5IZ/NKWLg5FYDHR7R1itYwyFfDmO6Kpmn+ptRarWVtEOnTof4OqL0Uu/sgJSYm8sgjjzB//nzmzp1b5vkuXbowY8YMhxooqGO0ftBprHK7gs7aKh8f/AcMANwrzSbLMllzPgcgZPw4VH72iVavREZhBuvTlTEszkivXcqj3R5Fq/JB7XOS2JiDeHvVXlRZZ8gyHPwTPu6tNHw05EOTbjBlFYyZC0FVC08rI67UQTrYwB2kL/YpEdLrW15PlL/jow32MKH9BLwkL7ZlbGPfuX32nSxJF6NIO76AEjvHl9QxzmoY+fE/RykxWujWPJjBcRFVn1BDJl6l6HBXH8gkLbvmacKG0kHbit3Jznbt2vHoo48yduxYVJdVMd1333106dLFUbYJXIW1s/bB3ysMf1vTbHkrV7hNmq1g3Tr0hw6h8vUl9G7H6jKWHFmCRbbQo3EPWgY5d3J1I59GxKpvBuCCbimFxsIqznATzh6ARaPh+zuV6KN/Yxj1Kdy7BqJr38XcmmI7fq4Qo9k5AzjdnWMXjvH3yb+RkJicMNnV5hDlH8XIlkoVk9Vxs4u4kRAeD/o82FGDKFQd4medy7b3XywlJQ5Z81ROEd9uTwPg8RFxTq3ObB3hz4C24cgyLNySWqM1GlIHbSt2OUjr16+v1o/Aw4lMgGY9KxVr+w8chKTVYjyRhv7wYRcYWBZZlsn+TOnNFXznHagvawBaG0wWk02cXdvO2dUlK70nFkMYxZacmv3hqUuKc+DPJ+DTfsqMLbVWiQw8nAhd7oIatoK4nKbBPvhp1RjMFlKzPMRpdDDzkpReb0OaD6FlsHMd9eoyJWEKEhJ/n/ybYxeO2XeySgX9HlNub/0EjO47SkYTE4NXRASy0Ujxnr0OWfP91UcwmmX6tQ7jqlaNHLLmlZjULxaAH3ecpEBvsvt8w4kTWAoKGoxAG+x0kAYNGsTgwYMZPHgwgwYNqvBn8ODBzrJVUJdYo0iJC8qJtdX+fviVdkzPX+H6NFvRjh0U79mDpNUSOsGxuowNpzZwtugsId4hDI0ZWvUJteRsfgknso3oz14PwML9CzmZf9Lp17Ubi4kW51bj9Wkv2P65UpHU7kaYth2GvqBMcHcgKpVEm8YNV6h9uuA0fx5XmvLd2/FeF1tzkZbBLRnSXCnUsTpwdtHxVghqDoXnSvVq7okkSQ5Nsx07V8DPu5R2F48Pr9k8R3sZ2Caclo38yNeb+DnxlN3nNzSBNtjpIIWEhBAdHc1zzz3HkSNHyMnJKfdz/vx5Z9kqqEs63ALeQZCTWqFY21bNtsr1DlJ2qfYoaMwtaCIcm8f/6bAizr659c1o1c7vR2Sdv9bKrxd9o/pitBh5Z+c7VZxVx2Qk4fXFIDqd+gqpOAciOsD4ZTD2awhtUfX5NSQ+suEKtRfsX4BJNtE7qjcJjdxLIGt12P48/ienC07bd7JaA1c9rNze/AGY7Y9s1BW+vZRUcZED5rK9u+owFhmGtouga/OQWq9XHVQqiQlXxQKwYHMqFot98oiLAu2GkV4DOx2kM2fO8MYbb7BlyxY6duzIlClT2Lx5M4GBgQQFBdl+BPUArS90Km2+mVi++WfA4MHg5YX+yFH0x4/XsXEXKd6XROGmTaBWEzZlikPXPl1wmo3pyogaZ4uzrewo7X/Uu0UYT/Z8ErWkZk3aGrad2VYn16+S9ERYcD3SuYPo1f6Yr30T7l8PLas/pLqmtG2gQu3s4mzbcFh3ih5ZSWiUQO+o3phkk214rl10vQd8G8GFNKWZqJtijSAV792L5ZK5nPaSfDqP3/9VWtfMHFY30SMrY7o3I8Dbi5SsQtYdtm9cVEMTaIOdDpJWq2Xs2LGsWLGCgwcP0qlTJ6ZPn050dDT//e9/MZnc1/sX1ABrT6SDf5QTa6uDgvAr7Truymq27M9Lo0c3XI+2mWPnOv185GdkZHpH9SYm0Lndfq1YHaQesaG0DmnN7XGKk/rGjjcwWVz8/jqxBRbeDCUXsDTtyZr2b2DpPhnUdRNut0WQGliK7ZsD36A360kIS6B3ZO+qT3ABVsdtyZElZBfb2WtH6wt9HlRub3y3wv5r7oC2RSzqRo2QDQZKSge21oTZqw4BcGPnJrRv4rgxVdXB39uL23tGAzBvU0q1zysr0HavCKYzqbGCsnnz5jz//POsXr2atm3b8vrrr1d7vonAQ2jc4aJYuwJ9wMVqNtc4SPqjR8kvHWQYNnWqQ9c2Woy2b+11Jc4u0JtIPq28h3qVNoic1mUaQd5BHMk5ws+HXfjt+vg6+PoWpXQ/tj/mu37C6OVYnVFVtC11kE6cL6LYYK7Ta7uKAkMB3x/8HlCckLqYQ1cTekf2JiEsAb1ZzzcHyhd2VEnPe0EbAOcOwGHHDZd2JJIk4Wfth1TDNNuutBxWHziLWiUxY2ibqk9wAhP6xiJJsOFIFkfPVu/LhjEtrcEJtKGGDpJer+fbb79l6NChJCQk0KhRI/744w9CQz2466+gYqxi7V3lO2sHXHMNqFTokw9gOFn3QuLsuUqFV8CwoXi3bu3QtdedXEdWcRahulCGRA+p+gQHsOtEDhYZokN9iAzSARDkHcS0LtMA+GjPR+TqXdAv5vBK+OY2MBZBq2vgrh9B61/nZjTy9ybMT4ssw5FqfrB7Oj8e/pF8Yz4tg1oyuLn7FsBIkmSLIn1/8HsKDPaNnMInGHqWpsg3zlZ6arkhF4XaO2t0/jsrlejRmG5NaRle9+8hgOZhvgxtp0yqqG7jyOLS9FpDEmiDnQ7S9u3befDBB4mMjOStt97ipptu4uTJk/z4449ce+21zrJR4ErKiLXXlnnKKzTU9oFR12k2w6l0cn//HYCw+xw/Edwqzh7dejQadd0MK7XNX4sp+0Xjtra30Tq4NRf0F/h0bx13HU5eBt/fBWY9xF0Pd36npERchG3kSAPQIenNehYlK6OdJidMRiU5bkaXMxjcfDAtg1qSb8znh0M/VH3C5fR5CNTecGoHpG50vIEOwKZD2rMHi8Fg17mbj2ax6Wg2GrXEI9e4JnpkxVryv2RXOrlFxiqPt05NaEgCbbDTQerTpw9//fUXjzzyCC+99BKxsbFs3LiRZcuWlfkR1CPKiLUXlHs6YIQyl6yu02zn530JZjN+V12FT8eODl37ZP5JNp/eDGAbp1AXbK9kQK2Xyounej0FKN/O7e43U1P+/Ql+mggWo+Io374QvJwzZqW6WIXaDcFBWnp0KVnFWUT5XWzI6M6oJJWtgeWi5EWUmOxsqBjQWBFsgxJFckO0rVqhDg1FLimhZF/1u4fLssxbpdGju3o1p1mI675kAPRtGUZ8ZADFRjPf70ir8viGKNCGGqTY0tLSeOWVVxg1alSFP6NHj3aGnQJXcqlYOz+zzFMBQ4eCJFGy91+MGRl1Yo7p3DkuLFb0OGH33+/w9a1an6uaXEV0QLTD168Ig8nCnpMXAOgZW77st09UH4ZED8Esm3lj+xvO72C+axEsmar0N+pyN4z5QinJdjENZWityWKy9RWa0GECGpXr/++rw8iWI4nyiyK7JJtlx2rwZbnfIyCp4djfcHq34w2sJTXth/T3wbPsTruATqNi2hDHygFqgiRJtijSV1tOYLpCd/oyAm3hIFWOxWKp8ic/v35/cDVILhVrX9ZZWxMRgU/XrgDkr1xVJ+acX7gQ2WDAp0sX2xBJR2E0G/nl6C9A3YmzAZJO51JitBDiq6FVJdqEx3s8jkalYcuZLaw7Vb43lcPY9jksmw7I0GMK3PQRqNxjJlxcA6lkW5G6gvSCdEK8Q7ilzS2uNqfaaFQaJnRQmrXOS5pnf+VlSKzSPBKUijY3xOYgba+eg2SxyLy9Upk4MOGqWCICdE6zzR5u7tKUEF8N6ReKWX0gs9LjGqpAG2pRxXY5er2e2bNn07Kle7TA92TMshtW6HQvjSJVJNa+ZDabszHn5pLz7XcAhN1/n8Orev4++TfnS84T7hPOwGjn9/axsvOS8v7K9hQdGM349uMBeGvHWxjM9mkgqsWm9+GvJ5TbfafD9e84bFyII7Cm2DLz9FwocsL+3QBZlvkySZlNdne7u/Hx8nGxRfZxS5tbCPEOIb0gnRWpNfhMuLp04HnyMsg64ljjHID1S1nR7t3Ixqr1O38mneHAmTwCvL14YID7OBg6jZq7ejcHYN4VxNplBNoaz4hkOgq7Pvn0ej3PPPMMPXr04KqrruLXX38FYN68ebRo0YJ3332XGTNmOMPOBsMfKX/wTt47JGcnu9qUsnQYXalYO3C4okMqTtyF6Zx9zcfs5fw332ApKsI7Lg7/QYMcvr5NnN1mdJ2mNbanKB20reX9lTG101Qa+TQiLT+tZuXUlSHLsPZ1WPW8cn/AEzD8/5Sp626Ev7cXzUIUh6G+6pDWn1rPkZwj+Hr5ckf8Ha42x258vHy4u50yMPrLpC/tTwdHtFMG2SLDpvccbl9t8W7dGnVwMHJxMcVJSVc81mS2MHuVEj26t39LQvyc343fHsb1icVLJbE95Tz7T1dcIWsdMdLQBNpgp4P0/PPP8+mnnxIbG0tqaiq33XYb9913H++99x6zZ88mNTWVp556ylm2Ngg2n9lMnpzHW4lvOV9nYg9aX+g8Vrm9s2xnbU2TJug6dgRZJn/NGqeZYCksJGfhVwCE3TfV4dGjE3kn2HZmGxISY9rUnTjbYpFJPGGNIF157ICfxo/Huj0GwJx/55BVnFV7A2QZVr8Ia19T7g95DobMcjvnyEpc4/qbZpNl2TageGzcWIK8PXMywR3xd+Dr5cuRnCOsP1WDAeZXz1T+3fsD5KY71rhaIqlU+PbsAVRd7v/L7nSOnyskxFfD5Ktj68A6+4gM0nFdxyig8pL/klInsKHpj8BOB+mnn37iq6++YvHixaxcuRKz2YzJZGLv3r3ccccdqNXuoVPwZB7p8ggaNOzN2stfKX+52pyyWHsiHfqzvFjbOpvNidVsOT/9hDk3F01McwKd0FZi8eHFAFzd9Gqa+Ddx+PqVcexcATlFRnQaFQlNq/6DeGOrG0kIS6DQWMgHuz6o3cUtFvjrqYvf1Ee8BgMer92aTsbaMLI+jhxJzExkz7k9aFVaxrUf52pzakyQdxBj45QvVF/s+8L+L3vRPSG2v1JBueUjJ1hYO3x7Vj2XzWCy8P4aJUX44KBWBOjcMz1lFWsv23OarIKyI1QaskAb7HSQTp06Rffu3QFISEjA29ubGTNmuG13V0+ksW9jBugGADA7cTZFxiIXW3QJjTtAs16lYu2ynbWtabbCbdsx5eQ4/NIWg4Hz85TIVdi99yI52Bk3mA0sPboUqFtxNsCO0gG1XaND0KirfkuqJJWt7P/Xo7+yP3t/zS5sMcPvj8L2OYAEN7wLfR+q2Vp1SH0eOfJFkhI9urn1zYT7hrvYmtoxrv04tCote87tITEz0f4FrFqkxAVQaOf4Eidj1SEV79qFXMmIrR92pHEqp5iIAG/G942tQ+vso1vzEDpHB2MwW/h2W9mSf5tAW6ttcAJtsNNBMpvNaLUXc6heXl74+7umG2h95mrvq4nyiyKzKJP5+8sPinUp1ihSYlmxtjYmBu/4eDCbKfj7b4dfNvfXXzGdPYtX48YE3Xyzw9dffWI1OfocInwj6N+sv8PXvxI7Kul/dCW6RHTh+pbXIyPXrOzfbIJfHoBdX4GkglGfQo/J9q3hIi4dWutWaehacvD8QTalb0IlqZjUYZKrzak14b7h3Nxaea9aHT+7aDUEojorHdy3z3GwdbXDu21bVEFBWIqKbBGWSyk2mPng76MAPDykNTqNe2dXJpdGkRZtPYHBdPFz3SbQbhff4ATaYKeDJMsyEydO5JZbbuGWW26hpKSEBx54wHbf+iOoHRpJw4yuyren+UnzOV1w2sUWXYJVrH3hBBz/p8xTzprNJptMZH+hVPWETZ6ESut4oaNVnD2mzRi8VHXbSt/mIFWhP7qcx7o9ho+XD7vP7mZ5qh3zq0wGWDwJ9v0IKi+4dR50udOua7uSluF+qFUS+SUmMvLsbEboxny5T/kdHxE7gujAuum/5WwmdZiESlKxKX0TB7IP2HeyJF3UIm2bA3r3iRhKKhW+PUp1SBWk2b7aksq5fD3NQnwY27N5XZtnN9clRBER4M25fD1/7jtje7whC7TBTgdpwoQJREREEBQURFBQEPfccw9NmjSx3bf+CGrPNdHX0KNxD/RmPbMT3air7KVi7cs6a9vSbJu3YHbg4OK8FSswpqWhDg4m+DbHp7+O5x5nZ+ZOVJKqznvOnMkt5lROMWqVRNfm9jlIkX6RTElQ5le9s/Mdik3FVZ9kLIEf7oEDy0CthdsXKU6vB+HtpaZlIz+g/lSypeWlsfKE8sXC+prWB6IDoxkROwLA1rrALtrdCGGtoeRChZ38XYlVqF14WcPI/BIjn65Tut0/ek0btF7u0yajMrReKsb1iQFg/qYUW2S2oXbQtmLXV+X5890s3VOPkSSJp3s9ze2/386K1BXcEXcHPSJ7uNoshe4TYfvnF8XaAcrgQ+/WrdG2aoXh2DEK1q4l6Kaban0pWZbJnvM5AKETxqPydXyLfqs4e0DTAUT6RTp8/Sth1R+1jwrE39v+yNWEDhNYcmQJpwtPMz9pPg91uYKGyFAI390JKevAywfu+AZaX1NT011K28gAjpwt4FBGPoPiIlxtTq2ZlzQPi2xhQLMBxIXGudochzIlYQp/pfzFqhOrOJF3gpjAmOqfrFJDv8eUxqVbPoZe97l83I0V21y2REWHZB3i+uXGFC4UGWkZ7sfork1daaJd3Nm7OR/+fZS9p3LZlXaBbtFBDd5Bcn/XtgETFxrHrW2UrrJv7HgDs8VNGkheQazt6DRbwdq16A8fRuXrS8hddzlkzUvRm/W2kQi3xdWtOBtgR4o1vVZ9/dGl6Lx0/KfHfwDlj+yZgjMVH1iSB1+PUZwjrT/cs9hjnSOA+Mb1Z+TI2aKztt/Bezve62JrHE9caBwDmg3AIluYn1SDL9mdxkJgU8g/A3u/c7yBNUQXH48qIABLQQElBw4CkFNo4IsNKQDMHNYWr2oUXbgLjfy9uamLUr07f1NKgxdog3CQ3J5pXacRoA3g4PmDthEYboF1PttlYm1bmm3DRiyFhbW6hCzLZH+miDND7roTtRPStytTV5KrzyXKL4p+Tfo5fP2qqKn+6FKGxQy7cjq26Dx8dTOkbVH0Y+N+hdira3w9d6BtPapk+2r/VxgtRrpFdKNrRFdXm+MUrI7f0mNLySysfKxFhXhpla7uABvfUwoM3ABJrca3tKrbOpfts/XHKNCbaB8VyMiEKFeaVyOsJf9/JWVwZsceALzjG6ZAG4SD5PaE6kJ5qLOSNvlw94fkGRyn7akV7UdVKNb2jo9HEx2NrNdTsL4GDeIuoWj7Dor37kXSagmdMKGWBlfMpeJsdR3PG8stNtoiID1qGEECJR37VK+nUEkqlqcuL1tSXXAOFt4Ep3eBTyhMWKb0mPFwrM0ij2QWYLZ4biVbrj6XHw//CNTP6JGVrhFd6RbRDZPFxFfJX9m/QPcJyu9vTgok/+pw+2rKxbls2zmbV8LCzakAPD6iLSqV57W/6dAkiF4tQjFbZP79eysAug7tXWyV6xAOkgcwNn4sLYJacL7kPHP2ukm5q9YXOpeOQUi8GDaXJInAEUoUqbZptuw5yl6Dbx2DV7jje8IczTnK7rO7UUtqRrepe6HyrhM5yDK0aORHeEDtdBXxofG27t9vbC9Nx+adgQXXQ+Y+8IuAiX9Aky4OsNz1NA/1RadRoTdZOJFdu0ilK/n24LcUm4qJC4nj6qaeHdWriikdFfH5T4d/Ildf8ViLStH6Qe8HlNsb31O6v7sBvr1KG0YmJvLJmkOUGC10ax7MYA/WxVlL/vWl7Qt8EhJcaI1rEQ6SB6BRaXiqp9IY8NsD35KSm+Jii0qxddb+C/IzbA8HlKbZCtatx1JSszLs4n37KNy8GdRqQic7p6rHGj0aFD2ICN+6/0Db7oD02qVM7zqdAE0AB84f4Nd982D+dZB1SNFvTPoLGtefb4IqlWTrh+SplWxFxiK+PfAtoDgP9b3hbv+m/YkLiaPYVMy3B7+1f4FeUxX9XOY+OLLK8QbWAF27eFR+fljy89myehsAj4+I8+jXclj7SJoG6YjJPgm4TqBtkS0cMh5yaa8z4SB5CP2a9mNgs4GYZBNv7XjL1eYoNG4P0b0Vsfbui2JtXceOeEVFIRcVUbhpU42Wzv5cqVwLuuEGtM0cXwlSbCrmt2O/AXXfOdvKzlTr/LWap9cuJVQXyoNdHgTgg13vk38hFYJjYNKf0Ki1Q67hTrT1cKH2z0d+5oL+AtEB0QyLGeZqc5yOJEm2KNK3B761f0qAb+jFL2Ub3aP1ieTlhU/3bgC0yzxGv9ZhXNWqkYutqh1qlcT9bbT4m0owqr3QtmxZ5zbsObuH8SvGs6hwEX+fcnzj4eoiHCQP4vEej+Ol8mJD+oaaDYB0BtYPrF0XxdqSJBForWZbscLuJfVHj5K/ajVIEmH3TXWUpWVYkbqCfGM+Tf2b0rdJX6dc40qUGM3sPamkGXo5yEECuCOsGy1MMudVEnOiYpTIUUisw9Z3Jzx55IjRbGTh/oUATEqYVOfNSV3FsJhhRAdEc0F/gZ+P/Gz/An2nK/270rbAiS2ON7AGlLTvAkDHrGM8Prx+tGgYrlbajxwLjGLrybrTvWYUZvD0hqcZ99c4ks8n4403+QbXvb+Fg+RBxAbFck+7ewB4a8dbGM1GF1uE0mRQFwQX0uD4RU/flmb7Zy2ywWDXktlz5yprDB3qtPJSa3rt1ra3opLq/m3w76lcDGYLjfy9iQlzUG+njH1oFt7EE1nnAPhGJ5GKG/yOOIlLR454Gr8f/53MokzCfcK5uZXjR+e4K14qLyYlKBWwC/cvtP8zLDAKOpd2fXeTKNIPBiVi1PVCKl2a1Y9GydKRQwAcDW7G/E2pTr9eiamEz/Z+xk2/3sQfx/9AQuLmljfzWOBjjGo1yunXrwzhIHkY93W6j1BdKKl5qXx30A16gmh8oJNVrL3A9rBP166owxthyc+ncOvWai9nOHWK3N//ACDsvvscaamNQ+cP8e+5f/GSvBjVepRTrlEV1vL+Xi1CHKNXSE+EBTdAURb9g+PoH9kHk2zm7Z1v135tNyWuNIKUmlVIidFNeoRVA7PFzLykeQCMbz8erdrxo3PcmZtb3Uy4TziZRZn8fvx3+xfo96gyP/DISsjY53gD7SD5dB4LsnQUq7X4lBSiP3zYpfY4CmuDyCNBzVh9IJO0bOcMTZdlmRWpK7j515v5eM/HFJuK6RrRle9u+I4X+rxAgCrAKdetLsJB8jACtAE82u1RAD7b+xnZxW4w5dqaZjv4p02sLalUBAwdCtiXZsv+8kswm/Hr1w+fjs6pnrBGj4b8f3v3HdbU2T5w/JuEEMLeS0RkKIqCA7VqtW6r1qq129bV8bavtrW2tnbvOvq2b1t/vt1Va3dd1Q6t29ZawYGKigKKIlNA9grJ+f0RiaCgjIQT8PlcVy5DcnLOfQDJnfPcz/0EDsNTK0+9QHWCFN3BDMNrZ/bAignG5RgC+sLU9cy74XlsFDbsPLeTv9L+av4xrJC3kwZXezUGCZLPF8sdToNtS91GSmEKzrbOsjQnlZutypapXacCxuamjW6A6xFibDMC8Nd/zRtcI723+QR6pYrzQeGAsTVJaydJkmkBXucekUgSrNiTYvbjJOQlMGPTDJ7e+TTpJen42PuwePBiVty8gggP6+jcLRKkVmhCyAS6uHehSFfEkoNL5A7nUrG2pK9VrO082rgGU/HWbUhV127upsvOpmD1GgA8/mWZq0elulLTp1a53pz0Bon9Z4xj/H07NjNBOrUTvr4NKosgaBDcvxa0rnR06ci9XYydxxfHLkZnaHtDbQpF65vJJkkSnx8xrmx/T/g9OKgdZI5IHnd0vgNnW2dSClPYenZr43dwo3Exb46uhdxk8wbXQAfOXmDL8WxUSgUdhxtbNJTGtv4ESXf2LIaiIhS2towbPwCAH2NTKa4wT4PO3LJcXv37Ve7ccCf7s/Zjp7Lj0ahH2TBpA2M6jrGqGYAiQWqFVEoV8/vOB2BN4prGr5JtCb0vdtauUaxtHx2NytUVfX5+g/5w5K1YgVRZibZnT1MDNnP7/fTvlOhKCHQKpK9vX4sc41pOZBZRVF6Fg63KVGjcJCf/gG/uAF0phAyHe38EjaPp6X9F/Qt3O3dOF5zmh4QfzBC59an+/rWWmWx7MvZwLPcYWhstU7pMkTsc2TioHbgn3FhL9PmRzxs/ldsvEkJHgmSAvz+0QITX9u4fxjqdyb3aETDkUoIk1VhZoDWqHl7ThIdzUxc/gj0dKKqoYvX+c83ab/XEhFvW3sLqxNVISIwJGsP6iev5d49/o7XRmiN8sxIJUivVy6cXY4LGICGxMGahrL0iAIiYeEWxtsLGBscRxvW+rtU0Up+fT/533wPGq0eW+hQhd3E2wL4zxuG1Xh3cmr5W07H18P29oK+AzuPgnu+MzTtrcLZ15rGejwHwv7j/kVee16y4rVFru4L0xRHjivaTwybjZmee/let1ZQuU9DaaDmed5w96U2YkTZorvHfuG+NTVFb0N9JOexOykWtUvD48DC03SJQaLXo8/OpSEpq0VjMrSy+eoHariiVCqZfbBy5/O8UDE3sWr/r3C5uW38b/9n3H4p1xXRx78Lym5ez+KbF+Dla75IsIkFqxZ7s/SR2KjsOZB9g05nGT6c3K7X20uySfZc6a1evzVa0eQuSvv5ag7xvvsFQWoqmc2ccb7rJIiEeyz3G0dyjqJVqJoTKN3MoppkL1HL4J/hpOhh0EHEb3Lmi3hXOJ4VOItw9nCJdEUsPLm1ixNarulD7ZCtIkA6fP0xMZgw2ShumRVhm6ZzWxM3OzdT9/fP4zxu/gw4DoP0NoK+Ef1rud1uSJN65ePXo3r6BBLjZo7C1xb5nD6D1D7NVX0HSXmwQOblXAE52NpzOKWHnyfON2tepglM8suURZm2dRUphCu527rw24DW+G/cdvX16mz12cxMJUivm5+jHzG4zAXhv33uUVZXJG1AdnbUdbrgBpZMT+pwcyg4erPNlhpISLny1EgDPFrh6NCJwBO525us91BiSJNVYoLYJMRxYCWseMtZ79ZgCkz8HVf0LSaqUKlMX9lWJqziRd6JJcVur6itI6QXlFJZbd51Vde3RLcG34OvgK3M01mFaxDRsFDbEZsZy6Pyhxu+g+irSvmVQdsG8wdVjW0I2B8/mY6dWMmvYpQasl9Zla70JUs0C7eoO2g4aG+6Kbg/Al7sbtopDYWUhi2IWMfnnyexO221s7xAxg18n/cptYbe1+LqXTSUSpFZuerfp+Dr4klGSwfKjy+UNxruL8ROdpIeDxoRHYWuL07ChQP3DbBd+/Al9QQHqDoE4XSzsNrcSXQm/nfoNkK84G+DchTKyCitQqxT0aO/auBfv/RTWzwYkiH4Abv0/aMAfmmjfaEYHjcYgGVgUu0j+4VgzctGq8XOxA6z7KlLShSS2p25HgcLUB0gAXwdfxgWPAy4lkI0SNgp8ukFlMcR8ZubormQwSPznD+NU/mkDgvB2sjM9Z1qXbd++Vvt/rGaBtib0UvI3tX8QCgX8mZhDUnb9/8/0Bj0/nviRW9bcwtfHv6ZKqmJIwBDWTVjH3Oi5ONo61vtaayQSpFZOa6Plqd5PAfDlkS/JLMm8xissrPoq0v6vTMXa1U0ji/7YfEUBo6GykrwvjT1hPB96CIXKMp8sfj31K6VVpQQ5BxHtE22RYzRE9dWjbu1c0No24lx3fwC/zzPe7z8bxr0Lyob/953bey4alYbYzFi2nN3SmJCtXmtYcqS679HwwOEEu7T80g3WbGb3mShQsCN1B0kXGlm/o1BcmtH2z0dQadmFi3+Lz+B4RiFOGhseGVy7ia1d9+4oNBr0ublUnjpl0TgspWaBtkJ96cp0oIc9I7r4ANTbODI2M5Y7f7mTN/55gwsVFwh2CebjER+zZPgSOjh3sHjsliASpDZgdNBoenn3olxfznv7Ze4uW12sXXAWko3F2g4DB6K0t6cqM5PyI7UbuxWsXUfV+fPY+PricuutFglJkiRWnVwFGNddk3MaaaOH1yQJdiyEzS8bvx48D0a9aXxjaAR/R3/TlYt3971LeVXTFhG2RuFWXoeUVpzGb6eNVy8f7P6gzNFYn2CXYIYHGidzVCeSjdJ1onE5nbI8OPCVWWOrqUpv4L3NxqtHDw4Kxs2hdoNPpa0t2h49ACiNibFYHJZUdvRSgfblZlws1l5zII2C0kvD2eeKzjF3x1xmbprJyQsncbJ1Yn7f+ay6dRUD2w1skbgtRSRIbYBCoeDZvs+iQMHvp3/nQNYB+YKpWay931isrbSzw3GIsfC65jCbVFVF7ufGy+oeM2egsLVMR+GjuUc5nnccW6WtrMXZALEpxjqJBiVIkgRbXoUdC4xfD3sJhr3Y6OSo2oyIGfjY+5BWnMZXxyz3RtLSrH3JkeXxy9FLem7wu4EIT+togGdtqhPH307/RlpxWuNerLIxdtcG+HsJVDVuaaOGWnswjVPnS3CzVzPzxqA6t7Hve7EOqZUWapcfNdYfVRdo19Q/2INwXyfKdHq+jz1Lqa6UDw98yIR1E9h8ZjNKhZK7Ot/Fr5N+ZUqXKaiV9ddGthYiQWojunp05baw2wBYGLMQgyRjL446irVrDbNdHJ8v3rQJXWoqKjc3XG+/3WLh/HjiRwBGBY3CRSPfWkl5JZUkZRs7Pkd3uMYUb4MBfn8Wdr9v/Hr0Ahj8dLOOb6+258nexuGIz498TlZJVrP2Zy0611i01tpqP3LKclibtBYQV4+uJsIzghv8bkAv6Vkev7zxO4i6Fxx9oDANjvxo9vgqqvS8vyURgEeHhOBkV/ebf3WhdklsrNX9Ll6LJEmmITa7OhIkhUJx8SqSgc/jVnHL2lv47MhnVBoq6efbj5/G/8SLN7zYptpXiASpDXms52M4qh05nnecn5N+li+QOoq1HQcNQqHRoEtNpTIhAQwGLnxu7AnjPm0qSnszLdh6maLKIjambASMw2ty2ndxeC3M2/GKy/O1GPTwyxMQ8wmggFv+C/3/bZYYxnYcSw+vHpRVlfHBgQ/Msk+5hXo7olTAhVId54sq5A6nlm+Pf0uFvoLunt1la0zaWlQnkGuT1pJTltO4F6vtoP8s4/2/3jf+HzKjH2JTScsvw9tJw9T+QfVup42KQmFri/58DpWnU8wag6XVV6BdU0hAHk7BH1Pu+jXny87TzrEd7w99n89GfUYnt04tHLHliQSpDfHQevBI1CMAvH/gfYorZVyfKvriTJ2LxdpKBwccBw8CoHjzFhwSEqhMSkLp4IDbvfdaLIxfTv1CWVUZIS4h9PTuabHjNISp/uhqy4voq2DtI8ZaCoUSJn4E0TPNFoNCoTB1Yd9wakPTplZbGTu1iiAP45Id1lSoXVxZzPcJxuanD3R/wKqWULBGfX370t2zOxX6Cr45/k3jdxA901j/mJsICU1YBLceZZV6lmwzFo8/NiwUO3X9kyuUGg3aqCig9Q2zmQq0O3euVaANkF2azQt/vcCMP+4HzVkkgy1eukn8PPFnhgcOb7O/2yJBamPuDb+XIOcg8srz+PTwp/IF0nUC2LnWKtauHmYr3rIF923bAXC7915Uzs4WCUGSJFPvozs6y1ucDTXrj+q5BF1VCatmGIcIlDZw+5fQ4x6zxxHhGcHE0IkALIpZJO9wrJlUD7NZU0ftH078QJGuiBCXEIa2Hyp3OFZPoVDwQPcHAPg+4XuKKhv5s9Q4Qd+Lazj++Z6xhs8MvtqTwvmiCgLctNzVJ/Ca25v6IbWyBMlUoN3t0vBahb6Czw5/xi1rb2F98noARrYfR/mpeZxK6kdiZtuZ7FEXkSC1MWqVmnl9jNPBVx5fyZnCMzIFcmWxtuOQIaBWozt9Gm1qKgqNBvdpUy0WwqHzh0i8kIhGpeGW4FssdpyGKK2sIj6tAKinQFtXDj/cB8fXg8oW7lwJEZMsFs8TvZ7A3saeIzlHTIv3tmbWtuRIeVU5K48Zh5dndp8p27I2rc3Q9kMJcQmhWFfMDyeasH5gv0fARgsZcShO72x2PEXlOj7aaVwM94nhYdjaXPvnaOqHFBPTquqQahZoS5LEljNbmLBuAh8e/JCyqjIivSL5duy3vDdsIWO6dgaMy4+0ZeJ/bRs0OGAwN7a7kSpDFf+J/Y98gdQs1i7MQOXkhMOA/qannSdNwsbT02KHr756NDpotKzF2QBxqflUGST8XOxo53rZooyVJfDtnZC4yfjH/Z7vIXysRePx1Hryr6h/AfD+/vcp0Vm2f4yl1SzUtgY/J/1Mbnku/g7+jOk4Ru5wWg2lQsnM7sYh5ZXHVja+HYWDJ/Q2LuOi/Pv9ZsfzxV+nyS/VEezlwKSe7Rr0Gm2PKBRqNVXZ2ejOnm12DC2hZoF2ZoADD/7xIE/ueJK04jS8td4sGLSAlWNW0t2rO3Bpyv/6uHRyiq2r7s+cRILURs3rMw8bhQ07zu1gd9pueYLwDofA/sZi7bivAXAeZeyULSmVuM6YbrFDF1QUsCnFuD7dnZ3vtNhxGir29KXp/bWG+soL4evJcHon2DrCfasgdHiLxHRfl/to79Se82Xnm9bF2IpcSpCKm7ygprlUGapYdtR41XRaxLQ2Md25JY3pOAZ/B3/yyvNYl7Su8TvoPxuUNijP/IVbSXKT47hQUsnnfxqX1pg7slODF5ZW2tlhFxkJtJ5htuoCbb2NknuPP0tMZgy2SlsejnyYDZM2cEvwLbWugvYKdCOqvSuVegPf7m0dSWBTiASpjQp2CeaeLsYhrsWxi9EZZFqnqlZnbT3OY27GcfRozt8yDrW/v8UO+8upX6jQV9DJrRORnpEWO05D7TtT3SCyRv1RaR58NQHO7gGNC9y/DoJubLGYbFW2zIs2DseuOLqC1KLUFju2uXVwt8fWRkmZTk/qhVJZY9mYspG04jTc7dyZFGa5YdK2Sq1UmxbzXX50OVWGqsbtwLU9RN4FQKfMdU2O4+NdyRRXVNHVz5mx3Rq34nx1P6SSVtAwUmfQsXnTJwCc9jKgU0qM7DCS9ZPW81jPx7BX1z3DeObFq0gr/zlDZVXrr2Osi0iQ2rBHoh7BTePGqYJTpl5ALa5WsfZ2lPb2+P7nHfIHWq7DqiRJ/HTiYnG2zJ2zwdiB98CZi1eQqmewFZ+HFbdC+gHQusO09dC+T4vHNqT9EPr79Udn0PHuvndb/PjmYqNSEuZtXOdJzjokg2TgiyPG9hVTukxBa6O9xiuEukwKm4S7nTtpxWn8fvr3xu/gxrlIChW+hYdQnGv8VZzswnJWXKyveXp0J5TKxv0NcTAValv3umy703Yzef1kjuxeB0BeBze+HP0l7w15j3aOVx9SHNPND28nDeeLKvjtSEYLRNvyRILUhjnbOvNYr8cAWBq3lAvlLbPadS11FGtb2oHsAyQXJKO10ZoWwpTT8YwiSir1ONvZ0MnbCQozYPk4yDoCDt4w/Vfw7yFLbAqFgmf6PINKoWLr2a38k/GPLHGYQ2crKNTedW4XSflJOKgduDv8btniaO20NlqmdJkCGJcfafRMS89QpEjj91+5461GH///tidRrjPQK9CVoZ29G/16bY8eYGNDVUYGurRGdgZvASkFKczeOptHtjzC6YLTdM62AWD8uDn08W3YBzVbGyX332BcY23Z7tNWnQg2lUiQ2rjbQm+js1tniiqLWBq3VJ4gLivWtrTq4uwxHcfgZOtk8eNdS8zF/kfRQe4oC1Nh2RjIOQHO7WDG7+Bz5bpHLSnULZS7OhuHJBbFLGr8kIaV6OQr76K1kiSZarnu7HwnzraWaV9xvbg7/G4c1A4k5SexM7XxM9L0g55GrzDWInFqR4Nfl5pXyncxxrqap0d3btIVaKW9PdruxoLm0r3WM8xWVFnEf2L/w6T1k9h5bic2Chvu73IfXXLsALDv1r1R+7u3XyC2NkoOnSvgwNl8C0QsL9kTpKVLlxIUFISdnR39+vUj5ipjtjqdjtdff52QkBDs7OyIiopi48aNtbbZtWsX48ePx9/fH4VCwbp1667Yz/Tp01EoFLVuN998s7lPzSqolCqe7fssYEwcTuSdaPkgahZrH/zaoofKL89nc8pmQP7O2dWqO2gP8ymGZWPhwmlw7QAzfgPPujvWtrR/9/g3LhoXkvKTTAv7tjZyz2Tbl7WPQ+cPYau0ZWpXy7WvuF442zqbJlh8Hv95469QuLTnjOfF/lNb32hwX6QPtyai00sMDPVgQEjTZ9laUz8kvUHP6pOruWXtLaw4toIqQxWD2g1i9YTVzPG9B+kaHbTr4+GoYUKUsZZ02e7TlghdVrImSD/88ANz587llVde4cCBA0RFRTF69Giys7Pr3P7FF1/kk08+YcmSJRw7doxHHnmESZMmcfDgQdM2JSUlREVFsXTp1a+W3HzzzWRkZJhu3333nVnPzZr08e3DqA6jMEgGFsUukudSaO+LnbUPfGX2ZQBq+jn5ZyoNlXRx70KEh/wLg0qSRGxKHiGKNO48/DAUpIJHqPHKkVuQ3OGZuGhcmN1jNmAcji2oKJA5osarHmI7db5ElqLR6tqjiaET8dRarn3F9WRq16nYKm05fP4w+7L2Nfr1J33GI9loIW0fnNx4ze2Tzxez+sA5AJ4e1bnRx6vJWhKkfZn7uPvXu3l1z6vklecR5BzE0uFL+d+I/xHsEnzVDtoNMf1isfbv8ZlkFJSZM3TZyZogvffeezz00EPMmDGDrl278vHHH2Nvb8+XX35Z5/YrV67k+eefZ+zYsQQHB/Poo48yduxY3n33UnHpmDFjePPNN5k06eqzRzQaDb6+vqabm1vbWWCvLnOj56JRaYjNjGXL2S0tH0DXW03F2opT2y1yCEmSTFc/bu90u+zF2QApuaV4lSTyo+0b2JZlg3dXY3Lk0rCeKi3p9k63E+oaSn5FPh8d+kjucBrNz8UOJzsbqgwSp3Jadpmd47nH2Z2+G6VCyfRu01v02G2Zp9bT1PW9OgFtjAq1K4Y+Dxm/2PamcRHoq/jv5pMYJBjRxZuegc17T7Dv1RNUKnRpabLUIaUXp/P0zqeZsWkGCXkJOKmdmBc9jzW3rmFwwGDTdmXx8QDYRTRtqD/C34W+Hd3RGyRW7pGpMbGF2Mh14MrKSvbv389zzz1nekypVDJixAj27NlT52sqKiqws7Or9ZhWq+Wvv/5q9PF37NiBt7c3bm5uDBs2jDfffBMPD496t6+oqKCi4lJDrMLCQsA47KfTmW8KffW+zLlPAG+NN1O7TOWz+M/4T+x/uMH7Buxs7K79QrOxQRl5F6qYT+DAcnC81+znuC9rHymFKdjb2DOq/Siz77+hav4MT+zfzne2b+KqKEHyjaTqnlWgcQOZYruWp3o9xaPbHuX7hO+ZFDyJYJfgOrez1O9pc3XydmT/2XyOpeUT4tH0GWSNPb/PDn8GwOjA0fja+Vrd9+Vy1vrzq8t94fexKnEVu9N3czjrMF3cuzToddXnVhH9CNoDy1BkxVN1ZBVS17o/PB/PKOKXw8YayceHhjT/e2NriyaiKxWHj1D4zz8433pr8/Z3mfp+hmVVZSw/tpyvjn9Fhb4CBQpuC72Nf0f+Gzc7NzBQq+1LdYKkDg9v8jlP7deemNN5fBdzlkcHB111vbqGsuTvaEP3qZBkKj1PT0+nXbt2/P333/Tvf6m78jPPPMPOnTvZu3fvFa+59957OXToEOvWrSMkJIStW7cyYcIE9Hp9reSlmkKhYO3atUycOLHW499//z329vZ07NiR5ORknn/+eRwdHdmzZw8qVd0/2FdffZXXXnvtise//fZb7C20Er25VUqVvF/4PoVSISPsRjDEbkiLHt+xPI3hx5/DgJLN3f5Ludq8V+1+KPmBI7oj9LHtwwT7CWbdd1O4F5+gd+K72FNOkk0YJ7rMpcrGQe6wrumbkm84rjtOiE0I0x2mW8WVuIb64ZSSv7OUjGhnYHxgywyz5ehz+KDoAyQkZjvNxlfl2yLHvZ78WPIjh3WH6abuxt0OjZ8d2CljHV0y11Cs8WVblwVIiiv/zn+WoCT+gpJeHgamdTLP747nb7/jvnMnBdHRZN1xu1n2WR9JkjisO8ymsk0USsYP8EGqIMZpx+FnU08fJ0ki5LXXUJWVc+bxx6ho17Qr2wYJ3jioIq9Cwd3Bevr7WPeMttLSUu69914KCgpwvspaoLJdQWqKDz74gIceeojw8HAUCgUhISHMmDGj3iG5+tx996X/YN27dycyMpKQkBB27NjB8OF1dzF+7rnnmDt3runrwsJC2rdvz6hRo676DW4snU7H5s2bGTlyJOomjAdfiyZFwwt/v8Bfur94+uan8bZv/BTW5jB89TPK1H+IOPcd3n0noVRrjAuzKlUX/1XXuF/9uNp0XzI9Xnu7PF0Rr/5xHIAnBz1CuEfXS69vYTqdjoNrPmDAmQ9QUM4efVfKJ33FqK5BLR5LU3Qv6s7tv95OclUyjlGO3BRw0xXbWPr3tKny9p7l718SMDj6MHZszybvpzHn98beN5CKJAb5D2LmkJlNPmZLstafX33C8sO467e7OKo7SsSNEXRw7nDN19Q6R8MgpKU7cCzLZGxAEVLUvbW2PZiaT/yeGFRKBQvvG0RHT/N8kClxciJj5048MjPpPda8ywfVPL/EwkTe2f8OhwoOAeDn4MeTPZ9kePvhV/2Ao0tN5UxZOQpbW4ZNn96kGqRqmS4pLNp0kgPFLrw+vX+zP1hZ8ne0egToWmRLkDw9PVGpVGRlZdV6PCsrC1/fuj+BeXl5sW7dOsrLy8nNzcXf35/58+cTHFz3MEBDBQcH4+npSVJSUr0JkkajQaPRXPG4Wq22yB8YS+13fOh4fkr8ibjzcfzf4f9jwaAFZj/GVUXPhNR/CMj/B/4wX8+dX12cqHJ3o1tFBd0/H1njGUWNZOtiwqRSX5aA2dRKwkzPqS5P1uq6Xbk/ZZWOG5I/QyHp2KmP5JGqJ/mnU1CreCMCCHYPZmrXqXwR/wX/PfhfBgcOxlZlW+e2lvo9baou/q4AnMwuNktc1zq/rJIsNpzeAMDDUQ9b1feiIazt51efrl5duSngJnae28nKEyt5bcCVV/PrYzxHexg0F/54EZs/34Eed4PNpb/n7281LkkyuVc7Ovm5mi1up759yVAqqTp3DnJzUdfz3tZURYYi3tr/FhtObUBCQmuj5YFuDzAtYlqDSijKThhnNWs6d8a2mSMh9/YL4sNtyZzMLmbf2UIGhJpnooIlfkcbuj/ZEiRbW1t69+7N1q1bTUNgBoOBrVu3Mnv27Ku+1s7Ojnbt2qHT6Vi9ejV33tm8tbbOnTtHbm4ufn6NayffGikUCub3nc/dv97NL6d+4a7Od9HDu0fLBdBtMvrMeDKP/4OfjxdKyQCGqvpv+ppf6y/+q6v1tcFQxSonYxflOwovL86VLm7fcrUW1desMnyH8VDKNEL8PHDRWv+bUE0PRT7Ez8k/c7boLN8c/4YZ3WbIHVKDVM9kO3ehjOKKKhw1lv0T99Wxr6gyVNHbp3fL/j+6Dj3Y/UF2ntvJ+uT1PBr1KL4OjUw2+jwIe5YaZ5LuXwH9Hgbg76Qc/k7ORa1S8PjwMLPGrHJ0xK5rV8rj4ymNjcVl/Hiz7De7NJufEn7iy8IvqSysBOCW4FuY02sOPg4+Dd5P9Qy2phZo1+Rir2Zy73Z8/c9ZvtydYrYESU6yDrHNnTuXadOmER0dTd++fXn//fcpKSlhxgzjH+OpU6fSrl07FiwwXuXYu3cvaWlp9OjRg7S0NF599VUMBgPPPPOMaZ/FxcUkJSWZvj59+jRxcXG4u7sTGBhIcXExr732GpMnT8bX15fk5GSeeeYZQkNDGT16dMt+A2QS4RnBxNCJrEtax6KYRXwz7ptaCxFalMoGw7CX2Vf+G2PHjkVphk8Ge9P3kLr5YRzVjtw8+y/j1aA6ky1djSSrvpv+4nZXT8qutj99VSXH0otZ4/UwlSnptddfayUc1A7M6TWHF3e/yCeHP2F8yPhWMXXdzcEWbycN2UUVJGYVNXsm0tXkl+ebmpI+2P1Bix1HMOrh3YPePr3Zn7Wfr459xTN9nrn2i2pSa2Hw0/DrU/Dnf6DnfUhqLe/8YbyKcm/fQALczF9Pat+3rzFBiolpVoKk0+vYdW4Xa5LW8FfaX6bu4hHuEczvN79JCXpZfHWCZJ6WKNMHdOTrf86yNSGLs7mlBHq0jvrc+siaIN11112cP3+el19+mczMTHr06MHGjRvx8TFmwGfPnkWpvPTGXV5ezosvvsipU6dwdHRk7NixrFy5EldXV9M2+/btY+jQoaavq+uGpk2bxvLly1GpVBw+fJgVK1aQn5+Pv78/o0aN4o033qhzCK2teqLXE/yR8gfxufFsSN7AhFD5i5qbqvpNalzwOOztXGSOBgw6Had++42YM8aGhX2C3GWOqGnGh4zn+4Tvic+N58MDH/L6wNflDqlBOvs6kV1UwYlMyyZI3yV8R1lVGeHu4Qz0t9zagsIlD3R7gP1Z+1l1chUPd38YVzvXxu2g51TY/QHkn4WYT9nmcQ8Hz+Zjp1Yya5hlmrba94km78svKY1pWj+k5Pxk1iauZcOpDeSV55ke7+HVg9CSUOaPno/GtvHvXZIkUX7sGABaMyVIod6ODO7kxa6T51mxJ4WXbpF3lYDmkr1Ie/bs2fUOqe3YsaPW1zfddBPHLv5A6zNkyJCrNkLUarVs2rSp0XG2NZ5aT/4V9S/+u/+/vH/gfUZ0GIGD2vpnWF0upyyH7WeNfZWspXM2QLneOG0YWm+CpFQoebbvs9z/+/2sS1rHXZ3vIsJT/uab19LJx4k/E3MsuuRIqa6UbxK+AYxv2q1ppl9rdmO7Gwl3DychL4FvE77l3z3+3bgd2NjCkOdg3aNIu9/nf5pwAKYNCMLbyTJtT+x79waFgsozZ9BlZaP2ufbEmOLKYjalbGJN0hoOnz9setzDzoNbQ29lYuhE2tu357fffmvy1X9daiqGwkIUanWjO2hfzYyBQew6eZ4fY1N5cmQniw9zW5LsS40I8rmvy30EOgWSU5Zj6uPS2qxLWkeVVEWkVySd3ZvX+dacUooUGCRo767F16Ul+02ZVw/vHtwSfAsSknxd2BupJZYcWXVyFQUVBQQ6BTKyw8hrv0AwC4VCwQPdHgDgm+PfUKorbfxOIu8Cz04oyi4wKOcHnDQ2PDI4xMyRXqJydsaui7F309W6akuSxP6s/bz414sM+2kYr+55lcPnD6NSqBjSfggfDv2QzXdsZm7vufX2J2sMUwft8HAUtnVPwmiKm8K8CPZ0oKiiitX7z5ltv3IQCdJ1zFZly7w+8wBjsWlqYarMETWOQTKYOmff2al5hfrmllxovKLQp0PrvHpU05xec9DaaDmYfZCNKdderkFu1YXaJzItkyBV6itZcWwFADO6zUAlQyuJ69nIDiMJdAqksLLQNLzeKEoV+puMDYofUP3O7BvccXMwX4JQl6stO3K+9DyfH/mc8evGM33jdH5O/pmyqjKCnIN4sveTbLljC0uGLWFo4FDUSvNN9jBngXZNSqXCtPzI8r9TMBis/0NVfUSCdJ27KeAmBvgPQGfQ8Z99/5E7nEbZk76HtOI0nGydGB1kXQX2py6+N/fp2PoTJB8HH1MR8rv73qWsyrrXWwrzcUShgJziSnKKr2wg21y/nPqF7NJsvLXe3Bpi3u7IwrWplCrTrMqvjn5Fpb6y0ftYU96bo4YOOCnKmME6M0d4Jfu+tRMknUHH1rNbmb11NiNXjeSDAx9wpvAMWhstk0In8dWYr1g/cT0zu8202OSIsqPmLdCuaXKvAJzsbDidU8LOk+fNvv+WIhKk65xCoeCZPs+gUqjYlrqNPel1L/Nijao/Pd4acmsLL5tydZVVBs4UXbyC1ApnsNVlatep+Dv4k1WaxbL4ZXKHc1X2tjYEuhtnz5w081UkvUFvOv+pEVPr7Q8lWNatIbfirfUmuyybX0790qjXVlTpeX9rMu9UGa862+7/HAozLBGmiakO6dQplmx5gxE/jWDO9jnsPLcTvaSnh1cPXh/wOtvv3M7rA1+np3dPi9a1SZJE+VHzFmjX5KCx4a7o9gB8ufu02fffUkSCJBDiGsLd4cbu4otjF1NlqJI5omvLLs1mR+oOwLqKswGOZhSikxS42asJ8XKUOxyzsLOx46nopwD4Mv5LMkos+4bSXJ2qh9nMXIe09exWUgpTcLZ15vZOll06QqifrcqWqRFTAePvo96gb/Brf4hNJS2/jGP2/TAE9IWqcuO0fwsp0ZXw8/ltZPkZP8Qd2fI9eeV5uNu5MyNiBj9P/JmVY1cyKWxSi02UsVSBdk3TBgShVMCfiTkkZVuuHtCSRIIkAPBo1KO4alxJyk9q2rh+C1uTuAa9pKeXdy9CXC1XYNkU+85cAKB3oGubmt00ssNIon2iqdBX8MHBD+QO56rCLVCoLUkSnx/5HIB7u9zbKmd9tiW3d7odZ1tnzhSeYcvZLQ16TVmlniXbjH3yHhsehnL4y8Yn9q+AC+ZbiV6SJA5kHeCl3S8x9MehvPL3K+z3LwdgRJ4/Hwz9gC13bGFutHkKrhvLVKDdubNZC7Rrau9uz4guxpY9y3anWOQYliYSJAEAF40Ls3sY2y3838H/I788X96ArkJv0LM6cTWAVX6K338mH4DoNjK8Vk2hUPBs32dRKpT8cfYP/ij7g93pu63yd6X6ClKCGYfY9qTv4XjecbQ2WqaETzHbfoWmcVA7cG8X45pqXxz5okEzLL/ak8L5ogoC3LTc1ScQOg6C4CHGRrA7FzU7ppyyHL448gW3rruVaRunsS5pnangOniIsV6td7qGYYHDzFpw3VjlFqw/qmnGwI4ArDmQRkFpy61mYC4iQRJMJneaTJhbGIWVhfzv0P/kDqdeu9N3k1mSiYvGhVFBo+QOp5bjGYXsPJkDQN82liABhLuHc3uYMSndVbGLx3Y8xqAfBjFm9Rjm7ZzHiqMr2J+1v2nTr83INNU/s8hsrQk+jzdePZocNrnxDQoFi5gSPgWtjZbjecf5O/3vq25bVK7jo53GNdeeGB6Grc3Ft79hF68iHfoOzp9sdAw6g45tZ7fx2LbHGPHTCN4/8D4phSlobbRMDJ3IiptXsH7iesZPfhaAyqRkqvLyrrFXyzIVaHezbIJ0Q7A74b5OlOn0fB971qLHsoTW28FJMDsbpQ3P9nmWB/94kB9P/Mgdne4gzM28axOZw08nLhVna1TW0/28Sm9g3qpDVBkkIt0NdPN3ljski5jfbz6hLqH8Gvcr+Xb5nCk6w7nic5wrPmdqA6BUKAlxDaG7Z3ciPCLo7tmdULfQFvvU3NHTAbVKQUmlnrT8smYvIXHo/CFiM2OxUdowLWKamaIUmsvVzpXJYZP5+vjXfH7kcwa2q7+j+Rd/nSa/VEewlwOTera79ERAb+g8Fk78BjvehjuWN+jYpwpOsS5xHeuT15Nbnmt6PMoritvCbmN00Ohaw7A2bm5owsKoSEykNHYfzqPl+XBn7KB9HLBMgXZNCoWCGQODeHb1Eb7ac4YHbuyIjar1XJcRCZJQSz+/fowIHMGWs1tYFLuIz0Z+ZlV1NJklmexK2wVY3/DaJ7tOEZ9WiIvWhts7llvV982c1Eo1t4fdjn2iPWPHjqXUUMqx3GPE58Sbbtll2SReSCTxQiJrEtcAoFFpCHcPp5tnN+PNoxuBzoEWWQdQrVIS4uVIQmYRJzKLmp0gVdcejQ8e3/hFUgWLmhYxje9PfM++rH3EZcfVuSbZhZJKPv/TOJtq7shOV75JD30BTvwOR9fCjXPBL7LOY5XqSo0drhPXEHc+zvS4u507t4YYO1xfrSbSvm9fY4IUEyNbgqQ7dw5DQYFFC7RrmtCjHQt/TyAtv4zNx7IY0731LAovEiThCnOj57Lr3C72Zuxle+p2hgUOkzskkzWJazBIBvr49pGluLE+SdlFfLAlEYAXxoSjyYiTN6AW5KJxob9/f/r79zc9llWSRXxuPEdzjnIk5whHc45SpCvi0PlDHDp/yLSdk62T6QpThKfxX2/7ay/F0BCdfZ2MCVJWEcO7NHyF88slXkhkR+oOFChM/XcE6+Hr4Mv44PGsTVrLF0e+YMnwJVds8/GuZIorqujq58zYbnW8Qft2g26TIX4VbH8L7v3B9JQkScSdj2Nt4lo2pmw09QFTKpQMajeISWGTGBwwuEFXR+379OHCN99ctaO2pZXHxwOWLdCuyU6t4t5+gSzdnsyy3SkiQRJat/ZO7ZkWMY3PjnzGO7HvMLDdQKsYyqoyVJmKs61par/eIDFv1WEq9QaGdvZiYg8/fr+OEqS6+Dj44OPgw/DA4YCx6/nZwrPE5166ynQ89zhFlUX8k/EP/2T8Y3qtt9b70lUmz25EeEbgbNv44crqQu3m9kL6Mv5LAEZ0GEFHl47N2pdgGTO6zWBd0jp2nNtB4oXEWqUB2UUVrPg7BYCnR3dCqaznyu7Q541XkE5uhNQYcjyD2ZC8gbVJazldcKmXTwfnDkwMnWjsxdTIZN6+TzQAFSdPUnXhAjZuLV+n2FIF2jXdf0MQn+w8RUxKHvFpBXRrJ/+i4g0hEiShTg92f5B1Ses4V3yOlcdWmjopy2nXuV1kl2bjpnEzvfFag2W7T3PwbD5OGhvevq17mx1aaw6lQkmQSxBBLkHcEnwLYCxuTbqQZLzClGu80pScn0x2WTbbUrexLXWb6fVBzkGmK0wRHhGEu4dfszloZzPMZEsrTuP3078D8ED3B5q8H8GyOrp0ZESHEWw+s5kv4r9g4aCFpuc+2nmKcp2BXoGuDO18lYTGI4SqqHv48+Rq1m6dzS5lJXrJ2F9Ja6NlZIeR3BZ2G728ezX5/7iNhwe2oSFUJiVTum8fziNbfh2/MgstMXI1vi52jOnux4ZD6SzbncK7d0a12LGbQyRIQp3s1fY82ftJnv/reT49/GmTPi2ZW3V/pomhE62mg/HpnBLe2XQCgBfGdcHPRYtO1/qms8pBrVTTxaMLXTy6mB4r1ZVyPO94rXqmc8XnSClMIaUwhV9P/QqAjcKGMLcwunl2Mw3PhbiE1FoXrXom26nzJej0BtRNKA796vhX6CU9/f36E+HRcp+4hcZ7oPsDbD6zmY2nNzK7x2x87HzILYcfDhsXTH16dOd6E5vTBadZm7SWDeVHyPHxAspAgkivSCaFTuLmoJtxtDVP01f7Pn2MCVJsbIsnSLUKtLt1a9FjzxgYxIZD6Ww4lM5zY8PxdJR/VOJaRIIk1Gtc8Di+T/iewzmH+eDAB7x141uyxZJWnMbutN2A9RRnGwwSz64+TEWVgYGhHtzVp73cIbV69mp7evv0prdPb9NjF8ovmK4wVdc05ZXncTzvOMfzjpsSZ62Nli7uXeju2Z1unt3o6h6Bg62SkkoDKTklhF28otRQxYZi1p9aD2AVV1CFq4vwiKC/X3/2ZOxh+dHlPNv7WTadU6LTSwwM9WBASO01zaoLrtcmreVg9kHT4+4KW8bn5zDRPojQMV+Dma8IO/TpQ/5331Mau8+s+22Ili7QrqlXoBtR7V05lJrPt3vP8vhw65shfTmRIAn1UiqUPNv3Wab8NoX1yeu5u/PddPfqLkssq0+uRkKin18/Ap0DZYnhcl/vPUPM6TzsbVUsvC1SDK1ZiJudGze2u5Eb290IGD8FZ5ZkciTniKmm6WjOUUqrSjmQfYAD2QdMr7UJdkBbEsD/xR1hcsQAIjwi8NB6NOi4f1f8TYW+gkjPSPr49rHIuQnm9WD3B9mTsYe1iWsZ6XMvMeeN/yefHtUZMP7uHDp/iLVJa9l4eiOlVcZ+XUqFkhvb3chtobcx2LUz6iXRkBdnrEfqPMasMdr3Mf4uVSQkoC8oQOXScvU4LdFB+2pmDgziie/jWPnPGR65KeRSLyorJRIk4aoivSK5NeRW1ievZ2HMQlaOXWmRadlXozPoWJu0FrCe4uzUvFIW/p4AwPwx4bR3b940cqHhFAoFfo5++Dn6mRqF6g16UgpTiM+JN11pSriQQJWhBBvHE2zLPMG2zG8A8HfwN9UzdfPsRlePrlcsG1JUWcTeir2AcehGJL+tQx/fPkR6RnI45zCv/fkpEoMY1tmL9l56lscvZ23SWk4VnDJtH+gUyKSwSVeWEPT7F+x+H7a9CWGjQWm+v3k2Xl7YduxI5enTlO7fj9OwlpslXD2DrSULtGsa082Pt5yOk11UwW9HMphYsx+VFRIJknBNT/R6gs1nNnM45zC/nvqV8SHjW/T4O1N3klOWg7udO8Pay99yQJIknltzhNJKPX2D3LmvXwe5Q7ruqZQqQlxDCHENYULoBAAq9ZX8Z/sOlu3fRXvfHJxcMjhdcJr0knTSS9LZfGYzAAoUBLsEm2bNdffszs6zO6mggmCXYIa0HyLjmQmNoVAoeKD7Azyx/QnOVm3BxskVyfsMI3/6hyrJuAh3dcH1pNBJ9PbpXXfyO/AJ2PclZMXDsbXGFgBmZN+3rzFB2hvTogmSHAXaNdnaKLn/hg68u/kky3afZkIPf6v+8CESJOGavO29eTjyYT448AHv73+f4YHDsVe33BWT6hqT28JuQ62Sb/2iaj/EpvJXUg4aGyWLbo+sf9qwICtblS1Dgnrx6RYdBqU9P08bSnFlMcdyj9WaOZdZkklyQTLJBcn8nPxzrX1M7zq9xa+YCs3T2bkfCp0PqLPQBnzDPuPKP0R6RjIprIEF1/buMOAxY0+k7W9DlwmgMt/bpX2fPuT/8EOL9kOqWaAt1xUkgHv7BbJkexKHzhVw4Gw+vTtY75JMIkESGuT+rvez6uQq0orT+PzI5zze6/EWOW5qUSp/p/+NAgWTw8z7Ka4pMgrKeOtX4x+ZeaM709FTrOhuzTpdnMl2Jq+Usko9jraO9PXrS1+/vqZtcspyas2ai8+Np6CiAC+lF6M7jJYrdKEJisp1PLB8P6UlI9EGfI0WeyaH38bkTpMJdWtkUfINj8LejyE3ybhOW6/7zRZndR1SeUIC+sJCVM6WX5aoZoG2XZh8BdIejhomRPnz0/5zLNt92qoTJPHRSGgQjUrDvOh5AKw4uoJzReda5LirTxobQw7wH0CAU0CLHLM+kiTx/JojFFVU0aO9q2mlasF6eTpq8HCwRZIgMbvufkieWk+GtB/C7J6z+Xjkx/x515/8MuEXHnF6RNYV14XG0ekNzPr2IAmZRbjRm+XD1vGsyzPM7TW38ckRgMYJbnzSeH/nIqiqMFusah9vbDt0AIOB0v37zbbfq5G7QLum6r+dv8dnklFQJmssVyMSJKHBhgUOo59vPyoNlby3/z2LH0+nt67i7LUH09h+4jy2KiXv3B6JSgyttQrV/ZBONLBhpEKhwN/BH43C+vu0CEaSJPHyz/HsOnkerVrFF9OiifQNxEbRzEGSPg+Ckx8UpML+FeYJ9iL7vsarSC013V+ODtr16ervTL+O7ugNEiv3nJE7nHqJBEloMIVCwbN9n0WpULL5zGZiMmIserytqVvJK8/DS+vF4PaDLXqsa8kuLOe1DccAeGJEWKN76gjyqV5ypKEJktD6fLzzFN/FpKJQwIf39CQywNU8O1ZrYfDTxvt//gcqS82zXy4Ns7VUHVK5zAXal6u+ivRdzFnKdXqZo6mbSJCERglzC+POTncCsCh2EVWGKosda9WJVQBMCpsk61CHJEm8uC6egjId3do58/Bg61kkV7g20xWkLJEgtUUbDqWzaKOx5cYrt3RlZNemL0xcp55TwTUQirMg5lOz7dZUh3T0KPriYrPtty6SJFF21PgBzxquIAGM7OpDO1ctF0p1rDuYJnc4dRIJktBos3rMwtnWmZMXTrImcY1FjnGm8Ax7M/daRXH2r0cy+ONYFjZKBYsnRzVpyQpBPtUJ0kmRILU5sSl5PPXTIQBmDuzIdEvUBdrYwpDnjPd3vw/lBWbZrdrPD3X79mAwUHbgwLVf0AzWUqBdk0qpYNoAY4uUZbtTkCRJ5oiuJP7SC43maufKrB6zAFhycAkFFeb5g1HTqpPGq0c3trsRf0d/s++/oXKLK3j5Z+Ol6VlDQ+nqb/nZJoJ5VQ+xZRVWkF9aKXM0grmczinhoa/2UVllYFRXH14Y1+XaL2qqyLvAszOUXYA9/zPbbltqmM1UoN2pk+wF2jXdFR2IVq3iRFYRe5Jz5Q7nCiJBEprkzs53EuoaSn5FPh8f+tis+67UV/JzkrEfjdzF2a9uOEZeSSXhvk7MGtqyaxcJ5uGosSHATQuIOqS2Ire4gunLYsgv1REV4MIHd/e07KQJpQqGPm+8v2cplJjnzby6ULskxrL1nKb6oxZeoPZaXOzVTO5t7Kb95e4UeYOpg0iQhCaxUdrwTJ9nAPgu4TuS85PNtu8tZ7ZwoeICPvY+DAoYZLb9Ntamo5lsOJSOSqngndujrH7dIKF+nX3EMFtbUa7T89BX+ziTW0qAm5bPp/VBa6uy/IG73Aq+kVBZBLv/a5ZdOlTXIcUfxVBSYpZ91sXaCrRrmj7AOCy6NSGLM7mW+x40hfiLLzRZf//+DG0/FL2kZ3HsYrONIVd3zp4cNhkbpTy9TPNLK3lxnXHdoocHB9M9oOUWlBTMr7phZIK4gtSqGQwST/14iANn83G2s2H5jD54ObVQOwalEoa9ZLwf8xkUZjR7l+p27VD7+4NeT+nBuGbvry7WWKBdU6i3I4M7eSFJsOJv65ryLxIkoVmejn4atVLN3+l/s+vcrmbv71T+KfZl7UOpUDIpbJIZImyaN345zvmiCkK8HHhiuHUUNQpNFy4KtduExZtO8OuRDNQqBZ/cH02odwu32wgbCe37QVW5cdq/Gdj3NXZ1L7XQMJs1FmhfbsbAIAB+2pdKcYXlZkY3lkiQhGYJdA7k/q7GFvyLYxej0+uatb/qq0eDAwbj6+Db7PiaYvuJbFYfOIdCAYtvj8JO3QKX7wWLqi7UTsgsssrZMsK1fbP3DB/vNA7lL749kv4hHi0fhEJx6SrS/hVwoflXPCxdqG2tBdo13RTmRbCnA0UVVaze3zKrNDSESJCEZns48mE8tZ6cLTrL18e/bvJ+yqvKWZ+8HpCvOLuwXMfza44AxmnD1rxOkNBwwV4OqJQKisqryCwslzscoZG2n8g2zSZ9ckQnJvWUcdmhjoMgeAgYdMYlSJqpulC7LD4eQ6n5GlFWs6YO2vVRKhVMv3gVafnfKRgM1vEhRiRIQrM5qB14otcTAHxy+BNyynKatJ/NZzZTWFmIn4MfA/0HmjPEBlvwWwIZBeV08LDn6VGdZYlBMD+NjYrgiwsLi5lsrcvR9AJmf3MAvUFicq8AHh9uBbNJh71s/PfQd3D+ZLN2pQ4IwMbPD3Q6yuLimh/bZS7NYLPeBAlgcq8AnOxsOJ1Tws6T5+UOBxAJkmAmt4bcSoRHBCW6Ej488GGT9lGzOFulbPlhrd1JOXwXcxaARZMjW2ZmjNBiOjVyTTZBfhkFZcxcHktJpZ4BIR4suK07CoUVrIEY0Bs6jwPJADvebtauFAoF9n2iASgx8zCbtRdo1+SgseGu6PYAfLn7tMzRGIkESTALpULJ/L7zAViXtI6jOUcb9fqkC0kczD6ISqGSpTi7pKKKZ1cfBuD+GzpwQ7AM9Q2CRVVP9RdLjrQOReU6ZiyLJauwgjBvRz66r7d1tdoY9gKggKNrIeNws3ZlqTokXVqa1Rdo1zRtQBBKBfyZmENStmWXX2kIK/ptE1q7Ht49GBc8DgmJhTELG1UMW331aGj7oXjbe1sqxHq9s+kE5y6U0c5Vy7Njwlv8+ILliSVHWg+d3sCsbw+SkFmEl5OGZTP64KKVbz3GOvlEQLeLyyBte7NZu3K4OJOt/NBhDOXmq5Erj7f+Au2a2rvbM6KLcS29r/45K3M0IkESzGxOrzlobbTEnY/j99O/N+g1ZVVlbEjeAMhTnB1zOo/lf6cAsHBydxw18vReEiyr+gpSYlYxeispAhWuJEkSL62LZ9fJ82jVKr6YFk2Am73cYdVt6POgUEHiJkht+jR9dWAgNt7eSDodZXGHzBZe+VFjLzdrH16racbF9fTWxaVTKvOMf5EgCWbl6+DLA90eAOC9/e9Rqrv2rIxNKZso0hUR4BjADf43WDrEWsoq9TyzyvgH6a7o9gwK82rR4wstJ9DdHju1kooqg9V17BUu+WhnMt/HpqJUwJJ7ehIZ4Cp3SPXzCIEe9xrvb329ybsx1iGZf5itNcxgu9wNwe6E+zpRpjOwJ0veejORIAlmNy1iGv4O/mSVZrHs6LJrbv/TiYvF2Z0mo1S07K/kf7ecJCW3FF9nO164xYKLXQqyUyoVpn5IolDbOm04lM7ijScAePmWrozo6iNzRA1w07OgsoWUP+HUjibvxtwNI1tTgXZNCoWCmRevIv2ZqaRKb5AtFpEgCWZnZ2PHU9FPAbAsfhnpxen1bnsi7wSHcw5jo7BhYujEForQ6ODZC3z+5ykA3prUDWc7K6txEMyukyjUtlqxKXk89ZPxau7MgR2ZfvFN0uq5tofeM4z3t74BTWxEWn0FqezQIQwVFc0Oq2aBtqaT9Rdo13RrD3/c7NVcqFSwJUG+Kf8iQRIsYmSHkUT7RFOhr+C9/e/Vu111cfawwGF4aj1bKjwqqvTMW3UYgwSTerZjeJdW8ElVaDax5Ih1Op1TwkNf7aOyysCorj68MK6VXc0d9BSo7SFtH5zc2KRd2HYMQuXpiVRZSfnh5s2Kg9oF2spWUKBdk51axd19ArC3kSgqb97qDM0hEiTBIhQKBc/2fRalQsmmlE3sy9x3xTalulJ+OfULAHd0btni7CVbk0jKLsbTUcMr461vhWvBMmouOSJYh9ziCqYviyG/VEdUgAsf3N0TldIKeh01hpMP9PuX8f62N8HQ+GEhhUKBw8Wu2iVmGGZrjfVHNT10YxCv9dJzR2/5uqaLBEmwmHD3cCaHGafBLopdhN6gr/X8pjObKNGVEOgUSF/fvi0WV3xaAR9dXNPpzYkRuNq3rk9XQtNVT/VPySmhXKe/xtaCpZXr9Dz01T7O5JYS4Kbl82l9Wm+D1gGPg8YZsuLh2Nom7eJSofaVHygbqzXOYKvJyU6N3L8KIkESLGp2z9k42TqRkJfA2qTafzRWJ60G4PZOt7dYcXZllYGnfzqE3iAxrrsfN3fza5HjCtbB20mDq70agwTJ5+VvRHc9MxgknvrxEAfO5uNsZ8PyGX3wctLIHVbT2bvDgMeM97e/DfrGz1E31SHFxWGorGxyKK21QNvaiARJsCh3O3f+HfVvAJYcXEJhZSEA6VXpHMs7hlqpZkLohBaL56MdySRkFuFmr+a1CeIPx/VGoRAz2azFok0J/HokA7VKwSf3RxPq7SR3SM13w6Ng7wG5ScZ12hrJNiQElbs7Unk55UeONDmM6gJtWmGBtjURCZJgcXeF30VHl47klefxyaFPAIipNI6xj+gwAnc79xaJIyGzkP/bngjAq7dG4OnYij+tCk1WXagtZrLJ55u9Z/hkp3EG6eLbI+kf0kaW9tE4wY1PGu/vXARVjZuNZq5+SNUF2natsEDbmogESbA4tVLNs32eBeDb499yNPcohyuNszRaqnN2ld7AM6sOo9NLjOjiw61R/i1yXMH6iCtI8tp+IpuXfza+gT85ohOTespXhGsRfR4EJz8oSIX9Kxr9clOCFNOMBKmVF2hbC5EgCS1iYLuB3BRwE1VSFbO2z6KSSoKcg4j2iW6R43/252kOnyvA2c6GtyZ1s44VwQVZmNZkEwlSizuaXsDsbw6gN0hM7hXA48ND5Q7J/NRaGPy08f6f/4HKa68mUJP9xZlspQcPIumaNsVdJEjmIRIkocU8Hf00NkobUx3SbaG3tUiikpRdzH+3nATgpVu64uNsZ/FjCtar+gpSekE5hTL2WLneZBSUMXN5LCWVegaEeLDgtu5t94NKz6ng2gGKsyDm00a9VBMaisrVFamsjLL4+EYf2ligLRIkcxAJktBiglyCuK/LfQDYYMP4juMtfky9QeKZVYeorDIwuJMXt8vYU0OwDi5aNX4uxiRZXEVqGUXlOmYsiyWrsIIwb0c+uq83tjZt+O3HxhaGPGe8v/t9KC9o8EsVSiX2fYxX1psy3V8UaJtPG/4NFazRvyL/xZgOYxijHYOLxsXix1v+dwoHzubjqLFp259YhUYRS460HJ3ewKxvD5KQWYSXk4ZlM/rgor0OlvWJvBM8O0PZBdjzv0a91L5P09dlMxVoh4WJAu1mkj1BWrp0KUFBQdjZ2dGvXz9irvILodPpeP311wkJCcHOzo6oqCg2bqzd1n3Xrl2MHz8ef39/FAoF69atu2I/kiTx8ssv4+fnh1arZcSIESQmJpr71IQ6ONo68tbAt+in6WfxY53JLeGdTQkAPDc2nHauWosfU2gdwkUdUouQJImX1sWz6+R5tGoVX0yLJsDNXu6wWoZSBUOfN97fsxRKchv80uo6pLIDB5CqGtdPyVR/1K1bo14nXEnWBOmHH35g7ty5vPLKKxw4cICoqChGjx5NdnZ2ndu/+OKLfPLJJyxZsoRjx47xyCOPMGnSJA4ePGjapqSkhKioKJYuXVrvcRcvXsyHH37Ixx9/zN69e3FwcGD06NGUl5eb/RwFeRgMEs+uPky5zsCAEA/u7Rsod0iCFRFLjrSMj3Ym831sKkoFLLmnJ5EBrnKH1LK63Aq+kVBZBLv/2+CXaTp1QunigqG0lPJjxxp1SFGgbT6yJkjvvfceDz30EDNmzKBr1658/PHH2Nvb8+WXX9a5/cqVK3n++ecZO3YswcHBPProo4wdO5Z3333XtM2YMWN48803mTRpUp37kCSJ999/nxdffJEJEyYQGRnJV199RXp6ep1Xm4TW6ZuYs/xzKg+tWsXC2yLF0JpQS+cai9ZKTVx9Xbi69YfSWbzxBACvjI9gRNfrcEFopRKGvWS8H/MZFGY06GUKpRL76It1SI0YZpMkSSRIZmQj14ErKyvZv38/zz33nOkxpVLJiBEj2LNnT52vqaiowM6u9gwkrVbLX3/91eDjnj59mszMTEaMGGF6zMXFhX79+rFnzx7uvvvueo9dUXGp6VdhoXEmlk6nQ9fEqZh1qd6XOfdpbSx9jmn5ZSz87TgAT40Mxc9Z3aLfT/EztH4d3DQoFXChVEf6hRK8L1viorWf37VY+vz2nbnAUz/GATBjQAfu7dOuxb+XVvMzDBqCKqAvynMx6HcuxnDz4ga9zK5XL4q3bqV4bwzO06Zd8Xxd56dLS0NfUAA2Nqg6Bsl/7s1gyZ9fQ/cpW4KUk5ODXq/Hx6f2pwofHx8SEhLqfM3o0aN57733GDx4MCEhIWzdupU1a9ag1zd80cnMzEzTcS4/bvVzdVmwYAGvvfbaFY//8ccf2Nubf0x98+bNZt+ntbHEOUoSfHRcSUmlko5OEh55R/ntt6NmP05DiJ+hdfPUqMguV/D1hm2Eu9Z9Fak1n19DWOL8ssvgv/EqdHoFke4GIg3J/PZbstmP01DW8DP0sBvBjcSgOLCC7WURlGm8rvkaTUU5HYCimBh+27ABVHWv3Frz/BwPH8EfKPfx4fctW8wUvbws8fMrLW1YbyrZEqSm+OCDD3jooYcIDw9HoVAQEhLCjBkz6h2SM6fnnnuOuXPnmr4uLCykffv2jBo1CmdnZ7MdR6fTsXnzZkaOHIla3TZneljyHH/an8aJf46isVHyycz+dPR0MOv+G0L8DFuH3wri2HQsG7egrowd0KHWc23h/K7GUueXW1LJnZ/upbSqjMh2znw9sw9amZZkt66f4VgM3/6N8vQORtjsQz92yTVfIen1nF62HIqKGBYcfMWQWV3nl3MykXzA+4Yb6DZ2rPlPowVZ8udXPQJ0LbIlSJ6enqhUKrKysmo9npWVha+vb52v8fLyYt26dZSXl5Obm4u/vz/z588nODi4wcet3ndWVhZ+fpdWcs/KyqJHjx71vk6j0aDRXLl2l1qttsh/Pkvt15qY+xwzC8pZcLHmYe7ITnTyczXbvptC/AytW7ifC5uOZZOYXVLvObTm82sIc55fuU7Pv7+N42xeGQFuWr6Y3hdnB/nXO7San+Hwl+HzHSiP/IBy0Fzw6nT17dVq7Hv3pnjHDioPHMSpnvenmuenO24sLbDv3t06ztkMLPHza+j+ZCvStrW1pXfv3mzdutX0mMFgYOvWrfTv3/+qr7Wzs6Ndu3ZUVVWxevVqJkxo+GrwHTt2xNfXt9ZxCwsL2bt37zWPK1gvSZJ4Ye0RisqriGrvyoODGp40C9enmoXaQvMYDBJP/XiIA2fzcbazYfmMPng5yZ8cWZWA3tB5HEgG2PF2g15i3/diP6QGLFxbq0BbTPE3C1lnsc2dO5fPPvuMFStWcPz4cR599FFKSkqYMWMGAFOnTq1VxL13717WrFnDqVOn+PPPP7n55psxGAw888wzpm2Ki4uJi4sjLi4OMBZlx8XFcfbsWcC4WvKcOXN48803Wb9+PUeOHGHq1Kn4+/szceLEFjt3wbx+jktna0I2apWCd26PRKUUs9aEq7uUIBVjMIiZbM2xaFMCvx7JQK1S8Mn90YR6O8kdknUa9gKggKNrIePwNTc3LVy7bx/SNWptdWnpxgJt0UHbbGStQbrrrrs4f/48L7/8MpmZmfTo0YONGzeaCqjPnj2LUnkphysvL+fFF1/k1KlTODo6MnbsWFauXImrq6tpm3379jF06FDT19V1Q9OmTWP58uUAPPPMM5SUlPDwww+Tn5/PjTfeyMaNG6+YISe0DueLKnh1g/GT0+PDwkw9bgThajq422Nro6RMpyf1QikdPFq+Xq0t+PqfM3yy8xQAi2+PpH+Ih8wRWTGfCOg2GeJXwbY3YcqPV93crks4SgcHDEVFVJw4gV3XrvVua7p6JDpom43sRdqzZ89m9uzZdT63Y8eOWl/fdNNNHLtG06whQ4Zcs6+JQqHg9ddf5/XXX29UrIJ1evnnePJLdXT1c+aRISFyhyO0EjYqJWHejhxNL+REZpFIkJpge0I2L/9sXFB17shOTOop1jq8pqHPG68gJW6C1Bho37feTRU2Nmije1OycxelsbFXT5AuLmwr+h+Zj+xLjQhCc/x2JIPf4zOxUSp4545I1CrxKy00XOfqNdlER+1Gi08rYNa3BzBIcHvvAB4bFip3SK2DRwj0uNd4f+u1P6Q7XBxmK4m5eh2SaBBpfuLdRGi18koqTZ9e/z0khAh/yy9+K7QtnXzForVNkVFQxgMrYimt1DMgxIO3J4mFoBvlpmdBZQspf8KpHVfdtLoOqWzfPiSDoc5tRAdtyxAJktBqvbbhKDnFlXTycWSW+PQqNIGYydZ4ReU6ZiyLJauwgjBvRz66rze2NuKtpFFc20P0TOP9rW8YO9zWw65rV5T29ugLCqioZ1H1WgXana/RPkBoMPFbLbRKm49l8XNcOkoFvHN7FBobeZrRCa1b9RDbqfMlVFbV/elcuESnNzDr24MkZBbh5aRh2Yw+uGjbRr+dFjfoKVDbQ9o+OLmx3s0UajXaXr0AKN1b97psokDbMkSCJLQ6BaU6Xlh7BICHBgcT1d5V3oCEVsvPxQ4nOxuqDBKncorlDseqSZLES+vi2XXyPFq1ii+mRRPgZv5llq4bjt7Q71/G+9vehHqGz6DGdP96+iGJ4TXLEAmS0Oq8+esxsosqCPZ04MkR4nKy0HQKhUIUajfQRzuT+T42FaUCltzTk8gAV7lDav0GPA4aZ8iKh2Nr693Mvm+Nfkh1JFIiQbIMkSAJrcrOk+f5af85FApjzxU7tRhaE5rHVKgtEqR6rT+UzuKLy/i8Mj6CEV19rvEKoUHs3WHAY8b7298GfVWdm2m7dUOh1aK/cIGKpKRaz0mSJKb4W4hIkIRWo6hcx3Orjd1npw8IIjrIXeaIhLYgXBRqX1VsSh5P/3gIgAdu7Mi0AUHyBtTW3PAo2HtAbhIc+q7OTRRqNfY9ewBXDrNVpYsCbUsRCZLQaiz8PYH0gnLau2uZN7qz3OEIbUR15/UEcQXpCqfOF/PQV/uo1BsYHeHD82O7yB1S26NxghuNKz6wcxFUVdS5mWldtsv6IVVcbJ6sCQsVBdpmJhIkoVX4OzmHb/Ya19NbNDkSe1vZm8ALbUR1DdK5C2UUV9Q9xHE9yi2uYMbyWPJLdUS1d+X9u3qKNQ4tpc8D4OQHBamwf0Wdm9Ral61GW4DqBEkbIRaoNTeRIAlWr7SyivmrjbPWpvQLZECIp8wRCW2Jm4Mt3hdXnk8Uw2wAlOv0PPTVPs7klhLgpuXzqdFobUW9n8WotTB4nvH+n/+BytIrNrHr3h2FRoM+N5fKU6dMj5cfO258XtQfmZ1IkASr986mE5zNK8XfxY75Y8LlDkdogzqLQm0Tg0HiqR8PceBsPs52Niyf0QeviwmkYEE97wfXDlCcBTGfXvG00tYWbc+eAJTGXOyHJEmmK0giQTI/kSAJVm1fSh7L/04BYMHkSJzsRFM6wfyq65DEkiOwaFMCvx7JQK1S8Mn90YR6O8kd0vXBxhaGPGe8v/t9KC+4YhP7PtHApUJtmwsXMOTniwJtCxEJkmC1ynV6nll1GEmCO3oHcFMnL7lDEtooseSI0df/nOGTncbhm8W3R9I/xEPmiK4zkXeCZ2couwB7/nfF09V1SCWxsUiShF1aGiAKtC1FJEiC1frvlpOcyinB20nDi+O6yh2O0IaJZpGwPSHbtPjz3JGdmNQzQOaIrkNKFQx93nh/z/9BSW6tp7VRUShsbdGfz0GXkoLmYoKkFcNrFiESJMEqxaXm89ku4yfZtyZ1x8VeDK0JlhPm44hCATnFleQU1z3Nui2LTytg1rcHMEhwe+8AHhOLP8uny63gGwmVxbD7v7WeUmo0aKOiACjbtx+7c8YEyU7MYLMIkSAJVqeiSs8zqw5hkGBCD39Giq69goXZ29oQ6G5cV+zkdXYVKT2/jAdWxFJaqWdgqAdvT+qOQiGm88tGqYThLxvvx3wGhRm1nq4eZiuLjTUNsYkCbcsQCZJgdZZuS+JkVjGejra8Ml78xxdaxvVYqF1UXsXM5bFkFVYQ5u3I/6b0xtZGvC3ILnQEtL8BqsqN0/5rqG4YWbJjB6rSUrCxEQXaFiL+JwhW5Wh6Af/bkQzA6xO64e4gCg+FlnG9LTmiN8DjPxwiIbMILycNy2b0wUUrhrKtgkIBw18y3t+/Ai6cMT2l7RGFQq1GKisDRIG2JYkEycp8sTuFxYdUPLf2KCv+TmFfSh4l10l3X53ewLyfDlNlkBjTzZex3f3kDkm4jlxPS45IksRPp5X8lZSLVq3ii2nRBLjZyx2WUFPQjRA8FAw64xIkFynt7LCLjDR9rekqJrBYilivwcocSi0grVTBqgNprDpgHF9WKKCjhwMR7VyI8He+eHNpc1dXPtmZzLGMQlzt1bw+QRQdCi3LNNU/s6jWUg5tRXZhOUfTCzmaXkDs6Tz2ZCtRKmDJPT2JDHCVOzyhLsNeglPbjYvYDpwDXsahNPu+fSjbvx8QCZIliQTJyjw3pjP+VenY+YWRkFnM0fRCMgvLOZVTwqmcEjYcSjdt6+diZ0qWIvydiWjngr+LXasssDyZVcSHW5MAeHV8hOjcK7S4jp4OqFUKSir1pBeUyx1Ok0mSxNm8UlMyZPy3kPNFV87Oe2FsOCPEJAjrFdAbOo+DE7/CjrfhjuUAOPTpQ+5HHwOgEQXaFiMSJCvj52JHpLvE2OGhqNXGeoCc4gqOpRcSf/GP3bH0Qk7nlJBRUE5GQTlbjmebXu9mr6arvzPd/F3oejF56ujpYNWLTFbpDcz76RCVegPDw72Z0MNf7pCE65BapSTEy5GEzCJOZBXLHU6DVOkNJJ0v5mhaoSkhOpZRSFH5lcPyCgUEezoQ4e9CF19HdGnHmHpDoAxRC40y7AU48RscXQs3Pgl+UWh79kTl4UGFTocmLEzuCNsskSC1Ap6OGgZ38mJwjU7SxRVVHM8oJD7t0ifExKwiLpTq2J2Uy+6kSw3GtGoVXfyciPB3oVs7Y9IU5uOIxsY6Fp/8cvdpDp0rwMnOhrfEFGNBRp19nUjILCIxq5j2cgdzmXKdnoTMItP/+WPpBSRkFlFRZbhiW1uVkk6+jkT4Gf/Pd/V3oYufE/a2xj/5Op2O33471tKnIDSFTwR0vx2O/ATb3oIpP6LUamn/4w9s3baNcFGgbTEiQWqlHDU29Alyp0+Qu+mxiio9iVnFHE0vID7N+GnyeEYRZTo9B87mc+BsvmlbtUpBqLcTEf7OdLs4PNfFzxlHTcv+Spw6X8y7f5wE4KVxXfF1sWvR4wtCTdWF2iezimnvIF8cBWU6jtUaIisg+XwJesOVtVEOtirT1eLqIfdQb0cxXb8tGfIcxK+BxE2QGgPt+2Lj7Y3e2VnuyNo0kSC1IRobFd3audCtnQt3GXuJoTdInM4pqfWH9mh6IfmlOo5nFHI8o5BVxlo/FAoI8nAwDdFVF4R7OFqmHshgkHhm1WEqqgwMCvPkjmixtIEgr84+l6b6Dw9umWPWLJ4+enEoPTWvrM5tPRxsTclQ9dXgDu72KK14CF0wA48Q6DkFDnwFW1+H6b/IHdF1QSRIbZxKqSDU25FQb0cm9GgHGIs40wvKOZpWQPzFS/VH0wvJKCjndE4Jp3NK+PXwpe6tvs52piLw6qSpnau22UNhK/eeZd+ZCzjYqlhwmxhaE+RXPZMtOacEfZB5911X8XR8WmG9S5u0c9XWmoTRrZ0LPs4a8f/kejX4GTj0PaT8Cad2QPuBckfU5okE6TqkUCho56qlnauWURG+psdziys4llFoGp47ll7I6dwSMgvLySwsZ2vCpWJwV3s1Xf2cTX+4I/yd6ejp2OBi8JxyeHdzIgDzx3YRPVgEq9DOVYuDrYqSSj3nmzGRrTHF00oFBHs51mrhEeHvjKu9qC0RanBtD9EzYe/HsPUNmPa73BG1eSJBEkw8HDUMCvNiUFjtYvCEy4vBs4vIL9Xxd3IufyfXLgYP96uua3Ihwt+FTr5XFoNLksT3yUrKdAZuCHZnSl8xk0awDkqlgjAfJ+JS88kobViy39ji6c6+TqZk6PLiaUG4qkFPGYfZ0vahSNwkdzRtnvhfKVyVo8aG6CB3ousoBq/ZeuB4RiGllXoOns3nYI1icJuLQ3w1hwkOpeaRWKjETq1k0eRIUT8hWJVw3/oTpMYWT0eY2m1cmj2qVoniaaGJHL2h37/gr/+i2rkA/OfJHVGbJhIkodFqFoPfeXEytN4gkZJbQnxawcU3EOObx4VSHQmZRSRkFrH6QO39zB0RRgcPGacKCUIdqmeypRTDjpPnOZFV0qDi6cs73YviacEiBjwOsV+gyD5KO/sY4Ba5I2qzRIIkmIVKqSDEy5EQr9rF4BkF5bWG546lF5BeUE6Ys0E0qROsUvWitScKlDy08uAVz1cXT3drd2lavSieFlqMvbsxSdr+JlGpy1H9eAra9QTfSPCLBOd2xinJQrOJBEmwGIVCgb+rFv/LisHzisrYufUPq+7uLVy/uge44O2k4XxROcFejsarpReHiLuK4mnBGtzwCNLhH1DnJkLiRuOtmtYd/KKMyZJvpPG+ewgoxdBuY4kESWhxTnY2iNxIsFZOdmp2PDWIX3/byMTxA01L/giC1dA4UfXANvau/Yj+QQ6oso9C5mHIPg5lecYFbk9tv7S92gF8u9dImiLBqwvYiGT/akSCJAiCcBm1SomtdazEIwh1U2vJdQzH0HcsquokXlcO2ceMyVLGYeO/mfGgK4HUf4y3ako1eHe5mDRFGa80+USAxlGe87FCIkESBEEQhLZAbQftehlv1Qx6yEm8mDQdMt4yD0N5wcUE6jDw9cWNFeARakya/KIuDdHZu9d1tDZPJEiCIAiC0FYpVeAdbrxF3ml8TJIg/+ylK03VSVNRBuQmGm/xqy/twzngsqTp+igGFwmSIAiCIFxPFApw62C8dRl/6fHi85B58SpT9RBd3ikoPGe8nfjt0rbXQTG4SJAEQRAEQQBHLwgdYbxVKy+ErPjaSdP5hOuiGFwkSIIgCIIg1M3OGToMMN6qXSfF4CJBEgRBEASh4a6TYnCRIAmCIAiC0DxmLgZXeHXFrjLXuA+ZiARJEARBEATzu2YxeI2k6bJicBtgNKD3yoEhz8gSvkiQBEEQBEFoOQ0oBpcyDiFlJyB5hcsWpkiQBEEQBEGQ12XF4FU6HRt/WcfNwcNkC6ntNCwQBEEQBKHNMChtwUYj2/FFgiQIgiAIgnAZkSAJgiAIgiBcRiRIgiAIgiAIlxEJkiAIgiAIwmVEgiQIgiAIgnAZq0iQli5dSlBQEHZ2dvTr14+YmJh6t9XpdLz++uuEhIRgZ2dHVFQUGzdubPQ+hwwZgkKhqHV75JFHzH5ugiAIgiC0PrInSD/88ANz587llVde4cCBA0RFRTF69Giys7Pr3P7FF1/kk08+YcmSJRw7doxHHnmESZMmcfDgwUbv86GHHiIjI8N0W7x4sUXPVRAEQRCE1kH2BOm9997joYceYsaMGXTt2pWPP/4Ye3t7vvzyyzq3X7lyJc8//zxjx44lODiYRx99lLFjx/Luu+82ep/29vb4+vqabs7OzhY9V0EQBEEQWgdZO2lXVlayf/9+nnvuOdNjSqWSESNGsGfPnjpfU1FRgZ2dXa3HtFotf/31V6P3+c033/D111/j6+vL+PHjeemll7C3t6/3uBUVFaavCwsLAeOQn06na8RZX131vsy5T2vT1s+xrZ8ftP1zFOfX+rX1cxTn1/x9X4usCVJOTg56vR4fH59aj/v4+JCQkFDna0aPHs17773H4MGDCQkJYevWraxZswa9Xt+ofd5777106NABf39/Dh8+zLPPPsuJEydYs2ZNncddsGABr7322hWP//HHH/UmVc2xefNms+/T2rT1c2zr5wdt/xzF+bV+bf0cxfk1XmlpaYO2a3VrsX3wwQc89NBDhIeHo1AoCAkJYcaMGfUOydXn4YcfNt3v3r07fn5+DB8+nOTkZEJCQq7Y/rnnnmPu3LmmrwsLC2nfvj2jRo0y69CcTqdj8+bNjBw5ErVabbb9WpO2fo5t/fyg7Z+jOL/Wr62fozi/pqseAboWWRMkT09PVCoVWVlZtR7PysrC19e3ztd4eXmxbt06ysvLyc3Nxd/fn/nz5xMcHNzkfQL069cPgKSkpDoTJI1Gg0Zz5ZowarXaIr+cltqvNWnr59jWzw/a/jmK82v92vo5ivNr2j4bQtYibVtbW3r37s3WrVtNjxkMBrZu3Ur//v2v+lo7OzvatWtHVVUVq1evZsKECc3aZ1xcHAB+fn7NOCNBEARBENoC2YfY5s6dy7Rp04iOjqZv3768//77lJSUMGPGDACmTp1Ku3btWLBgAQB79+4lLS2NHj16kJaWxquvvorBYOCZZ55p8D6Tk5P59ttvGTt2LB4eHhw+fJgnn3ySwYMHExkZ2aC4JUkCGn6prqF0Oh2lpaUUFha22U8Fbf0c2/r5Qds/R3F+rV9bP0dxfk1X/b5d/T5eL8kKLFmyRAoMDJRsbW2lvn37Sv/884/puZtuukmaNm2a6esdO3ZIXbp0kTQajeTh4SHdf//9UlpaWqP2efbsWWnw4MGSu7u7pNFopNDQUGnevHlSQUFBg2NOTU2VAHETN3ETN3ETN3FrhbfU1NSrvs8rJOlaKZRQF4PBQHp6Ok5OTigUCrPtt7r4OzU1tc32ZWrr59jWzw/a/jmK82v92vo5ivNrOkmSKCoqwt/fH6Wy/koj2YfYWiulUklAQIDF9u/s7Nwmf+lrauvn2NbPD9r+OYrza/3a+jmK82saFxeXa24jeydtQRAEQRAEayMSJEEQBEEQhMuIBMnKaDQaXnnllTp7LrUVbf0c2/r5Qds/R3F+rV9bP0dxfpYnirQFQRAEQRAuI64gCYIgCIIgXEYkSIIgCIIgCJcRCZIgCIIgCMJlRIIkCIIgCIJwGZEgWYkFCxbQp08fnJyc8Pb2ZuLEiZw4cULusMzmo48+IjIy0tT0q3///vz+++9yh2UxCxcuRKFQMGfOHLlDMZtXX30VhUJR6xYeHi53WGaVlpbGfffdh4eHB1qtlu7du7Nv3z65wzKboKCgK36GCoWCWbNmyR2aWej1el566SU6duyIVqslJCSEN95449prbrUiRUVFzJkzhw4dOqDVahkwYACxsbFyh9Vku3btYvz48fj7+6NQKFi3bl2t5yVJ4uWXX8bPzw+tVsuIESNITExskdhEgmQldu7cyaxZs/jnn3/YvHkzOp2OUaNGUVJSIndoZhEQEMDChQvZv38/+/btY9iwYUyYMIGjR4/KHZrZxcbG8sknnzR44ePWJCIigoyMDNPtr7/+kjsks7lw4QIDBw5ErVbz+++/c+zYMd59913c3NzkDs1sYmNja/38Nm/eDMAdd9whc2TmsWjRIj766CP+7//+j+PHj7No0SIWL17MkiVL5A7NbB588EE2b97MypUrOXLkCKNGjWLEiBGkpaXJHVqTlJSUEBUVxdKlS+t8fvHixXz44Yd8/PHH7N27FwcHB0aPHk15ebnlg2vw6qxCi8rOzpYAaefOnXKHYjFubm7S559/LncYZlVUVCSFhYVJmzdvlm666SbpiSeekDsks3nllVekqKgoucOwmGeffVa68cYb5Q6jRT3xxBNSSEiIZDAY5A7FLMaNGyfNnDmz1mO33XabNGXKFJkiMq/S0lJJpVJJv/zyS63He/XqJb3wwgsyRWU+gLR27VrT1waDQfL19ZXeeecd02P5+fmSRqORvvvuO4vHI64gWamCggIA3N3dZY7E/PR6Pd9//z0lJSX0799f7nDMatasWYwbN44RI0bIHYpFJCYm4u/vT3BwMFOmTOHs2bNyh2Q269evJzo6mjvuuANvb2969uzJZ599JndYFlNZWcnXX3/NzJkzzbrgtpwGDBjA1q1bOXnyJACHDh3ir7/+YsyYMTJHZh5VVVXo9Xrs7OxqPa7VatvU1dxqp0+fJjMzs9bfUxcXF/r168eePXssfnyxWK0VMhgMzJkzh4EDB9KtWze5wzGbI0eO0L9/f8rLy3F0dGTt2rV07dpV7rDM5vvvv+fAgQOtuh7gavr168fy5cvp3LkzGRkZvPbaawwaNIj4+HicnJzkDq/ZTp06xUcffcTcuXN5/vnniY2N5fHHH8fW1pZp06bJHZ7ZrVu3jvz8fKZPny53KGYzf/58CgsLCQ8PR6VSodfreeutt5gyZYrcoZmFk5MT/fv354033qBLly74+Pjw3XffsWfPHkJDQ+UOz+wyMzMB8PHxqfW4j4+P6TlLEgmSFZo1axbx8fFt7hNB586diYuLo6CggFWrVjFt2jR27tzZJpKk1NRUnnjiCTZv3nzFp7u2ouan8MjISPr160eHDh348ccfeeCBB2SMzDwMBgPR0dG8/fbbAPTs2ZP4+Hg+/vjjNpkgffHFF4wZMwZ/f3+5QzGbH3/8kW+++YZvv/2WiIgI4uLimDNnDv7+/m3mZ7hy5UpmzpxJu3btUKlU9OrVi3vuuYf9+/fLHVqbI4bYrMzs2bP55Zdf2L59OwEBAXKHY1a2traEhobSu3dvFixYQFRUFB988IHcYZnF/v37yc7OplevXtjY2GBjY8POnTv58MMPsbGxQa/Xyx2i2bm6utKpUyeSkpLkDsUs/Pz8rkjWu3Tp0qaGEaudOXOGLVu28OCDD8odilnNmzeP+fPnc/fdd9O9e3fuv/9+nnzySRYsWCB3aGYTEhLCzp07KS4uJjU1lZiYGHQ6HcHBwXKHZna+vr4AZGVl1Xo8KyvL9JwliQTJSkiSxOzZs1m7di3btm2jY8eOcodkcQaDgYqKCrnDMIvhw4dz5MgR4uLiTLfo6GimTJlCXFwcKpVK7hDNrri4mOTkZPz8/OQOxSwGDhx4RWuNkydP0qFDB5kispxly5bh7e3NuHHj5A7FrEpLS1Eqa7+tqVQqDAaDTBFZjoODA35+fly4cIFNmzYxYcIEuUMyu44dO+Lr68vWrVtNjxUWFrJ3794WqV8VQ2xWYtasWXz77bf8/PPPODk5mcZXXVxc0Gq1MkfXfM899xxjxowhMDCQoqIivv32W3bs2MGmTZvkDs0snJycrqgXc3BwwMPDo83UkT399NOMHz+eDh06kJ6eziuvvIJKpeKee+6ROzSzePLJJxkwYABvv/02d955JzExMXz66ad8+umncodmVgaDgWXLljFt2jRsbNrWW8D48eN56623CAwMJCIigoMHD/Lee+8xc+ZMuUMzm02bNiFJEp07dyYpKYl58+YRHh7OjBkz5A6tSYqLi2tdhT59+jRxcXG4u7sTGBjInDlzePPNNwkLC6Njx4689NJL+Pv7M3HiRMsHZ/F5ckKDAHXeli1bJndoZjFz5kypQ4cOkq2treTl5SUNHz5c+uOPP+QOy6La2jT/u+66S/Lz85NsbW2ldu3aSXfddZeUlJQkd1hmtWHDBqlbt26SRqORwsPDpU8//VTukMxu06ZNEiCdOHFC7lDMrrCwUHriiSekwMBAyc7OTgoODpZeeOEFqaKiQu7QzOaHH36QgoODJVtbW8nX11eaNWuWlJ+fL3dYTbZ9+/Y63/umTZsmSZJxqv9LL70k+fj4SBqNRho+fHiL/e4qJKkNtRgVBEEQBEEwA1GDJAiCIAiCcBmRIAmCIAiCIFxGJEiCIAiCIAiXEQmSIAiCIAjCZUSCJAiCIAiCcBmRIAmCIAiCIFxGJEiCIAiCIAiXEQmSIAhWJSUlBYVCQVxcnNyhmCQkJHDDDTdgZ2dHjx49mrUvhULBunXrzBKXIAiWIxIkQRBqmT59OgqFgoULF9Z6fN26dSgUCpmiktcrr7yCg4MDJ06cqLUu1OUyMzN57LHHCA4ORqPR0L59e8aPH3/V1zTHjh07UCgU5OfnW2T/gnA9EwmSIAhXsLOzY9GiRVy4cEHuUMymsrKyya9NTk7mxhtvpEOHDnh4eNS5TUpKCr1792bbtm288847HDlyhI0bNzJ06FBmzZrV5GO3BEmSqKqqkjsMQbAqIkESBOEKI0aMwNfXlwULFtS7zauvvnrFcNP7779PUFCQ6evp06czceJE3n77bXx8fHB1deX111+nqqqKefPm4e7uTkBAAMuWLbti/wkJCQwYMAA7Ozu6devGzp07az0fHx/PmDFjcHR0xMfHh/vvv5+cnBzT80OGDGH27NnMmTMHT09PRo8eXed5GAwGXn/9dQICAtBoNPTo0YONGzeanlcoFOzfv5/XX38dhULBq6++Wud+/v3vf6NQKIiJiWHy5Ml06tSJiIgI5s6dyz///FPna+q6AhQXF4dCoSAlJQWAM2fOMH78eNzc3HBwcCAiIoLffvuNlJQUhg4dCoCbmxsKhYLp06ebzmnBggV07NgRrVZLVFQUq1atuuK4v//+O71790aj0fDXX39x6NAhhg4dipOTE87OzvTu3Zt9+/bVGbsgtHUiQRIE4QoqlYq3336bJUuWcO7cuWbta9u2baSnp7Nr1y7ee+89XnnlFW655Rbc3NzYu3cvjzzyCP/617+uOM68efN46qmnOHjwIP3792f8+PHk5uYCkJ+fz7Bhw+jZsyf79u1j48aNZGVlceedd9bax4oVK7C1tWX37t18/PHHdcb3wQcf8O677/Kf//yHw4cPM3r0aG699VYSExMByMjIICIigqeeeoqMjAyefvrpK/aRl5fHxo0bmTVrFg4ODlc87+rq2pRvHQCzZs2ioqKCXbt2ceTIERYtWoSjoyPt27dn9erVAJw4cYKMjAw++OADABYsWMBXX33Fxx9/zNGjR3nyySe57777rkgy58+fz8KFCzl+/DiRkZFMmTKFgIAAYmNj2b9/P/Pnz0etVjc5dkFo1VpkSVxBEFqNadOmSRMmTJAkSZJuuOEGaebMmZIkSdLatWulmn8yXnnlFSkqKqrWa//73/9KHTp0qLWvDh06SHq93vRY586dpUGDBpm+rqqqkhwcHKTvvvtOkiRJOn36tARICxcuNG2j0+mkgIAAadGiRZIkSdIbb7whjRo1qtaxU1NTa61Sf9NNN0k9e/a85vn6+/tLb731Vq3H+vTpI/373/82fR0VFSW98sor9e5j7969EiCtWbPmmscDpLVr10qSdGkl8wsXLpieP3jwoARIp0+fliRJkrp37y69+uqrde6rrteXl5dL9vb20t9//11r2wceeEC65557ar1u3bp1tbZxcnKSli9ffs1zEITrgY1smZkgCFZv0aJFDBs2rM6rJg0VERGBUnnpYrWPjw/dunUzfa1SqfDw8CA7O7vW6/r372+6b2NjQ3R0NMePHwfg0KFDbN++HUdHxyuOl5ycTKdOnQDo3bv3VWMrLCwkPT2dgQMH1np84MCBHDp0qIFnaKzhsZTHH3+cRx99lD/++IMRI0YwefJkIiMj690+KSmJ0tJSRo4cWevxyspKevbsWeux6OjoWl/PnTuXBx98kJUrVzJixAjuuOMOQkJCzHcygtCKiCE2QRDqNXjwYEaPHs1zzz13xXNKpfKKxECn012x3eVDNAqFos7HDAZDg+MqLi5m/PjxxMXF1bolJiYyePBg03Z1DXdZQlhYGAqFgoSEhEa9rjpxrPl9vPx7+OCDD3Lq1Cnuv/9+jhw5QnR0NEuWLKl3n8XFxQD8+uuvtb43x44dq1WHBFd+f1599VWOHj3KuHHj2LZtG127dmXt2rWNOidBaCtEgiQIwlUtXLiQDRs2sGfPnlqPe3l5kZmZWevN3Zy9i2oWNldVVbF//366dOkCQK9evTh69ChBQUGEhobWujUmKXJ2dsbf35/du3fXenz37t107dq1wftxd3dn9OjRLF26lJKSkiuer28avpeXF2Csc6pW1/ewffv2PPLII6xZs4annnqKzz77DABbW1sA9Hq9aduuXbui0Wg4e/bsFd+b9u3bX/NcOnXqxJNPPskff/zBbbfdVmcBvSBcD0SCJAjCVXXv3p0pU6bw4Ycf1np8yJAhnD9/nsWLF5OcnMzSpUv5/fffzXbcpUuXsnbtWhISEpg1axYXLlxg5syZgLFwOS8vj3vuuYfY2FiSk5PZtGkTM2bMqJUsNMS8efNYtGgRP/zwAydOnGD+/PnExcXxxBNPNDpevV5P3759Wb16NYmJiRw/fpwPP/yw1nBhTdVJy6uvvkpiYiK//vor7777bq1t5syZw6ZNmzh9+jQHDhxg+/btpkSxQ4cOKBQKfvnlF86fP09xcTFOTk48/fTTPPnkk6xYsYLk5GQOHDjAkiVLWLFiRb3xl5WVMXv2bHbs2MGZM2fYvXs3sbGxpmMJwvVGJEiCIFzT66+/fsUQWJcuXfjf//7H0qVLiYqKIiYmplm1SpdbuHAhCxcuJCoqir/++ov169fj6ekJYLrqo9frGTVqFN27d2fOnDm4urrWqndqiMcff5y5c+fy1FNP0b17dzZu3Mj69esJCwtr1H6Cg4M5cOAAQ4cO5amnnqJbt26MHDmSrVu38tFHH9X5GrVazXfffUdCQgKRkZEsWrSIN998s9Y2er2eWbNm0aVLF26++WY6derE//73PwDatWvHa6+9xvz58/Hx8WH27NkAvPHGG7z00kssWLDA9Lpff/2Vjh071hu/SqUiNzeXqVOn0qlTJ+68807GjBnDa6+91qjvgyC0FQrJktWFgiAIgiAIrZC4giQIgiAIgnAZkSAJgiAIgiBcRiRIgiAIgiAIlxEJkiAIgiAIwmVEgiQIgiAIgnAZkSAJgiAIgiBcRiRIgiAIgiAIlxEJkiAIgiAIwmVEgiQIgiAIgnAZkSAJgiAIgiBcRiRIgiAIgiAIlxEJkiAIgiAIwmX+H0gRJa0FoYmuAAAAAElFTkSuQmCC", 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", 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QIAiFgyTB7gmoLiynEqC7WB8ajjR2VIKQ93kfhc0DIS4MrF3h/XVQoraxo8o20Q1eEISCT5Jg33dwYXnyU8qDUyHkgfFiEoS8TpLg7FJY3U1OforVguFHCkTyAyIBEgShMDj6Pzi9AABtx7kEW1dGoY2DbSNApzVycIKQB2kT4Z9P4L8v5MrOXn1g8G6wdTN2ZAYjEiBBEAq2k/PgyI/y4/b/Q6rZn0ulhiGZ2YLvBTg517jxCUJeEx0Mf3WFS6sABbSZAd1+B7WFsSMzKJEACYJQcJ3/A/ZPkh+/MxkayGt+4k2Lomv3k/z8kZ/A/6qRAhSEPMb/GixrCU9Pg5kt9N0Ejcfli8rOmSUSIEEQCqYr6+X+RABNxkPTCSlelqr2goqdQa+FbSNBE2+EIAUhD7m5DZa3g4hn4FAWPjoIFdoaO6ocIxIgQRAKnls7YMfH8uN6I+TRn9cpFNDlN7BygqBbcnE3QSiM9Ho4/ANsHgSaWCj7Dgw7CE4VjB1ZjsoTCdDChQvx8PDA3Nyc+vXrc+7cuXSPXblyJQqFIsWXuXn6HWdHjhyJQqHg119/zYHIBUHIc+7tgy1DQdJDzX7Q/qf0h++tHOUkCODUfHhyKvfiFIS8ICEaNvWXNwoANBwjT3tZFDFuXLnA6AnQxo0bGT9+PFOmTOHSpUtUr16ddu3aERQUlO57bG1t8ff3T/568uRJmsdt27aNM2fOUKxYsZwKXxCEvOTRcfnDXK+BKt2hyzxQvuVjrmInqNEPkOSpsISoXAlVEIwu7DH82Rbu7AKVKby3GNp9D6rCUSLQ6AnQnDlzGDZsGIMHD6Zy5cosWbIES0tLli9fnu57FAoFrq6uyV8uLi6pjvH19WXs2LGsXbsWtVqdk7cgCEJe8Ow8rOsD2nio0AG6L814if72P4JdSQh/ItcLEoSC7tFxWNoSgm6CtQsM2g01+ho7qlxl1DQvMTGRixcvMnHixOTnlEolrVu35vTp0+m+Lzo6mlKlSqHX66lVqxY//PADVapUSX5dr9fTv39/vvjiixTPpychIYGEhITkP0dGRgKg0WjQaDRZubV0JZ3P0OfNK8T95X/58h4DrmOytgcKTQz60s3RdVsGeuSRoNekeX8qCxSdf8NkbTe4uBJtuXZI5drkUvCGlS+/f5lU0O8xp+9PeXE5yn3foNBr0btWR9drNdgWg1z6+8zJ+8vMORWSJEkGjyCD/Pz8KF68OKdOnaJhw4bJz3/55ZccPXqUs2fPpnrP6dOnuX//Pl5eXkRERDB79myOHTvGzZs3KVGiBAA//vgjhw8fZu/evSgUCjw8PPj000/59NNP04xj6tSpTJs2LdXz69atw9LS0jA3KwhCjrCO96XJ/R8w00YRalWe02W/RKcyy9K5qvqspWzwXuJN7DhU6Qc0JjYGjlYQjEeh11LNdw2lQw4B8KxIQ66UHIpeaWrkyAwnNjaWvn37EhERga2t7RuPzXcTfQ0bNkyRLDVq1IhKlSrx+++/M2PGDC5evMhvv/3GpUuXUGSwbsHEiRMZP3588p8jIyNxd3enbdu2b/0LzCyNRsP+/ftp06ZNgZyaE/eX/+Wrewx7jMlfX6LQRqF3rY7th9toZ/7m/7NvvD9NS6Q/W2Eeep/22n3ouv6Zg8HnjHz1/cuign6POXJ/MSGotg5BGXIKCQX6lpNwbTiW9kao75OT37+kGZyMMGoC5OjoiEqlIjAwMMXzgYGBuLq6ZugcarWamjVr8uCB3NPn+PHjBAUFUbJkyeRjdDodEyZM4Ndff+Xx48epzmFmZoaZWerfGNVqdY7958rJc+cF4v7yvzx/jxG+sK47RAeAUyWU/behtCqa4beneX9qtbx26I/WKG/vQHmnK1TraeDAc0ee//4ZQEG/R4PdX8ANWP8BRDwFUxsUPf5A5dmeDK6QyzE58f3LzPmMugja1NSU2rVrc/DgweTn9Ho9Bw8eTDHK8yY6nY7r16/j5ib3J+nfvz/Xrl3jypUryV/FihXjiy++YO/evTlyH4Ig5LLoILlUf/hTcCgDA7ZDJpKfNypeC5p/KT/+dwJE+hnmvIJgDLd2yju9Il78X/noAHi2N3ZUeYLRp8DGjx/PwIEDqVOnDvXq1ePXX38lJiaGwYMHAzBgwACKFy/Ojz/KvXymT59OgwYNKFeuHOHh4cyaNYsnT57w0UcfAVC0aFGKFk35QahWq3F1dcXT0zN3b04QBMOLfS53pw59AHbuMGAn2GRsxDjDmk6Au/+B/xXYMQb6/V0gWwEIBZheD8d+ftkHr0wL6LkCLB2MGlZeYvQEqE+fPgQHBzN58mQCAgKoUaMGe/bsSd7a/vTpU5Sv1PEICwtj2LBhBAQEUKRIEWrXrs2pU6eoXLmysW5BEITckhAFa3tC4A156+6AHWDvbvjrqF5MhS1pCg8PwoXlUHeo4a8jCDkhMUauaXV7p/zn+qOg7cxCU98no/LE38aYMWMYM2ZMmq8dOXIkxZ/nzp3L3LmZ696c1rofwfB0eom1557h81xBR2MHIxQ8ibFynR/fi2DhAP23Q9GyOXc9J09oPRX2TpRrA5VpkbPXEwRDCH8K6/tC4HVQqqHzXKjV39hR5UlGL4QoFAyh0QkMXH6Oqf/cZsU9JaExicYOSShItAlyhecnJ+UO1f23gksujPrWHwkeTeX+SNtHgV6X89cUhKx6cgqWtpCTHysnGLRLJD9vIBIgIdsuPw2j8/wTnHgQAoBOUrD7eoCRoxIKDJ0WtgyBBwdAbQkfboZiNXPn2kolvLcITG3g2Vk4NS93risImXVhBazqArGh4FYdhh+Bkg2MHVWeJhIgIcskSWL16cf0/v00/hHxlHa0ol99eT3G9iti54xgAHq9PPKS1Kvo/XW5/6FuXxI6/CQ/PvS9vKVYEPIKnQb+/Rx2fQp6rdwDb/AesCth7MjyPJEACVkSl6hj/KarTNpxE41Oon0VV3aOacyYFmVQInHNN5IHQaKppJANkgT/fgbXN4HSBHr/BWVbGieWGh+CZ0e5tca2EfKUnCAYW0yovCPy/DL5z60mQc/lYCo6GGSESICETHsUEkO3RSfZdtkXlVLBNx0rsrhfLWzM1RS1NqNSEbm7ytZLvkaOVMi3JEleeHxxJaCQd2R5djBePAoFdPkNLIvKO9CO/GS8WAQBIPAmLGsJj4+DqTW8vx6afS7KNWSCSICETNl7M4Cu809wJyAKR2sz1n5Un+HNyqZoO1LXSU6Atl/2Ra83Wqs5IT878hOcXiA/7jofqvYwbjwA1s5yEgRw8ld4mrpXoSDkitu74I82EP4EinjIxQ0rir23mSUSICFDtDo9P/53mxGrLxKVoKVOqSL8O64JDcqkrr5btYiEjbkJfhHxnHkUaoRohXzt5G9w9MUIS4ef89YulkpdwOt9kPTyVFhCtLEjEgoTSYKjs2Djh6CJgdLNYNhhcK5k7MjyJZEACW8VHJVA/z/P8ftRbwCGNC7N+uENcLE1T/N4tRI6VpUr84ppMCFTzi2D/ZPlx+9MhvojjBtPWjr8D2yLQ9ijl7EKQk5LjIHNg+DwTPnP9UZAv62isnM2iARIeKOLT57Tef5xTnuHYmmqYkHfmkzuUhm16s3/dLrVkHuz/Xfdn9hEbW6EKuR3V9bB7s/lx00nyF95kYU9vLtQfnzhT3l7viDkpPBnsLwd3NouFzfs8ht0/FmuWC5kmUiAhDRJksSKk4/o8/sZAiMTKOtkxY7RjensVSxD769V0p6SDpbEJOrYdzMwh6MV8r2b22HHaPlx/ZHybpa8rGxL+TdwkHuFxYUZNx6hQNEGB6MOCpL/8OS0XNww4DpYOsLAnVB7kDHDKzBEAiSkEpOg5ZMNV5j2zy20eolO1dzYMaYJ5V1sMnwOhUJBt5rFAdh6WUyDCW9wby/8PVReV1OzP7T7MX/sZGk9FYqWgyh/2P2FsaMRCgh9fDw+H/aj1G/z0O5b8KK4YQi4VoPhh6FUI2OHWGCIBEhI4UFQNO8tPMnOq36YKBVM6lyZBX1rYm2W+bZx3WvJCdCJ+8EERsYbOlShIPA+Chv7ywXcqvaQh/aV+eRjydQSuv0OCiVc3ww3tho7IqEACN+0Ca2/P0qtlri1s+TaU5XfhSF75aKcgsHkk08aITfsvu7PuwtOcD8oGmcbM9YPb8DQJqVTbHHPjFJFrahTqgh6CXZcEaNAwmuenYP1H4AuQS4y2O13UKqMHVXmlKjzcq3Sv+MhSrSAEbJOHx9PyNJlyX+O9jODlt9Cr1VgamXEyAomkQAJaHR6vv/3Fh+vvURMoo76pR3YNa4JdT2yv7ugey25HPvfF32RJFETSHjB/yqs6Slv5S3TEnquyL8LOpt9Ca5e8jqgnePkrcqCkAXhmzahCwlBZaoHIDbECn2d0fljSjgfEglQIRcUGc+Hy86y7PgjAIY3K8Paj+rjbJP2FvfM6lTNDVMTJXcDo7jlH2mQcwr5XPBduXx/QgSUbAjvrwW1Yf69GYWJqVypWmUG9/fCpb+MHZGQD706+uNUPRJsTZG0OmJOnzZyZAWXSIAKsXOPntNp/gnOPX6OtZkJS/rV4puOlTB5yxb3zLCzVNO6kjMgagIJwHNvWNX1RcfqGtB3Y8EY2neuBK2+kx/v/QaePzJuPEK+kzT6o7bSYlfJnIgq1QGIPnrUyJEVXCIBKoQkSeKP4958sOwMwVEJVHCxZseYxrSv6pYj1+teU54G23HFD61OnyPXEPKBCB9Y9S5EB4BzZei/DcztjB2V4TQcDSUbQWI0bP8Y9DpjRyTkE/Loz1IAilaORmo+gahK1QCIPnpMLB/IISIBKmSiE7SMWXeZmf/eRqeXeLdGMbaPbkxZJ+scu2ZzTyccrEwJiU7g+IOQHLuOkIdFB8Ff70LEU3AoA/23F7wKtkoVvLdIbkz59BScXmjsiIR8Qh79CUVtpcW+phP62oOJK1MahYU52qAgEm7fNnaIBZJIgAqR+4FRvLvgBP9e90etUjCtaxV+7VMDS9PMb3HPDLVKSdfqcgFFMQ1WCMU+h7/eg9AHYOcOA3aCjYuxo8oZDqWh3Q/y40MzIPCWceMR8jx9fDwhv/8OyKM/irZTQWWKpFZjWb8BIKbBcopIgAqJf6768e7CkzwMjsHV1pwNwxsysJFHlre4Z1aPF7vB9t0MIDJekyvXFPKA+EhY0wOCboK1CwzYAfbuxo4qZ9UaAOXbgS5RbpiqTTR2REIeFr5pE7rQ5/LoT+OKUKVb8muWzZoBEH1EJEA5QSRABVyiVs+0f24ydv1lYhN1NCxTlF3jmlC7VJFcjaNqcVvKOVuToNXz33X/XL22YCSJsbD+ffC7BBYOcvJTtKyxo8p5CgV0nQcWRSDgGhz72dgRCXmUPj6ekCVLgBejPx2+T7Hl3appEwDirl1D+/y5UWIsyEQCVIAFRMTzwbIzrDj5GIBRLcqyemg9HK3Ncj0WhUKRXBn6bzENVvBpE2BjP3hyEsxs5QXPzpWMHVXusXGFznPlx8d/gWfnjRuPkCeFb9qE7nkYakst9u2ap2pzYeLqilnFiiBJxBw/bqQoCy6RABVQpx6G0Hn+cS4+CcPGzISl/WvzVfuKBt3inlnv1SiOQiFvv3/2PNZocQg5TKeBLUPg4UFQW8KHm6FYDWNHlfuqdINqveQeZ9tHyiNigvCCPPqzCICiVWJRtJue5nHWzZsDYh1QThAJUAEjSRJLjj6k3x9nCYlOpKKrDf+MbULbKq7GDo1i9hY0KlsUgO2iQWrBpNfB9lFwZ5dcGPCD9VCygbGjMp6Os8DGTV4AfmCqsaMR8pDwjZvQPY+QR3969ASnCmkel5wAnTiJpNXmZogFnkiACpDIeA0j11zkp//uoJege83ibPu4MR6OeafQXFJNoK2XRWuMAkeSYNdncmNQpQn0XgVlWhg7KuOyKALvLpAfn/sdHh42bjxCnqCPjydk8XwAinolomj9bbrHWlT3QmVvjz4ykrjLl3MrxEJBJEAFxN2AKN5dcJK9NwMxVSmZ+V5VfuldHQvTvNVcsn1VVyzUKh6FxHD5WbixwxEMRZLkCsiXVsnd0bsvBc8Oxo4qbyjXGuoMlR/vGA1x4UYNRzC+8PXr0IVHy6M//UaAtXO6xypUKqyaNQXENJihiQSoANh+2Zf3Fp7kUUgMxezM2TSyIf0alMq1Le6ZYWVmQvuq8nTc1ks+Ro5GMJjDP8AZeT0DXedD1R7GjSevaTsDipSGSF/47ytjRyMYkbz2Ry6SWbSWEkWzT976HrEOKGeIBCgfS9TqmbzjBp9uvEKcRkfT8o7sGteUGu72xg7tjZJ2g/1z1Z8ErWgXkO+d+PXlVu8Os6BmP6OGkyeZWkG33+XRsWsb4NZOY0ckGEn42lXoImLl0Z9hX2aoF551kyagUpFw/wGJPmL9pKGIBCif8guPo/fvp/nr9BMAxrUqx8rB9XCwMjVyZG/XqKwjLrZmRMRpOHwn2NjhCNlxbhkcmCI/fmcK1B9u3HjyspL1ofGL3/Z3fSq3BxEKFX18PKG/v6j7U98aRZ2BGXqfys4Oi5o1AIg+eiSHoit8RAKUD518EELn+Se48iwcW3MTlg+qw/i2nqiUeW/KKy0qpYL3asqjQGIaLB+7sg52fy4/bvo5NB1v3HjygxYTwaUqxIbCznHy2imh0AhftRRtZLw8+jNmJqgy3oZITIMZnkiA8hG9XmLh4Qf0//Msz2MSqexmy66xTWlVMf/1VUraDXb4bhDPY0SrgHzn5jZ5QS9A/VHQ6jvjxpNfmJjJU2FKNdz7D66sNXZEQi7Rx8cT+sefABRt5oaicsdMvT8pAYo9ew59XJzB4yuMRAKUT0TEaRi++gKz9t5FL0Gv2iXY+nEjSha1NHZoWeLpakPV4rZodBK7rvkZOxwhM+7thb8/kgv81RoA7X9MUb5feAvXqtDqxbbn/76GsCfGjUfIFeF//Io2KlEe/fl0dqb/z5iVL49JMTekhARizpzJoSgLF5EA5QO3/CLpuuAEB24HYWqi5Kfu1ZjVqzrm6ry1xT2zur0YBRKtMfIR76OwsT/otVC1J3T+VSQ/WdFoHLjXh8QoeSRNrzd2REIO0sfFEbpyDQBF23ii8KiX6XMoFAoxDWZgIgHK47Zc9KHbopM8CY2lRBEL/h7ZiPfrlTR2WAbRtXoxVEoFV5+F8zA42tjhCG/z9Cys/wB0CeDZCbotAWX+TsKNRqmC9xbLrUIeH4ezS4wdkZCDwhd9jzZah9pKh/2EX7N8npcJ0DFRSNYARAKURyVodXyz7Tqfb75KglZPC08ndo1tQrUSdsYOzWCcbMxoXsEJgG1iFChv87sCa3uBJgbKtoJeK0ClNnZU+VvRstB2pvz4wFQIumPUcIScoY+NIXTdVgCKdq6Dwrlcls9lVb8+CjMztP7+JNy7b6gQCy2RAOVBPmGx9FpymnVnn6JQwGetK7B8YF3sLfP+FvfMSqoJtO2yL3q9+I0mTwq6A2u6Q0IElGwIfdbKi3mF7KszBMq+I4+qbRshN5IVCpTw375BGyOhttJjP35uts6ltLDAskF9QEyDGYJIgPKYo/eC6Tz/BNd8IrC3VLNiUF0+aV0eZT7Z4p5ZrSu5YGNugm94HGcfPTd2OMLrnnvDX+/K27aL1YS+G8E0fy68z5MUCrlXmLk9+F+B478YOyLBgPSRzwndvBeAoj1bobDL/o5dsQ7IcEQClEfo9RK/HbjPoBXnCI/V4FXCjl1jm9DCM/0eMQWBuVpFZy83QNQEynMifWHVuxAdAM6Vod9WMC84U7B5hm0x6PQi8Tn6M/heMm48gsGE/zIBbawCE2uw/3SWQc5p8yIBirt8GV14uEHOWViJBCgPCI9NZOiq88w9cA9Jgg/qlWTTiIaUKFI4ftNO2g22+7o/cYmiNUZeYKYJx2RtN4h4Cg5lof92sHQwdlgFV9UeUKUbSDp5Kkwj6rzkd/oQH0J3ngLAsW9XFBbWBjmvunhxzMqXB72e6BMnDXLOwkokQEZ2wzeCzvNPcPhuMGYmSmb19OLH7tXy/Rb3zKhTqgjuDhbEJOrYdyvA2OEIsc9p9OBnFM+9wa4kDNwJNvmv2Ga+olBApzlg7QIh9+DgdGNHJGRT+OxP0cYpMbFRYv/xNIOe27qFmAYzBJEAGdHG80/pvvgUPmFxlHSwZOvHjehVx93YYeU6pVKRPAq0VewGM66EKFQbemMb74Nk7QIDtoNdCWNHVThYOkDXBfLjM4vg0THjxiNkmd73JqF7rwHgOOgDFObmBj1/0jqgmOPHkXRi1DyrRAJkBBo9fLP9Jl/9fZ1ErZ53Kjrzz5gmVClWeNdXdH/RG+z4/WCCIuONHE0hJUmwbSRK/yskqKzR9t0qb9UWck+FtlB7kPx4+8cQH2nUcISsCZ/9Gdo4FSZ2arnju4FZ1KiB0s4OXXg4cVevGfz8hYVIgHLZs7BYfr2hYvNFX5QK+KKdJ8sG1MHOsnDXVPFwtKJ2qSLoJdhxRbTGMIoTc+HOLiSVKWfLjgcnT2NHVDi1nQn2pSDiGeyZaOxohEzS3z9O6OHHADgOG4rC1PDlSxQmJlg3bgxA9JEjBj9/YSESoFx0+E4Q3RafwSdGQRFLNX8Nqc/oluUK7Bb3zEqqCfS32A2W+x4ehkMzANC3/YEwq6wXaxOyycxGrrKNAq6sgTv/GjsiIaMkifC5X6CNV2Fib479gFE5dimxDij7RAKUiy48eU5EnJZS1hI7Pm5Ik/KOxg4pT+lcrRimKiV3AqK45SeG/nNN+FPYMkRublqjH/qaA40dkVCqETQaKz/+5xOICTFuPEKG6K/+TegpuZ6Z46iPc2T0J4lV06agUJBw9y4af/8cu05BJhKgXDS+jSeTOlVkXBUdbnaGXRRXENhZqnmnklz3aNtlMQqUKzTxcnPTuOfgVh06Zb5LtZBDWn4r11+KCZaTINH7KW/TaQhfME0e/XGwwv6DnP1FwqRIESyqVwfk3mBC5okEKBeplAoGNCiJifhbT1f3WvKOo+1X/NDqRIfsHLf7c7kCsUUR6L0a1BbGjkhIojaHbr+DUg13dsG1jcaOSHgD/allhF5IAMBx9LgcHf1JIqbBskf8KBbylOYVnHCwMiU4KoETD8Swf466uBIurwYU0ONPKFLK2BEJr3PzghZfyY93fwERYmQ0T4qPJPzPOfLoT1Eb7Hu9nyuXTd4Of+YM+oSEXLlmQSISICFPMTVR0rV6MUDUBMpRPhflH6gArb6Dcu8YNx4hfY0/g+J1ICFS3hqvf/PI6Ia7G5gZMZMLgRdyKUBBf+QXQq/IP04dx3yWK6M/AGYVK2Li4oIUF0fsuXO5cs2CRCRAQp7T7UVNoL03A4iKF92xDS4mBDYNAF0ieHaCJuONHZHwJioTeSrMxAIeHYXzy9I9NFGXyLIby4iX4plzaQ6SWDeU8yJ8CV+zQh79cbLHvkePXLu0QqHAulkzAKKPiGmwzBIJkJDneJWwo6yTFQlaPf/dEK0xDEqnhS2DIdIHipaDbotBKT4G8jzHctBWLlPA/ikQcj/Nww4+PUhYQhgAd8LucNRH/FDMafp9Mwi9IW9qcRzzaa6N/iSxbtkCkNcBiYQ3c8Qnn5DnKBSK5MXQokO8gR2aLrdYUFtBnzWiu3t+UmcolGkB2ji5YapOm+qQTXc3AWCtkBtvLrqySPxQzEkBNwjftlMe/XEuin23brkeglWDBihMTdH4+JDo7Z3r18/PRAIk5Env1SyOQgFnvJ/jExZr7HAKhls74ORv8uN3F4BzJePGI2SOUgnvLgQzO/C9KFfufoV3uDcXAi+gVCgZYDUACxMLbj+/zTEfsUU6p+j/+47QW3Ky6fjx2Fwf/QFQWlpiWa8eIKbBMitPJEALFy7Ew8MDc3Nz6tevz7k3LOZauXIlCoUixZf5a43mpk6dSsWKFbGysqJIkSK0bt2as2fP5vRtCAZU3N6ChmWKArD9slgMnW3Bd+UFtAANx0DV7saNR8gauxLQcZb8+OhP4Hcl+aXN9zYD0Kx4M4qZFKNPhT4ALLoqRoFyxMNDhO8/J4/+uDhh3z33R3+SJO0GE9vhM8foCdDGjRsZP348U6ZM4dKlS1SvXp127doRFBSU7ntsbW3x9/dP/nry5EmK1ytUqMCCBQu4fv06J06cwMPDg7Zt2xIcHJzTtyMY0MtpMF/xAZ4dCVGwsR8kRkOpJtB6mrEjErLDqzdU6gp6LWwbCZp44rRx7Hi4A4Ae5eRFuP0r9sfCxIJbobfEKJCh6fXo/5v0cvRn1GijjP4kSaoHFHvpErpIUUU/o4yeAM2ZM4dhw4YxePBgKleuzJIlS7C0tGT58uXpvkehUODq6pr85eLikuL1vn370rp1a8qUKUOVKlWYM2cOkZGRXLsmuubmJ+2rumKuVuIdEsOVZ+HGDid/kiR55CfkHtgUg14r5F1FQv6lUEDnuWDlDMG34fBM9jzaQ1RiFMWti9PQrSEARcyL8H5FuR6NGAUysOubCD/1SB79cXUx6ugPgKm7O6ZlyoBWS8zJk0aNJT8x6idhYmIiFy9eZOLElx2PlUolrVu35vTp0+m+Lzo6mlKlSqHX66lVqxY//PADVapUSfcaS5cuxc7Ojuovyoa/LiEhgYRXikhFvsigNRoNGo1ht2Ennc/Q580rDHl/ZkpoW8mFndf8+fviM6q6WWf7nNmV375/ytPzUN3eiaRUo+uxHMmsCLwl9vx2j5lVIO7P1A5FxzmYbO6HdGoBm6KaAPLoj06rA+T761ehH+tvr+dW6C0OPTlEs+LNjBm1wRj1e6iJQ7l3WvLoT5Fhw9EqFG/9f5WpS2Th/iybNiHR25vIw4exaN3aYLHkhJz8/mXmnEZNgEJCQtDpdKlGcFxcXLhz506a7/H09GT58uV4eXkRERHB7NmzadSoETdv3qREiRLJx+3atYv333+f2NhY3Nzc2L9/P46OaTcf/fHHH5k2LfW0wL59+7C0tMzGHaZv//79OXLevMJQ91dcowBUbL34lJo8yjNtRPLD988x6iaNHvwMwLVifXl8NQiu7s7w+/PDPWZHQbi/Gg7NiIk+zY3oJ6hQYultyf7H8n0l3V9dk7oc1x3n5xM/E2UdhaIA9XozxvewXOAu3K5EoI23Q2Nnx0lzM9id8f9XmZGZ+7MwM8MdCDt4iIsNd+WL8hY58f2Ljc34phmFZMRxUT8/P4oXL86pU6do2LBh8vNffvklR48ezdDCZY1GQ6VKlfjggw+YMWNG8vMxMTH4+/sTEhLCsmXLOHToEGfPnsXZ2TnVOdIaAXJ3dyckJARbW9ts3mXqePfv30+bNm1Qq9UGPXdeYOj70+klms0+RlBUAos+qEGbyqm/f7kp33z/InwwWf4OithQ9F7vo+s8P8NNTvPNPWZRgbq/hCh+WNuMLeYK2qud+KHX3lT3FxYfRqcdnYjXxfNb899oWrypsaPONqN9D2NDUc6vg/cWS7TxKpwmTcKudy+DXyYr9ydpNDxq1hx9dDQl1q7B3MvL4HEZSk5+/yIjI3F0dCQiIuKtP7+NOgLk6OiISqUiMDAwxfOBgYG4urpm6BxqtZqaNWvy4MGDFM9bWVlRrlw5ypUrR4MGDShfvjx//vlnium2JGZmZpiZmaV57pz6z5WT584LDHV/auTK0L8f82bHNX86Vi+e/eAMIE9//zTxsHUIxIaCqxfKLr+iVGd+gWaevkcDKAj3Fy2Z8q+lOegT6P30BupHh6B0K+Dl/Tmrnfmg0gesuLGCpTeW0rJUywIzCpTr38NTv/L8lv7F2h9XivbqiSIHr5+p+1OrsWrcmKi9e4k/eRKb2rVzLC5DyYnvX2bOZ9QxMlNTU2rXrs3BgweTn9Pr9Rw8eDDFiNCb6HQ6rl+/jpub2xuP0+v1KUZ5hPwjaTfYoTtBhMUkGjmafOC/L8Hvktzhvc8a0eG9APvX+1/i9AmUVllTJz4BdoyRE9/XDKoyCAsTC26G3uS473EjRFoAPPdGf+aPlzu/Ro406s6vtCRvhxf1gDLE6JOE48ePZ9myZaxatYrbt28zatQoYmJiGDx4MAADBgxIMWozffp09u3bh7e3N5cuXaJfv348efKEjz76CJCnvr755hvOnDnDkydPuHjxIkOGDMHX15devQw/VCnkPE9XGyq72aLRSey65mfscPK2i6vg0ipEh/eCT5IkNt7bCEDv6sNROHpCTBCq/76Qd/+9wsHcgfc9X+wIE9Whs+bgdMLvm8qjP25uRt/5lRbrZvL0ZvytW2jeUEpGkBk9AerTpw+zZ89m8uTJ1KhRgytXrrBnz57khdFPnz7F398/+fiwsDCGDRtGpUqV6NixI5GRkZw6dYrKlSsDoFKpuHPnDj169KBChQp06dKF0NBQjh8/nu5OMSHv615LnvraKooips/3Iuz+XH7c6lvR4b2Auxp8lfth9zFXmdOlQnfo/jsoTVDe2UnxsNS7aAdWGShGgbLK5wL6a9sIvf1i9GfEiDw3+gNg4uiIebVqAMQcE7Wf3sboCRDAmDFjePLkCQkJCZw9e5b69esnv3bkyBFWrlyZ/Oe5c+cmHxsQEMC///5LzZo1k183Nzdn69at+Pr6kpCQgJ+fHzt27KBu3bq5eUuCgXWtUQyVUsHlp+F4B0cbO5y8JyYENiZ1eO8ITSYYOyIhhyX1/Wpfuj12ZnZQrCY0+xIAL5+/IPxpiuOLWhQVo0BZIUmwbxLhDy3RxuXd0Z8kSUURRVXot8sTCZAgvI2zjTnNystlDLaJUaCUdFrYMkTu8O5QFrotyRdbYIWsC48PZ+/jvQD0rtD75QtNx6N3q4mpLhaTvzpDUMpyImIUKAvu7kb/6BSht+UdRXl19CeJdfMWAMScPIU+UayZfBPxKZnbooNQ6FN3cRbe7tXWGHq9+O012aEZ8OgoqC1Fh/dCYsfDHSTqE6nkUImqjlVfvqBSo+u5kigzNxRRfrC8HTx72Vvx1VGgxVcWi1Ggt9FpYP+UF6M/yjw/+gNgXrkSKidH9LGxxF24YOxw8jSRAOWms79jsqguHqGHjB1JvtSmsgs2Zib4hsdx7vFzY4eTN9zaCSd/lR+/uwBcKhs1HCHnSZKU3Pi0l2ev1FvabYtzvMJ36IvVhvhwWNUV7u1NfjlpFOhG6A0xCvQ2l/5CH3Sf0DvyLxV5ffQHQKFUYt1MrvgtpsHeTCRAuUmlRqGJwdN/O8SLhnWZZa5W0bGaXO5g6yUfI0eTBwTfg+2j5McNRkPVHsaNR8gV5wLO8STyCVZqKzqV7pTmMRoTG3QfboVybUAbB+s/gCvrAXkUqI+n3ClejAK9QUIUHPlRHv2JVeSL0Z8kYjt8xogEKDfVHIBUtDxmumiUp38zdjT5UtJusN3XA4jX6IwcjRElRMHGD190eG8MbUSH98Ji411563vnMp2xVL+hVY+pFXywHrz6gKSD7SPh5DxArgtkrjIXo0Bvcmo++shgQu/aA+A4YnieH/1JYtWoMajVJD55QsKjR8YOJ88SCVBuUpmgazUZAOW53yFCjGJkVl0PB0oUsSA6Qcu+W4Fvf0NBlKLDuxv0Wgmq/F3RWMiY4NhgDj89DECvChmoa6ZSw3tLoOEY+c/7J8G+SRQ1d0juFC9GgdIQ6Q+n5sujPzFg4uaGXffuxo4qw1TWVljWkStBi2mw9IkEKJdFeTQhxNoThTYeDn1v7HDyHaVSQfeaL2oCFdZpsFPz4PZOUKqh919gbdz+aELu2fZgG1pJSw2nGng6eGbsTUoltJ0JrV+MEp6aB9s/ZlClfsmjQCd8T+Rc0PnRkR/Qx8cSeq8oII/+KPPJ6E+S5GkwkQClSyRAueh8wHm6/vMua10byE9cXQ8B140bVD7U7cVusGP3ggmKijdyNLnM+ygcmCo/bv8juNczajhC7tHpdWy5twWA3p6933L0axQKaPIpvLsIFCq4uo6i28fyfnl53djiq2IUKFnQbbi8Rh79idblu9GfJEkJUOyFi+iiY4wcTd4kEqBcdOjpIcITwlmlO8GNim0BCfZPNnZY+U5pRytqlbRHL8HOK4WoNUaED2wZDJIeqn8AdT8ydkRCLjrpdxL/GH/szOxoU6pN1k5S80N4fy2YmMP9vQy6vg9zlRnXQ66LUaAk+6eg1+oJve8E5M/RHwCz0qVRlyoJGg0xp04aO5w8SSRAuWhCnQk0dmuMBg1jCSRAbQYPD8GDg29/s5BCt1dqAhUK2gTYNOBFh/dq0Hmu/Fu9UGgkVX5+t+y7mJuYZ/1Enh1gwA4wt6Ooz0X6xMmbCcQoEPDoGNzfS7i3DdooTb4d/UkipsHeTCRAuchEacKPTX7ERelCSEIYYz3KE6tQyKNA+kK8oykLuni5oVYpuOUfyW3/QlBS4L8v5V5f5vaiw3sh5BftxzEfubdThhY/v03JBjB4D9i4Mcj/MeaSJEaB9HrYNwm9DkLvy1Xn8+voT5LkBOjYMSS93sjR5D0iAcpl1mpr+ln1w8HcgTvaSL52dUUXeAOubjB2aPmKvaUp71SUG+YW+NYYl1bDxZW87PDuYeSAhNy25d4WJCTqu9XHw87DMCd1qQxD9+FYpCx9IqMAWHx+duEdBbrxN/hfIfyJA9rIhHw/+gNgWbcuCktLdMEhxN+6bexw8hyRABlBEVURfmn6C6ZKUw6bq/m1iD0cmgmaOGOHlq8k1QTadtkXra6A/nbjewn+fdHYtOU3UL61ceMRcp1Gr2Hr/a3Aa32/0nHlWTgbHyoJjkp4+8ntS8KQvQwy98Bcr+d6pDcnzy/Ibsj5jzYBDk6XR3/uOQD5f/QHQGlqinXjRgBEHz1i3GDyIJEAGUl1p+rMbDITgJX2tvwtRcCZRUaOKn9p4elMEUs1wVEJnHwYauxwDC8mVF73o0uACh2g6efGjkgwgsNPDxMaH4qjhSMtS7Z847HPnscybPVlTgUpmfJPBn/jtyqK48B/6aOUf/AvvrIA6erG7Iadv5xbChFPCfcthjY8tkCM/iR5uQ7omJEjyXtEAmREHUp34OPqHwMw09GBs2fnQ0yIkaPKP0xNlHSpXgwogDWB9Dr4ewhEPAOHMqLDeyG26Z68+LlbuW6olekXvIzX6Bi19iLhcRoA9t8O4ui94IxdxMyaQd03YY6Sa2amnNwzDk4Xkl/IYp/DsVny6M9ta6BgjP4ksXrRFyz++nW0oQXwF8VsEJ+oRjay+kg6eHRAq1DwmYMVjw+KbfGZkdQhfu/NAKITtEaOxoAOzQTvIy86vK8FC3tjRyQYweOIx5z1P4sCBT0r9Ez3OEmSmLT9Bjd8Iyliqaa2ozwlPHXnTRK0Gdtg4WjjRp/K/QBYbG+HtHci7J8iVx4vyI7/AvERhAeVRRsWXaBGfwDUzs6YV64MkkT0MdH25FUiATIyhULBjCYzqG5TmiiVktFBhwn3v2zssPKN6iXsKONkRbxGz3/X/Y0djmHc3gUn5siPu84XHd4LsaSu781KNKOYdbF0j1t37imbL/qgVMDc3l70Lq3H0dqURyExLD/xOMPXG1R1MOYqc66Zm3HSwhxO/go7x4CuAP1y8aqwx3BuqTz6c8MMKFijP0msW4jt8GkRCVAeYKYy47cOKyiOmqdqEz7bPwqNTmPssPIFhUJBj4JUEyjkPmwbKT9u8DFUS/+3fqFgi9fGs+PhDuDNlZ8vPw1j6s6bAHzezpPGZYtibgJftasAwPxD9/GPyNgGC0cLx+RrLS5dHUmhhMtrYFP/grlJ4+AM0CUSHlEDbWh4gRv9SZK0DijmxAkkjfjZkkQkQHlEUYuizG/8A1Z6PRekGKYfGFt4t6Nm0nsveoOd9g7FJyzWyNFkQ0IUbPgQEqNedHifbuyIBCPa/2Q/EQkRuFm50bhY4zSPCYlOYNSaS2h0Eu2quDCqednk196t7kadUkWITdTx/b8Z3wI9OGkUKM6fU+0my1Wj7+6G1d0gLizb95Vn+F6CG1vQ6xSEXpJHuAri6A+AebVqqBwc0EdHE3tJzDAkEQlQHlK+XHtm21RHKUlsDzjJihvLjR1SvlDc3oKGZeSmhTvya2sMSYIdoyHkrtzhvecK0eG9kEuq/NyzQk9USlWq17U6PWPWXSIgMp4yTlbM7lUdxSvVwRUKBdPerYJSAbuu+XPqYcY2WLw6CrTo+QWkflvBzA6enoYVHSEyn/4fe5X0sg1ReHxTtCHPC+zoD4BCqcS6aVMAoo8cMW4weYhIgPKYJm1/4atwuXHdr5d+4+AT0SYjI7q9qAn09yWf/Dlydmo+3Nohd3jvtQpsXIwdkWBEd5/f5UrwFUwUJnQr1y3NY37ee5cz3s+xNFXxe7/a2JinTpirFLOjX4NSgLwgWpPBelnJo0DB1zilBgbvBmtXCLoFf7aTp2rzs/v74PFx9JgReiYcAMfhwwrk6E8SsQ4oNZEA5TW2bvT1+ogPIqKQkPj6+NfcDL1p7KjyvA5VXTFXK/EOjuGaT4Sxw8mcR8fgwBT5cfsfoWR948YjGF3S4ueWJVviZOmU6vV/r/mz9Jg3ALN6Vqe8i0265xrfpgIOVqbcC4zmr9NPMnT9FKNAVxchuVSBoXvBoSxEPIXl7eTWLPmRTvty9EfXGm1wCCaurtj16GHkwHKWVePGoFKR6O1N4rNnxg4nTxAJUF7UeBxfJpjQODaOeF084w6OIzAm0NhR5Wk25mraVXEF8llNoAgf2Pyiw7vX+6LDu0CsJpZd3rsA6OPZJ9Xr9wOj+GLLVQCGNytDJy+3N57P3tKUL9t5AvDr/nsERcVnKI7BVQdjpjKTR4H8TsktWIbsBbcaclPelV3kZs75zZU1EHwHvboIoUflz4qCuvbnVSpbWyxr1QIg+ogYBQKRAOVNZjaYtPiaWUEhlNPqCYoLYuyhscRq8vEC31yQVBNo51U/ErX5oDVGcof3EHARHd4F2b+P/iVGE4OHrQf1XOuleC0qXsOI1ReJTdTRsEzR5MTmbXrXcad6CTuiErT89N+dDL0n1SiQJIG1EwzaBaWbgyYG1vaG61syd4PGlBgDh38AIFxqjzYouFCM/iQR02ApiQQor6o1EBuHcsz398dBacbt57eZeHwieikf/GA3ksZli+JsY0ZYrIYjd4OMHc7b/ffVKx3eV4OppbEjEoxMkiQ235Wnv3pW6JliUbMkSXy++SreITG42Zkzv29NTFQZ+whXKhVMf7cqCoVcLuLC4+cZet+QqkOSR4FO+52WnzSzgQ83Q5XuoNfA30PhzJLM3aixnFoA0YHobUoRul/eGVcYRn+SJG2Hjz13Dn2s+IVaJEB5lUoNbaZRQqvjN39/TJVqDj07xK+XfjV2ZHmWiUqZvCU+z9cEurwGLq5A7vD+BziUNnZEQh5wI+QGt5/fxlRpyrtl303x2uKjD9l7MxBTlZJFH9bC0dosU+eu7m5PnzruAEzecROd/u2bBdIcBQIwMYMef0K94fKf93wl19TJyxsQogLh5G8AhOvbow0MKlSjPwCmZcuiLlECKTGRmDNnjB2O0YkEKC/z7AglG1EjNprpZmUAWHFjRXJnaCG1bi8SoEN3ggiPTTRyNOnwuwy7xsuPW0yE8m2MG4+QZyT1/Wrn0Q57c/vk54/fD2b23rsATOlamZoli2Tp/F+088TW3IRb/pGsO5uxBdFJo0BXg6++HAUCuTddh5+h5bcvgpwN/4zLu1Wjj/4Emhj0LrUI3XUOKFyjPyCXRkhujirWAYkEKE9TKKDtDAA63TrAqLLybyozTs/gfMB5Y0aWZ1Vys6WSmy2JOj27ruXB1hixz2FjUof39tDsC2NHJOQREQkR7Hm0B0hZ+dknLJZx6y+jl6BX7RL0rVcyy9coam3G5y/WDc3ae5fQ6IS3vifdUSCQP6OafwmdfwWFEi79BZsH5r2q0cH34OIqAMK176ANDCx0oz9JktcBHTuWP0uGGJBIgPK6EnWgSjdAYpT3VblxqqTl08Of8jjisbGjy5N61EqaBstju8H0OtgyRN5GXKQ0dPtddHgXkv3z8B/idfFUKFKB6k7VgRcd3tdcIixWQ9Xitsx4r2qKdUFZ8WH9UlR2syUyXsvsfXcz9J50R4GS1Bks169SmcGdXbCmB8SFZytOgzowFSQd+rIdCN1yACh8oz9JLOvVQ2FhgTYggIS7Gfv+F1Ti0zc/eGcyKNUovA8xo9g7eDl5EZkYyZhDY4hIyGc1b3JB1xrFUCrg0tNwHoXEGDuclw5/D96HwcQC+qwRHd6FZJIkJU9/9a7QG4VCgSRJTN5xg+u+Edhbqln8YW3M1akrQmeWSqlg+rtVANhw/hlXn4W/9T2OFo70qtALSGMUKEnlrtDvbzCzhScnYWUniArIdrzZ9uQU3P0XFCrCY+sX6tEfAKWZGVYNGgBiGkwkQPmBQ5nk+jBmB2bwW/NfKWZVjCeRT/jsyGeiceprnG3MaVZBLh63La+MAt35F47/Ij/uOh9cqxo3HiFPuRB4gUcRj7AwsaBTmU4ArD/3jE0XfFAoYN77NXF3MNwuwToeDnSvWRxJgsk7b6LPwILoFKNA/mmMAgGUbgqD/gUrZwi8AX+2hdCHBos70yQJ9n0HgN6rH6Hr5OayhXX0J0nyOqBCvh1eJED5RfMv5X48gddxvH+ABe8swEptxfmA88w4M6PQz+W+Lqkm0NbLvhn6cM9RIQ9ednivPxK8ehk3HiHPSdr63qlMJ6xNrbnyLPxlh/e2nskJvSF93aEi1mYmXH0WzuaLb68M7GTplDwKtPjK4vQ/c9y85KrRRUpD+BM5CfIzUgPOm9vkUhNqK8KfVy70oz9JrJs3AyDu6lW0YQWowW0miQQov7B0gKYvdg4dmkl56xLMajYLpULJtgfbWHlzpVHDy2vaVnbB2swEn7A4Ljwx4n/whGjY+CEkRELJRtB2pvFiEfKk0LhQ9j/dD8jTX3KH94sk6vS0rezCxy3KvuUMWeNsa86nrcsD8L89d4mIfftIctIo0JXgK+mPAoE8aj10H7h6yYU+V3YG7yMGijyDtAlwcBoA+npjCP1rAyBGfwDUbm6YeXqCXk/M8ePGDsdoRAKUn9QfCXbuEOkDZxbTtERTvqz7JQBzL87l4FPRODWJuVpFx2pGbo0hSbBzDATfkRtJ9lopOrwLqWx/sB2tXks1x2qUt/dk7LrL+EfEU8bRil96V8/2ouc3GdjIg/LO1jyPSWTO/rcviM3wKBCAtbM8HebRFBKjYW0veUQmt5z/E8Ieg7UL4f7FxOjPa8R2eJEA5S9qc2glz2dzYi7EhNK3Yl/6ePZBQmLi8YncCr1l3BjzkKRpsH+v+ROv0eV+AKcXyh/4ShPo/Zfo8C6kopf0yY1Pe3v2Ztbeu5z2DpU7vPdPu8O7IalVSqZ1lRdErz7zhJt+b99UkeFRIABzW/hwC1TqCrpEue/duWWGCP3N4sLh2M8A6Jt8SeifKwEx+vOq5O3wJ04gafNo7aYcJhKg/KZab3lYOSESjv2MQqHg63pf06hYI+K0cYw9OFY0Tn2hnocDxe0tiErQsv9WLv+dPDqe3HGadqLDu5C2U36n8I32xcbUBmKq83sGO7wbUqNyjnTyckMvwZQdN9+6njBTo0Ag/+LWayXUGQJIsPtzuR9XTq5bPDEH4sLAqSLhD8zF6E8aLKpXR2Vnhz4ykrgrV4wdjlGIBCi/USqTiyNy/g8IfYiJ0oTZzWdT1q6saJz6CqVSQXdj1ASK8IUtg0HSgVcfqDcs964t5Cub7spb35u7deDbv+UpqGFNS7+1w7uhfdepEhZqFReehLH9ytvbyLw6CnTGPwMtFZQq6DRHrnwOcPR/sOszuTaWoYU/S+5Npm8xmdBlfwBQdPgwMfrzCoVKhVXTpkDh3Q0mEqD8qEwLKNcG9NrkRX42pjYseGcBRcyKcPv5bb458Y1onMrL1hjH7ocQHPX2qrfZpk2QK+HGBL/o8P6r6PAupCkgJoCjPvIPnpNXyhOTqKNBGQe+al8x12Nxs7Ng7DvlAPhh9x2i4t+8IDrFKNDVDIwCgfz/oMXX0OkXQCH3wts8CDTx2Yz+NYdmypXWPZoSfiVCHv1xccG+Z0/DXqcAKOzrgEQClF+1mSaXnr+1A57JbTFK2JTgt1a/oVaqOfj0IL9d+s3IQRpfGSdrarjbo9NL7Lzql/MX3DMRfM6DuR30+Ut0eBfStfX+VvSSHls8eRpgg6utOQv61spwh3dDG9qkNKUdrQiOSuC3A/ffenzSKNDloMsZGwVKUvcj6LUCVKZweyes7QnxBiro6n8Vrm0EQN98EqFLlwJQVKz9SZN10yagVJJw/z4av1z4fMxjRAKUX7lUgRp95cf7vkueT6/pXJPpjacDsPzGcrbdz8VdF3lUrrXGuLwWLvwJKKD7H/JWYEFIg1av5e97fwMQ6FsLtUrBon6Z7/BuSGYmKqZ0qQzAilOPuRcY9cbjszQKlKRKN3lxtKkNPD7+omp0NtfpSRLsmwRIULUn4acfitGft1DZ22NRsyZQOKfBRAKUn7X8Vm6r8OyM3H/nhc5lOjPCawQA089ML/SNUzt7FUOtUnDTL5I7AZE5cxG/K/KaBpCH+Su0zZnrCAXC0WdHCYoLQtJaoY2swpQuVaiVxQ7vhtTC05m2lV3Q6SWm7nz7gugsjwIBlGkOg3aBlRMEXIflbeG5d9aDf3AQHh0FlSn6pl8T+rsY/cmIwjwNJhKg/My2GDQcLT8+MBVeaYkxusZo2nu0R6vX8tmRz3gS+cQ4MeYBRaxMaVXRGYBtl96+wDPTYp/Dxv7yuoPy7aDZl4a/hlCg/HVzPQCJ4XXpWduDD+tnvcO7oU3qXBkzEyWnHoay+/qbe3k5WTrRs4I8upLpUSCAYjVgyF6wLyXX7PmznTyNlVl63ctdl/WGE37wvBj9yaCkBCjm7Fn08QZej5XHiQQov2v8CVg6QugDuLgy+WmFQsGMxjPwcvQiIiGCMQcLd+PUpJpA2y77ojNkawy9Dv4e+qLDuwd0Fx3ehTd78Pwxl4LPIkkKSpu1YqYBOrwbkruDJaNeVJ+e+e8tYhLeXCNmSNUhmCpNuRx0mbMBZzN/waJl5arRLtUgJghWdIJHxzJ3jqvrIegmmNuhrz9WjP5kglmF8pi4uSHFxxN7Ngvfv3xMfFLnd+a28pQLwJGfIP7lFI+5iTm/tfoNNys3Hkc+ZvyR8TnaODXRxwff4SNw2bgJSZ+3dqC19HTG3lJNUFQCJx+EZOkcftF+TDk9hUVRi3gW9aJ30uEf4OGhFx3e14KF8acxhLxtwp7fAVDEe/JH33YG6fBuaCObl8XdwQL/iHgWHn7wxmOdLZ3p5ZmJukBpsXGFwf9CqSaQGAVresgbPDIiMVbe+QXQ7AvCdx8Soz+ZoFAoknuDFbZ1QCIBKghqD4Ki5eSeOydT7vxytHBkfqv5WJpYci7gHDPPzsyRxqkxZ87yuGcv4k6fxu7SJSI2bDT4NbLD1ERJF69igDwKlBkRCRH8cuEXumzrwj+P/sFP58eCqwtedHifLR/UdZ7o8C681ZozD3kQdxiA4TU+NGiHd0MyV6uY3FmuEL3suDfewdFvPD5pFOhS0KWsjQKBvHOy399QsbNcNXrTQLiw/O3vO7MIovzBriT66gPF6E8WvLoOqDA11hYJUEGgUkPrqfLj0wshMuV2Rk8HT2Y1lxunbr2/lVU3Vxns0pIk8XzdOp4OHYouPByVk9y1OvTXX9H45sB6m2xIKoq450YA0W8Z1gdI0CWw6uYqOm7tyMqbK0nUJ+Ll6AXA/qf7ufvPx/KB9UaAV+8ci1soGK4+C2fmkY0oTWKwUhVlVL3Oxg7pjVpXcqaFpxMancS0f2698QejQUaBQK4a3fsvqDUQkOSNBUf+l37V6JhgOPGr/PidyYTv2FUgRn/uht3lgebNI2+GZNWgAQozMzR+fiQ+yL3rGptIgAqKip3BvQFo4+Dw96leblaiGV/U+QKAORfncOjpoWxfUkpMJGDKVAKnzwCdDtvOnSm16x/iPEohxcXhP3Vanvptooa7PWUcrYjT6NhzI/3FnXpJzz8P/6HLti7MvjCbyMRIytmXY+E7C1nRZgXVTOStwousTOS/c9HhXXiL0Bcd3hV2cu+sAVX7YKI0MXJUb6ZQKJjSpQqmKiVH7wW/tZ2MQUaBQK4a3eU3aCZ/XnHkB9j9RZpVo5XHZ8tTZm410FfoUiBGf3Y+3Em/Pf1YGbOScwHncuWaSgsLLOvXAyDqyJFcuWZeIBKggkKhePmD+PJaCLyZ6pAPK32Y3Dj16+Nfczv0dpYvp33+nCdDhhC+aRMoFDh/PoFis35GaWlJQM+eKExNiTl+nMidO7N8DUNTKN7eGuO032ne3/U+35z4Bv8Yf5wtnZneaDpbumyhWYlmKIAxoUEoJYlDVpbcbPMtmOTPD1ohd2h1esZtuExA3BNMLB+jUqjoUb67scPKkNKOVnzUtDQA03fdemNTYWdL55c7wrIzCgTy51mr76DDz4ACzi+DLUPkSusvWMX7o7z8YjS77QzCt27N96M/K26s4NsT36KT5L/nhVcX5tovkcnTYIVoHZBIgAoS97pQ+T1Aerkl9BUKhYKv6n1FQ7eGxGnjGHNoDEGxQZm+TPydO/J6nwsXUVpbU2LxIop+9FHyThaNkxNFRsp1iAJ/+BFtaGh27sqg3nvRGuO0dyh+4XHJz999fpcR+0cwfP9wbj+/jbXamk9qfcKubrvoVr4bKqW8UFV5Zj6Nnl+iY4z83oX3NuT+TQj5yux99zj5IBSLovJv8y3cW+Bi5WLkqDJuTKtyuNmZ4xMWx5KjD9947KujQAYZvag/Anr+CUo13NoOa3tBglygsbLfZhR6LZRvh754g3w9+qOX9Pxy4RfmXJwDQI9yPVCj5nro9eR2KTnNunkLAOIuX0EXUTh2DIsEqKB5Z7L8YfHgADw8nOpltVLN7BazKWNXhqDYzDdOjdyzl8cf9EXj54e6VEk8Nm7ApkWLVMcVGTQIs4oV0UVEEPh96ik5YylRxJL6pR2QJNh+xRf/aH++PfEtvf7pxSm/U5goTehXqR+7u+/mo2ofYWFi8fLNZ5agOiRX2R5eZSgqhYrjvse5EnTFODcj5Hl7bvjLSYMiEUuHKwD0rpC/1otZmprwXSd52nfxkYc8e57+54WLlUvyKNCiK4sMM3pRtQd8uAnUVnKhw5WdUNzdTbGIC0gKJbSZRviWLfl29Eej1zDp5CRW3lwJwITaE/i23rc0MGsAwLzL83Klr6NpieKYlisLOh3RJ07k+PXyApEAFTRFy0LdofLj/ZMhje3otqa2yY1Tb4Xe4tsT3771P5ik1xM8bz6+n36KFBeHVaNGlN60CbOyZdM8XqFW4zZzJiiVRO7+j6hD2V9zZCg9apUAZSyr7iyk87bO7Hy4EwmJ9h7t2fnuTr6q9xVFzF/bzn5mCez5CoB7Ll0o0ehzupbtCsDCKwtz+xaEfOBBUDQTNslF/VrW9iNeH0MJ6xI0KNbAyJFlXsdqrjQqW5QErZ7pu2698ViDjwIBlG0lV422LAr+VzHZMgAAqXpf9PZlCV26DMh/oz+xmlg+OfQJOx/uRKVQ8X2T7xlUdRAATc2aYq225n7YffY82pMr8RS2aTCRABVEzb4EM1sIuAbXN6V5iLuNO7+2/BW1Us2BpweYd2leuqfTx8Tg+8knhCxaBIDDwIG4L/0dlZ3dG8OwqFqFokMGAxAwdRq6qDf3FsoNibpEwk0PYF1uFrEWB0jUJ1LHpQ7rO61nVvNZuNu6p37TK8mPrtGn3HbrCQoFI6qPwERpwhn/M1wIuJDLdyLkZdEJWkasvkBMoo76pR2IMz8OQC/PXigV+e9jV6FQMK1rFUyUCvbfCuTw3fSnznNkFAigeC0Ysg/s5KrZWqUpumZfEfH332gDAvLd6E9EQgTD9g/juO9xzFXmzGs1L/mXKgBLpSUDKsmJ3sIrC9Hoc66GW5Kk0fyYY8eRdOmv9yoo8t//ROHtrIpCkxd9qQ7OAE3a5c1rudRiWqNpAPx540+2P9ie6phEHx8ef9CXqP0H5FGdH37AZeLXKEwytoPFccwY1KVKog0KImjW7CzdjiHoJT27vHfRdXtX5l+Zg0IVhy7ehUbWX7K83XKqOqZTw+eV5Icm49G3+FZeoAkUty5O93LyYtYFVxbkqR1vgvFIksQXm6/yMDgGV1tzxnWw4GboTdRKNe+Ve8/Y4WVZeRcbBjf2AGD6P7dI0Kb/AzJHRoEAHMvB0H3oa/TjYqlRSGZFCUla+zN8WL4Z/QmICWDgfwO5FnwNW1NblrVdRrMSzVId94HnBziYO/A06ik7HmSwMGQ2WNSsidLWFl14OHHXruX49YwtTyRACxcuxMPDA3Nzc+rXr8+5c+n/h1m5ciUKhSLFl7m5efLrGo2Gr776imrVqmFlZUWxYsUYMGAAfn5+6Z6zQGowCmxLQKQPnF2S7mFdynZhuNdwAKadnpaicWrM2XM87tmLhHv3UDk6UvKvVdh375apMJTm5rhNnwFA+KZNxJzNnW2dr0ra2TXx+ER8o31xtnCmb5nPiX30CedvuaLRpZO4vJb88M7k5OQnyTCvYZgqTbkYeDHzzSCFAmnpMW/+uxGQ3OF9v892ANqUaoODuYNxg8umce+Ux8nGjEchMfx54lG6x+XYKBCArRu6Tr8SYF+byG3b8t3oj3e4N/3/68/DiIc4Wzqzqv0qajjXSPNYK7UVH1X7CJB7rSXoEtI8zlAUJiZYN2kMFI5pMKMnQBs3bmT8+PFMmTKFS5cuUb16ddq1a0dQUPpDrLa2tvj7+yd/PXnystFnbGwsly5dYtKkSVy6dImtW7dy9+5dunbtmu75CiS1hbyNFOD4HLlhZzpG1xhNO492yY1Tn0Y+JWz9+uTihuZVqlB6y2Ysa9bMUihW9eth36cPAP6TJ+Vaw727z+8ycv/I5J1dVmorxtUcx67uu/iiUX+cbCx4HpPI0XvBqd+cgeQHwNXKNbkAnBgFEk49COF/e+4AMLlLFcq7mrD70W4Aenvmr8XPabExV/NNx4oAzD/4IMVOytcNqToEtVLNpaBLKX6xMhSFVkvYsj+AF6M/ZmYGv4ahXQ2+yoA9AwiICaC0XWnWdFhDuSLl3vie3p69cbF0ISg2iI13cr7C/st1QJnsx5YPZSoBelNSAqDVat84epOWOXPmMGzYMAYPHkzlypVZsmQJlpaWLF+efgl0hUKBq6tr8peLy8stpXZ2duzfv5/evXvj6elJgwYNWLBgARcvXuTp06eZii3f8+ojNxhMiICjP6d7mFKhZGbjmVRzrEZ0bDgHxvYiYNp00Gqx7dSJUmvXoHZ1zVYozp9PwMTZGc2Tp4QsWJCtc73Nqzu7TvqdxERpwoeVPmR3990M8xqGhYkFJiol71ZPao3xWk2gDCY/ST6q9hHmKnOuBV/juO/xnLotIY/zC49jzPrL6CV5oX2/+iXZ5b2LOG0cZe3KUsu5lrFDNIj3ahSnrkcR4jQ6vt+dfi2xFKNAVw08CgTYnr+Qr3Z+Hfc5zrB9w4hIiMDL0YtV7VfhZu321veZqcwYVX0UAH9c/4MYTUyOxmnVtCkoFCTcvo0m8M3FL/O7TJUidXNzw9/fH2dnZwCqVavG7t27cXeXF46GhobSsGFDdBlcPJWYmMjFixeZOHFi8nNKpZLWrVtz+vTpdN8XHR1NqVKl0Ov11KpVix9++IEqVaqke3xERAQKhQJ7e/s0X09ISCAh4eXQYmSk3FBUo9Gg0Rh24VnS+Qx93vQoWk3BZH1PpPN/oK09BIqUTvM4FSpmVZ3EpfnvU+5xJJICiowbS9GhH6FTKNBlMN5078/cHKdJ3+E/dhyhy1dg0bo15m/4nmVFVGIUy28uZ/3d9STqEwFoW7Ito6uPxt3GPVVcXb1c+ePEI/bfCiQkMhY7CzXK80tR7fsGkBc865tNBO3Lthlp3Z+diR29KvRi9e3VLLi8gAbODfJUd+/Myu1/o7ktJ+4vQaNj5OoLPI9JpLKbDVM7e6LRaNh0R96E0KNcD7Tat7dfMYTc+P5N6liR9xaf5t9r/vSpHUDDMkXTPG5gxYFsubeFi4EXOe17mroudQ1y/cSYGBwOy2U+7IcORadUZvgzyhh2P9rN1DNT0UpaGro1ZHbT2VioLNL9Hr3+PexQqgPLbyznadRTVl1fxbBqw3IuWBsbzKpVI+HaNSIOHcIuB5LLnPw3mplzKqRMpOVKpZKAgIDkBMjGxoarV69SpkwZAAIDA3Fzc0OfwU7gfn5+FC9enFOnTtGwYcPk57/88kuOHj3K2bOpy6mfPn2a+/fv4+XlRUREBLNnz+bYsWPcvHmTEiVKpDo+Pj6exo0bU7FiRdauXZtmHFOnTmXatGmpnl+3bh2WlnmzWWFmNHgwC5eo6/ja1+NC6TFpHmPq50fxv/5CHRZOnCn81lWJsko93rV416A/zF3XrsP22jUS3Fx5MnYsqLLfCVsraTmbcJYjCUeIk+QheQ+VB+0t2lPCJPW/iVf976oKv1gFvcvo6Kfci5fPGkDe6p602ysjYvQx/BL5C4kk0teyL5VNK2fvpoR8ZcNDJaeDlFiqJD730lHUHJ5on7Asehlq1Hxp+yUWSou3nygf2fJIyfEAJa4WEl966VClM5+wK3YXZxLP4KHyYKj1UIN8ntidPoPL9u1obG15/OUXSGp1ts+ZU07Gn+S/+P8A8FJ70d2yOyaKzLdBuZZ4jU2xmzDDjAm2E7BU5tzPJoeDB3Hct5/oypXxGzggx66TE2JjY+nbty8RERHY2tq+8ViDN6PJ6d98GzZsmCJZatSoEZUqVeL3339nxowZKY7VaDT07t0bSZJYvHhxuuecOHEi48ePT/5zZGQk7u7utG3b9q1/gZml0WjYv38/bdq0QZ1b/2kDSyH90YLi4edwqe6CVLx2ipej9+8ncOlSpLh41CVLEvztAK48+Rl94gWaVWlGv0r9Mnypt92ftn59nr77Hmb+ATQICMBhWNZ/k9FLevY+2cvSq0vxi5cXuZexK8O4GuNoWqxphv4t+ts95qc996gQeRKvaDn50TX6lNItvqV0Gu9/0/0FXg1k+c3lnDM9x/gO4/Pldmcw0r/RXGTo+9t80YfTp2+hUMCCD2vTtLwjAN+e+haioVPZTvSo3yPb18mo3Pr+NY7T0ObXEwTEagguUpkhL3aIva5ObB267OzCY91jnOs4Z3sUSB8by5M5c9EBLqNGUendd7N1vpwiSRLzrszjv9ty8tPXsy/ja2XscyGt72F7qT2X/7vM/fD7+JXwY1yNcTkWe0Lp0jzbtx+bR4/o0Lo1CgPvrsvJf6NJMzgZYdRufI6OjqhUKgJfm2cMDAzENYNrTtRqNTVr1uTBax1sk5KfJ0+ecOjQoTcmMmZmZpilsYBOrVbn2AdITp47lRI1oMaHcGUNJoemwuD/QKFA0usJWbiIkIVyIT+rRg0pPmcO5ezt+fyWnp/P/8zcy3PxsPegZcmWmbpkevendnXF9ZuJ+H31NWGLl2Dfvj1mL0YQM+OM/xnmXJjD7efyGgRnC2dG1xxN17JdM9VksnstdwL2z+ej6Bc9hZp8huqdKajekjyldX9Dqg1h071NPAh/wGG/w7T3aJ+5m8pjcvXfqBEY4v6u+YQzdZe86Hl86wq0qiyv6QiLD+PA0wMAvF/xfaP8Peb0989RrebrDhX56u/rzD/sTbda7jjbmqc6rrhdcXpW6Mn6O+tZemMpjUo0yvI14+/cwXf8BHSBgWhsbbHv1TNP/hvV6rVMPz09ubTIp7U+ZUjVIZkeIHj9e/hJrU8Yc2gMG+5uYECVAThZOhky7GQm1aph4uyMNiiIxMtXkneGGVpO/BvNzPky9SuqQqEgKiqKyMjI5HU10dHRREZGJn9lhqmpKbVr1+bgwYPJz+n1eg4ePJhilOdNdDod169fx83t5WKypOTn/v37HDhwgKJF056fLlRafgMmFvD0NNzd/aK44afJyY/DwAG4L12K6sU6qX6V+tG7Qm8kJL46/hV3nt8xWCi2Xbti1bQpkkaD/3eTkDI4ZQovdnYdGMmwfcNS7ezqXr57pjtsO99exVS1nPycKz4Q3pmS4Wmv19mZ2TGgsjxcvPjKYnRpdK8WCo7nMYmMWnOJRK2e1pWcGd3y5W6eHQ92oNFrqFy0MlUcDbvWLS/pVdud6u72RCdo+em/9D8jknaEXQy8mKUdYZIk8XzNWh737kOitzcqJyf8P/wwT+78itPG8dnhz9j+YDtKhZLpjaYztJphpv6alWhGdafqxOviWXptqQGiTZtCocC6uVyXKLoAd4fPVAIkSRIVKlSgSJEiODg4EB0dTc2aNSlSpAhFihTB09Mz0wGMHz+eZcuWsWrVKm7fvs2oUaOIiYlh8GC5gvCAAQNSLJKePn06+/btw9vbm0uXLtGvXz+ePHnCRx/JtRI0Gg09e/bkwoULrF27Fp1OR0BAAAEBASQmJmY6vgLDrjg0/BiAxL8nvShuuF8ubvj997hMnJiiuKFCoeDr+l+/bJx6cAzBsWlsF88ChUKB29QpKC0tibt0ibD169/6noCYAL478Z28s8v3JCYKE/pW7JtiZ1emnf0d/vsSgEXarkx4/i7Z3afSr3I/bE1t8Y7wTt7+LBQ8Or3EuPWX8Q2Pw6OoJb/0roFSKf+A00t6Nt/bDOS/vl+ZpVQqmN61CgoFbL3sy/nHaZfbcLVypUd5eRpw0ZVFmbqGNiwMn9FjCJw5EykxEesWLSj59xbiPUplO35Di0iIYMT+ERzxOYKZyoxfW/xKt/KZq532JgqFgk9qfQLAlvtb8Inyecs7su7VthgFtbxHpn5dPnw4dXPN7OrTpw/BwcFMnjyZgIAAatSowZ49e5K3tj99+hSl8mWeFhYWxrBhwwgICKBIkSLUrl2bU6dOUbmyvOjU19eXnTt3AlCjRo1U8bdIo3FnodH4U2J2rcL3UDS6BLm4YYl587CslXZ9n6TGqf129+NRxCPGHhrLivYrspZsvH7u4sVxGj+ewJkzCf5lDjYtW6IuVizVcZGJkfx5/U/W3l6bXASsnUc7xtUcR0nbklkP4JXkR9PwUxaebEBMWDwXnoRR1yPrxepsTG0YXHUwv136jSVXl9ChdIdMj0oJed/sfXc58SAEC7WK3/vXwc7i5bD7Wf+zPI16irXamg6lOxgxytxR3d2e9+u6s/7cMybvuMmusU1QKVOPdgytNpS/7//NhcALnA84T13Xt68Fijl7Dr8vv0QbGIhCrcb5yy8p0u/DXNtRlxmBMYGMPDCSB+EPsDG1YX6r+dR2qf32N2ZSXde6NHBrwBn/Myy+upjvm+RMs2mrhg1RqNVonj0j8dGjLC1VyOsy9cnc/EVGaGhjxoxhzJi0dycdeW34be7cucydOzfdc3l4eBTYbDW7wrbvJmCfOej0mDvqKbF2BepSby7CZWtqy8JWC+m7uy83Q2/y7Ylvmd18tkEW+Bbp+wGRu3cTd+kS/lOn4v7778nDxIm6RDbc2cDS60uJSIgAoLZLbSbUnkA1p2rZu/AryQ9NPkP9zhQ6Rl5j80Uftl7yyVYCBNC3Yl/+uvkXT6Oe8s/Dfwz6G6BgfHtuBLD4yEMA/tfTC09XmxSvJ43+dC7TGUt1/t9FmhFftKvI7usB3PaPZO3ZJwxo6JHqmKRRoA13N7DoyiJWtF+R7vkkrZbghQsJXfI7SBKmpUtTfM4vmFeqlIN3kXWPIh4xYv8I/GP8cbZwZnGbxVQoUiHHrjeu5jjO+J9hl/cuhlQdQln7tJtSZ4fSygrLunWJOXWK6CNHC2QClKmfYlqtNkW9HJAXLE+bNo0vv/ySEydOGDQ4wTAkjQb/adMImDoNdHpsy6ko1SIQ9cOMVRV1t3Xnt5a/oVaq2f9kPwsuG6aQoUKpxG3mDBRqNTHHjhP5zz/oJT3/ev9L1+1dmXVhFhEJEZS1K8uCVgtY0W6FwZOfpDU/3WvJ2+V3XfMnXpO9tTuWakuGVB0CwJKrS9Do8m59EiFzHgRF8/lmucP7kMal6Vo95ahlUGwQh54eAgpG5eeMcrAy5fN28hKI2XvvEhqddsuGodWGolaqk0eB0qLx9eVJ/wGELl4CkoRdzx6U/ntLnk1+rgdfZ8B/A/CP8cfD1oO/Ov6Vo8kPQDWnarRyb4Ve0rPwysIcu471ixmTgtoWI1MJ0LBhwxg37uXWu6ioKOrWrcvChQvZu3cvLVu2ZPduse4hL9E+f87TIUMJX78BFAqcxo+n2P++R2kiwekFEOmfofPUcqnF1EZTAVh2fZnBGvOZlSmD42h5bZLPzBkM3dCTr49/jW+0L04WTkxtOJUtXbfQ3L159hcRnl2aZvIDUL+0A8XtLYiK13Lgdvarn/ap2AdHC0f8YvzY9mBbts8nGF90gpaRay4SnaClXmkHJr5oCfGqrfe3opN01HKuRfki5Y0QpfH0rVeSym62RMZrmbX3bprHvG0tUOSePXi/1424y5dRWltTfM4vFJs5E2Uercd2yvcUQ/cNJTwhnCpFq7CqwyqKWxfPlWuPqTkGBQr2P9nPzZCbOXIN6xbyrE/sxYvooqJy5BrGlKkE6OTJk/To8bKexV9//YVOp+P+/ftcvXqV8ePHM2vWLIMHKWRN/N27PO7Zi9jz51FaWVFi4UIchw9DUbkruNcHTSwczvj8cdeyXZMrkE49PZWLgRcNEmdoj2YEF7dCGRlN4013sFJbMbbmWHZ120WPCj0Ms4bm7FL47wv58WvJD8iLObvVlD+4tl3yzfblLEwskpsYLr22NMebGAo5S5IkvtxylQdB0bjYmrGgb03Ur1X+0+q1bLm3BSC5P1xholIqmP6uvONt44VnXHkWnuZxaY0C6ePi8J80Gd9PP0MfFYVF9eqU3r4N244dcyv8TNvtvZvRB0cTp42joVtD/mz3Z642uy1fpDydynQCYP7l+TlyDdOSJTEtXRq0WmJOnsqRaxhTphIgX19fypd/+VvNwYMH6dGjB3Z2dgAMHDiQmzdzJhMVMidy3z4ef9AXjZ8f6pIl8di4AZtWL2r5KBTQ5kXRyCtrIfBWhs87puYY2pRqg1av5dPDn/I0Muv91ZJ3du1+n1/axKNXQOPbEtuKfsNwr+GGWz/xluQnSbdacgJ05F4wIekM4WdGzwo9cbF0ITA2MPkHo5A/LTvuze7rLzq8f1gLZ5vU9W5O+J4gMDYQezN72pRqY4Qoja+OhwPdaxVHkmDKjhvo9anXY7paudK9fHdA7nAef/cuj3r2InzzZlAoKDpiBKXWrMY0jcr+ecXa22v56vhXaCUtHTw6sPCdhViprXI9jo+rf4yJwoSTfidzpOEspNwNVtBkKgEyNzcnLu5l998zZ85Qv379FK9HR0cbLjoh0yS9nuAFC/Ed9wlSbCxWjRpSetNGzMq9tti5ZH2o1BUkPRyYkuHzKxVKvm/yPVWLViU8IZzRB0cnL1LOqMjESH69+Cudt3Vmx8MdSEiUa9AOsw/lnjPRP8wx3HDrq8lP40/fWOenrJM11d3t0ekldl7xy/alzVRmDPcaDshNDOO06XfOzis0Oj2Xn4UTUYgrRrzu1MOQ5Bo3kzpXpnaptH/L33RX7vv1Xrn3MFPlvfo0ueXrDhWxMTPhqk8Emy48S/OYj6p9hFphgsO/Z/Hu2YvEhw8xcXKi5PI/cf7sUxR5sLghvKjufGkeP537CZA3PPzU7CfUKuPE627rnpxMzr88P0c2ACVNg0UfO5apmm35QaYSoBo1arB69WoAjh8/TmBgIK1atUp+/eHDhxRLYyuzkDv0MTH4fvpZcrf1IgP6pyhumErrqaA0gfv7wDvj2b2FiQXzWs3D1cqVx5GPmXB0Ahr92xf6JuoSWX1rNR23duTPG3+SoEugtktt1nZcyy8tfqHMhG9QlyyJNjCQoF9+yXA86Xo9+Wk99a1FDnu8GAXa+nqH+CzqVq4bxa2LExIXkvwDMq+RJImrz8KZsuMG9X84SO+l55h80YRO808xc9ctjtwNIi6xcBZ19AuPY+w6ucN795rF6d8g7dozvtG+nPCVN4EkdUAvrJxtzPm0jbwI+H977hAemzqbdkw0Y9Z/jgzdp0eh0WDdvDmld2zHKoMFcI1Bq9cy9fRUll1fBsDYmmP5ut7XRm95M9xrOGYqMy4HXea473GDn9+yVi2UVlboQkOJL2AzPJn6zk2ePJnffvuNsmXL0q5dOwYNGpSiAvO2bdto3DhnSmYLb5bo48vjvh8StW8fqNW4fT8T12++SVHcMJWiZaHOUPnxvu8gE9m9k6UTC1otwMLEgrP+Z/nh7A/p/vahl/Ts9t5N1+1d+fn8z0QkRFDGrgzzW81nRbsVeDl5AaC0sMBt+nQAwjdsJObcuQzHk0oWkh+Azl7FUKsU3PCN5G5A9keh1Co1I7xGAPDn9T+J1cRm+5yG4hMWy4JD93lnzlHeXXiSVaef8DwmEVtzExRI3AuK5o8Tjxi04jzVp+3jwz/OsOToQ276RaQ5tVHQJGh1jFp7idCYRCq72fJ9t2rpLsTfcm8LEhIN3RpSyjbvFejLbQMalqKCizVhsRrm7L+X4rWYc+d49F43il32QaOCFa2VBEwdgolD7q2fyax4bTzjj4xn6/2tKBVKpjScwnCv4Tne+zIjXKxc+KDiBwAsuLwAvWTYURqFqSlWL36uRx8pWNNgmUqAmjdvzsWLFxk3bhwrVqxg2bJlKV6vUaMGn332mUEDFN4u9vx5HvfqRcLdu6iKFqXUqpXY98hg88XmX4KZLQRcgxuZW6fi6eDJz81+RoGCLfe2sPrW6lTHnPM/xwf/fsBXx79KsbPr765/08K9RaoPEKsG9bHvJS8g9Z80CX18fKZiArKc/IC8nbelpzNguFGgLmW7UNKmJGEJYay7s84g58yqyHgNG849pffvp2nyv8PM3ncP7+AYzNVKulYvxorBdTn7dQu+r6NjXh8v3q/rTnF7CxJ1ek4+COWn/+7Qad4J6v1wgE82XGbLRR8CI7PwPcoHpv1zi6vPwrGzUPN7/9pYmKrSPE6j07D1/lagcG19fxO1SsnUrvKC6DVnnnDTL0Ku7TNvHk8HDkIbGIiphwdHJ3fgv7pKFl9bYuSI0xeZGMmI/SM4/OwwpkpT5rSYk+dG+YZUHYKV2orbz2+z/8l+g5+/oK4DyvT2mkqVKlEpnXoMw4cPz3ZAQuaEbdhIwMyZoNViXrkyJRYuQP3KqNxbWTlCk0/h4HT5q1JXUKde4JmeFu4t+LzO58y6MIvZF2ZTzFKeAr0ffp8FVxckD8laqa0YXGUw/Sv3f+viZucvPif66FE0T54SsnAhzhMmZPx+spH8JOleqwT7bgWy47IfX7armGZV28wwUZowsvpIvjnxDSturKCPZx9sTG3e/kYD0ej0HLsXzNbLvuy/FUiiVv4NUaGAhmWK0q1mcdpXdcXGXF7HoNFosFJDh6qudK3pjiRJPAqJ4fj9EI7fD+b0w1BCohPZccWPHS/WSnm62NC0vCNNKzhRz8Mh3WQhv9h04Rnrzj5FoYDf3q+Bu0P6/2YPPjvI8/jnOFk40dw9Z4rF5keNyjrS2cuNXdf8mbP6KJOubCDu8mUA7Hp0x/Wbb+hFFCu2HuF8wPkMV4fOTUGxQYw8MJL7YfexVlszr9W8PBcjQBHzIgysPJBFVxex4PIC3in5jkEr0Fs3awpA/I0baIODMXHKmSasuS1Tf0PHjh3L0HHNmjXLUjBCxkkaDQE//CDX9wFsO3bA7fvvUVpkoU1Fg4/h/J8Q8QzO/Q6NP8nU2/tX7s+jyEdsubeFb059Q3lFea7vvo6EhInChF6evRjhNYKiFhlrSquytcV1ymR8Ro8hdPkKbNq3x6JKBhpKnluW7eQHoGVFJ+ws1ARExnP6YShNyjtm+hyv61i6I39c/wPvCG/W3FrDqBqjsn3ON5EkiWs+EWy77Ms/V/0IjXm5DqO8szXda5Xg3RrFKGb/9n8vCoWCMk7WlHGyZmAjDxK1ei4/DZMTogchXPMJ525gFHcDo/jjxCNMVUrqli5C0/JONC3vSCVX2+Q+WfnBdZ8Ivtt+A4DPWlegxYsRwfRsvitXfu5evjtqZd5cvGss33aqROz+fYz4dxNxmjiU1ta4Tp2KXWd5+7YrVnQv352Ndzey5OqSPJVcPI54zMgDI/GN9sXRwpElrZfg6ZD5fpe5pX/l/qy7s47HkY8NXoHexMkJ86pVib9xg+hjx7Hv0d1g5zamTCVALVq8nLJIb72HQqFApyucCyZzizYsDN9PPiX23Dm5uOEnn1B0RDbmo9UW0PJb2PExHPsFavYHy4zPxysUCr6p/w3Pop5x1v8s17gGQNtSbRlXa1yW1kTYvPMONh3aE/XfHvy/m0TpTRvfvDPk3DLY/bn8OBvJD4CZiYou1d1Yc+YpWy/5GCQBUilVjKoxii+OfsFft/6ib6W+2JnZZfu8r/MJi2X7ZV+2XvbFOzgm+XlHa1O6Vi9O91rFqVLMNltrF0xNlNQvU5T6ZYryeTtPwmISOfUwlOP3gzl+PwTf8DhOPgh9MWUmX7tJOcfkhMjZNuMjjLktLCaRkWsuJnd4H9Pyza1ivCO8ORdwDqVCmVzgT5Dp4+Lgl5/44rS8+P+BowdNVi7CrlzpFMd9VO0j/r7/N+cCzuWZUaCboTf5+MDHPI9/TkmbkixpswR3G3djh/VG1qbWfFTtI2ZfmM3iq4vpVKYTpipTw52/eXM5ATpypHAmQEWKFMHGxoZBgwbRv39/HB2z/4NByJz4u3fx+Xg0Gl9flJaWFJs9+2V9n+yo/j6cWQSBN+DYbGj/Q6berlaq+aX5L0w4MoHQkFC+a/Udtd2y1wjQ9dtviTl1moTbtwldvgLHEelMsRow+UnSvVYJ1px5yn83ApjxnhYrs+wPJ7ct1ZZlRZZxL+weq26uYlytcW9/UwZExmv477o/Wy/5cvbRy27c5molbSu70q1WcZqWc8RElTO7VYpYmdLJy41OXm5IkoR3SAwnXpsu237Fj+15fLpMp5cYtyHtDu/pSarv1Kx4M9ysMzH1XMDF372H74TxJD54CAoFe73aMr9UKwbdiWPSazllUnXovDIKdNrvNJ8e/pRYbSyVHCqxuPXiDI9eG1sfzz78dfMv/GP82XxvMx9W+tBg57Zu0ZyQhQuJOXUKKTERhanhkitjydQnor+/P//73/84ffo01apVY+jQoZw6dQpbW1vs7OySv4ScEbl/v1zc0Nc3dXHD7FKqoI28A4tzS+H5o0yfws7MjsWtFjPIehBejl7ZDsnE0RGXiV8DELJwIQneacSUA8kPQE13e0o7WhGn0bHnRkC2zwdyDaWPa8htP9bcXsPz+OdveUf6NDo9B28HMnrdJerOPMBXf1/n7KPnyet6ZvX04vy3rZn3QU1aejrnWPLzOoVCQdkXU2V/DKzL5clt2Ti8AWNalqN6CTsUCpKnygYuP0f16Xlnd9mc/Xc5fl/u8L6kf+0UHd7TEq+NT24JIxY/yyRJ4vm6dTzu1YvEBy9r+9Sc8S06pYqVpx5zLzD17sqPqn2EidIkeRTIWPY82sPHBz8mVhtLfdf6LG+3PN8kPwDmJuaMqC7vOl16balBd52aV6mCytERfUwMsRcN0wXA2DL1qWhqakqfPn3Yu3cvd+7cwcvLizFjxuDu7s63336LVqvNqTgLNUmvJ3jhQnzHjkOKjcWyYQO5uOErVbkNotw7ULYV6DXygug8wO7dd7Fq0gQpMRH/SZNSFuLKoeQH5B/k3WsatiYQQCv3VlQuWpk4bRwrbqTfDTst8rqecKbuvEmDHw4ydNUF/r3mT4JWT3lna75s78nJr1qxfngDetVxT17UbExJ02Wft/Nkx5gmXPquDQv61uT9uu4UszMnUZt6d9mnGy7z90UfgnJxd9m+mwEsPCx3eP+pRzUqutq+9T17H+8lMjGS4tbFaVSsUU6HmOdpw8LwGTOWwOkzkBITsWreLLm2T/MKTrSr4oJOLzFlx81USyhe7RG25KpxdoStu72OL499iVavpW2ptixqvQhrU2ujxJId3cp3o4R1CZ7HPzforlOFUon1i/W9BWU7fJZ/LSxZsiSTJ0/mwIEDVKhQgZ9++onIyEhDxiYA+thYubjh/BfFDfv3p+SyZekXN8yuNtMBBdzcCj7Gz/IVCgVu06aisLQk7uJFwjbIi75TJj+fGDT5SfLeiwTo1MNQ/CMMU8VZoVAwusZoADbc2UBIXMhb3+MTFsvCww9oPecoXRecZOWpx4TGJOJobcqQxqXZNbYJ+z5rxsctymVoUbMxFbEypbNXMX7q4cXJr1txcEJzpnapzDsVnbE0VSVPl03YfJV6Pxyk3dxjzNx1i6P3gnOsGKN3cDQTNskd3gc39uDdGhlrZrnpnry2pWeFnqiUeWMaz1hizp3jUbfuRB88CGo1LhO/xn3JkhS1fb7rVBkzEyWnvUP593rqJsyvjgJdCLiQa7FLksSCywv48dyPSEj08ezDz81+Nuj6mdykVqqTR5qX31ie6Ur9b1LQtsNnKQFKSEhg3bp1tG7dmqpVq+Lo6Mi///6LQx4uZJUfaXxfK244cwau376luGF2uVaD6nJRLfZPghworZ5Z6uLFcX5RXyp49i9odv/yWvIzzeDJD4C7gyX1SjsgSbD9cvZbYyRpWrwpXk5exOvi+eP6H2keExWvYdP5Z/R5Ua9n1t67PAyOwcxESZfqxVgxqC5nJr7D5C6VqVrcLk8UZMuspOmyQY1L8+egulyZ3JYNb5ku6/fHWX434HRZTIKWEasvEpWgpZ6HA990TLvEx+vuPL/DteBrmChMeK/ce9mOI7+Sa/vM5+mgwWgDAjD18MBjw3ocBg5M9W/S3cGSj1vIC4Bm7rpNTELKGQNXK1e6l5MX1+bWKJBOr2P6men8fu13AD6u8THf1v823ye0HUt3pJx9OaISo1h1c5XBzmvVuBGo1SQ+fkzikycGO6+xZCoBOnfuHKNGjcLV1ZVZs2bRtWtXnj17xqZNm2jfvn1OxVgoxV64wKOevUi4c+dlccOeuVR8q9V3YGIOT07C3f9y55pvUaTvB1jUqIE+Nhb/WQvkvCwHk58kya0xLvkYrM+OQqFgTI0xgNw/KiBGXmOk0ek5dCeQMesuUWfmAb78+1qKdT0/9/Tiwnetmf9BTVpWzL11PbnF1ERJgzSmy/rUeTldduJBCD8aaLpMkiS+/Psa94OicbYxY8GHqTu8pydp6/s7pd7B0aJwbgbR+PnxZMBAQhYtAr0eu+7dKf33ljeWrBjRvAzuDhYERMaz4PCDVK8njQKdDTib46NACboEJhydwJZ7W1CgYFKDSYyqPipf/jLxOpVSxZia8mfMmttrMjTSnKHzWltjWVve3FIQRoEyNZTQoEEDSpYsybhx46j94i/hxIkTqY7r2rWrYaIrpMI2biJgxgzQajGrXAn3BQtQ52aPNbvicm2gE3PkRqnl24IqB0edMkChUuHWpzqPrl0mxt+cSPNu2OVw8gPQoZobk3fc5H5QNDd8I6noYpgO9Q3cGlDbpTYXAy/y0+kF2Md+kKpeTzlna7rXKs57NYrn+amtnJA0XdbZq1jy7rLj9+St9qe9U+8uq+hqI2+3z+DushWnnvDvNX9MlAoW90u7w3taYjQx7PLeBUDvCoVz8XPk3n1ypfbISJRWVrhOm5Zc2+dNzNUqpnSuwkd/XeCP4970ql2CMk4v19m4WbvRvVx3Nt3bxJKrS/jDNe0R0uyKSoxi3KFxXAi8gFqp5n/N/kebUm1y5FrG0sq9FVWLVuVG6A3+uP4HX9f72iDntW7enNgzZ4g+chSHAQMMck5jyfRPtadPnzJjxox0Xxd1gLJO0mgI/PFHwtatB8CmQ3uK/fBD1oobZleTT+HSKgi5B5f/gjpDcj+GV51bhtnVHyla2ZqQG7YE7riL1YiwHO8fZGuupm0VV/656sffl3z4tkMFg5zXLyKe4lI3LnKRAz67iHnoiaRxwNHalC7Vi9G9ZgmqFs9evZ6CJGm6LGnKLFGr59LTsOTaQ9d9I7gTEMWdgBfFGE2U1PNwkLfbl3eioqtNii3t9yMULD57H3hzh/e0/Ov9L7HaWDxsPYy+ZTu36ePiCPzpf4Rv3AiAuZcXxX+Zjal7xmvkvFPJmZaeThy+G8zUf26xanDdFP/OP6r2EVsfbE0eBarjWseg9xASF8LI/SO5G3YXK7UV81rOo55bPYNeIy9QKBSMqzWO4fuHs+nuJgZWHmiQUg3WzZsT9L//EXv+PPqYGJRWVgaI1jgyNYau1+vf+hUVlf0GkoWRNiyMp0M/Sk5+nD79lOJz5hgn+QEwt4PmX8mPD/8ICUb8vr6y4Nlx6GDMKlRAFx5O4A8/5srlu7+YBvvnqh8aXdYbDSat63l/6Wka/3SItUdN0EaXQ6HQUa7CGVYMqsvpie8wpUsVqpXIn+t6ckvSdNkX7Sqy87XpMrfXpss6zjtOvR8OJk+X3fSLZOV9JTq9RLeaxRnQMOOFOiVJYtNdefFzb8/ehep7FH/vHo97905OfooO+wiPtWsylfyA/IN5cpcqmKqUHLsXzL5bgSleTxoFAsOvBXoa+ZT+u/tzN+wuRc2LsqLdigKZ/CRp4NaAeq710Og1LDFQvzXT0h6oS5ZE0miIOX3aIOc0FoMtIkhISGDOnDmUKVPGUKcsNOLv3uNxz17EnjuH0tKSEosW4jhyhPE/XGsPBocyEBMEp+YbJ4bXdnsp2s/A7fuZoFQSuWsXUYcP53gITcs54mhtRmhMIscfhGbqvVqdnsN3glKs6znjLdf/aVDGgWHV5JYYwdJJyhaLz/AaFCGlpOmy//X04tTXrTgwvjlTulSmVfLusoTk3WXvLT5DtEZBRVcbfnhDh/e0XAu5xt2wu5ipzOhatnBM9UuSRNj69Tzu1ZuE+w9QOTni/ucfOE+Y8Obq7G9Q2tGKYc3kitDT/7lFvCblrMGra4EuBhpmN+qt0Fv0/68/PtE+lLAuweoOq6lUNGOL3vMrhULB2JpjAdjxYAePIjJf3y2tcxaU3WCZ+rRNSEhg4sSJ1KlTh0aNGrF9+3YAli9fTunSpZk7d67oBp9JUQcO8PiDD+Tihu7uL4obtjJ2WDITU3hnivz41HyIMkxBwAxLtdVdXvNjUa0aDgMHAhAwbTq66OgcDcNEpeTdGvIarIzsBpMkies+EUz75yYNfjzI4JXn2fWiXk85Z2u+aOfJya9bsWF4Q8Y3a0uT4k3QSToWX12co/dRWCgUCso5WzO4cWmWv7K7bHTLsni92F1mZSKx4IPqma5CnTT6086jXY60MslrdOHh+IwdS8C06UgJCVg1a0qZ7duxbtw42+ce3bIcxezM8Q2PY/GRhylec7N2o1s5uZeVIf5fnPU/y5C9Q3ge/5yKDhVZ3XE17rZ5u7WFodRwrkGLEi3QSToWXVlkkHO+TICOGWxziDFkKgGaPHkyixcvxsPDg8ePH9OrVy+GDx/Or7/+ypw5c3j8+DFfffVVTsVaoEiSRPCiRfiMGSsXN2zQAI+cKG6YXZXfhRL1QBMLhzPXHiNb0kl+kjiNG4va3R1tQABBv/yS4+EkTYMdvBtMbDr1Pn3D41h4+AFt5h6jy4ITrDj5mJDoRIpamTK4sQf/jGnC/s+aMbplOYq/sqg5aUfY7ke78Q73zvF7KWxeny4793VLJtXUUeoNHd7TEpEQwd7He4HCUfk59vx5vN/rRvSB12r7FDVMZWRLUxO+61wZgMVHH/I0NGXV4uRRIP/sjQLte7yPUQdGEaOJoa5rXZa3W17odu4l7Qjb83gPd57fyfb5LOvVRWFpiTYoiITbt7N9PmPJVAK0efNm/vrrL7Zs2cK+ffvQ6XRotVquXr3K+++/j0qVv2sn5BZFYiIBEz4nZJ48rVSkXz9KLluKSZEiRo4sDQoFtJ0pP768GoJy4R/7q8lPo3FpbnVXWljgNkOuVh2+fgOx53O2fH5lN1squtqQqNVzJfRlLFHxGjZdkNf1NPnfIWbtvcuDoGjMTJR09nJj+aA6nPnmzet6qjhWoaV7S/SSXowC5QJ7SzUWWdjUuPPhThJ0CXgW8TRIq5e8StJqCZ6/gCcDB8m1fUqVelnbR2nYKdoOVV1pXK4oiVo903fdSvFaMeti2R4F2nhnI58f/RyNXkPrkq1Z3HoxNqY22Y47v/F08KRD6Q4AzL+c/eUMSlNTrBo2BCDqyJFsn89YMvWv2cfHJ3n7e9WqVTEzM+Ozzz4z/lqVfETj54f7osXE7N8PajWuM6bj+t23WZ5LzxUl60OlLiDpYf+UnL3W68lPm+npbnW3atAA+15ybST/7yahj8+51gkKhSJ5FOhskJIj94IZu/6yvK5ni7yuR5LkdT0/9/Di/HetWdC3Fq0qumRoXU9Sdeg9j/dwL+xejt2HkDWFZfGzxs+PJ4MGEbJwoVzbp1s3Sm/9+421fbJDoVAwrWsVTJQKDtwO5PCdoBSvZ3UUSJIkFl1ZxMyzM5GQ6FWhF7Obz8ZMZWboW8g3RtcYjUqh4pjPMS4HXc72+aybv2iLkY/XAWUqAdLpdJi+0gHWxMQEa+v81yvFWGIvXMDngw8w9/dH5eBAqZUrKNKrl7HDyph3poLSBO7vhUfHcuYamUh+kjh/8QUmTk4kPnlCyELDzG+n590axVEq4HG0gmGrL/PPVT8StHrKOlnxRTtPTnzVkg3DG9K7rju2mezD5engSdtSbQEMNk8vGM75gPM8jnyMpYklncq8vd5NfhS5bx/e3boTd+EiSisris2aRbEff8jxbc7lnG0Y0kReED3tn5skaF8uiM7KKJBOr+P7s98nHz+y+kgmNZiU76s7Z1cp21LJVct/u/RbttfuJK0Dir92He3zrDd2NqZMJUCSJDFo0CC6d+9O9+7diY+PZ+TIkcl/TvoS0ha5dx+652HEF3OjxIb1yRU18wXHcvKuMIB9k0Cf9e3gacpC8gOgsrXFdcpkAEKXLyf+1q23vCPrXGzNeaeiMwAOVmoGNfJg55jGHBjfnNEty1GiSPaKJH5c42MUKDj49OD/27vvqCiuPozj31lYei9SBLF3QSxR7Eas0VgSNcbYezS2FPVNrEksKXYTWywxRhNjj10TO2LFLmJBjQIC0uuyO+8fxE2IjbIwlPs5Z8+B2Zk7zwWFHzN37uVadP71Q8i5p+t+dSzfEUt10Z335Hl0qamETZvGw9Fj0MXFYVarFuW2bsG2U8cCy/DB6xUpZW1KaHQyK49lfVLp31eBzkecf2k76dp0Pj76Mb8E/4KExP8a/I+RtUcW2yt2OTXcZzgmKhPORZwj4FHeHmFXu7hgWr0ayDKJR/Ppj+J8lqMCqF+/fpQqVQpbW1tsbW157733cHd313/+9CU8n8snH+PwwSgejBiB2i3vE1IVuBYTwcQawoLgymbDtZvL4ucpa39/rNu2Ba2WR599hpzxglHKBvDVWzUZXyuD4x83Z9qbNfD2sDPYD9cKdhXoUL4DAEuClhikTSHvolKiOHTvEFD8Bj+n3rxJaPfuxG78z9w+ZcoUaA5rM7V+HbbFf9ziYew/iw9n9ypQYnoiIw6O4MC9AxirjPmq+Vf0qtorf4MXMa6WrvSs2hOABRcMdxWoqN4Gy9FQwNWrV+dXjhJBUqtxGDoUefdupaPkjqVT5gzRf3wOh2ZkjgtSZ2/5gBfKY/HzlOtnn5J06hRp164TvXo1TkOG5C3XC1iZGuNlRb7N1zPCZwR77+7l6F9HuRh5ER9nn3w5j5B9225tI0POwNvZmyoOVZSOYxCyLBO7cSMRs+cgp6Vh5OSE+5zZBnm8Pbc613bn58D7nA59wsxd11nSu47+vcG1BrP11lZOhZ3ifMR5ajnUynJsVEoU7x98n+tPrmNhbMGC1xfQ0K1hQXehSBhUcxC/3fyNa9HXOHT/EP5e/rluy7p5c6K/X0rS8RPIGk3hHsv6HGLWNSFnGr4P1u4Qdx/OrMhbWwYqfgCMnZ1xmZi51k3UosWk3c37hF9K8LLxolOFTgAsuSCuAilNq9Py283fgOKz7pc2NpaHo0f/M7dP06aU326YuX3yQpIkpneugUqCXZfDOHHrnwU83a3c9eNX/nsV6EHCA/ru6cv1J9dxMHNgVbtVovh5CUdzR/pU7wNkPhGm1eV+6SqzWrUwsrdHl5BA8oW8D6wuaKIAEnLGxAJe/zTz46NfQ3IuB78ZsPh5yrZLZywbN0ZOTyds8mRkQ49TKiDDvIdhLBkTEBZgsFlwhdw5+egkDxMfYmNiQ9uybZWOk2fJZ89yp2s3Eg4cBLWaUhMm4LnMcHP75FU1Nxv6+pUFYOqOq1mWnhlSawjGkjGnwk7pn2IKjgmm756+PEh4QGmr0vzY/kdqOObPE2vFSb8a/bAxseFO3B123839HQnJyAirZk2BonkbTBRAQs759IJSNSA1Do7lYhLCfCh+IPMvSNfp05EsLEg5e47YX3/Nc5tK8LD2oGulzDEPiy8sLtIzrRZ1Tx9971yxM2bGebzdqyA5I4PIxUu417cfGWFhqL3KUHbDBhwH9Df43D55Na51ZRwtTbj1OJE1J0L1292t3OlSqQsAy68s547mDkMODiEqJYrK9pX5sf2PeNlkf123kszGxIaBNTMXuF4StASNVpPrtoryOKDC9S9fKBpURplFC8Dp5RATmv1jz6z8V/HzgcGKn6dMPEpTauwYAB5//Q2a8AJevsNAhnoPRa1SczbiLIHhgUrHKZHCEsM4+jDz6ZbulYvIdBXPoQkLy5zbZ/HizLl9unSh3OYtmNcsnFdKbM3VTGhXFYD5B2/yOP6f+b2eXgUKDA9kbdJaEjWJ1HWpy+p2qyllUUqpyEVSr6q9cDJ34mHiQ7aEbMl1O5ZNmoCREem3bpP+10MDJsx/ogAScqdiKyjfArTpcOjz7B1zZiXs+jDz40YfQOvPDVr8PGXfuzfmPj7okpIInzqtSF5BcbV01f/SFVeBlLE5ZDM6Wcdrrq9Rzrac0nFyJf7AAe506Zo5t4+FBe5ff4X77FkYWRXuR/nfrutBbU87ktK1zNrzz9IN/74KpEVLS4+WLPVfio2JjUJJiy4LtQVDvYcCsOzSMlIyUl5xxPMZ2dhg4esLQOKRw4aKVyBEASTkjiRlFjBIcOU3ePjy+TkKqviBzPvSbl98Dmo1iUeOEL+raD51N7jWYEyNTLkYeZHjD48rHadE0eg0+r+Ku1cpeld/JI2Gx198wcMPRv8zt8+2rdh26qR0tGxRqSRmdK6BJMHWCw85ffefsYbDvYfj6+xLY9PGzGkyp0jfmlTa25XeprRVaSJTItl4Y2Ou27FqUTRvg4kCSMg9N2/weSfz4/2T4UVXKQqw+HnKtFIlnIYPAyDiyy/JiInJ1/PlB2cLZ96pkvn1XRK0RFwFKkBHHhwhMiUSRzNHWnm2UjpOtskZGSSdOEGZxYuJ/yVz/JLj4EGKzO2TV94edrxTPzPzlO1XyPh7QLSLpQs/tP6B9ubtMVblYlE3QU9tpGaEzwgAfrjyAwnpCblq5+k4oOTA0+hScnclSQmiABLypuWnYGQK947DzX3Pvq9A8fOU05AhmFaqhDYmhoiZswrknIY2sNZAzI3NuRp9lT8f/Kl0nBLj6eDnbpW6oTYq/HObpN64QcTsOYS0bEnY8BGYhkdg5OiI58qVlProI6R/LWFUlHzctgp2FmpuhCewPvC+0nGKpY7lO1LetjxxaXH8eO3HXLVhUrEiand35LQ0kk6dMnDC/CMKICFv7DyhYeZfEByYArp/zcKsYPEDIJmY4PblF6BSEb9zZ5FctdjBzIHe1XoDmVeBdHLRfLS/KLkXf4+AsAAkJN6q/JbScV5IExFB9A8/cOfNztzt0pUna9agjYxCZWtLrJ8fnr/9hlUTZef2ySsHSxM+apM5+eS3+4OJSkxTOFHxY6QyYpTvKAB+vPojT1JzPrWJJEn/3AY7XHRug4kCSMi7puPB3AGigpGC1gOgOrdK0eLnKXNvbxz69gUgfPoMtImJBZ4hr/rX6I+V2oqbMTc5cO+A0nGKvacTHzYp3YTSVqUVTpOVLimJ2G3buD9wILdatOTx19+QdvMmklqNdZs2eCxZTLk//+Bxl84YOxWOuX3yqtdrZajhbkN8agZf7w1WOk6x5F/Gn+qO1UnOSOaHyz/kqg2rFi2AzHFAReV2vSiAhLwzs4XmEwAwOjqHChF7MNr7SeZ7ChY/TzmP/gC1hwcZYWFEzp2rWI7csjW11c/c+l3Qd3mauVV4uTRtGttubQMKz7pfslZL4vETPPzkE242aUrYxEkknQwAWca8Th1cp02j0vFjeCxcgHWrVkVuOYJXMfp7QDTAL2cfcOF+0RvPV9hJksRo39EAbLyxkfCknE8fYvHaa0hmZmSEh5N286ahI+YLUQAJhlFvINiXQ0p6TM1HGzK3FYLiB0BlYYHb55nzFsX8vIHks2cVzZMbfar30c/cuid0j9Jxiq0D9w4QmxaLq6UrTUs3VTRL6o0bRMz5ilstWvJg8GDid+xETklB7VUGpw9GUeHAfsr+vB77d3piVMwXoa7r5cBbdTwAmLL9Klpd0bjCUJQ0cm9EXZe6pOvSWXZpWY6PV5mZYdkwcwmSonIbTBRAgmEYm4D/VP2n2oYjC0Xx85Slnx+2b3UDIOyzyejSitZYAmsTa/rX6A/A0otLydDl34r3Jdmm4E0AvFXpLYxURgV+fk3E46zjelavJiMyEiNbW+zf7UXZjRuosHcvziNHYuLpWeD5lDSxfVWsTY25/DCO384XrQn3ioJ/XwXaGrKV+/E5H3Re1B6HFwWQYDjVu6BtNZ0gzwHoXp9WaIqfp1w++QQjZyfSQ0OJ+u77Vx9QyLxb7V3sTO24F3+Pnbd3Kh2n2AmJCeH84/MYSUa8VangBj/rkpKI276d+wMHcavlf8b1tG6Nx+JFVDp2FNcpUzCvXRupkP2/KijO1qaMa10ZgG8PhJCU+9UbhBeo41KHJqWboJW1LAnK+WLMVs2aAZASFFQkph4RBZBgOJKEruFI7jm1LHTFD4CRrS2uU6YAEL1yJanXryucKGcs1Zb69XuWXVqGRid+AxjS00ffXy/zOs4Wzvl6rv+O63k0YSJJJ0+CTpd1XM+ihVj7+xfZx9gNra+fF1VcrIlJ1vDzbRUxyelKRyp2PvD9AIA9d/dwMyZnY3nU7u6YVq4MOh1Jx0/kRzyDEgWQUKLYtG6NdZs2oNUS9ulnyBlF61bSO1XfwdHMkYeJD/WDdYW8S9Yks/NO5lW1/Fz3KzU4WIzryQNjIxUzOtfASCVxJUZFu4Un2B70sMg8dVQUVHesThuvNsjILL6wOMfHF6XFUUUBJJQ4rpM/Q2VjQ+q1azxZs0bpODlibmzO4FqDAVh+aTnpWvEXsCHsubuHJE0SZazL0MCtgUHbzhzXs4o7nbtwt3OXLON67Hq9g9eGn0vsuJ7caFDekQ2D6uNqLvMkScOYjUEMWHOGv2KSlY5WbIz0HYlKUvHngz+5FHkpR8c+HQeUdOwYsrZwP7EqCiChxDF2dsZlQuZj+5GLFpMeGqpsoBzqXqU7pSxKEZ4Urp+zRsibX29m3v7qXrk7KinvPxafHdfzNWnBwc+M63GbOhULX98SO64nt3zL2PGxt5Yxr1fAxEjF4eBI2sw7yg/H74onxAygvG153qzwJgALLyzM0bHmPj6obG3RxsWRcvFifsQzGFEACSWSbbeuWDbyQ05LI2zyFGRd0Zlh2dTIlKG1MldxXnl5JakZqQonKtquRl3lWvQ1TFQmdK7YOdftyFotiSdO8GjCBG42bZZ1XI+vb+a4nmNHxbgeAzFWwaiWFdg9pimvlXUgOV3L579fo9t3J7j2KF7peEXeCJ8RGKuMCQwL5FRY9pe3kIyNsWrSBCj8j8OLAkgokSRJwnXGDCRzc5LPnCH2101KR8qRbpW64W7pTmRKJL8E/6J0nCLt6dWfNmXbYG9mn+PjU4ODifjqa261fJ0HgwYTt30HcnIy6jJlcBo1igr791F2w8+Z43rs7AycXqhYyoqNQxvyZdeaWJsac/GvON5cfJw5e2+Qqinct2AKM3crd3pUzpwMdNH5RTkaZ1VUHocXBZBQYpl4eFBq7BgAHn/zDZrwnM9+qhS1kZphPpmr3a+6sopkjRj/kBvx6fHsvrMbyNnMz8+M61m1iozHj7OO69m3F+dRI4vcKuxFkUol0buBFwc/bE67Gq5k6GS+P3ybdvOPcvJ2lNLxiqwh3kMwNzbnUtQlDj84nO3jLJs0AZWKtOBgNGFh+ZYvr0QBJJRo9u+9h5mPN7rERMKnTS9ST5N0qtAJT2tPnqQ+4ecbPysdp0jaeXsnqdpUKtpVpLZz7Zfuq0tOJm7HDu4PGpxlXA9qNdat/cW4nkLAxcaMpX3qsqxPXVxsTAmNTubdFYF88ttFYsUj8znmZO6kX4x54YWF2V6M2djeHnMfHwASjxzNt3x5JQogoUSTjIxw/+ILUKtJPHyY+N27lY6UbWqVmhE+IwBYc3UNielFb6FXJcmyrJ/5uUeVHs8tWLKM62nSlEefTCDpxIl/jeuZSuVjR/FYtEiM6ylE2tZw5cD45rzXMPPq269n/8J/7hF2XnxUpP7IKQz61+iPtdqaW7G32HM3+8vw6BdHPXw4f4IZgCiAhBLPtFIlnIZl3k6K+HJmkZjB9KkO5TpQzrYccWlxrLu+Tuk4RcqFyAvcjruNubE5Hct3zPJeavDNbI7reUeM6ymkbMzUfNGlFr8N96NiKSuiEtP5YMMFBq09y8PYFKXjFRm2prYMqDkAgCVBS7I9Aav+cfhTp9ClFs4HNRQvgJYsWULZsmUxMzOjQYMGnD59+oX7rlmzBkmSsrzMzMyy7LNlyxbatGmDo6MjkiQRFBSUzz0QigOnoUMwrVQR7ZMnRMyapXScbDNSGfG+z/sArLu6jri0OIUTFR2bQzYDmUWktYk1msePiV61mjtdunK3c2f9uB6VrS127/QU43qKqHplHdg1uglj/SuhNpL448Zj2sw9wuoT4pH57OpdrTcOZg48SHiQ7QlYTStXxtjVFTk1leSX/F5XkqIF0C+//ML48eOZOnUq58+fx8fHh7Zt2/L48eMXHmNjY0NYWJj+de/evSzvJyUl0aRJE+bMmZPf8YViRDIxwe2LL0CSiN+xk8Sjhfe+9X+1KduGSvaVSNAksPbqWqXjFAlJuiQOPjiIabpM91CXzHE9LVry+KuvSLtxQz+up/SihZnjeqZNE+N6ijBTYyPG+ldmz5im1POyJyldy/Sd13jr+5PcCBePzL+KhdqCod6ZU28svbiUNO2rF5OWJOmfWaEL6ePwihZAc+fOZciQIQwYMIDq1auzdOlSLCwsWLVq1QuPkSQJV1dX/cvFxSXL+3369GHKlCn4+/vnd3yhmDH38cGhbx8AwqZOQ5uYpHCi7FFJKkb6jARg/fX1xKQWnVt4SpC1WsKv72fo9lR+WCSjmrHwheN6bFq3RiXG9RQbFUtZ8+swP77okvnIfNCDWDouPM43+4LFI/Ov0L1yd1wtXXmc/JhfbmRv6o1/L4tRGMdeGSt14vT0dM6dO8ekSZP021QqFf7+/gQEBLzwuMTERLy8vNDpdNSpU4eZM2dSo0aNPGVJS0sjLe2fijY+PvMvAo1Gg0Zj2AUnn7Zn6HYLi6LeP7v33yf+4EEyHj4i4ttvcf7fpCzvF9b+NXVrSlX7qtyIucHKSysZ6zs2120V1j4agpyRwYP+A2ivn6FWxtjDA+tOHbHu2FF/a0sH6Ipo/4vz9++pvPaxZ113mldyYMbvNzhw/TGL/7zFrkuP+LxzdRqUczBk1FwpjN9DCYmhNYcyI3AGKy6v4M1yb2KptnzpMSZ16yCZmKB5+JDk4GBMKlQA8rd/OWlTkhUqyx49ekTp0qU5efIkfn5++u2ffPIJR44cITAw8JljAgICCAkJwdvbm7i4OL755huOHj3K1atX8fDwyLJvaGgo5cqV48KFC9SuXfulWaZNm8b06dOf2f7zzz9jYWGRuw4KRZZFSAgeK39AliQeDBtGarmySkfKlmBNMOuS1qFGzXib8VirrJWOVOhYn7+A2y+/kKqGEzWNKduwP1qvCiBubZVYF6MlfrurIl6T+W/Ar5SON710WCh2eaDw0spaFiUsIkoXRSuzVrQ0a/nKY0r/sArLmzeJ7NCemL+vCOWn5ORk3n33XeLi4rCxsXnpvkXqW+zn55elWGrUqBHVqlVj2bJlfP7557lud9KkSYwfP17/eXx8PJ6enrRp0+aVX8Cc0mg0HDhwgNatW6NWqw3admFQXPoXERVNwrZtVNi7F8/fNqEyNQUKd//ay+25sP8CV6KvcN/tPh/X/ThX7RTmPuaFrNNxf9lyNMCWRipM+vZg2GujlY5lcMX1+/dvhuxjB2BkioavD4Sw8cxfBDxWEZJsxpQ3qtKuhosi474K8/fQ5J4Jk05MIlAbyKetPsXO1O6l+8fGxhE1axaejyPx69ABWZY5ezeaJbvPMqZTfXy9HA2a7+kdnOxQrABycnLCyMiIiIiILNsjIiJwdXXNVhtqtRpfX19u3bqVpyympqaY/v0L7r/t59c/vvxsuzAo6v1zmzSR5BPH0YSGErdyJaXGjs3yfmHt3wd1PmDYgWFsDtnMwFoDcbXM3v+l5ymsfcwNWZY58+sirO/cIdkU9tWR+LFy92LTv+cpTt+/FzFUHx3Vama/5UO3Op5M2nKJ25FJjP7lEv7VSvF5l5q42ZobIG3OFcbvYYcKHVhzbQ3BMcGsC17H+LrjX7q/bavXiZo1i9QLF1i25zK/BccRGp0MqCh3+TGvVcz9z6jnycnXS7FB0CYmJtStW5dDhw7pt+l0Og4dOpTlKs/LaLVaLl++jJubW37FFEooI1tbXD+bDED0yh9IvXFD4UTZ4+fmR51SdUjXpbPy8kql4xQK16KvMWT/YCK+/x6AP+qb0sa+CxXsKiicTChsXivnwO4xTRndKvOR+YPXH9N67lF+DAhFJx6ZBzIfuhhdJ/PK6YbrG3ic/OKntuOSNWx6qOWxgxtotQRu2kNodDIWJkbUd9LRvqbLC48tCIo+BTZ+/HhWrFjB2rVruX79OiNGjCApKYkBAzInXerbt2+WQdIzZsxg//793Llzh/Pnz/Pee+9x7949Bg8erN/nyZMnBAUFce3aNQCCg4MJCgoivAit8yQUDjZt22DdujVkZBD26WfIGRlKR3olSZIY5TsKyJzn5lHiI4UTKedh4kMmHptIz997khxwiorhoDUxou/UTdQ3ra90PKGQMjU2Ynzryuwa3ZQ6ZexITMtgyvarvL30JDcjEpSOVyg0Ld2U2s61SdWmsvzS8izvpWfo2Hc1nOHrzlH/y4N8uvUKRx0qA9Ax+S7zevoQMKE571XS8VpZZQecK1oA9ezZk2+++YYpU6ZQu3ZtgoKC2Lt3r/7R9vv37xP2r4XUYmJiGDJkCNWqVaNDhw7Ex8dz8uRJqlevrt9nx44d+Pr68sYbbwDwzjvv4Ovry9KlSwu2c0Kx4DL5M1Q2NqRevcqTtUVjjp36rvVp4NaADF0Gyy4tUzpOgYtLi+PrM1/TaWsndt3ZBcCQ85mrvDu98y4OrmUVTCcUFZVdrPlteCNmdK6Blakx5+/H8sbCY8zdLx6ZlyRJfxVo883NPIh/wPn7MUzedoXXZh5k2Lpz7L0aTrpWR1VXa6p0bgeAb9h1uvi4Y2FSOIYfK55i1KhRjBo16rnvHf7PGiLz5s1j3rx5L22vf//+9O/f30DphJJOXaoULhM+IezTz4hcuAizv9e3KexG1R5FYFgg229tZ1DNQZSxKf4zF6dp0/j5+s+suLyChPTMv9QbuDZgnEl7VDc/A7Uax4EDFU4pFCUqlURfv7K0ru7C5G1XOXg9goV/3OL3y2HM7ubNa4XgkXml1HetTx3nBpyPDKTrhmlE3e2mf6+UtSldfEvT1bc01dxskDUabi6fifbJE1IvX8b4XxctlKT4UhiCUNjZduuGhV9D5LQ0Hk+dBrrsrYispNqlatO4dGO0srbYXwXSyTp23t5Jp62dmHtuLgnpCVSyr8T3/t+zos0KrDfuB8CuS2fU2XzAQhD+zc3WnBV96/Jd7zo4W5tyJzKJHssCmLTlMnEphWeunoIQl6zh58D7dF96kmOnM28lp5qewdwykq6+pVk36DUCJrXifx2qUc0t8ylqSa3GskljIHNSxMJCFECC8AqSJOE2YwaSmRmpZ89ie+aM0pGyZVTtzCurv9/5nTtxdxROkz9OPjpJj509+N/x/xGWFIaLhQtfNP6CTR030aR0E9Ju3CDpyFFQqXD811hBQcgpSZLoUMuNg+Oa0+s1TwA2nL5P67lH2Hsl7BVHF23pGToOXItgxE+Z43r+t/UyZ0JjkNM8sNXVQZJk/BtdYF7P2jSt5IyR6tmpA57OCp1QiFaHFwWQIGSDiacnzmPHAOC0azcZL1mvrrCo6VSTFp4t0Mk6lgYVrzFwN57cYOj+oQw7MIzgmGCs1FaMrTOW37v+TueKnTFSGQEQtTxzgKZN+/aYeHkpGVkoJmwt1Mzq5s3GoQ0p72TJ44Q0hv90nqE/niU8rnCuep4bsixz4X4MU7ZfocHMgwz58Sx7rvwzrmdS+6oETGzF2q5TkJA4/NcfXI26+sL2rJo1A0ki7dr1QvPzUxRAgpBNDn36YFqzJkZpaTxZWjRuKz29CrQ3dC8hMSEKp8m7R4mP+N+x/9FjZw8CwgIwVhnzXrX32N1tN4NqDcLM2Ey/b9qduyTs3QeA49AhSkUWiqmG5R3ZPaYpo1pWxFglsf9aBK3nHmHdqXtF+pH5B0+SWXQohFbfHqHrdyf5MeAeMckanK1NGdK0HLtHN2Xv2GYMa14BV1szKthVoFOFTgAsurDohe0aOzhg5l0LgKRjxwqkL68iCiBByCbJyAinjz4EIH7rVtLv3VM40atVcahCa6/WyMh8F/Sd0nFyLS4tjm/PfkunrZ3YeWcnMjLty7VnR5cdTHhtAvZm9s8cE71yJcgyVi1bYlaligKpheLOTG3ER22r8PvoJtT2tCMhLYPJ267QY1kAIUXokfm4FA0bTt+nx9IAmn71J98euMmdqCTM1UZ0qe3O2oGvETDxdT59ozrV3Z9dHWGEzwiMVcaceHSCM+EvHiLw9DZY8lFRAAlCkWNety5JVapARgaRi5coHSdb3vd5HwmJg/cPcj36utJxciRNm8baq2vpsKUDa66uIV2XTn3X+mx8YyNfNfsKT2vP5x6nefiQuB07AHAaNrQgIwslUFVXGzaPaMS0TtWxNDHi7L0YOiw8xrwDN0nLKJyPzGu0Og5ei2Dk+vPU//Igk7Zc5nToEyQJGld05JvuPpz5zJ/57/jSvLIzxkYvLhc8rD14q9JbQOZVoBctMaovgAICkArBvGqKPwYvCEVNVNs2WAYHE//77zgOHoxZlcpKR3qpivYVaV+uPbvv7mZJ0BIWt1qsdKRX0sk6dt/dzaLzi3iUlDmZY0W7ioyrO46mpZu+cn2m6FWrISMDi4YNMX/FYsiCYAhGKon+jcvRpoYrk7dd4dCNxyw4FMKuy2HM7laLegpP+geZ43ou/RXHlvN/sfNSGE+S0vXvVXaxolsdDzrXds/V0h/DvIex/dZ2Ljy+wLGHx2jm0eyZfcyqV8fY2ZmMyEjM79zNU18MQRRAgpBDaaVLY9WmDYn79xO5cCGeSwp/QTHCZwR7Q/dy5K8jXIq8hLezt9KRXijgUQDzzs3j+pPMq1WlLEoxqvYo3qzwpn5w88tkREUR+9tvADgNH5avWQXhv9ztzFnZrx67LocxbcdVbj1O5O2lAfRuUIYJ7atiY1bwa3v9FZPMtgsP2XLhIXcik/TbnaxM6Vzbna6+panhbpOnhV+dLZzpVa0Xq6+sZtGFRTQp3QSVlPWqkSRJWDZvRtxvm7EsBMsLiQJIEHLBYeRIEg8eJPHQIVKCggr9VYaytmXpVL4T229vZ0nQEpa1LnyDuIOfBDPv3DxOPDoBgJXaikG1BtG7Wm/MjbP/F+mTtWuR09Iw8/HGokGD/IorCC8kSRIdvd1pUtGJmbuv8+vZv1gfeJ+D1yOY/mZN2tXM//mo4lM17LkcxpbzDwm8+0S/3Uytok11V7rVKU2Tik4vvbWVUwNrDGRT8CZuPLnB/nv7aVe23TP7WDVvLgogQSjKTMqXw7ZLF+K2bOHx/AV4rVmtdKRXGu4znF13dnHy0UnOR5ynjksdpSMBEJ4UzqILi9h5O3Nws7HKmHeqvMNQ76HPHdz8Mtq4OGJ+3gCA07BhefqLVhDyys7ChK/e9qGLb2n+t+UyodHJDP/pHO1quDK9cw1cbMxe3UgOaLQ6jt6MZMuFhxy8FkFaRuakrZIEfuUd6epbmnY1XbHOp6tQdmZ29K3Rl++CvmPJhSX4l/HHWJW1zLD0awTGxphER5MeGoq6UqV8yZIdYhC0IOSS88j3Qa0m+dQpkgIClI7zSh7WHnSp1AWAxUHK37aLT49n7rm5vLHlDXbc3oGMTLuy7djR+cVPdr1KzM8/o0tKwrRyZayKyLIlQvHXqIITe8c24/0WFTBWSey9Go7/3COsD8z7I/OZ43pimbbjKg1nHmLQ2rPsuhRGWoaOSqWs+KRdFU5MeJ2fhzSkez3PfCt+nupbvS/2pvaExoey8/bOZ943srLEvF49QPmnwcQVIEHIJXXp0tj37EnMTz/xeN58yjZsWOivODwdqHgm/AyBYYE0cCv4W0Tp2nQ23tjI8svLiUuLA6CeSz3G1x1PLedauW5Xl5TEk7U/AuA4dCiSSvx9JxQeZmojPmlXlU4+7kzcfImLf8Xx6dYrbL/wiJndalGxlFWO2nsYm5I5ruf8X9zOMq7HhDd9StOtTt7H9eSGpdqSQbUG8c3Zb/ju4ne8Uf4NTIxMsu7TrClJZ8+ijY0t0Gz/JQogQcgDp2FDid28mdRLl0j84w+sW7VSOtJLuVq68nblt9lwYwOLLyzmNdfXCuwHpE7WsffuXhZeWMjDxIcAVLCtwLi642jm0SzPOWI2bUIbG4u6TBls2rU1RGRBMLhqbjZseb8xa0+G8s3+YE6HPqHDgmOMbFmRES0qYGL84sI9IVXDnsvhbLnwF6fu/DOux9RYRZsarnTzLU3TSoYd15MbPav05MdrPxKeFM6mm5voXa13lvdtunUjwNqayl26KBPwb6IAEoQ8MHZ2xqFPH6KXLydy/gKsWrRAMnr1k0pKGlJrCFtCthAUGcSJRydoUrpJvp8zMCyQuefmci36GgDO5s6M8s18suu/YwRyQ5eezpNVmeOwHIcMRjIWP9qEwstIJTGwSTna1HBh8rYr/BkcybyDN/n90iNmv1ULb3dr/b4ZWh3HQqLYfP4vDvxrXA9Aw/IOdKvjQft8HNeTG2bGZgz3Gc6MgBksv7ScrhW7YqG20L+vsrRENjF5SQsFQ/yUEIQ8chw0kJgNG0gLCSF+925sO3VSOtJLOVs46/9CW3xhMY3dG+fbVaCbMTeZd24exx8eBzIvjw+sOZD3qr2X5QdiXsVt3UbG48cYu7hg27mzwdoVhPzkYW/Bqv712XHxETN2XiPk70fm363viXMyfLH7BrsuhxOV+M98PRVLWdHVtzRdfEtT2i7n8/UUlC4Vu7D6ymoeJDxg/fX1DPEufMvRiJvkgpBHRra2OA4aBEDkwkXIGo3CiV5tYM2BmBubczX6KocfHDZ4++FJ4Uw+MZm3d7zN8YfHMZaMebfqu+zutpuh3kMNWvzIGRmZy16QWYyqCsFfloKQXZIk0bl2aQ6Ob87bdT2QZVh/+gHzrxizNuA+UYnpOFqa0L9RWXaMasyBcc0Y2bJioS5+ANQqNSNrjwRg9dXV+vF+hYkogATBABz6vIeRoyOaBw+I3bxF6Tiv5GjuyLtV3wVgSdASdLLuFUdkT0J6AgvOL6Dj1o5su7UNGZk2Xm3Y3mU7kxpMwsHM8LPhxu/Zg+bBA4zs7bF7+22Dty8IBcHe0oRvuvuwfnADyjtZoJZk3qjpyqr+9Tj1v1ZMe7MG3h52hf5Bi39rX649lewrkZCewNqra5WO8wxRAAmCAagsLXEaljnrcNR336FLTVU40av1r9EfS7UlwTHBHLx3ME9tabQafrr2Ex22dGDl5ZWkadOoU6oO6zus59sW31LGpoyBUmcl63REL18OgEO/vqgsDHdlSRCU0LiiE/vGNOGrBlrm9/Tm9aouqBUe1JxbKknFB7U/AOCn6z8RlRKlcKKsiuZXVRAKIbt3emLs5kbG48f6yfgKMzszO/pU7wPAd0HfodXlfNFGWZbZe3cvb257kzln5hCbFkt52/IsbLmQNe3W5PuSG4l//EFayC1UVlbYv/tuvp5LEAqSquhc6HmpFp4t8HbyJiUjhZWXVyodJwtRAAmCgahMTHAelXnPO3r5crSJiQonerU+1ftgbWLN7bjb7A3dm6Njz4Sf4d1d7/Lx0Y/5K/EvnMydmOo3lc1vbqZlmZb5fqlelmWilmVe/bF/912MbGzy9XyCIOScJEmMrjMagF+Df+VR4iOFE/1DFECCYEC2nTtjUrYs2thYnqwpfPe8/8vGxIZ+1fsB8P3F78nQZbzymFsxtxh5aCQD9w3kSvQVLIwtGFl7JLu67uLtym8b5LH27EgOCCD18mUkMzMc+vUtkHMKgpBzDdwa0MCtARqdhqUXlyodR08UQIJgQJKxMc5jMv/aebJ6NRkxMQonerX3qr+Hnakd9+LvsevOrhfuF5EUwdSTU3lr51sc/esoxlLmml27u+1muM9wgz7ZlR1RSzMXdLXr3h1jR8cCPbcgCDkz2jfz5+L229sJjQ9VNszfRAEkCAZm3bYtptWqoUtKInpF4brn/TyWaksG1BwAwNKLS9Hosj7Gn5ieyMLzC+m4tSNbQragk3W09mrN1s5b+bThpziaF3zxkXz+AsmnT4NajePAAQV+fkEQcsbb2ZuWni3RyTq+v/S90nEAUQAJgsFJKhWlxo4BIGb9ejQREQonerV3qryDg5kDfyX+xc47mQsYarQa1l9fT4ctHVhxeQWp2lR8S/myrv065raYS1nbsorlffrkl23nN1G7uSmWQxCE7BvlOwoJiQP3D/AoQ/mxQKIAEoR8YNmsGeZ16yKnpRH1feH4a+dlLNQWDK41GICVV1ZyMf0ib+16i9mnZxOTFkNZm7IsaLmAte3WUrtUbUWzpt64QeLhw6BS4TR4sKJZBEHIvsr2lWlfrj0AB1PzNvWGIYgCSBDygSRJlBo3FoDY3zaTfv++soGyoUeVHpQyL0V4cjibkjfxV+JfOJo5MrnhZLZ23srrZV4vFJOwPb36Y9OuLSZlyyobRhCEHBlZeyRGkhE3M25y4fEFRbOIAkgQ8olFvXpYNm0KGRlELl6sdJxXMjUyZXjt4QCYYMLwWsPZ3W03Par0KLAnu14l7e5d4vdkPq7v+PfEk4IgFB1lbMrQuULmen0/3fhJ0SyF46eaIBRTzmPGkHTsGPE7f8dx8GDMKldWOtJLvV3pbbwsvQg5HUKPWj1QqwvPCtNA5ppfsoxVixaYVamidBxBEHJhSM0hxP8VzzS/aYrmEFeABCEfmdesgXXbtiDLRC5cqHScV5IkCd9SvliprJSO8gzNo0fEbd8BgNNwcfVHEIoqFwsXWpi1KPCpM/5LFECCkM+cR38AKhWJBw+RcumS0nGKrOhVqyEjA4sGDTCvXVvpOIIgFHGiABKEfGZaoQK2nTPveUfOn69smCIqIzqa2E2bAHAaNlThNIIgFAeiABKEAuA0ciSo1SSdDCDp1Cml4xQ5T9asRU5Lw8zbGws/P6XjCIJQDIhB0LkkyzIZGRlotTlbQVuj0WBsbExqamqOjy0Kikv/1Go1RkZGBmvPxKM09j16ELN+PZHz5mOxcUOheKS8KNDGxxPz889A5tUf8XUTBMEQRAGUC+np6YSFhZGcnJzjY2VZxtXVlQcPHhTLH+TFpX+SJOHh4YGVleEGAzsNH0bs5s2kXLxI4p+HsX69pcHaLs5ifv4ZXVISppUqYtVSfM0EQTAMUQDlkE6n4+7duxgZGeHu7o6JiUmOftHrdDoSExOxsrJCpSp+dyCLQ/9kWSYyMpK//vqLSpUqGexKkLGzMw59+hC9YgWR8+dj1aI5UhH9GhUUXXIyT9asBcBx6DDx9RIEwWBEAZRD6enp6HQ6PD09sbDI+SN8Op2O9PR0zMzMimyB8DLFpX/Ozs6Ehoai0WgMeivMcdBAYjZuJO3mTeJ378G24xsGa7s4it20CW1sLGpPT2zat1M6jiAIxUjR/Q2lsKL8y114tfy6fWdkZ4fjoIEARC5aiKzRvOKIkkuXnk70D6sAcBwyGMlY/L0mCILhiN/iglDAHPr0wcjBAc29+8Ru2ap0nEIrbts2Mh4/xrhUKWy7dFE6jiAIxYwogAShgKksLfUzGUd99x261FSFExU+ckYG0St/AMBh4ABUJiYKJxIEobgRBVAJ0qJFC8aOHWvQNtesWYOdnZ3+8+nTp9O0adNsHdu/f3+6lNC/7O169sTYzY2MiAhiNmxUOk6hE79nL5r79zGys8O+Rw+l4wiCUAyJAkgwqA8//JDt27crHaPQU5ma4jzyfQCily1Dm5iocKLCQ9bpiF6+HACHfn1R5eJhA0EQhFcRBZBgUFZWVjg4OCgdo0iw7dIFEy8vtLGxPFm7Vuk4hUbi4cOkhYSgsrTEvndvpeMIglBMiQLIAGRZJjk9I9uvlHRtjvZ/0UuW5VxnLlu2LF988QV9+/bFysoKLy8vduzYQWRkJJ07d8bKygpvb2/Onj2b5bg1a9ZQpkwZLCws6Nq1K9HR0Vnez8ktsP/au3cvTZo0wc7ODkdHRzp27Mjt27f176enpzNq1Cjc3NwwMzPDy8uLWbNmAZnfg2nTplGmTBlMTU1xd3dn9OjR+mNjYmLo27cv9vb2WFhY0L59e0JCQnKV01AkY2Ocx2RmfLJqNRkxMYrmKQxkWSZq6TIA7N99FyMbG4UTCYJQXInnSg0gRaOl+pR9BX7eazPaYmGS+2/hvHnzmDlzJpMnT2bevHn06dOHRo0aMXDgQL7++msmTJhA3759uXr1KpIkERgYyKBBg5g1axZdunRh7969TJ061WD9SUpKYvz48Xh7e5OYmMiUKVPo2rUrQUFBqFQqFi5cyI4dO/j1118pU6YMDx484MGDBwBs3ryZefPmsXHjRmrUqEF4eDgXL17Ut92/f39CQkLYsWMHNjY2TJgwgQ4dOnDt2jXUarXB+pBT1u3aYbp8BWk3bhC9ciUuH3+sWJbCIPnUKVIvXUIyNcWhX1+l4wiCUIyJAqgE69ChA8OGZT6NNGXKFL7//nvq169P9+7dAZgwYQJ+fn5ERETg6urKggULaNeuHZ988gkAlStX5uTJk+zdu9cged56660sn69atQpnZ2euXbtGzZo1uX//PpUqVaJJkyZIkoSXl5d+3/v37+Pq6oq/vz9qtZoyZcrw2muvAegLnxMnTtCoUSMA1q9fj6enJ9u2bdP3VwmSSoXz2DH8NXwEMT+tx6FvP9QupRTLo7SnV3/sunfH2MlJ4TSCIBRnogAyAHO1EddmtM3WvjqdjoT4BKxtrPM8maK5Om8zFHt7e+s/dnFxAaBWrVrPbHv8+DGurq5cv36drl27ZmnDz8/PYAVQSEgIU6ZMITAwkKioKHQ6HZBZ3NSsWZP+/fvTunVrqlSpQrt27ejYsSNt2rQBoHv37syfP5/y5cvTrl07OnToQKdOnTA2Nub69esYGxvToEED/bkcHR2pUqUK169fN0j2vLBq3hxzX19SLlwgaun3uBnwqlpRknzhAsmBgWBsjOPAAUrHEQShmBNjgAxAkiQsTIyz/TI3McrR/i965XW24n/f+nna1vO2PS1E8lunTp148uQJK1asIDAwkMDAQCBz7A9AnTp1uHv3Lp9//jkpKSn06NGDt99+GwBPT0+Cg4P57rvvMDc35/3336dZs2ZoisBMy5Ik4TxuLACxm34j/e/beiVN9PIVANi++SZqd3eF0wiCUNyJAkjItmrVqumLkqdOnTplkLajo6MJDg7ms88+o1WrVlSrVo2Y5wwKtrGxoWfPnqxYsYJffvmFzZs38+TJEwDMzc3p1KkTCxcu5PDhwwQEBHD58mWqVatGRkZGluxPz1e9enWD5M8ry9dew7JxY8jIIGrxYqXjFLjU4GAS//wTJAnHIYOVjiMIQgkgboEJ2TZ69GgaN27MN998Q+fOndm3b5/Bbn/Z29vj6OjI8uXLcXNz4/79+0ycODHLPnPnzsXNzQ1fX19UKhWbNm3C1dUVOzs71qxZg1arpUGDBlhYWPDTTz9hbm6Ol5cXjo6OdO7cmSFDhrBs2TKsra2ZOHEipUuXpnPnzgbJbwjO48aRdOIEcTt24jh4MKaVKikdqcBEL8uc98e6XVtMy5VTOI0gCCWBuAIkZFvDhg1ZsWIFCxYswMfHh/379/PZZ58ZpG2VSsXGjRs5d+4cNWvWZNy4cXz99ddZ9rG2tuarr76iXr161K9fn9DQUHbv3o1KpcLOzo4VK1bQuHFjvL29OXjwIDt37sTR0RGA1atXU7duXTp27Iifnx+yLLN7925FnwD7L/OaNbBu0wZkmciFC5WOU2DSQ0OJ/7uQdvp7UL4gCEJ+E1eASpDDhw/rPw4NDX3m/f/OK1S2bNlntg0cOJCBAwdm2fbhhx/qP546dSrjxo3LVp41a9Zk+dzf359r1669MNOQIUMYMmTIc9vq0qXLS5fVsLe358cff8xWLiU5j/6AhIMHSThwkJTLlzH/16D04ipq5UrQ6bBq3hyzqlWVjiMIQgkhrgAJQiFiWrEitm++CUDkvPnKhikAmrAw4rbvAMBRXP0RBKEAiQJIyDdWVlYvfB07dkzpeIWW06iRoFaTdPIkSacCX31AERa9ajVoNFi89hoWdXyVjiMIQglSKAqgJUuWULZsWczMzGjQoAGnT59+4b5r1qxBkqQsLzMzsyz7yLLMlClTcHNzw9zcHH9/f8WXPSiJgoKCXviqV6+e0vEKLRMPD+z/npwxcv78PC15UphlREcTu2kTAI7DhiqcRhCEkkbxAuiXX35h/PjxTJ06lfPnz+Pj40Pbtm15/PjxC4+xsbEhLCxM/7p3716W97/66isWLlzI0qVLCQwMxNLSkrZt25Kamprf3RH+pWLFii98mZubKx2vUHMcPgzJzIyUoCAS/zV2qzh58uM65NRUzGrWxPLvGboFQRAKiuIF0Ny5cxkyZAgDBgygevXqLF26FAsLC1atWvXCYyRJwtXVVf96OmMxZF79mT9/Pp999hmdO3fG29ubH3/8kUePHrFt27YC6JEg5J26VCkc+rwHQOT8BcgFNBllQdHGxxOzfj0ATsOH5XlST0EQhJxS9Cmw9PR0zp07x6RJk/TbVCoV/v7+BAQEvPC4xMREvLy80Ol01KlTh5kzZ1KjRg0A7t69S3h4OP7+/vr9bW1tadCgAQEBAbzzzjvPtJeWlkZaWpr+8/j4eAA0Gs0zMwlrNBpkWUan0+VqhuSntzOetlHcFJf+6XQ6ZFlGo9FgZPTPkiNP/z0UxAzTNv36EbNhI2nBwcT8/jvW7dvn+zmhYPr45Kef0CUmYlKxAqZNmxbojN0F+T1UQnHvHxT/Por+5b3t7FC0AIqKikKr1Wa5ggOZa1DduHHjucdUqVKFVatW4e3tTVxcHN988w2NGjXi6tWreHh4EB4erm/jv20+fe+/Zs2axfTp05/Zvn//fiwsLLJsMzY2xtXVlcTERP0SDbmRkJCQ62OLgqLev/T0dFJSUjh69CgZGRnPvH/gwIECyeHQyA+n/Qd4MOcrQjMywChv67/lRH71UUpPp9wPqzAG7tWrxxUDTaaZUwX1PVRKce8fFP8+iv7lXHJycrb3LXLzAPn5+eHn56f/vFGjRlSrVo1ly5bx+eef56rNSZMmMX78eP3n8fHxeHp60qZNG2xsbLLsm5qayoMHD7Cysnpm8HV2yLJMQkIC1tbWxfKyf3HpX2pqKubm5jRr1izL91mj0XDgwAFat25dIJMo6po3597Zs5hER9M4PR3bt97K93Pmdx9jf/qJqKQkjEuXpsmECUjGBftjqKC/hwWtuPcPin8fRf9y7+kdnOxQtABycnLCyMiIiIiILNsjIiJwdXXNVhtqtRpfX19u3boFoD8uIiICNze3LG3Wrl37uW2Ymppiamr63Lb/+83RarVIkoRKpcrVau5Pbws9baO4KS79U6lUSJL03H8D8Px/G/nCzg6nYcOImDWbmKXLcOjaFdVz/q3mh/zoo5yeTuyatQA4DRmCiYKD4Qvse6iQ4t4/KP59FP3LXZvZpehvKBMTE+rWrcuhQ4f023Q6HYcOHcpyledltFotly9f1hc75cqVw9XVNUub8fHxBAYGZrvNkqZ///4vnUVZUJbdO+9g7OpKRng4sRs3Kh0nT2K3bycjIgJjZ2dsu3VVOo4gCCWY4n+ijx8/nhUrVrB27VquX7/OiBEjSEpKYsCAAQD07ds3yyDpGTNmsH//fu7cucP58+d57733uHfvHoMHZ64gLUkSY8eO5YsvvmDHjh1cvnyZvn374u7uLn7JC0WSytQUp5HvAxC1bDnaxCSFE+WOnJFB9IqVADgMHIjKxEThRIIglGSKjwHq2bMnkZGRTJkyhfDwcGrXrs3evXv1g5jv37+f5VZKTEwMQ4YMITw8HHt7e+rWrcvJkyepXr26fp9PPvmEpKQkhg4dSmxsLE2aNGHv3r25GrMjCIWBXZcuPFn5A+n37hGz7kecRoxQOlKOxe/dh+b+fYxsbbHv0V3pOIIglHCKXwECGDVqFPfu3SMtLY3AwEAaNGigf+/w4cNZFs2cN2+eft/w8HB27dqFr2/WKfQlSWLGjBmEh4eTmprKwYMHqVy5cv51QJYhPSn7L01yzvZ/0SuHMwT/9ttv1KpVC3NzcxwdHfH39ycp6Z+rCd988w1ubm44OjoycuTILI8Trlu3jnr16mFtbY2rqyvvvvtulskqDx8+jCRJ7Nq1i8aNG2NhYUHDhg25cuVK3r++ApJajdPoDwCI/mEV2thYZQPlkKzTEb1sGQD2/fqisrRUOJEgCCWd4leAigVNMsx0z9auKsDOUOf93yMwyd4vkrCwMHr16sVXX31F165dSUhI4NixY/p5e/7880/c3Nz4888/uXXrFj179qR27dr61dc1Gg2ff/45VapU4fHjx4wfP57+/fuze/fuLOeZMGECX375JeXLl+ezzz6jU6dO3Lx5s1gP5CsoNu3bE718BWnBwUT/8AOlPvxQ6UjZlnj4CGkhIagsLXHo3VvpOIIgCKIAKinCwsLIyMigW7dueHl5AVCrVi39+/b29ixevBgjIyOqVq3KG2+8waFDh/QF0MCBA/X7li9fnoULF1K/fn0SExOxsrLSvzd58mRatmyJjY0Na9euxcPDg61bt9KjR48C6mnxJalUOI8dw18j3ufJup+w79MHdalSSsd6JVmWiVq2FAD7d3thZGurcCJBEARRABmG2iLzakw26HQ64hMSsLG2zvtj4mqLV+/zNx8fH1q1akWtWrVo27Ytbdq04e2338be3h6AGjVqZJnx2M3NjcuXL+s/P3fuHNOmTePixYvExMToH3e/f/9+lvFX/37SzsHBgSpVqnD9+vVcd1HIyqpFC8xr1yYlKIjopUtxnTJF6UivlBwYSOrFS0impjj066d0HEEQBKCQjAEq8iQp81ZUdl9qi5zt/6JXDiYaNDIy4sCBA+zZs4fq1auzaNEiqlSpwt27d4Fn506QJElf5CQlJdG2bVtsbGxYv349Z86cYevWrQB5mg1byDlJknAeNw6AmF83kf7ggcKJXi3q77E/dm+9hbGTk8JpBEEQMokCqASRJInGjRszffp0Lly4gImJib6QeZkbN24QHR3N7Nmzadq0KVWrVs0yAPrfTp06pf84JiaGmzdvUq1aNYP1QQDLBq9lrp6ekUHU4iVKx3mplIsXSQ44BcbGOA4a+OoDBEEQCogogEqIwMBAZs6cydmzZ7l//z5btmwhMjIyW8VJmTJlMDExYdGiRdy5c4cdO3a8cNmRL774giNHjnDlyhX69++Pk5OTmH8pHziPGwtA3I4dpIWEKBvmJaKWLQfAtlMn1KVLK5xGEAThH6IAKiFsbGw4evQoHTp0oHLlynz22Wd8++23tM/GCuPOzs6sWbOGTZs2Ub16dWbPns0333zz3H1nzpzJxIkTqV+/PuHh4ezcuRMTMeGdwZnXqoV1a3+QZSIXLlI6znOlBt8k8Y8/QJJw/HswvSAIQmEhBkGXENWqVWPvC1bd/vc8S0/Nnz8/y+e9evWiV69eWbbJz5mHqEmTJgQEBGBjY1Ok1wIrCpxHjybh4CESDhwg5fJlzP/1VF9hEL088+qPddu2mJYvp3AaQRCErMRvKEEookwrVcL2zU4ARM5foHCarNLv3SN+zx4AnIaKqz+CIBQ+ogAShCLM6YMPQK0m6cQJkgJPKx1HL3rlStDpsGzeDLN/TZMgCIJQWIgCSDCIFi1aIMsydnZ2SkcpUUw8PLDv/jYAkfPnP/e2ZEHThIcTu207AE7DhimcRhAE4flEASQIRZzj8OFIZmakXLhA4pEjSschetUq0GiwqFcPizp1lI4jCILwXKIAEoQiTl2qFA7vZa6vFTl/AfLfE1gqIePJE2J/3QRkFmaCIAiFlSiABKEYcBg0CJWVFWk3bpDwgqf9CsKTH39ETk3FrEYNLBs3UiyHIAjCq4gCSBCKAWN7exwGDgAgcsFC5IyMAs+gTUggZv3PADgOH4aUg6VaBEEQCpoogAShmHDo2w8je3vS790jbtu2Aj9/zM8b0CUkYFKhAtatWhX4+QVBEHJCFEAlSIsWLRg7diwAZcuWfWayQ6FoM7KyxHHYUAAil3yHLi2twM6tS0nhydq1QOa8P5KYBFMQhEJO/JQqoc6cOcPQoUOVjiEYmH2vXhi7upIRFkbsL78U2HljN/2G9skT1KVLY/PGGwV2XkEQhNwSBVAJ5ezsjIWFhdIxBANTmZri9P4IAKKWLkOXlJTv55TT0zMffQcchwxGMhYr7AiCUPiJAqiE+u8tMEmSWLZsGR07dsTCwoJq1aoREBDArVu3aNGiBZaWljRq1Ijbt29naWf79u3UqVMHMzMzypcvz4wZM8hQYACu8A+7rl1Re5VB++QJT9aty/fzxe3YQUZ4OMbOzth27Zrv5xMEQTAEUQAZgCzLJGuSs/1KyUjJ0f4vehl61t/PP/+cvn37EhQURNWqVXn33XcZNmwYkyZN4uzZs8iyzKhRo/T7Hzt2jL59+zJmzBiuXbvGsmXLWLt2Ld9++61Bcwk5I6nVOH8wGoDoH1ahjY3Nt3PJWi1RK1YA4DBgACpT03w7lyAIgiGJa9UGkJKRQoOfGxT4eQPfDcRCbbjbWAMGDKBHjx4ATJgwAT8/PyZPnkzbtm0BGDNmDAMGDNDvP336dCZOnEi/fv0AKF++PNOnT2fChAl8+eWXBssl5JxNh/ZEL19O2s2bRP+wilIfjs+X8yTs24fm3n2MbG2x79kjX84hCIKQH8QVIEHP29tb/7GLiwsAtWrVyrItNTWV+Ph4AC5evMiMGTOwsrLSv4YNG0Z4eDjJyckFG17IQlKpcP77ib8n69aRERlp8HPIskzUsuUA2Pftg8rS0uDnEARByC/iCpABmBubE/huYLb21el0JCQkYG1tjSqPjwqbG5vn6fj/UqvV+o+fTmL3vG26v5daSExMZPr06XTr1k2/j06nIzExETMzM4NmE3LOqmULzH18SLl4kaily3Cd/JlB2088fJi04GBUFhY49O5t0LYFQRDymyiADECSpGzfitLpdGQYZ2ChtshzAaS0OnXqEBwcTMWKFfXbdDod8fHxRb5vxYEkSTiPG8f9/v2J+fVXHAYMwMSjtEHalmWZ6KXLALDr9Q5GdnYGaVcQBKGgiN9SQq5NmTKFH3/8kenTp3P16lWuX7/Oxo0b+eKLL5SOJvzNsmEDLBv5gUZD1JIlBms3OfA0KRcvIpmY4Ni/v8HaFQRBKCiiABJyrW3btvz+++/s37+f+vXr07BhQxYsWICnp6fS0YR/eToWKG77dtL+M41BbkUv//vqz9tvYezsbJA2BUEQCpK4BVaCHD58WP9xaGholvf++0h92bJln9nWokWLZ7a1bdtW/5QY/HMLTCg8zL29sfJvReLBQ0QuXITHgvl5ai/l0iWSTgaAkREOAwcZJqQgCEIBE1eABKEEcB49GiSJhH37SLlyNU9tPX3yy7ZTJ4ONKRIEQShoogAShBLArHJlbDp1BCBywYJct5N68yaJhw6BJOE4dIih4gmCIBQ4UQAJQgnhPGoUGBuTdOwYyWfO5KqN6OWZsz5bt2mDafnyhownCIJQoEQBJAglhEmZMti9/RYAj+fNz/FSKun37xO/ezeAuPojCEKRJwogQShBnEaMQDI1JeX8eZKOHs3RsdErVoJOh2XTppjXqJFPCQVBEAqGKIAEoQRRu7hg//eszY/nL0D+e1bvV9FERBC7bRsATsOH5Vc8QRCEAiMKIEEoYRyHDEZlaUna9esk7NuXrWOerFoNGg3m9epiUbduPicUBEHIf6IAEoQSxtjeHoeBAwCIXLAQOSPjpftnxMQQ8+uvADgNG57v+QRBEAqCKIAEoQRy6NcfI3t70kNDidu+/aX7PvnxR+SUFMyqV8eySeMCSigIgpC/RAEk5DtJktj29/gRoXAwsrLEcehQACIXL0GXnv7c/bQJCcT8tB4Ax2HDkCSpwDIKgiDkJ1EACYXOtGnTqF27ttIxij37Xu9g7OJCRlgYsRt/ee4+MRs2oktIwKR8eaxb+xdwQkEQhPwjCqASLP0Ff/ULJYPKzAyn998HIGrZMnRJSVne16Wk8GTNGiBz3h9JJX5cCIJQfIifaCVIixYtGDVqFGPHjsXJyYm2bdsyd+5catWqhaWlJZ6enrz//vskJiYCmQukOjs789tvv+nbqF27Nm5ubvrPjx8/jqmpKcnJyQCEhITQoUMHLCwsqF69OgcOHHgmx4QJE6hcuTIWFhaUL1+eyZMno9FoAFizZg3Tp0/n4sWLSJKEJEms+fuX8MuyCrlj160r6jJl0EZH82TdT1nei9+yFe2TJ6hLl8b2jTcUSigIgpA/RAFkALIso0tOzv4rJSVn+7/gldOZfAHWrl2LiYkJJ06cYOnSpahUKhYuXMjVq1dZu3Ytf/zxB5988gmQOXanWbNm+lXkY2JiuH79OikpKdy4cQOAI0eOUL9+fSwsLNDpdLz99tuYmJgQEBDA0qVLmTBhwjMZrK2tWbNmDdeuXWPBggWsWLGCefPmAdCzZ08+/PBDatSoQVhYGGFhYfTs2RPgpVmF3JHUapw/+ACA6B9+QBsXl/lGRgaxT6/+DB6EpFYrlFAQBCF/GCsdoDiQU1IIrpOzuVEiDHDeKufPIVlY5OiYSpUq8dVXX/3TRpUq+o/Lli3LF198wfDhw/nuu++AzKtGy5YtA+Do0aP4+vri6urK4cOHqVq1KocPH6Z58+YAHDx4kBs3bnDp0iWqVKmCSqVi5syZtG/fPkuGzz77LMs5P/roIzZu3Mgnn3yCubk5VlZWGBsb4+rqmuW4sWPHvjSrkDs2b3QgesUK0m7eJPqHVdh/MAqbC0FkhIdj5OyEbbduSkcUBEEwOHEFqISp+59J7A4ePEirVq0oXbo01tbW9OnTh+joaP0trebNm3Pt2jUiIyM5cuQILVq0oEWLFhw+fBiNRsPJkydp0aIFANevX8fT0zPLLTI/P79nMvzyyy80btwYV1dXrKys+Oyzz7h///4rs78qq5A7kkqF89gxADxZt46Mx49x+Puqn2P/AahMTRVMJwiCkD/EFSADkMzNqXL+XLb21el0xCckYGNtjSqPg0olc/McH2Npaan/ODQ0lI4dOzJixAi+/PJLHBwcOH78OIMGDSI9PR0LCwtq1aqFg4MDR44c4ciRI3z55Ze4uroyZ84czpw5g0ajoVGjRtk+f0BAAL1792b69Om0bdsWW1tbNm7cyLfffvvS47KTVcg9q5YtMfPxJvXiJR4NG45JVBQqGxvs/r79KAiCUNyIAsgAJEnK/q0onQ5VRgYqC4s8F0B5de7cOXQ6Hd9++60+y69/z/j7lCRJNG3alO3bt3P16lWaNGmChYUFaWlpLFu2jHr16umLqmrVqvHgwQPCw8OxsbEB4NSpU1naO3nyJF5eXnz66af6bffu3cuyj4mJCVqtNsdZhdyTJIlS48Zxv/8A0m/dAsC297sYWVm+4khBEISiSdwCK8EqVqyIRqNh0aJF3Llzh3Xr1rF06dJn9mvRogUbNmygdu3aWFlZoVKpaNasGevXr9eP/wHw9/encuXKvP/++1y8eJFjx45lKXQgcwzS/fv32bhxI7dv32bhwoVs3bo1yz5ly5bl7t27BAUFERUVRVpaWrazCrln2bAhFn4NAdCZmGD396KpgiAIxZEogEowHx8f5s6dy5w5c6hZsybr169n1qxZz+zXvHlztFqtfqwPZBZF/92mUqnYvHkzKSkpNGzYkMGDB/Pll19maevNN99k3LhxjBo1itq1a3Py5EkmT56cZZ+33nqLdu3a0bJlS5ydndmwYUO2swp54zJxImpPT6LatcXI1lbpOIIgCPlGknPzLHUxFx8fj62tLXFxcfpbOU+lpqZy9+5dypUrh5mZWY7b1ul0xMfHY2Njo/gtsPxQXPr3ou+zRqNh9+7ddOjQAXUxfTS8uPdR9K/oK+59FP3LvZf9/v6vovsbShAEQRAEIZdEASQIgiAIQokjCiBBEARBEEocUQAJgiAIglDiKF4ALVmyhLJly2JmZkaDBg04ffp0to7buHEjkiTRpUuXLNsjIiLo378/7u7uWFhY0K5dO0JCQvIhuSAIgiAIRZWiBdAvv/zC+PHjmTp1KufPn8fHx4e2bdvy+PHjlx4XGhrKRx99RNOmTbNsl2WZLl26cOfOHbZv386FCxfw8vLC39+fpKQkg2YXD88Vb+L7KwiCULwpWgDNnTuXIUOGMGDAAKpXr87SpUuxsLBg1apVLzxGq9Xql1IoX758lvdCQkI4deoU33//PfXr16dKlSp8//33pKSksGHDBoNkfvrInlh/qnhLT08HwMjISOEkgiAIQn5QbCmM9PR0zp07x6RJk/TbVCoV/v7+BAQEvPC4GTNmUKpUKQYNGsSxY8eyvJeWlgaQZd4WlUqFqakpx48fZ/Dgwc9tMy0tTX8sZM4jAJlzFWg0mmf2t7a2JiIiAp1Oh4WFBZIkZaPHmWRZJj09nZSUlBwdV1QUh/7pdDoeP36MmZkZsixn+Tfw9OPn/bsoLop7H0X/ir7i3kfRv7y3nR2KFUBRUVFotVpcXFyybHdxceHGjRvPPeb48eP88MMPBAUFPff9qlWrUqZMGSZNmsSyZcuwtLRk3rx5/PXXX4SFhb0wy6xZs5g+ffoz2/fv3//CRTatra1JSkoq0pP9CS+m0WiIjIzk0qVLz33/wIEDBZyo4BX3Por+FX3FvY+ifzmXk7szRWYx1ISEBPr06cOKFStwcnJ67j5qtZotW7YwaNAgHBwcMDIywt/fn/bt2790TMekSZMYP368/vP4+Hg8PT1p06bNS2eS1Gq1ZGRk5Gi8SEZGBidPnqRRo0YYGxeZL3+2FYf+SZKEWq1+bnGr0Wg4cOAArVu3LpYztELx76PoX9FX3Pso+pd7T+/gZIdiv6GcnJwwMjIiIiIiy/aIiAhcXV2f2f/27duEhobSqVMn/TadTgeAsbExwcHBVKhQgbp16xIUFERcXBzp6ek4OzvToEED6tWr98IspqammJqaPrNdrVa/9JuTm2+cRqMhIyMDKyurYvsPuzj376lX/dsoDop7H0X/ir7i3kfRv9y1mV2K3b8xMTGhbt26HDp0SL9Np9Nx6NAh/Pz8ntm/atWqXL58maCgIP3rzTffpGXLlgQFBeHp6Zllf1tbW5ydnQkJCeHs2bN07tw53/skCIIgCELRoOg9ivHjx9OvXz/q1avHa6+9xvz580lKSmLAgAEA9O3bl9KlSzNr1izMzMyoWbNmluPt7OwAsmzftGkTzs7OlClThsuXLzNmzBi6dOlCmzZtCqxfgiAIgiAUbooWQD179iQyMpIpU6YQHh5O7dq12bt3r35g9P3793M8yDgsLIzx48cTERGBm5sbffv2ZfLkyfkRXxAEQRCEIkrxUaqjRo1i1KhRz33v8OHDLz12zZo1z2wbPXo0o0ePzlOmp4OaczKYKrs0Gg3JycnEx8cXy3u7on9FX3Hvo+hf0Vfc+yj6l3tPf29n5+EkxQugwighIQHgmXFFgiAIgiAUfgkJCdja2r50H0kWc/4/Q6fT8ejRI6ytrQ0+md/TR+wfPHjw0kfsiyrRv6KvuPdR9K/oK+59FP3LPVmWSUhIwN3d/ZVDaMQVoOdQqVR4eHjk6zlsbGyK5T/sp0T/ir7i3kfRv6KvuPdR9C93XnXl5ykxjbEgCIIgCCWOKIAEQRAEQShxRAFUwExNTZk6depzZ54uDkT/ir7i3kfRv6KvuPdR9K9giEHQgiAIgiCUOOIKkCAIgiAIJY4ogARBEARBKHFEASQIgiAIQokjCiBBEARBEEocUQAVgFmzZlG/fn2sra0pVaoUXbp0ITg4WOlYBvX999/j7e2tn9jKz8+PPXv2KB0r38yePRtJkhg7dqzSUQxi2rRpSJKU5VW1alWlYxncw4cPee+993B0dMTc3JxatWpx9uxZpWMZRNmyZZ/5HkqSxMiRI5WOZhBarZbJkydTrlw5zM3NqVChAp9//nm21nwqShISEhg7dixeXl6Ym5vTqFEjzpw5o3SsXDl69CidOnXC3d0dSZLYtm1blvdlWWbKlCm4ublhbm6Ov78/ISEhBZZPFEAF4MiRI4wcOZJTp05x4MABNBoNbdq0ISkpSeloBuPh4cHs2bM5d+4cZ8+e5fXXX6dz585cvXpV6WgGd+bMGZYtW4a3t7fSUQyqRo0ahIWF6V/Hjx9XOpJBxcTE0LhxY9RqNXv27OHatWt8++232NvbKx3NIM6cOZPl+3fgwAEAunfvrnAyw5gzZw7ff/89ixcv5vr168yZM4evvvqKRYsWKR3NoAYPHsyBAwdYt24dly9fpk2bNvj7+/Pw4UOlo+VYUlISPj4+LFmy5Lnvf/XVVyxcuJClS5cSGBiIpaUlbdu2JTU1tWACykKBe/z4sQzIR44cUTpKvrK3t5dXrlypdAyDSkhIkCtVqiQfOHBAbt68uTxmzBilIxnE1KlTZR8fH6Vj5KsJEybITZo0UTpGgRkzZoxcoUIFWafTKR3FIN544w154MCBWbZ169ZN7t27t0KJDC85OVk2MjKSf//99yzb69SpI3/66acKpTIMQN66dav+c51OJ7u6uspff/21fltsbKxsamoqb9iwoUAyiStACoiLiwPAwcFB4ST5Q6vVsnHjRpKSkvDz81M6jkGNHDmSN954A39/f6WjGFxISAju7u6UL1+e3r17c//+faUjGdSOHTuoV68e3bt3p1SpUvj6+rJixQqlY+WL9PR0fvrpJwYOHGjwBZ2V0qhRIw4dOsTNmzcBuHjxIsePH6d9+/YKJzOcjIwMtFotZmZmWbabm5sXuyuyd+/eJTw8PMvPUltbWxo0aEBAQECBZBCLoRYwnU7H2LFjady4MTVr1lQ6jkFdvnwZPz8/UlNTsbKyYuvWrVSvXl3pWAazceNGzp8/X2Tvx79MgwYNWLNmDVWqVCEsLIzp06fTtGlTrly5grW1tdLxDOLOnTt8//33jB8/nv/973+cOXOG0aNHY2JiQr9+/ZSOZ1Dbtm0jNjaW/v37Kx3FYCZOnEh8fDxVq1bFyMgIrVbLl19+Se/evZWOZjDW1tb4+fnx+eefU61aNVxcXNiwYQMBAQFUrFhR6XgGFR4eDoCLi0uW7S4uLvr38psogArYyJEjuXLlSrGr5gGqVKlCUFAQcXFx/Pbbb/Tr148jR44UiyLowYMHjBkzhgMHDjzz11lx8O+/or29vWnQoAFeXl78+uuvDBo0SMFkhqPT6ahXrx4zZ84EwNfXlytXrrB06dJiVwD98MMPtG/fHnd3d6WjGMyvv/7K+vXr+fnnn6lRowZBQUGMHTsWd3f3YvX9W7duHQMHDqR06dIYGRlRp04devXqxblz55SOVuyIW2AFaNSoUfz+++/8+eefeHh4KB3H4ExMTKhYsSJ169Zl1qxZ+Pj4sGDBAqVjGcS5c+d4/PgxderUwdjYGGNjY44cOcLChQsxNjZGq9UqHdGg7OzsqFy5Mrdu3VI6isG4ubk9U4xXq1at2N3qu3fvHgcPHmTw4MFKRzGojz/+mIkTJ/LOO+9Qq1Yt+vTpw7hx45g1a5bS0QyqQoUKHDlyhMTERB48eMDp06fRaDSUL19e6WgG5erqCkBERESW7REREfr38psogAqALMuMGjWKrVu38scff1CuXDmlIxUInU5HWlqa0jEMolWrVly+fJmgoCD9q169evTu3ZugoCCMjIyUjmhQiYmJ3L59Gzc3N6WjGEzjxo2fmX7i5s2beHl5KZQof6xevZpSpUrxxhtvKB3FoJKTk1Gpsv7KMjIyQqfTKZQof1laWuLm5kZMTAz79u2jc+fOSkcyqHLlyuHq6sqhQ4f02+Lj4wkMDCywsaPiFlgBGDlyJD///DPbt2/H2tpaf3/T1tYWc3NzhdMZxqRJk2jfvj1lypQhISGBn3/+mcOHD7Nv3z6loxmEtbX1M2O2LC0tcXR0LBZjuT766CM6deqEl5cXjx49YurUqRgZGdGrVy+loxnMuHHjaNSoETNnzqRHjx6cPn2a5cuXs3z5cqWjGYxOp2P16tX069cPY+Pi9eO9U6dOfPnll5QpU4YaNWpw4cIF5s6dy8CBA5WOZlD79u1DlmWqVKnCrVu3+Pjjj6latSoDBgxQOlqOJSYmZrmKfPfuXYKCgnBwcKBMmTKMHTuWL774gkqVKlGuXDkmT56Mu7s7Xbp0KZiABfKsWQkHPPe1evVqpaMZzMCBA2UvLy/ZxMREdnZ2llu1aiXv379f6Vj5qjg9Bt+zZ0/Zzc1NNjExkUuXLi337NlTvnXrltKxDG7nzp1yzZo1ZVNTU7lq1ary8uXLlY5kUPv27ZMBOTg4WOkoBhcfHy+PGTNGLlOmjGxmZiaXL19e/vTTT+W0tDSloxnUL7/8IpcvX142MTGRXV1d5ZEjR8qxsbFKx8qVP//887m/+/r16yfLcuaj8JMnT5ZdXFxkU1NTuVWrVgX6b1eS5WI2jaYgCIIgCMIriDFAgiAIgiCUOKIAEgRBEAShxBEFkCAIgiAIJY4ogARBEARBKHFEASQIgiAIQokjCiBBEARBEEocUQAJgiAIglDiiAJIEIQCExoaiiRJBAUFKR1F78aNGzRs2BAzMzNq166dp7YkSWLbtm0GySUIQv4SBZAglCD9+/dHkiRmz56dZfu2bduQJEmhVMqaOnUqlpaWBAcHZ1mX6L/Cw8P54IMPKF++PKampnh6etKpU6eXHpMXhw8fRpIkYmNj86V9QSjpRAEkCCWMmZkZc+bMISYmRukoBpOenp7rY2/fvk2TJk3w8vLC0dHxufuEhoZSt25d/vjjD77++msuX77M3r17admyJSNHjsz1uQuCLMtkZGQoHUMQCh1RAAlCCePv74+rqyuzZs164T7Tpk175nbQ/PnzKVu2rP7z/v3706VLF2bOnImLiwt2dnbMmDGDjIwMPv74YxwcHPDw8GD16tXPtH/jxg0aNWqEmZkZNWvW5MiRI1nev3LlCu3bt8fKygoXFxf69OlDVFSU/v0WLVowatQoxo4di5OTE23btn1uP3Q6HTNmzMDDwwNTU1Nq167N3r179e9LksS5c+eYMWMGkiQxbdq057bz/vvvI0kSp0+f5q233qJy5crUqFGD8ePHc+rUqece87wrOEFBQUiSRGhoKAD37t2jU6dO2NvbY2lpSY0aNdi9ezehoaG0bNkSAHt7eyRJon///vo+zZo1i3LlymFubo6Pjw+//fbbM+fds2cPdevWxdTUlOPHj3Px4kVatmyJtbU1NjY21K1bl7Nnzz43uyCUBKIAEoQSxsjIiJkzZ7Jo0SL++uuvPLX1xx9/8OjRI44ePcrcuXOZOnUqHTt2xN7ensDAQIYPH86wYcOeOc/HH3/Mhx9+yIULF/Dz86NTp05ER0cDEBsby+uvv46vry9nz55l7969RERE0KNHjyxtrF27FhMTE06cOMHSpUufm2/BggV8++23fPPNN1y6dIm2bdvy5ptvEhISAkBYWBg1atTgww8/JCwsjI8++uiZNp48ecLevXsZOXIklpaWz7xvZ2eXmy8dACNHjiQtLY2jR49y+fJl5syZg5WVFZ6enmzevBmA4OBgwsLCWLBgAQCzZs3ixx9/ZOnSpVy9epVx48bx3nvvPVNETpw4kdmzZ3P9+nW8vb3p3bs3Hh4enDlzhnPnzjFx4kTUanWuswtCkVdgy64KgqC4fv36yZ07d5ZlWZYbNmwoDxw4UJZlWd66dav87x8HU6dOlX18fLIcO2/ePNnLyytLW15eXrJWq9Vvq1Klity0aVP95xkZGbKlpaW8YcMGWZZl+e7duzIgz549W7+PRqORPTw85Dlz5siyLMuff/653KZNmyznfvDgQZZVzps3by77+vq+sr/u7u7yl19+mWVb/fr15ffff1//uY+Pjzx16tQXthEYGCgD8pYtW155PkDeunWrLMv/rIQdExOjf//ChQsyIN+9e1eWZVmuVauWPG3atOe29bzjU1NTZQsLC/nkyZNZ9h00aJDcq1evLMdt27Ytyz7W1tbymjVrXtkHQSgpjBWrvARBUNScOXN4/fXXn3vVI7tq1KiBSvXPhWQXFxdq1qyp/9zIyAhHR0ceP36c5Tg/Pz/9x8bGxtSrV4/r168DcPHiRf7880+srKyeOd/t27epXLkyAHXr1n1ptvj4eB49ekTjxo2zbG/cuDEXL17MZg8zx9Dkl9GjRzNixAj279+Pv78/b731Ft7e3i/c/9atWyQnJ9O6dess29PT0/H19c2yrV69elk+Hz9+PIMHD2bdunX4+/vTvXt3KlSoYLjOCEIRI26BCUIJ1axZM9q2bcukSZOeeU+lUj3zi1+j0Tyz339voUiS9NxtOp0u27kSExPp1KkTQUFBWV4hISE0a9ZMv9/zbkflh0qVKiFJEjdu3MjRcU8Lw39/Hf/7NRw8eDB37tyhT58+XL58mXr16rFo0aIXtpmYmAjArl27snxtrl27lmUcEDz79Zk2bRpXr17ljTfe4I8//qB69eps3bo1R30ShOJEFECCUILNnj2bnTt3EhAQkGW7s7Mz4eHhWX55G3Lunn8PHM7IyODcuXNUq1YNgDp16nD16lXKli1LxYoVs7xyUvTY2Njg7u7OiRMnsmw/ceIE1atXz3Y7Dg4OtG3bliVLlpCUlPTM+y96TN3Z2RnIHGf01PO+hp6engwfPpwtW7bw4YcfsmLFCgBMTEwA0Gq1+n2rV6+Oqakp9+/ff+Zr4+np+cq+VK5cmXHjxrF//366dev23AHqglBSiAJIEEqwWrVq0bt3bxYuXJhle4sWLYiMjOSrr77i9u3bLFmyhD179hjsvEuWLGHr1q3cuHGDkSNHEhMTw8CBA4HMgcFPnjyhV69enDlzhtu3b7Nv3z4GDBiQpRjIjo8//pg5c+bwyy+/EBwczMSJEwkKCmLMmDE5zqvVannttdfYvHkzISEhXL9+nYULF2a5nfdvT4uSadOmERISwq5du/j222+z7DN27Fj27dvH3bt3OX/+PH/++ae+EPTy8kKSJH7//XciIyNJTEzE2tqajz76iHHjxrF27Vpu377N+fPnWbRoEWvXrn1h/pSUFEaNGsXhw4e5d+8eJ06c4MyZM/pzCUJJJAogQSjhZsyY8cwtqmrVqvHdd9+xZMkSfHx8OH36dJ7GCv3X7NmzmT17Nj4+Phw/fpwdO3bg5OQEoL9qo9VqadOmDbVq1WLs2LHY2dllGW+UHaNHj2b8+PF8+OGH1KpVi71797Jjxw4qVaqUo3bKly/P+fPnadmyJR9++CE1a9akdevWHDp0iO+///65x6jVajZs2MCNGzfw9vZmzpw5fPHFF1n20Wq1jBw5kmrVqtGuXTsqV67Md999B0Dp0qWZPn06EydOxMXFhVGjRgHw+eefM3nyZGbNmqU/bteuXZQrV+6F+Y2MjIiOjqZv375UrlyZHj160L59e6ZPn56jr4MgFCeSnJ8j/ARBEARBEAohcQVIEARBEIQSRxRAgiAIgiCUOKIAEgRBEAShxBEFkCAIgiAIJY4ogARBEARBKHFEASQIgiAIQokjCiBBEARBEEocUQAJgiAIglDiiAJIEARBEIQSRxRAgiAIgiCUOKIAEgRBEAShxBEFkCAIgiAIJc7/AUvaIPFTXp27AAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the data, with nclust on x-axis and the rest being lines\n", + "i = 0\n", + "for data_result in data_results:\n", + " data_result = data_result.set_index(\"nclust\")\n", + " data_result.plot(grid=True)\n", + " # y axis label is RMSE\n", + " plt.ylabel(\"RMSE\")\n", + " # x axis label is number of clusters\n", + " plt.xlabel(\"Number of Clusters\")\n", + " # title is the dataid\n", + " plt.title(\"DataID: \" + dataids[i])\n", + " # move legend to side\n", + " # plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + " # remove legend\n", + " #plt.legend().remove()\n", + " # show legend\n", + " plt.legend()\n", + " plt.show()\n", + " i+=1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nclustlmdi_lassoshaplimerawdata
020.8310650.8350680.8104000.823859
130.8350680.8414730.8224000.812650
240.8510810.8502800.8224000.809448
350.8598880.8502800.8528000.819856
460.8414730.8509620.8526820.800641
570.8486790.8581730.8397440.841473
680.8502800.8445510.8444270.802885
790.8429490.8397440.8380110.813152
8100.8461540.8517630.8468320.813953
\n", + "
" + ], + "text/plain": [ + " nclust lmdi_lasso shap lime rawdata\n", + "0 2 0.831065 0.835068 0.810400 0.823859\n", + "1 3 0.835068 0.841473 0.822400 0.812650\n", + "2 4 0.851081 0.850280 0.822400 0.809448\n", + "3 5 0.859888 0.850280 0.852800 0.819856\n", + "4 6 0.841473 0.850962 0.852682 0.800641\n", + "5 7 0.848679 0.858173 0.839744 0.841473\n", + "6 8 0.850280 0.844551 0.844427 0.802885\n", + "7 9 0.842949 0.839744 0.838011 0.813152\n", + "8 10 0.846154 0.851763 0.846832 0.813953" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_results[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the data, only shap and aloo_l2_unsigned_nonnormed_leafavg_rank\n", + "# i=0\n", + "# for data_result in data_results:\n", + "# data_result = data_result.set_index(\"nclust\")\n", + "# data_result[[\"shap\", \"nonloo_l2_signed_nonnormed_leafavg_rank\"]].plot(grid=True)\n", + "# # move legend to side\n", + "# plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "# # call aloo_l2_unsigned_nonnormed_leafavg_rank lmdi+\n", + "# plt.legend([\"SHAP\", \"LMDI+\"])\n", + "# # y axis label is RMSE\n", + "# plt.ylabel(\"RMSE\")\n", + "# # x axis label is number of clusters\n", + "# plt.xlabel(\"Number of Clusters\")\n", + "# # title is the dataid\n", + "# plt.title(\"DataID: \" + dataids[i])\n", + "# plt.show()\n", + "# i+=1" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "auroc_ranks = []\n", + "for data_result in data_results:\n", + " auroc = data_result.drop(\"nclust\", axis=1).apply(np.trapz, x=data_result[\"nclust\"])\n", + " # convert to ranking, i.e. 1 is lowest, 2 is second lowest, etc.\n", + " auroc_rank = auroc.rank()\n", + " auroc_ranks.append(auroc_rank)\n", + "# merge series in auroc_ranks by averaging the ranks corresponding to the same names\n", + "auroc_ranks = pd.concat(auroc_ranks, axis=1)\n", + "auroc_ranks_mean = auroc_ranks.mean(axis=1)\n", + "auroc_ranks_median = auroc_ranks.median(axis=1)\n", + "auroc_ranks_mean = auroc_ranks_mean.sort_values()\n", + "auroc_ranks_median = auroc_ranks_median.sort_values()\n", + "auroc_ranks_mean.plot(kind=\"bar\")\n", + "plt.show()\n", + "auroc_ranks_median.plot(kind=\"bar\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 ... 23 24 \\\n", + "lmdi_lasso 3.0 3.0 2.0 4.0 4.0 4.0 3.0 4.0 3.0 1.0 ... 3.0 2.0 \n", + "shap 4.0 2.0 3.0 2.0 3.0 1.0 4.0 2.0 2.0 2.0 ... 4.0 4.0 \n", + "lime 2.0 1.0 1.0 3.0 2.0 3.0 1.0 3.0 1.0 4.0 ... 2.0 1.0 \n", + "rawdata 1.0 4.0 4.0 1.0 1.0 2.0 2.0 1.0 4.0 3.0 ... 1.0 3.0 \n", + "\n", + " 25 26 27 28 29 30 31 32 \n", + "lmdi_lasso 1.0 2.0 4.0 3.0 1.0 3.0 3.0 2.0 \n", + "shap 2.0 4.0 3.0 4.0 3.0 4.0 4.0 4.0 \n", + "lime 4.0 3.0 2.0 1.0 2.0 1.0 2.0 1.0 \n", + "rawdata 3.0 1.0 1.0 2.0 4.0 2.0 1.0 3.0 \n", + "\n", + "[4 rows x 33 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auroc_ranks" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2drbrdWZmpvcKBgAAfsWqPTIZGRlyOByqWrVqoX0SExMVFhbmOqKjo8uvQAAAUK6sCTLnz5/XM888o3vvvVehoaGF9hs7dqwyMjJcR1paWjlWCQAAypNPby0VV05OjgYOHChjjObMmVNk34CAAAUEBJRTZQAAwJf8PshcCjGHDx/WunXrilyNAQAA1xa/DjKXQsy+ffu0fv161ahRw9clAQAAP+LTIHP27Fnt37/f9To1NVUpKSmqXr26atWqpT/96U/atm2bli9frtzcXB0/flySVL16dTmdTl+VDQAA/IRPg8yWLVvUtWtX1+vRo0dLkuLj4zV+/Hh99NFHkqTWrVu7Xbd+/Xp16dKlvMoEAAB+yqdBpkuXLjLGFHq+qHMAAADWPH4NAABwOYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWMunQWbTpk3q27evateuLYfDoQ8//NDtvDFGzz//vGrVqqWgoCD16NFD+/bt802xAADA7/g0yGRlZalVq1aaNWtWgeenTZumV199VXPnztU333yj4OBg9erVS+fPny/nSgEAgD+q5MsP7927t3r37l3gOWOMZsyYof/+7/9Wv379JEkLFy5UZGSkPvzwQ91zzz3lWSoAAPBDfrtHJjU1VcePH1ePHj1cbWFhYbr55pv11VdfFXpddna2MjMz3Q4AAHB18tsgc/z4cUlSZGSkW3tkZKTrXEESExMVFhbmOqKjo8u0TgAA4Dt+G2Q8NXbsWGVkZLiOtLQ0X5cEAADKiN8GmZo1a0qSTpw44dZ+4sQJ17mCBAQEKDQ01O0AAABXJ78NMnXr1lXNmjW1du1aV1tmZqa++eYbdejQwYeVAQAAf+HTp5bOnj2r/fv3u16npqYqJSVF1atXV506dZSQkKBJkyapYcOGqlu3rp577jnVrl1b/fv3913RAADAb/g0yGzZskVdu3Z1vR49erQkKT4+XsnJyXr66aeVlZWlRx55RL/88otuueUWrVq1SoGBgb4qGQAA+BGHMcb4uoiylJmZqbCwMGVkZLBfBoBfiB2zwtcllNihKXG+LgHXmOL+/PbbPTIAAABXQpABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAa/l1kMnNzdVzzz2nunXrKigoSPXr19fEiRNljPF1aQAAwA9U8nUBRZk6darmzJmjBQsWqFmzZtqyZYuGDh2qsLAwPf74474uDwAA+JhfB5kvv/xS/fr1U1xcnCQpNjZW7777rr799lsfVwYAAPyBR7eWDh486O06CtSxY0etXbtWP/zwgyRpx44d+vzzz9W7d+9Cr8nOzlZmZqbbAQAArk4eBZkGDRqoa9euevvtt3X+/Hlv1+QyZswY3XPPPWrSpIkqV66sG2+8UQkJCRo0aFCh1yQmJiosLMx1REdHl1l9AADAtzwKMtu2bVPLli01evRo1axZU8OHDy+T2z3vv/++Fi1apHfeeUfbtm3TggUL9OKLL2rBggWFXjN27FhlZGS4jrS0NK/XBQAA/IPDlOIRoIsXL+qjjz5ScnKyVq1apUaNGunBBx/UAw88oPDw8FIXFx0drTFjxmjEiBGutkmTJuntt9/Wnj17ivUemZmZCgsLU0ZGhkJDQ0tdEwCUVuyYFb4uocQOTYnzdQm4xhT353epHr+uVKmS7rrrLi1ZskRTp07V/v379eSTTyo6OlqDBw/WsWPHSvP2OnfunCpUcC+xYsWKysvLK9X7AgCAq0OpgsyWLVv02GOPqVatWpo+fbqefPJJHThwQGvWrNHRo0fVr1+/UhXXt29f/f3vf9eKFSt06NAhLVu2TNOnT9eAAQNK9b4AAODq4NHj19OnT1dSUpL27t2rPn36aOHCherTp49r9aRu3bpKTk5WbGxsqYp77bXX9Nxzz+mxxx5Tenq6ateureHDh+v5558v1fsCAICrg0dBZs6cOXrwwQc1ZMgQ1apVq8A+EREReuONN0pVXEhIiGbMmKEZM2aU6n0AAMDVyaMgs2/fviv2cTqdio+P9+TtAQAAisWjPTJJSUlasmRJvvYlS5YU+Wg0AACAN3kUZBITE3X99dfna4+IiNDkyZNLXRQAAEBxeBRkjhw5orp16+Zrj4mJ0ZEjR0pdFAAAQHF4FGQiIiK0c+fOfO07duxQjRo1Sl0UAABAcXgUZO699149/vjjWr9+vXJzc5Wbm6t169Zp1KhRuueee7xdIwAAQIE8empp4sSJOnTokLp3765KlX57i7y8PA0ePJg9MgAAoNx4FGScTqfee+89TZw4UTt27FBQUJBatGihmJgYb9cHAABQKI+CzCWNGjVSo0aNvFULAABAiXgUZHJzc5WcnKy1a9cqPT093x9xXLdunVeKAwAAKIpHQWbUqFFKTk5WXFycmjdvLofD4e26AAAArsijILN48WK9//776tOnj7frAQAAKDaPHr92Op1q0KCBt2sBAAAoEY+CzN/+9je98sorMsZ4ux4AAIBi8+jW0ueff67169dr5cqVatasmSpXrux2funSpV4pDgAAoCgeBZmqVatqwIAB3q4FAACgRDwKMklJSd6uAwAAoMQ82iMjSRcvXtSnn36qefPm6cyZM5Kko0eP6uzZs14rDgAAoCgercgcPnxYt99+u44cOaLs7GzddtttCgkJ0dSpU5Wdna25c+d6u04AAIB8PFqRGTVqlNq1a6fTp08rKCjI1T5gwACtXbvWa8UBAAAUxaMVmc8++0xffvmlnE6nW3tsbKz+/e9/e6UwG8SOWeHrEjxyaEqcr0u4Jtg4P5gbAGzj0YpMXl6ecnNz87X/+OOPCgkJKXVRAAAAxeFRkOnZs6dmzJjheu1wOHT27FmNGzeOP1sAAADKjUe3ll566SX16tVLTZs21fnz53Xfffdp3759uv766/Xuu+96u0YAAIACeRRkoqKitGPHDi1evFg7d+7U2bNnNWzYMA0aNMht8y8AAEBZ8ijISFKlSpV0//33e7MWAACAEvEoyCxcuLDI84MHD/aoGAAAgJLwKMiMGjXK7XVOTo7OnTsnp9Op6667jiADAADKhUdPLZ0+fdrtOHv2rPbu3atbbrmFzb4AAKDcePy3li7XsGFDTZkyJd9qDQAAQFnxWpCRftsAfPToUW++JQAAQKE82iPz0Ucfub02xujYsWOaOXOmOnXq5JXCAAAArsSjINO/f3+31w6HQ+Hh4erWrZteeuklb9QFAABwRR4Fmby8PG/XAQAAUGJe3SMDAABQnjxakRk9enSx+06fPt2TjwAAALgij4LM9u3btX37duXk5Khx48aSpB9++EEVK1ZUmzZtXP0cDod3qgQAACiAR0Gmb9++CgkJ0YIFC1StWjVJv/2SvKFDh+rWW2/V3/72N68WCQAAUBCP9si89NJLSkxMdIUYSapWrZomTZrEU0sAAKDceBRkMjMzdfLkyXztJ0+e1JkzZ0pdFAAAQHF4FGQGDBigoUOHaunSpfrxxx/1448/6p///KeGDRumu+66y9s1AgAAFMijPTJz587Vk08+qfvuu085OTm/vVGlSho2bJheeOEFrxYIAABQGI+CzHXXXafZs2frhRde0IEDByRJ9evXV3BwsFeLAwAAKEqpfiHesWPHdOzYMTVs2FDBwcEyxnirLpd///vfuv/++1WjRg0FBQWpRYsW2rJli9c/BwAA2MejFZlTp05p4MCBWr9+vRwOh/bt26d69epp2LBhqlatmteeXDp9+rQ6deqkrl27auXKlQoPD9e+ffvcnpYCAADXLo9WZJ544glVrlxZR44c0XXXXedqv/vuu7Vq1SqvFTd16lRFR0crKSlJ7du3V926ddWzZ0/Vr1/fa58BAADs5VGQWb16taZOnaqoqCi39oYNG+rw4cNeKUySPvroI7Vr105//vOfFRERoRtvvFH/+Mc/irwmOztbmZmZbgcAALg6eXRrKSsry20l5pKff/5ZAQEBpS7qkoMHD2rOnDkaPXq0/uu//kubN2/W448/LqfTqfj4+AKvSUxM1IQJE7xWAwAA5SV2zApfl1Bih6bE+fTzPVqRufXWW7Vw4ULXa4fDoby8PE2bNk1du3b1WnF5eXlq06aNJk+erBtvvFGPPPKIHn74Yc2dO7fQa8aOHauMjAzXkZaW5rV6AACAf/FoRWbatGnq3r27tmzZogsXLujpp5/W7t279fPPP+uLL77wWnG1atVS06ZN3dpuuOEG/fOf/yz0moCAAK+uCgEAAP/l0YpM8+bN9cMPP+iWW25Rv379lJWVpbvuukvbt2/36kbcTp06ae/evW5tP/zwg2JiYrz2GQAAwF4lXpHJycnR7bffrrlz5+rZZ58ti5pcnnjiCXXs2FGTJ0/WwIED9e2332r+/PmaP39+mX4uAACwQ4lXZCpXrqydO3eWRS353HTTTVq2bJneffddNW/eXBMnTtSMGTM0aNCgcvl8AADg3zy6tXT//ffrjTfe8HYtBbrjjju0a9cunT9/Xt9//70efvjhcvlcAADg/zza7Hvx4kW9+eab+vTTT9W2bdt8f2Np+vTpXikOAACgKCUKMgcPHlRsbKy+++47tWnTRtJvm29/z+FweK86AACAIpQoyDRs2FDHjh3T+vXrJf32JwleffVVRUZGlklxAAAARSnRHpnL/7r1ypUrlZWV5dWCAAAAisujzb6XXB5sAAAAylOJgozD4ci3B4Y9MQAAwFdKtEfGGKMhQ4a4/gTA+fPn9Ze//CXfU0tLly71XoUAAACFKFGQufwvTt9///1eLQYAAKAkShRkkpKSyqoOAACAEivVZl8AAABfIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGtZFWSmTJkih8OhhIQEX5cCAAD8gDVBZvPmzZo3b55atmzp61IAAICfsCLInD17VoMGDdI//vEPVatWzdflAAAAP2FFkBkxYoTi4uLUo0ePK/bNzs5WZmam2wEAAK5OlXxdwJUsXrxY27Zt0+bNm4vVPzExURMmTCjjqgAAgD/w6xWZtLQ0jRo1SosWLVJgYGCxrhk7dqwyMjJcR1paWhlXCQAAfMWvV2S2bt2q9PR0tWnTxtWWm5urTZs2aebMmcrOzlbFihXdrgkICFBAQEB5lwoAAHzAr4NM9+7dtWvXLre2oUOHqkmTJnrmmWfyhRgAAHBt8esgExISoubNm7u1BQcHq0aNGvnaAQDAtcev98gAAAAUxa9XZAqyYcMGX5cAAAD8BCsyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsJZfB5nExETddNNNCgkJUUREhPr376+9e/f6uiwAAOAn/DrIbNy4USNGjNDXX3+tNWvWKCcnRz179lRWVpavSwMAAH6gkq8LKMqqVavcXicnJysiIkJbt27VH//4Rx9VBQAA/IVfB5nLZWRkSJKqV69eaJ/s7GxlZ2e7XmdmZpZ5XQAAwDf8+tbS7+Xl5SkhIUGdOnVS8+bNC+2XmJiosLAw1xEdHV2OVQIAgPJkTZAZMWKEvvvuOy1evLjIfmPHjlVGRobrSEtLK6cKAQBAebPi1tLIkSO1fPlybdq0SVFRUUX2DQgIUEBAQDlVBgAAfMmvg4wxRn/961+1bNkybdiwQXXr1vV1SQAAwI/4dZAZMWKE3nnnHf3rX/9SSEiIjh8/LkkKCwtTUFCQj6sDAAC+5td7ZObMmaOMjAx16dJFtWrVch3vvfeer0sDAAB+wK9XZIwxvi4BAAD4Mb9ekQEAACgKQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsZUWQmTVrlmJjYxUYGKibb75Z3377ra9LAgAAfsDvg8x7772n0aNHa9y4cdq2bZtatWqlXr16KT093delAQAAH/P7IDN9+nQ9/PDDGjp0qJo2baq5c+fquuuu05tvvunr0gAAgI9V8nUBRblw4YK2bt2qsWPHutoqVKigHj166KuvvirwmuzsbGVnZ7teZ2RkSJIyMzO9Xl9e9jmvv2d5KIuxQH42zg/mRvlgbqAwzI3872uMKbKfXweZn376Sbm5uYqMjHRrj4yM1J49ewq8JjExURMmTMjXHh0dXSY12ihshq8rgL9ibqAwzA0UpqznxpkzZxQWFlboeb8OMp4YO3asRo8e7Xqdl5enn3/+WTVq1JDD4fDa52RmZio6OlppaWkKDQ312vterRiv4mOsio+xKj7GqvgYq+Iry7EyxujMmTOqXbt2kf38Oshcf/31qlixok6cOOHWfuLECdWsWbPAawICAhQQEODWVrVq1bIqUaGhoUz0EmC8io+xKj7GqvgYq+JjrIqvrMaqqJWYS/x6s6/T6VTbtm21du1aV1teXp7Wrl2rDh06+LAyAADgD/x6RUaSRo8erfj4eLVr107t27fXjBkzlJWVpaFDh/q6NAAA4GN+H2TuvvtunTx5Us8//7yOHz+u1q1ba9WqVfk2AJe3gIAAjRs3Lt9tLBSM8So+xqr4GKviY6yKj7EqPn8YK4e50nNNAAAAfsqv98gAAAAUhSADAACsRZABAADWIsgAAABrEWQAAIC1CDKF2LRpk/r27avatWvL4XDoww8/vOI1GzZsUJs2bRQQEKAGDRooOTm5zOv0ByUdqw0bNsjhcOQ7jh8/Xj4F+1BiYqJuuukmhYSEKCIiQv3799fevXuveN2SJUvUpEkTBQYGqkWLFvr444/LoVrf8mSskpOT882rwMDAcqrYd+bMmaOWLVu6frtqhw4dtHLlyiKvuRbnlFTysbpW51RBpkyZIofDoYSEhCL7lffcIsgUIisrS61atdKsWbOK1T81NVVxcXHq2rWrUlJSlJCQoIceekiffPJJGVfqeyUdq0v27t2rY8eOuY6IiIgyqtB/bNy4USNGjNDXX3+tNWvWKCcnRz179lRWVlah13z55Ze69957NWzYMG3fvl39+/dX//799d1335Vj5eXPk7GSfvtV6b+fV4cPHy6nin0nKipKU6ZM0datW7VlyxZ169ZN/fr10+7duwvsf63OKankYyVdm3Pqcps3b9a8efPUsmXLIvv5ZG4ZXJEks2zZsiL7PP3006ZZs2ZubXfffbfp1atXGVbmf4ozVuvXrzeSzOnTp8ulJn+Wnp5uJJmNGzcW2mfgwIEmLi7Ore3mm282w4cPL+vy/EpxxiopKcmEhYWVX1F+rFq1aub1118v8Bxzyl1RY8WcMubMmTOmYcOGZs2aNaZz585m1KhRhfb1xdxiRcZLvvrqK/Xo0cOtrVevXvrqq698VJH/a926tWrVqqXbbrtNX3zxha/L8YmMjAxJUvXq1Qvtw9z6TXHGSpLOnj2rmJgYRUdHX/Ff2lej3NxcLV68WFlZWYX+TTrm1G+KM1YSc2rEiBGKi4vLN2cK4ou55fd/osAWx48fz/dnEyIjI5WZmalff/1VQUFBPqrM/9SqVUtz585Vu3btlJ2drddff11dunTRN998ozZt2vi6vHKTl5enhIQEderUSc2bNy+0X2Fz61rYU3RJcceqcePGevPNN9WyZUtlZGToxRdfVMeOHbV7925FRUWVY8Xlb9euXerQoYPOnz+vKlWqaNmyZWratGmBfa/1OVWSsbqW55QkLV68WNu2bdPmzZuL1d8Xc4sgg3LXuHFjNW7c2PW6Y8eOOnDggF5++WW99dZbPqysfI0YMULfffedPv/8c1+X4veKO1YdOnRw+5d1x44ddcMNN2jevHmaOHFiWZfpU40bN1ZKSooyMjL0wQcfKD4+Xhs3biz0B/S1rCRjdS3PqbS0NI0aNUpr1qzx6w3OBBkvqVmzpk6cOOHWduLECYWGhrIaUwzt27e/pn6gjxw5UsuXL9emTZuu+K+6wuZWzZo1y7JEv1GSsbpc5cqVdeONN2r//v1lVJ3/cDqdatCggSSpbdu22rx5s1555RXNmzcvX99rfU6VZKwudy3Nqa1btyo9Pd1tpTw3N1ebNm3SzJkzlZ2drYoVK7pd44u5xR4ZL+nQoYPWrl3r1rZmzZoi77vi/0tJSVGtWrV8XUaZM8Zo5MiRWrZsmdatW6e6dete8ZprdW55MlaXy83N1a5du66JuXW5vLw8ZWdnF3juWp1ThSlqrC53Lc2p7t27a9euXUpJSXEd7dq106BBg5SSkpIvxEg+mltlto3YcmfOnDHbt28327dvN5LM9OnTzfbt283hw4eNMcaMGTPGPPDAA67+Bw8eNNddd5156qmnzPfff29mzZplKlasaFatWuWrr1BuSjpWL7/8svnwww/Nvn37zK5du8yoUaNMhQoVzKeffuqrr1BuHn30URMWFmY2bNhgjh075jrOnTvn6vPAAw+YMWPGuF5/8cUXplKlSubFF18033//vRk3bpypXLmy2bVrly++QrnxZKwmTJhgPvnkE3PgwAGzdetWc88995jAwECze/duX3yFcjNmzBizceNGk5qaanbu3GnGjBljHA6HWb16tTGGOfV7JR2ra3VOFebyp5b8YW4RZApx6RHhy4/4+HhjjDHx8fGmc+fO+a5p3bq1cTqdpl69eiYpKanc6/aFko7V1KlTTf369U1gYKCpXr266dKli1m3bp1vii9nBY2TJLe50rlzZ9fYXfL++++bRo0aGafTaZo1a2ZWrFhRvoX7gCdjlZCQYOrUqWOcTqeJjIw0ffr0Mdu2bSv/4svZgw8+aGJiYozT6TTh4eGme/furh/MxjCnfq+kY3WtzqnCXB5k/GFuOYwxpuzWewAAAMoOe2QAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYK3/B20kZy7dMKAmAAAAAElFTkSuQmCC", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# for each row in auroc_ranks, plot a histogram of the ranks\n", + "for i in range(auroc_ranks.shape[0]):\n", + " auroc_ranks.iloc[i].plot(kind=\"hist\")\n", + " plt.title(\"Method: \" + auroc_ranks.index[i])\n", + " plt.show()\n", + "\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/current/subgroup-debug-pipeline2.ipynb b/feature_importance/subgroup/current/subgroup-debug-pipeline2.ipynb index 27a657a..8ffc0cb 100644 --- a/feature_importance/subgroup/current/subgroup-debug-pipeline2.ipynb +++ b/feature_importance/subgroup/current/subgroup-debug-pipeline2.ipynb @@ -23,7 +23,7 @@ "\n", "# sklearn imports\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LogisticRegression, LinearRegression\n", + "from sklearn.linear_model import LogisticRegression, LinearRegression, LogisticRegressionCV\n", "from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \\\n", " accuracy_score, r2_score, f1_score, log_loss, root_mean_squared_error\n", "\n", @@ -38,10 +38,11 @@ "outputs": [], "source": [ "# set inputs\n", - "seed = 1\n", + "seed = 5\n", "dataids = [361247, 361243, 361242, 361251, 361253, 361260, 361259, 361256, 361254, 361622]\n", - "dataid = dataids[0]\n", - "clustertype = \"hierarchical\"" + "dataid = 361617\n", + "clustertype = \"hierarchical\"\n", + "standardize = True" ] }, { @@ -53,12 +54,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1179881/2839099260.py:2: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.\n", - " X, y = get_openml_data(dataid)\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/openml/tasks/functions.py:442: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.\n", - " dataset = get_dataset(task.dataset_id, *dataset_args, **get_dataset_kwargs)\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/openml/tasks/task.py:150: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.\n", - " return datasets.get_dataset(self.dataset_id)\n" + "/tmp/ipykernel_1091322/3355156882.py:2: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.\n", + " X, y = get_openml_data(dataid, standardize)\n" ] }, { @@ -72,16218 +69,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 8.1s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 9.3s finished\n", - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 17.1s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 0.5s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.5s finished\n", - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3810066790141156e-05, tolerance: 1.757894395872719e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001154366291390499, tolerance: 1.757894395872719e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0195362774456938e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3064499234826813e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.941126769598652e-05, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5078253626636612e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7866902417353224e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4289396143962966e-05, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5072253770536818e-05, tolerance: 1.767001228590262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1509055134373914e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9561424820101836e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9766137020954533e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011746066488938639, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.113324227810299e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2878230354586582e-05, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.607926957106822e-05, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9754260771931718e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9800454663721568e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020723557308415658, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5236491382887166e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2511093337656413e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8581172942292177e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.713474911232328e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.25402237571909e-05, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.400616813631964e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.171831572601317e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.274453912757407e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.635435635049311e-05, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.550069550119778e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9819826061840424e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.323995093567524e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2856666085160904e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.404627979220064e-05, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.392459545573805e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.432116327758492e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4902754975045464e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9815833965016252e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9813176308096656e-05, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.282913168328432e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.780663482509457e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.213383542650775e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.238529789794566e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7685593933471364e-05, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4098428154754066e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8448683601646546e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.087907158261851e-05, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014450629785263056, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.86287753503294e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9343999057463285e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1093921765545642e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.581697848709792e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.935995413497043e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015722435668969435, tolerance: 1.757894395872719e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1336402889225725e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.101720954351715e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032387137563585564, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9227231336264514e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016345474934155446, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8921264300591698e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.464297104568143e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.51425012771124e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.944127841783724e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.114906365501174e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010905303709383973, tolerance: 1.757894395872719e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.012422861855765e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.054508685574684e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3516302843487602e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9187888750506364e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.796774014865577e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3164526311077183e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000250421670305416, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002426430615739758, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8458113371016515e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.63549688383244e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6082128023470486e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.29044948717623e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3116934777914905e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.380984668195907e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.73292295827985e-05, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.084625696841246e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.294184847242975e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036631338626723527, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021123923921980044, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.251987713712034e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6549155355163473e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2780757712231522e-05, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.321969332818018e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1697138694147522e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.908317195405058e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022166867523615644, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.253346609581251e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.034119717878689e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048472909518705327, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.419814531621483e-05, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2646959553546346e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.94087933287658e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020281184103085846, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8658067653710386e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003959695070434007, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016713903102148764, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011614189577406099, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005207262428258341, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010009850869783544, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002556692941912556, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.074801737960406e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.320212666462451e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011262823309827856, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005131098656067602, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.429280231861647e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002981230311229783, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015134126647726922, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2335012056552e-05, tolerance: 1.767001228590262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002844904846799985, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004266644126576201, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.168582098224588e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001417243822798087, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017981183596531454, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.410308070026877e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9405684246128216e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006031825505243334, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033866258733171537, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.799080637259533e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002105799316341429, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4209007035636693e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018626506030247451, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.15973153558036e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.795176648555213e-05, tolerance: 1.767001228590262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.310122280593633e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003604439354715251, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021048461400170922, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015412745008140834, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.319423732817822e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006624611824040897, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033654081291742063, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.26840565613567e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017053253388157538, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.429854243611758e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003719735993909911, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.849862533964048e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.24499193361423e-05, tolerance: 1.767001228590262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.172327062730758e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016711927349195334, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000296369455633114, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001992972628142558, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.22841858555762e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.698198481702844e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006124905034111758, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037484167519785845, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6833734075851785e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.548294070079109e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005002319086184689, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017734059860100015, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.623326039815555e-05, tolerance: 1.767001228590262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012209879337662585, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.6379017335879e-05, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022536966996238549, tolerance: 1.7474356552538402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9933483193283752e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002082254382346398, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003652353348679114, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.871908310180803e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025833782411734466, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.039066265291603e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0874623971605874e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.604630389528063e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005062211932977314, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0357419523980612e-05, tolerance: 1.762775120188722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.09113158596396e-05, tolerance: 1.767001228590262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010318291326943431, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016079104897689933, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004144088471101466, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.208698454777817e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001678683289501777, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017025165683537229, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005982997611775724, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.415456501484117e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6141077248915956e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012126795673160322, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1102078881349235e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.015089830654341e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.579095978953952e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043871799694496054, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.667163982638802e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017228520602440012, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020968927344388807, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9952654145137527e-05, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012813108170255365, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.177018369065955e-05, tolerance: 1.708335702550683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.700355616270955e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004658302790737541, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004008088035231741, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0625653706975514e-05, tolerance: 1.759372960473415e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.951226902672688e-05, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010338432551390293, tolerance: 1.775212262652814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1488041035128483e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018820867000080878, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.608278135236586e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.838853520957703e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037350189174431385, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010048078672685035, tolerance: 1.6784924950840113e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.023011955688825e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016947505995186692, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000357899865503007, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.360559076971692e-05, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036413775174340326, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.039760157664555e-05, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017239680804541444, tolerance: 1.7905876283999246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002992276248148587, tolerance: 1.6784924950840113e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8405390101944247e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004233822665109209, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.390284544809886e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018479552496767672, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003427352433025936, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.957496134449027e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010925674267730622, tolerance: 1.777446180899249e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035109893901238195, tolerance: 1.6784924950840113e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003001147488872948, tolerance: 1.7286562432417856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6375528324355975e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042990789536336655, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.581551462347385e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030717193426190066, tolerance: 1.7178096197299652e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002515337430624544, tolerance: 1.6784924950840113e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0015605680900415e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.97904703102987e-05, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.903657978711987e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041537028977707644, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021252497634728293, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001367150372418918, tolerance: 1.6784924950840113e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.551566008455596e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040045805554059666, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9268483621266127e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011617434585546246, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017745008947728236, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.693832633261957e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.432364213070039e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000386122015955173, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011505789415631237, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8703617443570353e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9187901274972664e-05, tolerance: 1.7707194631025308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017262494007338688, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5765849893425756e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037101847526012496, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011158023572841597, tolerance: 1.790241273189875e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1397747690113664e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.43718193173134e-05, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016397077285947546, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9805091463771796e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034609283228450183, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018068044538318076, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.012820868218211e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016197316148463918, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015908380291419777, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0117635153370922e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003275083040121217, tolerance: 1.794004999983534e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002981202178245386, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012065968256589829, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016190826102826204, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.091412904266232e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.629538791882351e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003094721867955459, tolerance: 1.7957478025714862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.340533130949958e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016707363171163114, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8348572252390935e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.206427820021853e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.896122325020289e-05, tolerance: 1.7645624085944457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.474423595524386e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8773157982666586e-05, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001508129925766634, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5586469647505966e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0693274171048344e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002137248966706303, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.724822992800774e-05, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.502253969716947e-05, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.801403456889444e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0451396321171517e-05, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.21092049986632e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017786994849558826, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.777475415420918e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0536929644448966e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.707241957124705e-05, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001866704762464675, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7682255879978024e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012817801077670207, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.266693364955455e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018616874097686711, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2701741595876693e-05, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.518977243185909e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8153814227008245e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.870060129901719e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.607758667323018e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.73421672332612e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.208822477255052e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.658364246930321e-05, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003523398187477902, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9718430510920074e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0648741764460465e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.062062623961265e-05, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0092365441294613e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026617670001520804, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.67879346456191e-05, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.245705395974469e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.929134338249025e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9720967639500625e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5634057318372344e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.657928690654603e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.995563295597441e-05, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011537563998188956, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035602780181308996, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4703357777787482e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7214041907136846e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4295048607294104e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.075780968380114e-05, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.705221228606897e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0775901137404934e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025317945958552857, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.713049065867893e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021887509921595522, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.059661710412176e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3686623308262836e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1566524869467973e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8020953396969547e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026178251515773625, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.211669954337199e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010402960422724143, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020397823837134052, tolerance: 1.741373428099177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.063882544650497e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.28458014888568e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012694943634827469, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.276878491216826e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013366809761699953, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025045801209471787, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.899854455208975e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003766776474674934, tolerance: 1.741373428099177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000166422283262517, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016251235434561542, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.001198332170575e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9991695527799515e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.954869261031893e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022228315535335798, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.227539900117025e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00045594834103336503, tolerance: 1.741373428099177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011469384661639583, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.577635710740954e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.119701915588538e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015445621368663856, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023922151660740743, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.469066665623069e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.487971785382358e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.742623851937983e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6784303378122286e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8124633160260823e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013224905156461637, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000490327111714076, tolerance: 1.741373428099177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002756354602061403, tolerance: 1.7188282806755853e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0729115070638494e-05, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011681126082626735, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.931415365253688e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5264655514793616e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.289308077774003e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.150071167390208e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010285037196473834, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.243524583801445e-05, tolerance: 1.679574344834139e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.970932822255999e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1730342650809415e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1994431274717794e-05, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012631371643787633, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.549119443473311e-05, tolerance: 1.6957375814230217e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.581915641250494e-05, tolerance: 1.682573162967857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.889237342456265e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.220530038518509e-05, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0600332808863e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016462231716602663, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037985540437050595, tolerance: 1.741373428099177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.64809267652642e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.783325058696334e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.055376746469821e-05, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.533923461271441e-05, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.539511186065249e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011037688944042987, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3993410966559325e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033362431862200056, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003022955434533155, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.497834544243227e-05, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.23778517831408e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020228863113188125, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.67147027564328e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.188261622286321e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.874242764768677e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013483127909380728, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019140887127699034, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005337162195310574, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8617691528891728e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6628948610742803e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.648961276088048e-05, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.683283756387699e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028230257442701974, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9562833024835726e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014109989239297592, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.436260018447156e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019289943048115144, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.617239516441912e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007036630172686936, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011446032499113148, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004857981095878622, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.387450242979927e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8238096818743e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.638869306342226e-05, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.544191722167558e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002131399872058945, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008080518939734445, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012031527079649565, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3611642786224192e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005277468737743931, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.33781841695287e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.51395227642954e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.344356447025509e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001937248006794682, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.37100794276335e-05, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1126108691886464e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.518433961934974e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.847106622197489e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.594876006033734e-05, tolerance: 1.7308388183510554e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018087456842649959, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.418399996754048e-05, tolerance: 1.742217264454565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005549467065780762, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.312967096084918e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006476215207953826, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037547707245717647, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.000340231492122e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.88941684949591e-05, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5612062329543814e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005797850997016777, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.907973891633537e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017440508011932164, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.008981411273969e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.592108492350658e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8068811977734198e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040825950373455863, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1255462096655617e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0245465123214634e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.588673439862411e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006212355899539741, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006967287576431945, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013855427054498305, tolerance: 1.7324747938515865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4852502200758556e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042313592968179405, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013216918397126846, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.550509975711741e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.740887835070958e-05, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1548277386179704e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.709051513919829e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006087015005499701, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.044936422088619e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7645439511850278e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.116183695269826e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3143206602687857e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006777588666971677, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048451362367040937, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.660315610984742e-05, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.307839104544634e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005241882699538211, tolerance: 1.7026810650021725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013946097599376248, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.768843993889789e-05, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3783484021256546e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4558500879113e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.439111483982289e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.763987149007542e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.278687586118518e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3206410400848296e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00047418484933361375, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.130811698915449e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.481198161221007e-05, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.351518327504924e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003741096499226878, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.475651400658593e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.342922397435842e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9066906280630277e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7694152422633933e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3206764507736665e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00045068178167985244, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.772529389783693e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.101847490022372e-05, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0031039177627963e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013425579574172924, tolerance: 1.707000886174053e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.996682956431975e-05, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.935492177863894e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4272240354279596e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.791147034340898e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002844708528979764, tolerance: 1.6832603820560627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003957349349287204, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017902450279372838, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.249102991101035e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014215253577823384, tolerance: 1.707000886174053e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5203843726159803e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001445685055030347, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0127617200923634e-05, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.164488057236513e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7473105450646967e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.940122171198178e-05, tolerance: 1.745260390273283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004158498582963882, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9703527558230455e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.824321524614025e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001908591927100127, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4641037675734653e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012431103430849202, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9900020546659744e-05, tolerance: 1.707000886174053e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.649434857263243e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.000631287012126e-05, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041633677844912613, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.353663511603989e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0173193172851045e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022009060108749406, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013362494383166446, tolerance: 1.707000886174053e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.056454036486089e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011769957063034568, tolerance: 1.7448606715841463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.582488788923769e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014521049426868826, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003993648573814342, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.50429094107415e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0180539040714745e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6206311569382246e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.41616256983231e-05, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0991768921469617e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2196165598324944e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.611109118054905e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033988957230347144, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039843146865688097, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.671615760947506e-05, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4993570949391128e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.238942175670014e-05, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8579945308853235e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001285819410122022, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1155284625723756e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.54194173039598e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.599295674236886e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003838745842861162, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002273928297429166, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3526464675830208e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.946224652177105e-05, tolerance: 1.7071091289437614e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011313499481167485, tolerance: 1.7605797294438754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.724815504974974e-05, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0842408510175423e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.496957742814507e-05, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020478977440703278, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.860090841767457e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.839909325929166e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.897150117436159e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000189372742063397, tolerance: 1.7071091289437614e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003881475163939989, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.222047184300205e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003244806161730356, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.306855548842024e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016869737785460762, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025530268062885775, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6808793575512288e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001961439657418131, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011368778547657735, tolerance: 1.7071091289437614e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.990742649975665e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003916967769401645, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.975161961824054e-05, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030928918344875524, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.610978970391762e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4946245040118054e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037552857186384965, tolerance: 1.739593300551998e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.506821037475125e-05, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024901129803450717, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.08522429523723e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.22789538667472e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003819757604767663, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018493541772897706, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.438217473229581e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020080353232500032, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.517297171045728e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.032145795672312e-05, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.90667977065701e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.797755044944369e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.57557395292905e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020748488079954525, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003476025934656237, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.198048963376856e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001647015896856837, tolerance: 1.720536760873616e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.783379462034969e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001717999728556678, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4310695035258123e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.132624656137194e-05, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001600998930287173, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.324298873653251e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010895634974432931, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003198556650243099, tolerance: 1.7517759570398256e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026874226276863734, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.695737366057679e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1971620918485868e-05, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.283319868486705e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.88974936431599e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012773130740690447, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.145929113326927e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4995559771298637e-05, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012295974711098306, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013070429275001405, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001881017141871777, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6738401403820014e-05, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.869883407181979e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.497239161739869e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.473703270941857e-05, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002071223796224833, tolerance: 1.7285277113736954e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001025951066117789, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9228736212725805e-05, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.582927483144614e-05, tolerance: 1.768080901676179e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.81567013237366e-05, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017039765845163618, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001051578928442324, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.190225624478021e-05, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3933499071353975e-05, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.166581617858853e-05, tolerance: 1.7241020232830764e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.63959931364674e-05, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.514046778434995e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001610380898829939, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8834187143212944e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016773807719968475, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.513150584050524e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2822848410319896e-05, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.257888141420528e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.91052246388268e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4204374626947056e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016847673098885525, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020087799745512058, tolerance: 1.7444466960904464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.86945776138953e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0682745577007416e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.985686575448636e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.469576547982146e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8545493212033327e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.150758194361193e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.564961732138063e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021438061208692554, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.804666818762014e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.395905675552927e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013082359181883219, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011002729272593416, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.457402076669218e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.719335344609583e-05, tolerance: 1.7071091289437614e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.01196617484013e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1180604134865026e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5302657596667708e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1089830137648036e-05, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015080323725772359, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4709391141915227e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.502116435429557e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001537387504285566, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8611771002410788e-05, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012462764708855677, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028096991171451015, tolerance: 1.7071091289437614e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.543079173783328e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.806722201387102e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9252332770634817e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013820867292454325, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011835900204874503, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.937769858918891e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.241238629469114e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.178252635790621e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.382134883380932e-05, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004088029883216769, tolerance: 1.7071091289437614e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.32143440300144e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.38734829460844e-05, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010136777246411502, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019583328453147766, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.782618019766112e-05, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.610094767200725e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034337396089489415, tolerance: 1.7071091289437614e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7045709258639514e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.636916335787864e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6245279045837506e-05, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0869223978980944e-05, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014242959942422848, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7746052765631182e-05, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.793027870694325e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020228145296336507, tolerance: 1.7650910020591233e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.910680811973488e-05, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019779163896882675, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4498217857982924e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.553208699963073e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.193608377813147e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.985886324642476e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.371836535164476e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.831275183090575e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6867742152401897e-05, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015675749940465, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2928171498088484e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3585817639974402e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.784498237705176e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.72222718282443e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4343617945531137e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023643054410272223, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.628330439067577e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011161693077405346, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.255269093826313e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.108368990443951e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.835520281882467e-05, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013054702250495185, tolerance: 1.7780820684585603e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.385531238523157e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7731352597749198e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.579556724987067e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001103923289379645, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024227961476076115, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.91985127761344e-05, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004254306875289809, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6583153207535875e-05, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.045375274102549e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.365236868529107e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010491335693079903, tolerance: 1.7404188872990525e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8773951810841247e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4249973078677156e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7196239364283767e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.230589980540195e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000405579394060533, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014438092207341886, tolerance: 1.7430594054058126e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004989227338997888, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.865876212635603e-05, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014650386286773134, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0286690008790584e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.185589709954697e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.265536798577329e-05, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8569146189049164e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8444554978997926e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030253912908453744, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.699906953671353e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000686870683705626, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3079903581885164e-05, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.898897269479339e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.600743048441412e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001455273747461433, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.616168233866549e-05, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000323426279991732, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4759575935966164e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8784721850803538e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9575363331963908e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.789256019535105e-05, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.480643141409029e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8210688389907904e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9088611233865176e-05, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008005439213410711, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013225960290931538, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004218614920321632, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5453262129918045e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.805e-03, tolerance: 2.148e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6637093863752255e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9418875895453195e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.371659618059449e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030304043233972833, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6105361724020346e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8147070450759417e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005857004990174426, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005076774513261117, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.202150272910113e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.391600876725414e-05, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011052750705628674, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7423508051454635e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.825715942317633e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.604605260199359e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.841610663245589e-05, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002084455357304147, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021625312092939802, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1668400656102774e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.913548116841678e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023596279874285673, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010652284782334924, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005362869433648684, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.36300956881057e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006261303036222462, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.196475604764568e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001156374453265014, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017961364610740316, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.446361318773915e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029772233844439604, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.160699349873553e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011523804662827777, tolerance: 1.792947422915112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011468922682254194, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029355458554345456, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9967660948073257e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008275459223565139, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.88646777001148e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013961104777800073, tolerance: 1.76882294811416e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004231525882268626, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9130602016246357e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.587103187777904e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015006550119463918, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000262222896500487, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.924645662683166e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037715734190333066, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3399554363458253e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005222700337803206, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.817155582295755e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6797466217607794e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.665457459577179e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.193746898833495e-05, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001653907024252322, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001780913629534052, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.480790506674424e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003390177056410296, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00045759841263515004, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7783190741516306e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.244506332520907e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002583125743409022, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.847542065301939e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6255481919437718e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.238534692896541e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.829156727624752e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021958376741416583, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.057650084641427e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027446623414001566, tolerance: 1.7556554787492374e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020767915688345295, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004859685677728173, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.490299827307833e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0821931592206883e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013199124631189767, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024619580850565245, tolerance: 1.70252800586063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7512862860555945e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.823564097611576e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.318496740318884e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011237816930165061, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8389389357008632e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004109901320179625, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021979392941534961, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016503298179336087, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.398344409582179e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.76226855955585e-05, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9574978956885675e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.281717019117415e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6606988765743092e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9873641781405502e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.25702829317511e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016538889262130568, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.243065005698573e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00047247347813419993, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.040933615303386e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013356347529127136, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018662888397760063, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.208317033571525e-05, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4786383257601602e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.203363552073851e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.441525309227014e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.913603141304371e-05, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004798686571466284, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020022738049764455, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.666110176672314e-05, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5353837597129336e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.332790487476557e-05, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4294948337241426e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.595073321940398e-05, tolerance: 1.7173507245099585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3897776043661778e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.8959902140473e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.340533136417042e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010344320302938664, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043963665136826724, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.741965481216934e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010127490745093747, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8639946829034534e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013123116123910962, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1684562976650654e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.533525345490785e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.269180127423511e-05, tolerance: 1.736506805342936e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010932070435559771, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040689014524947567, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.879984858386995e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021228851595816306, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.914511891347101e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001669953089098626, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.836743059482128e-05, tolerance: 1.786406135856044e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.124171594345482e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.259049509284216e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4563096516388647e-05, tolerance: 1.7355299591404428e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017343659783623268, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039138959007359265, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012466927594594404, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8816287839489715e-05, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022993204180633484, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8744245982276085e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018728628877385037, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.425949270365088e-05, tolerance: 1.7355299591404428e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003858140848614312, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021867854125178984, tolerance: 1.71806007245991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014851535263751478, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9386802270423414e-05, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011619854533888654, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6971641647254705e-05, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000251003350498177, tolerance: 1.7343261900259156e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017168019179001487, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.521783450797066e-05, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037164188413434714, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001446070980821064, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.196717366368585e-05, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010031029107437917, tolerance: 1.7238561297477725e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010709873813107007, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016606231751517802, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013782367300937091, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036109300072216435, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.53712284906629e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.822266131548821e-05, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.827300055472561e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001324394916843146, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001935786100162075, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017681245263224087, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.132410962745929e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.369897518563578e-05, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.226789894966903e-05, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012675597574621743, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034488025285562976, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4524550481300894e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002458742063093846, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015304545636734591, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3666812599784648e-05, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.972044997926498e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001280668794199722, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001684575321232798, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.082099122734411e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010929572294777166, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002800290180894095, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032500184666957484, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.981243908458219e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011949542292228645, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.055962914752825e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.069992118926717e-05, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.941559969466337e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001325471010517245, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001380987403605538, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.661072199187074e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024369646435455118, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031354202046868316, tolerance: 1.7704302033484342e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.571330172200704e-05, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1458660580799096e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013118753791941176, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010520293018874069, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2087243923149535e-05, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001380296231069094, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.507057260556019e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.290733519047741e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.213064129421362e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.898612772352717e-05, tolerance: 1.7534150472803565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003076401753012671, tolerance: 1.7570259836116876e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0171976775694026e-05, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.101258499459239e-05, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3085490759390537e-05, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000124486236435707, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.55350178011549e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014303680322856442, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3402036040915536e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.969037728020484e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.327121201810513e-05, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9648950615352642e-05, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013135528078035003, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.393705992950582e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1925835507641267e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.799816053740687e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.603029472688853e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.819963735321146e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001238323191844323, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032992238087396764, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002006496278285884, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.028780258980789e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.116154334335176e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.993089923230077e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.084e-04, tolerance: 2.157e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.924450176101801e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.544120978933728e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.657177008312972e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004776042066808456, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021719383123906116, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0217618273252825e-05, tolerance: 1.7688605173824983e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.833025572048056e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.386387219545678e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.819878499227452e-05, tolerance: 1.7608109494922808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006329047741539017, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.275467059088994e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3754879834393724e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4142265223484694e-05, tolerance: 1.7355299591404428e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002201747789815035, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.728606860777534e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.034327943738691e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.693869339107785e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.210945372873855e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.546844188200877e-05, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9198951061739564e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.588320818299253e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008216361582759554, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.041415800506119e-05, tolerance: 1.7355299591404428e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002653304965067292, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.985951582744171e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5803504277832623e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.07500359070626e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.099777121373231e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.081141911266295e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.691076210724964e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0953540350155883e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006350847296091233, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.193243431693412e-05, tolerance: 1.7355299591404428e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017053410200025803, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.453423781653707e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6408923232742326e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018666505265722225, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.174325986863134e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.717470613363779e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.325093417984111e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007131598061016287, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.450505745371574e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.886264063751108e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.23807145345274e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.410544465929821e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.754251438885131e-05, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034223178349108464, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.939226419640875e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.141921808232054e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040623297858881965, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011013852243592012, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.428830569046862e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.972775530042017e-05, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.864059693389036e-05, tolerance: 1.770906031813532e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00047985937428188935, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1854150646670456e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1199844642150883e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.582043254871287e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2642181630805683e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031853142295104435, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.037674642556551e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3096722756532854e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.40012255986519e-05, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000524441878849681, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.761686907173545e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.448236608892888e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001275596464028611, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.256388599654416e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027441358899828704, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.510018175771062e-05, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004597089899654339, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9906255180723457e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7983024776485612e-05, tolerance: 1.7061532228268827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.200486806974976e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.45388234552509e-05, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.047931354262969e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8945950860001226e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.0901055525755e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5189353849108115e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029672188015188866, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023071295203895906, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.185059579695164e-05, tolerance: 1.7061532228268827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.096865061825483e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010825788347148743, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9892751590684307e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048579501735731504, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.816014912245036e-05, tolerance: 1.7437021240266367e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.953955813848804e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014407313021817716, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011977806653865573, tolerance: 1.725513907772591e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.570513567919577e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.806495067559118e-05, tolerance: 1.7061532228268827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024142124165083597, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.473934502771283e-05, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001973834490339027, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3919018936831987e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9872974541460323e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9706404416462127e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.215069568802454e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002391744105716834, tolerance: 1.7010463857777805e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.478291591351757e-05, tolerance: 1.728889579519518e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005099867672592193, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.170537249418167e-05, tolerance: 1.7061532228268827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012456667587774743, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1942013834895524e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.32998077039602e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4336673144193886e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021678781409456734, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.781769076995108e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005211412537552773, tolerance: 1.735992647691564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.243134313788947e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013212006868223644, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010506607327447244, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.200071825023825e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.686510986861176e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001120993728877407, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022707823566131893, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.333406236321514e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9769571904769004e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4058796059728965e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012008822258485763, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1168889773853783e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.454037178065496e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016779162821260393, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010175655714805571, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0795324707282024e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011316812386542342, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.920115695238771e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2728968252625284e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.036869753771762e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012728327827275007, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4227001679321134e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4560766739290894e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9083769808453666e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020303448170970982, tolerance: 1.7278882349543706e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8128759448260016e-05, tolerance: 1.7061532228268827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.059102785370153e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001137142606617101, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3874881332597298e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.341568232053226e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.292375416810201e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2865990790394558e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.456595346100101e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001374141081146368, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002121825333707382, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8159311379992386e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8656046186296682e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.531406636738207e-05, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3394037265209514e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3031326551016876e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.081722543491234e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.124455495618101e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001232270592465834, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.547389132528336e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.732814865323349e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021067140912723985, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.177465760379825e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.490643403218263e-05, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1784224676937164e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.637705657695556e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.681149387561622e-05, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.165271169339526e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.445703673857735e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.544918342533742e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8976797654014534e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015439036907146948, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.78880498277706e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.800e-03, tolerance: 2.149e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0294567853643142e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001072060081278919, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.996758997927563e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002633364759222073, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.826571613611696e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001532727066772925, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9942302045479234e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.190938881487899e-05, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9200664559144293e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5412092574968815e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.214900903631694e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001560610773125061, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2192556720975615e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030743686232739473, tolerance: 1.7501881211817967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001568651090029776, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.393404098721165e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.037727606229929e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7816604673464437e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013344322606385472, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.087430171810166e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1057430001102566e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017521418973751902, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.281207593714211e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.003520186928258e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.950445351505889e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.201220884014552e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.898295885143739e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014727845242920243, tolerance: 1.778739674770508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.297915778763781e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7796055738424363e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015516204789699922, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.494572545516313e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.660726751369622e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.208e-03, tolerance: 2.169e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.909457205311835e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.74969697243371e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1779848094826554e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001743815987439723, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.574e-03, tolerance: 2.169e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.744592375179475e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.872832975071629e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.100639528901092e-05, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.128401202121724e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.976597134393382e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.306523863901012e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.128544721461713e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6061835722308163e-05, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016868532423074643, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.985021274834159e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020258668941434792, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8524148369676585e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.896557194218262e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0991587818163654e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.547898327641629e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0050739592603062e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010508475545752474, tolerance: 1.7634145137576173e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7541502853941904e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9668050582774543e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025681794512189774, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.662328055395846e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016127840390447578, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.741727644213967e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8332443868176833e-05, tolerance: 1.741934081512913e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.664260447861435e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.43291595002681e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001515177673976579, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9244653051636524e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026786990173420944, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.310915473642975e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.833102582656485e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8003059751458984e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.788338416013364e-05, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025614459152825024, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.989608982255937e-05, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032876743489380564, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.530523772268164e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.362266029729489e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.745691213197927e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001254598267541561, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032812747105210455, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002928741993453144, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017700602195515683, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024383023583014057, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.220560495422478e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.91140328298663e-05, tolerance: 1.7310282634645602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.030920400292139e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.742091523073487e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014325494958007147, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002980264806944971, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012063916945802451, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014780538974274587, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004022971021412358, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0726180227445884e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7954447535998425e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7562676012457075e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001993112377103954, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026900119670306144, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001065461456548171, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005590155308867386, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.401665138770202e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.743608993409751e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.871488442799193e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.264333734081387e-05, tolerance: 1.753699130129038e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.498733556487522e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040718950821262874, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002648229827207126, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012097018416784913, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006584864082062863, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.748020967561243e-05, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.775681014286109e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.750812326034763e-05, tolerance: 1.7587651311937508e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.400310793684564e-05, tolerance: 1.748235398347111e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005086996980796397, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0886086661693026e-05, tolerance: 1.753699130129038e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.841122807824853e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002486368333304151, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1586140155424247e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014768324818727023, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.225e-03, tolerance: 2.205e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007635011321183204, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011898491774780737, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005373932563284244, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.019622878350746e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5005819418069675e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024253404277389985, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0270548132338826e-05, tolerance: 1.753699130129038e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5004333677038655e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015239956471453393, tolerance: 1.7735555087104495e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013688663313633602, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008662875462030319, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004953268860344532, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3945997238931233e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022192287335386042, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001353096041047351, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.193795290559834e-05, tolerance: 1.753699130129038e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1226845351170198e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006916767441413809, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.472391563424243e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004684397266208713, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7954059415486593e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022215086104573727, tolerance: 1.787046085497678e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013015852911890148, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8704729436243234e-05, tolerance: 1.6971386823257352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.159249920333984e-05, tolerance: 1.753699130129038e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7772733324160637e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.031e-03, tolerance: 2.191e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027977868434909785, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7382748635482466e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0120469078356226e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005003290614287309, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.22705575132939e-05, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.867481184641238e-05, tolerance: 1.6971386823257352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4997838215838906e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5445142695760398e-05, tolerance: 1.7573079279021003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.807866837623886e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011965051714227584, tolerance: 1.753699130129038e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2316077357457413e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002468027831654899, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.840334595357764e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041558382662408664, tolerance: 1.7198759729820992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.015574011137645e-05, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.83707813180014e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9387480023322357e-05, tolerance: 1.6971386823257352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3994010149051143e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2019553410149162e-05, tolerance: 1.7573079279021003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2904382163487294e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.522912900668154e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013272398556047477, tolerance: 1.753699130129038e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003094814546295184, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018612818921619548, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.960678584512839e-05, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.827999277259935e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9222973084558e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.403529735867411e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002289821109319641, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002696104091590738, tolerance: 1.7021051214886385e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5873988596509384e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012551812867076483, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9968838187141638e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9459501208859915e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.230423091850649e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.459498484538858e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023517335113764498, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015736166645311314, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.149438129165763e-05, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.550295053566995e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.784159739725958e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.441e-03, tolerance: 2.219e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018637129868556354, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.123738623411203e-05, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.481597855295234e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018539645499493062, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.883529273937067e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015185387472089595, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001380384155451747, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.508939391383802e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.826042745854092e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.222434184191601e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.237534781000111e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001444622099996554, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015288018579885827, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0849250664669444e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.154e-04, tolerance: 2.198e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.680097614808437e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022924976295404364, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.156071159183838e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3938666032990302e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018148467447685977, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.14193381000822e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013980750505713114, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.42354709425512e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001588886961414685, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.198566985167974e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001874310217845317, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3873986034747144e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012826348549475897, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.893881001589913e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9707653132336806e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.673379795679948e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017535282729449868, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.779787580306809e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014099263795276477, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.451e-03, tolerance: 2.196e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.505598340822254e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.863e-03, tolerance: 2.161e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.572061826361689e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.37522010753203e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016649929913936483, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013963554409454626, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.254364085019753e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.226922775684485e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.229586563243409e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.789449564447407e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.424e-03, tolerance: 2.127e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017720629262042188, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012972749504343295, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.508649633653728e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8895366480252176e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2148901543152244e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.405058293240632e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011198202723529133, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.936639800786583e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010603640550885859, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.230326693982928e-05, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.584596713514335e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.18206073426281e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.323757334785244e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0150625439071275e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001419579295146682, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.077376727320818e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5957704167579804e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010107811580942899, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8796842419864105e-05, tolerance: 1.6971386823257352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001403913077985177, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.800996793820141e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7033810748142324e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.725880900612663e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2517482174188326e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.013262655118809e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.545343367764643e-05, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6178267236319844e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001437031694222603, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.500513850640847e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.401500406077784e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1482877574080456e-05, tolerance: 1.6971386823257352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.769780533439108e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.107711991981337e-05, tolerance: 1.716822785604813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.980711748060271e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4823218987515827e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018451074012328659, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.323178463499014e-05, tolerance: 1.763849133310132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.250341742096086e-05, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021144590431289127, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3514834015484444e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3798955111686143e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.454065312990043e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016076546258292357, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.654258965614561e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.412182663928685e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019691627776644233, tolerance: 1.7339371358074535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038051409543003734, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001293237341147592, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018869093589660504, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.297482993985627e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020466788878766278, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.388391466694143e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4048769916962714e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000488514243467587, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019791922969446957, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014153507664677913, tolerance: 1.7619633525101552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4328526081116714e-05, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8285400328140258e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.62562483319091e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020403511455277003, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.758114114203604e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002705965656670459, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5611387499350352e-05, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041420813710365927, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.016895058534884e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.575088064298974e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.820003810595633e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1678677768616206e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.953459628187363e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5040012285450913e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.882947066278451e-05, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004834833426074839, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.904863951957778e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.762043389850256e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001656538777681394, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8737489087125407e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.432446982915352e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005102724695643882, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9963074870448473e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9077045293315314e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021279032306626647, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8725557570754142e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8495939835702635e-05, tolerance: 1.713150705178726e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7661293887201446e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.19251366689297e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3826804375487046e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.973929482824605e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004384386896246206, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022223594408940332, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7535917040412356e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.979985983656396e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.78066159540679e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.371115020343965e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0256988616420705e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033370921464445473, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021274988017183204, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.285189894568005e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6095247961606394e-05, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.493950462938043e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9387370346346522e-05, tolerance: 1.713150705178726e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.791796127331116e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003090167749414787, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.888e-03, tolerance: 2.191e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020404121496380226, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.78714664043116e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.570169997702126e-05, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7984522850534104e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010733312687609235, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.109360153967613e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2286660310885664e-05, tolerance: 1.713150705178726e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.62199442341706e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030358623800485346, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019490021029802554, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013878464292634528, tolerance: 1.7191255998833336e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.901584410669558e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9390539537039383e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011671826200152294, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.330e-03, tolerance: 2.209e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.963733314288093e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7103453927309886e-05, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.648229895099553e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018222682785508284, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002900755732336202, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.974457522546332e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8673439639311368e-05, tolerance: 1.7683393331430438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002837633327297121, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6722269165084175e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012001417950988774, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1307795223943554e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017542081171772377, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026103995271061116, tolerance: 1.797200944982133e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.446620035650054e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00044851732140749334, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.376018028210381e-05, tolerance: 1.6875141468943673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.362926984322323e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1151902415978844e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014912353719835972, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001702459765906693, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.414878818171004e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020728934562729787, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8323479924477645e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011811195450060069, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2468813700108475e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015023677690812268, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015693490115728428, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.529401416587518e-05, tolerance: 1.703840397110618e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001287487247908243, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.14268802433867e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001719775896057013, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001523076884151018, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001453056210906187, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4170426259403216e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001315525026481506, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014610319106138185, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002296035536710362, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011965944314196262, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9496735493132467e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.648138896244189e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001378422204448542, tolerance: 1.7714297517044055e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.761330253306311e-05, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025610271659985435, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.862639375962916e-05, tolerance: 1.7835756912707626e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.984990465513358e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.762223755901048e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9893440495379342e-05, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027378720687420196, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9294269488462357e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.084581520590527e-05, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.432283145865522e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.978340898507909e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8981048289730693e-05, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.872476712784439e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029523008703043196, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.947667124064037e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7519110045789016e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014158629552230712, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.051419586759356e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.147262090099934e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.123848216897919e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002551679098975962, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.152787182473908e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.779865318188791e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.400752542859658e-05, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9728006067176382e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.569746251185171e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001492979552165999, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.231648736850214e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3076985922007576e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.284649469772945e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010779052094542912, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.267349385491756e-05, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.393207034504066e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.536279373388232e-05, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018115423193499486, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.523689242519121e-05, tolerance: 1.8076961260591675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016846775629999079, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010671679727003794, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.105735853360352e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.293042805116544e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9401498980294203e-05, tolerance: 1.713150705178726e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013256397393042463, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1716380337923284e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8604978737435905e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017509356319015453, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022475358118289896, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.084965001158568e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.687120881088016e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010379300970184656, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.391662515526538e-05, tolerance: 1.713150705178726e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014924145948138137, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001327055478127486, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.81800804382196e-05, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.559e-03, tolerance: 2.189e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.10259904215196e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020511323803728936, tolerance: 1.7383129633960392e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.671756474019419e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013040051485203874, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010062900619012098, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021630567176667698, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014108216075415896, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.411983776304658e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.143805471066477e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.936670925849746e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021192111274941083, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014147467283926254, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001278447515200348, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.797419578893823e-05, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.582013499394778e-05, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9129381929225767e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.99201706599906e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.311925168417249e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.580e-03, tolerance: 2.222e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021597404235634485, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002603768272794616, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012178753712617334, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001217528578707789, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.305966930714158e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.648448738070262e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001618075327928046, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.203363753596141e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017707903505458956, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002874431717553045, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.571564016356145e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011667238813892374, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.095129268355535e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000120657064567967, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.76946963857047e-05, tolerance: 1.731696766942152e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7626320371550222e-05, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030350625728249245, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0675506406891628e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.88979545110441e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.42699990214407e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001873195162144948, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028093688726252373, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012211544931764462, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001424442816957642, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7523329757009676e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011290045179129446, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.166018219682089e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4336930119061946e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003906287297528663, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002177486843940703, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024259670814284848, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011563282192397772, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5124541780908476e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016499342113612467, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023750164445562189, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.40569853281704e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6020176763993115e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.766724788646137e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015289373174160424, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043092905562418585, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023625229428006836, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010998174116095064, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.207272089981352e-05, tolerance: 1.731696766942152e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.1020346012012616e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018990234675622317, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0784774751060895e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9548287389229555e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031330650413830593, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.260698640695283e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001458540203729923, tolerance: 1.655675068262973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.546223441757603e-05, tolerance: 1.731696766942152e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013172455016999696, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002283156422958352, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004310070571021396, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.213899133202135e-05, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.401510374042616e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.249276766491507e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020685006420714282, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.550551030920718e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.146590351280759e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002897792682642541, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0986088391069385e-05, tolerance: 1.731696766942152e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019125633296158137, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020831115868388123, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5562092216876303e-05, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.389644991109956e-05, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040152166991065837, tolerance: 1.7496786590729448e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.636926167090905e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002727650310023824, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026420350484172503, tolerance: 1.688568067126522e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1340309489889806e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.372760034752174e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0795555210952914e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7741304611157416e-05, tolerance: 1.731696766942152e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.97695405002295e-05, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012752825285809212, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004338785201598262, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.436453806564827e-05, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015656964885155285, tolerance: 1.7287986749899477e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.686541567661871e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003166753825162901, tolerance: 1.729872163751689e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0984753732511694e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015897822007866422, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.824837935813765e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.206873092902328e-05, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014194103144271174, tolerance: 1.787603021473023e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006036299442972425, tolerance: 1.6663767177371814e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5510643860665594e-05, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.073669663687366e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.016881344502023e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.931682743123405e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0800956118279673e-05, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014499271099908256, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.116886528204294e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9340440597954917e-05, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3732514132962165e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.428249028016944e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6988262186381524e-05, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014186937334263912, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.888156081683803e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5774262805618575e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8125696578697466e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9997139766018968e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.122919467846418e-05, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2226494367019733e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7016428029850264e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.883649341840251e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.14796853370781e-05, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010090971087704098, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8262319285975923e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.106145853860702e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.001283471335727e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.907927744918431e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.431795075358739e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4280004781405554e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.427432291250065e-05, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1131732751141635e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.897195373296549e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3310915070118574e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3549807084903562e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.075898172637015e-05, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.905657825930588e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0718747358858258e-05, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.480234536957943e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.87033830573074e-05, tolerance: 1.7314978514426063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1863676844122446e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.208119157647362e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.277855140023731e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.450711035625427e-05, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012093414822434041, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.87164554054152e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8123356952067065e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9632820141873147e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7167451068897735e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.06829738815216e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0521388347793934e-05, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7902497933683656e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012724073470412114, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.51874344275426e-05, tolerance: 1.7286686796914425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8336860126340684e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.656666714518288e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.475954206408788e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1400559844203526e-05, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7441318882819032e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0705703951002727e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001429231788922236, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.731369897862612e-05, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.042413114108522e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8119546483393728e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7710748709266694e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.506741471155048e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.76517032754329e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.240196166821169e-05, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.515042154954683e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.143783574614852e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001533841040835173, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020981240328908635, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8084939644125798e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6084046402911898e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.692194927784823e-05, tolerance: 1.731696766942152e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.434118429108406e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.259709432393124e-05, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.873674460907252e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.947340042924005e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019369324804824513, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.789487051602397e-05, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8118839044723176e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8179701256327424e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0977959233659665e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.152682419362671e-05, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.815427950630909e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.542090260501387e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.871358447258641e-05, tolerance: 1.7378049840577864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020978782780672108, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6999124103454716e-05, tolerance: 1.731696766942152e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.382675616689929e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9636079655629274e-05, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.7763771761356e-05, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8175821458926997e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2359290855179792e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.631440825858795e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012973544583744223, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002475204240007375, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5230500477862955e-05, tolerance: 1.731696766942152e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1445381796457034e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.718176262710148e-05, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001711616441552686, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.981671218321713e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011088394054692766, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4748905280150566e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002732121025114283, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.300820878778717e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010876145237291232, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.056981633148546e-05, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.18182304998825e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8693981111877686e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0096561988047993e-05, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.976878100681179e-05, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029092137089755006, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014103019153294694, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.034021410039777e-05, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.410110157458894e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.080413159826342e-05, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0408673777651265e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001063039834830002, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.417077154650161e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6712993696169996e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030526343833494826, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011454397412615294, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010736529028909127, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014167240772114485, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011367757907917823, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7820875441527064e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.851240612053308e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2918604898383875e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016506156376947662, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011552053670141338, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5637878111274028e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037160936283143955, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001689613524706226, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023453801331829206, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001700353688993692, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001276293837039217, tolerance: 1.664401941400863e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.002848695891127e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010704602476687905, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1520124750498693e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001238828249458205, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.709465984699789e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022311832400088993, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039518784105355576, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022708857838523756, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016358112075312656, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003032251650163728, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002082656878852132, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1768171824122773e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012523357058311958, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.224635733722463e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.022345024354759e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026057573939801144, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030689604213874743, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015427396313287024, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029022345093076933, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026456718332097586, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.521171529984781e-05, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026338474130044136, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.221449220921138e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0889038008772842e-05, tolerance: 1.7666985295141408e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014177003561303622, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.627998439768425e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002317245595544904, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011257490213427424, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027212236570103073, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034236526038603984, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4857402966526636e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018879629951675664, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.169658452603172e-05, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030823280565112684, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001372706213852732, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.177e-04, tolerance: 2.146e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002605719259237745, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5518061795521425e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001014046772543787, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018899920579077724, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5312299568577834e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028943252498279774, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001748804713475223, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000116625955162597, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035842269703815436, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.25866330359478e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4254704728100176e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.827260677480293e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013270631010185892, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002336471471709638, tolerance: 1.723951467005183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.252820074767034e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.734768013392495e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.661534565787636e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016058864011017464, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020905618378671588, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031237000906023105, tolerance: 1.7282750405682754e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.951342287002347e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003988611706400346, tolerance: 1.761046239810329e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001472115609459975, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.125336429858811e-05, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012457197665496043, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021341528199638783, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.183408473026987e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020205378596844722, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.891746938359698e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.005908513142741e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8317171922219034e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015625662727368483, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4554170519369466e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012663610078724602, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002800599909380131, tolerance: 1.8014010579119555e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017182530107789137, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.504020190735896e-05, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.394010088602755e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.817583337212011e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.350005405220168e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021773192934292172, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001258613171697901, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.666266780067573e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012149774794370386, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.69083657917649e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001585092693284166, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.672078858544316e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5227116838695898e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.766438633762576e-05, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.290498851565328e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018937070006138073, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011516029255091015, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7772887757182563e-05, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.76882948687299e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.36501336464487e-05, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000183116839313947, tolerance: 1.6673254623392162e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.535299195533281e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.526838125170062e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.210485477297056e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.527153225106911e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.030520647412648e-05, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014484102372840525, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011059627399321453, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3477431926410536e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.499743286764252e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7535025123037737e-05, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011144486070753176, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.370650882754278e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.552131349877357e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3590822065650396e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013085102510806274, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.962235558557317e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001238365103406495, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6555295108472536e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013476564054695078, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.760039836358964e-05, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.687919803290837e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.649343479721415e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.299493503668629e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019027278798567932, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.308415885331078e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012081698999068767, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023730082834233675, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.973613490855903e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8879562083812564e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011975758721997215, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011001361209139054, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001112341741238464, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.192800110113461e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.458730859812881e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024254040972518896, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3814211772864936e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.920522242878161e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011117840314403419, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003321396880401478, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0451791242514185e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.453119682213598e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011387706428745044, tolerance: 1.7804494139173316e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.158993668063886e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020097326459205412, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0700027321213232e-05, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.157560446016879e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028219720506204686, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.151885201468964e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010517604736339772, tolerance: 1.737204348761966e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.246424431983114e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.797901079262688e-05, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042345002881538987, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.826688010843582e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.521634786805594e-05, tolerance: 1.692405739024892e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031579371013618074, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030775482607326224, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8999732574900494e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.803279949686836e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4583791503225705e-05, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.060270190098084e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5454619394254964e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041880083648148693, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020435677420531373, tolerance: 1.6976815724847893e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033659158406243537, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003850771703906089, tolerance: 1.6891227878217105e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7907342467263915e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.678915860437856e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.561776092388645e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4727367883059514e-05, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.452711438527156e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004123468463193957, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0934446219660055e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029923080466439517, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.18963174198196e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.978059816684825e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.610068394106682e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1828183135619295e-05, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040758344828887606, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.91318335799494e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022220655811365855, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.325568510456888e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9647487723317295e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.966383151520701e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2232308545804885e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9440527298663843e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005579479433014201, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017073296148361044, tolerance: 1.7649983255851295e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.228814225394056e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9175832484017745e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4440698356088808e-05, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1238275780244807e-05, tolerance: 1.7279195948105952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9916229990007565e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006390920590863321, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.330178770521701e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010679708537595506, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.742501453636021e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.578301264328023e-05, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6582165086044074e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.807999575679329e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006687026087595985, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011992163270400924, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.781134878572215e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0584139939414797e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.579601961820668e-05, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.968541173279618e-05, tolerance: 1.7939220122583673e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.505786063565496e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.260093627339064e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9054589051221574e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012017122412277814, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.392490702376627e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0589036695316194e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005006604952995982, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9334350430641754e-05, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8087953949165745e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010014694516935138, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9850174980653767e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013035577340650764, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.78095959737605e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018210472339232758, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.154592561869715e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032858464408974056, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8424328885525568e-05, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8717023275181414e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1538884241584233e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.858505788621516e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001248427567430311, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003133054480518975, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8739289009972052e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.815051562706736e-05, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035646611810425007, tolerance: 1.7505459599935474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.754510670852431e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.74345514440781e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001393246211849608, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.104092223138802e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.462774886593036e-05, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6196867317201597e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2733494360814913e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.095236427639076e-05, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011539161107868793, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.267052419163869e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011504924782150192, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.441351477852239e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8271483680790124e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7776644507489234e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.429056836199139e-05, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.66353607094604e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.31408129065247e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6885202449472047e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7560393033588624e-05, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.170592880805256e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3044747311044486e-05, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013441665844667397, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9389484898096827e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.501490294982253e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012062283295975751, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.13336753819133e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1913448020555336e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6010062742722398e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7052296142623536e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.106650506178339e-05, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.220481929157518e-05, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014673889025615915, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.078854016793809e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.486400009561796e-05, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.681051769005266e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.379726726111952e-05, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010826191086883187, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.295701485705158e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.938968847221752e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6388045711745606e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.718241684995405e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023710794206005525, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001249052577941244, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.801681930622243e-05, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.962413465219151e-05, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.916434154286816e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.523300290182412e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.087107922941176e-05, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.474403159909819e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5949325014877796e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011441365944115517, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8869188496762005e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.834695072867417e-05, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027313690969001277, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.540540771500514e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.73219000567045e-05, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6387380001172134e-05, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012532919929997798, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9590374524261776e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.743833808310046e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.612283430195101e-05, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010590533086402704, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.019496724014249e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8244985160848918e-05, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2960992257219787e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.562514153743651e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3528303727313816e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030342435228874944, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001251218571551773, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010901323128015283, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7661131783905894e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.971458326749408e-05, tolerance: 1.6875350475191756e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.995564954088515e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.277222338530887e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001147671803786443, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.440442221237447e-05, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5187819187492905e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001282187321478669, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002864227942904127, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012790610829194193, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.085628634313248e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.570562456129122e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.515451335309999e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.737634343196691e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010934479671268968, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000129158561507893, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.177218907268742e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.172317092826169e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001577602508235406, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.328060500236256e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011561859420749707, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.742910245284071e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015340267303373158, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012704321613847373, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.751687520270851e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014873975787493832, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8294084787613096e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.732062782095107e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010386227584074925, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.947388638262563e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035081379895473067, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010703748097743056, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.571583004292032e-05, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.777123572988778e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.828576358337171e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012250066916502395, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015925468125154926, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.168558746334538e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.047695649043309e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.94920957742405e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3551838103000834e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004214844765220889, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001091148485532871, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001770872804428567, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7729991088500106e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.229308581849225e-05, tolerance: 1.7146516897131493e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020264458226090364, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.888830258348875e-05, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012548757362633098, tolerance: 1.7962895132385283e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002814013773320481, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5440749994914154e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4445216627043695e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4674251101169534e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012606234906900084, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8694886499629436e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.39041449815004e-05, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004323512441455797, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.655784437098109e-05, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.740304809769442e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4970454245865775e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001722311745817988, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002278871302570911, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002271899368216524, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001072087779551258, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.869097252604328e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.135793493555788e-05, tolerance: 1.7311179934348993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.80730350142918e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.771828876282708e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012097927108316395, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005345493145943701, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.995298681514937e-05, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010192370157678307, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7086598426805863e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014015495900510447, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022006980780643615, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.960324762530189e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011300749523254251, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012443895517360096, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2639755226471284e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024162662552592098, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006885192911330416, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.373658675604173e-05, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017990390054943486, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.354019114564343e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039109910545800236, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002725762170777673, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001230568966821945, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002457394781459465, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.014298191152391e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010808933501181214, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014265403815028575, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019861001379368418, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0545413652112033e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016265041579716782, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006737861652625328, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011553298280875257, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.9873904779999e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022411847064993367, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.383903522627805e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000544805356214529, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.89699510833067e-05, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019049362413638138, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021899671873855386, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014809752655481172, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010890426918560778, tolerance: 1.7347170313585085e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002659461735240124, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.199250297696287e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000668402421771774, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027970067503326444, tolerance: 1.7479139251458932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.947947032513678e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000210391992479457, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.679905270935704e-05, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4193288294254044e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005500837908774432, tolerance: 1.7294440883791563e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017754211774215298, tolerance: 1.6847272869449654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024083562624136, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011286432468974976, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003512072755200461, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.918252520975591e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1482493767272125e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9502887222421226e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014094471059343874, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002203310550966597, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9445167712947575e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000256899459092139, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019985943559175118, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.362792602168562e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3855856976311265e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003594377698289252, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002688928702520091, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.47123530794786e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.863935859046985e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002511596442182056, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025637227650910244, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.069200887226807e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.572282784473831e-05, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032497734683939895, tolerance: 1.7468249408595227e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00045939289263369123, tolerance: 1.6668748064872403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012836589893021593, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7387007703566736e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.676236505536362e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.337206102860309e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002562285507734137, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.124966233203393e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000257450495799603, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.744678177351575e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010613268240111488, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014282659641971004, tolerance: 1.7456464414705464e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011043896563756386, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001734048669899367, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9347517020929066e-05, tolerance: 1.7947830084517835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025221459003737113, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.86507445297173e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002010654323092529, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013822038587633402, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023055422636002014, tolerance: 1.7948919450952378e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001950270966470779, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.682355307044806e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001388112146212192, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002837657190107165, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9726263144526426e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002068670684179149, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.530430601071023e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9499321518314655e-05, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001288779153155832, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033238502790686707, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7883630485071517e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.448087768488222e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001549762482880978, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7499692620901878e-05, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012217962512455285, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.634255702678792e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.102863006484164e-05, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012200523066244716, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010030094654404909, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003478161203055094, tolerance: 1.7285798025332246e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.506646154192994e-05, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.413801819867994e-05, tolerance: 1.721409651032601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011473365451596454, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011765103892694313, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0325008016730826e-05, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020375190186862693, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.285462910725164e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015190226365779588, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024371900944579906, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013826024033985508, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.959040700482097e-05, tolerance: 1.6566049150467347e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011313550299554831, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6354457915690336e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016781957758072244, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003694190529210592, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.118079993026616e-05, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002840299327420666, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9878396331583087e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002404608325044669, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2985160719714987e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010629213664630974, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017280070867145905, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003978804810979529, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.199881970353564e-05, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002371987445484603, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5934646257986388e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.278932629463161e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028275463078879584, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.748662280342747e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.730046511384656e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8554701929724494e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010569807474038531, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039722052443827774, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4856834692338e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003187912055135529, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.076591315843765e-05, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022660797793999394, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.800824927160858e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.274955430664528e-05, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4603531922652864e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1112134174135558e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7360826691710865e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8011069320706822e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004082260888720324, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033870926505497403, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024032552415281447, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9055150005568376e-05, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.662549092918078e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.368822599819642e-05, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9218946029252865e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3173154789498523e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8648133619164846e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033655721522030287, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5136411651725116e-05, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004061920620854649, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002450301631079276, tolerance: 1.6878290288896657e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.704946198133767e-05, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022385548353251634, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000141960309031847, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8747656144825486e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7598195049735657e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8829105778671593e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.994037380880249e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9708023581824324e-05, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.067802912206299e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003631490330121929, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005595346081778921, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023656665914797634, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015054990869766034, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6029906872308073e-05, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3052554393315078e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9289121214557947e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.465451831145399e-05, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.788150176615908e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004023696120379668, tolerance: 1.804773296392992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010075866258423688, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5210873101538046e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013794804209718917, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006797199016766696, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.550e-03, tolerance: 2.155e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 37.8s\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003006387593501754, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4081796374055692e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8855625334502435e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.748776165381912e-05, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012134312623943502, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.748976143726489e-05, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9362844609020075e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2921189956815894e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007181525368254102, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0959373702180446e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017704656745221817, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9749123692767295e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3313599745015895e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.642704825145796e-05, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018983020560759886, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.693257301803804e-05, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.780785744760413e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006831422951619317, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.09897476065326e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.581610041922256e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017901844188762672, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025457050583919255, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.302970982026199e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.683865201346419e-05, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014144479626203126, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2273135304957947e-05, tolerance: 1.766156643129619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000440304333342037, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010897875027346578, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.083277919171783e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011039316048096996, tolerance: 1.7241163076013955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031328438065698905, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.781501584366285e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.899186908319855e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016542929922807974, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010504754674644894, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.064422101117792e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.214658489909163e-05, tolerance: 1.766156643129619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018469301499369692, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028865338578736195, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003602738378114846, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013957169654782442, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9141365748755876e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.10589459137367e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021228060529647833, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7146134427300534e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e-03, tolerance: 2.128e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022994112158886658, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000372861610530394, tolerance: 1.7257176637542146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003915479751991453, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001245263939980007, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.242617691784702e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016247697472598042, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0741906723791004e-05, tolerance: 1.766156643129619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.795173985491091e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024620450364631116, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00044866311533152837, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001128669315961714, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.777e-03, tolerance: 2.223e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.422307655161951e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1426297037415235e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001409971457414002, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.099601404689319e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.219646485077982e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002634792534291037, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8859747945949614e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001093050019843056, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012254955313267465, tolerance: 1.7704202931973968e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5700695089263546e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9141461629811063e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025345314984904794, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.846768401621694e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.361179593162298e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.60021288885247e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9956952583806767e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.082368903222827e-05, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.515235772400503e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.013974605237419e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6774809450309106e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023356875459478593, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4705626132693355e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.915119460563046e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.497821556641613e-05, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.554422571778505e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.930952909099756e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9247014399845913e-05, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.187775038873645e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.70410016528171e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9232503169503155e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022762761642500934, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.161113110144569e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1893694201286125e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011812896292269634, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.470800112981558e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3007997866520552e-05, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.553282330396288e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8338164219918194e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000196767025974434, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.614051226531796e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.062293409741539e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.874873507175164e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.09863210850203e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011659596514001364, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012097802222738215, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013525865460883163, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.863434863903779e-05, tolerance: 1.766156643129619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001628225362300846, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.609335288136191e-05, tolerance: 1.6946342497877326e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0979221929072764e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4699980644237243e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.526967377512878e-05, tolerance: 1.7752252200252407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0480020921661776e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014702125993335674, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6883301453936174e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031638132330878525, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011194806515203368, tolerance: 1.8033649356418244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012231489749181448, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012598424811667217, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0017023502297976e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9664580497876297e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.946944821027752e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4977402858616937e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003440372469932368, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.288715528824594e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018679984768506762, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.557516821902338e-05, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9537517442509873e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8195980654044037e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011551942664321177, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.405379178166677e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9910034879929853e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003333090797976654, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.296142406347639e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.154457691973908e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022412056085654997, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0378274065245908e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000162320747089063, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8947144529337396e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.383474092935948e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.867516801721969e-05, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023098498877171907, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.610e-03, tolerance: 2.197e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012063435675408862, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.576258444232797e-05, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.367168625574007e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002449382687620596, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001779188399464067, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.635876225046395e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.310374181876574e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023200719861170283, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010792131721816403, tolerance: 1.7063087590065642e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001714503575896368, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.974839207671014e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002440349722095357, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.672722761389769e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018202779868814294, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011444231671638855, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.675915982035658e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.188955918482714e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002863907672359285, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.4967751021459e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021211915398392043, tolerance: 1.7536423498652365e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025805610138165784, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017564507826596347, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.621038030007581e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.882638568616543e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032975134909384897, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7846990180917935e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.431712024238204e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012587452677393501, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021528306585746112, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.27710931637468e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.989655146103814e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.069588510892714e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005004036889293077, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002631066347570498, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023100115356564956, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.256e-03, tolerance: 2.187e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017591170355790806, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.521649450849399e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1563352243243384e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.771214262376156e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.725279535336454e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023860480276993203, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006336570392897582, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002263117625806643, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e-03, tolerance: 2.228e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021681239625944912, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.795819908167946e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1874974182065597e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.762680178741594e-05, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006039128007964663, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012625015771245383, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017708673734508074, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.991799908692779e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002057450945845557, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011717663550627443, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.284133124152686e-05, tolerance: 1.7652376317087273e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013863887359822323, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019718222086098115, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013625708791869043, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000481265998767807, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.4521301691625e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.834283305147832e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.492258489820875e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0246987717849616e-05, tolerance: 1.7652376317087273e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.862794232609412e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001125212541630595, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000125521772532753, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004009659141173379, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012422805748831325, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014834220054130848, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.201338650205912e-05, tolerance: 1.738061301714817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.877052460186849e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.102606029342341e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.523858354692079e-05, tolerance: 1.7652376317087273e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.877735578809165e-05, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.805588420257265e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001260515296912434, tolerance: 1.6718199030514324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001036870065652569, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024006677423602484, tolerance: 1.7333103184772147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012345376608418474, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.889e-03, tolerance: 2.148e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0005430926079565e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.830968831834968e-05, tolerance: 1.7652376317087273e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6102256942083183e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.126647210297372e-05, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3928515346012354e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001027477832370241, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001472129751373362, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013007541797761292, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.894290189389039e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.257139675324263e-05, tolerance: 1.7652376317087273e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022910555005763514, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0134894517254776e-05, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.69062458612488e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021127863950459042, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1129765577317626e-05, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012984369112793107, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.909585341820224e-05, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010537529946292624, tolerance: 1.7652376317087273e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9564224902327e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.607449823750529e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6697507028531145e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8787203323155844e-05, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.415796918413607e-05, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001413281279653046, tolerance: 1.744477281082777e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013359245389011568, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038419153632732184, tolerance: 1.7858465130297282e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7813377688487105e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018779528300126233, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.503245886144195e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1198393516198843e-05, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000103862688470676, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013676914882056667, tolerance: 1.7195643454509507e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.672988049900974e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5325178650274835e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.4429548250681e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9063451334247472e-05, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.860767491226327e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019972107052018163, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0914528113801816e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017376269795056458, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6636074052586185e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002736080995507789, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010551104693687016, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.398e-03, tolerance: 2.159e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.446553754679881e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002530472159317787, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010465183831277758, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030136084972830126, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011504865495000523, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0720902461082185e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e-03, tolerance: 2.100e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002865780714394585, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7524549243200932e-05, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031383800772160405, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022904336126383998, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.874414253316692e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000123040230898183, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032323088438441366, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.461393088451074e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013246154066915393, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032695867276621906, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7550290988160508e-05, tolerance: 1.721488063506033e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4390698111904224e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6563673599630348e-05, tolerance: 1.7257356740264212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8212049720672113e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003507816683656394, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011385626473164922, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9487707533258105e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002709069444807224, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016074692316241784, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003298442911880209, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3992415799325983e-05, tolerance: 1.721488063506033e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8953194589370623e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8644118460611064e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015045382814421037, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3240094268454506e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.302306041601878e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.958354205642681e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030129519369897703, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002566385846178758, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015925620001020456, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.330174432580012e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.690295028384928e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.776555099786443e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2842044350437786e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5524508309566225e-05, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.819209695507357e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001745665991444295, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031916670806758987, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014938514494641265, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.828270023657765e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8981935294828944e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.792346488573037e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.221898081191097e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000287879472695529, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.215407392333919e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.519464030189365e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001481154980241539, tolerance: 1.763197933083236e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016262232423656323, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003209220888285985, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9877837170771276e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.650654371835474e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9065792410471895e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041497829837509187, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016519322165501824, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003220291982091849, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.68732647189171e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8124748219642932e-05, tolerance: 1.716986111155881e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.650097257817085e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020402582367792625, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8514561378177308e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.018474250008842e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.282062504438062e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004707887996759155, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.089633449754366e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024004787419844992, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4409885610749207e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018651826290198587, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004318281803980181, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000199191312359865, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010280088310788901, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2596023350831005e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039572648796788127, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.952594903710525e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1868802012835892e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002962956950659651, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.652397831418896e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000192428652835544, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004132401320183004, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8237409019696985e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.150679787210634e-05, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8301044217620965e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004378513446616425, tolerance: 1.7953299279524905e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.000993310769146e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.117665824488272e-05, tolerance: 1.7021169112239683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7876712049710456e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.654018706932443e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003729478167548844, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005203043364704289, tolerance: 1.746089814814818e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.099423093620406e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002312820366010323, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.704762197641014e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4410601541648026e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9672576588271604e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.175598550001374e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027537218122793833, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9276151212655607e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.756816309933783e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.979855194708812e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.603738904401065e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028203411571886376, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018142992832305763, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.362554910315649e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.744661194279302e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.838898616137591e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026082626050280555, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023573792538091921, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002601509971376162, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9499919215373168e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.721859969260503e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.93394179673987e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.502e-03, tolerance: 2.229e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011874653994185629, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048773936228288343, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.071336194755529e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002525608527118254, tolerance: 1.7581089675712314e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5270948703024973e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9572969727694664e-05, tolerance: 1.721488063506033e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002912429480703641, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010224377085051028, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2969063707956325e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021096436673807319, tolerance: 1.7705334848882716e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005966735607606643, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8950567437121405e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1343022288049934e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.950033506844227e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004023760977854545, tolerance: 1.7581089675712314e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.379504218160167e-05, tolerance: 1.721488063506033e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024900568002660453, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0385865046495846e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.186358246191003e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7305992319218153e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.164601729012742e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006763138680353576, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.207079604637818e-05, tolerance: 1.721488063506033e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002297437869114658, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00046540393623407256, tolerance: 1.7581089675712314e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016289947553727036, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.75680873482329e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.47702996713889e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013424082955883557, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.802297186542616e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.418e-03, tolerance: 2.120e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010910056923466721, tolerance: 1.721488063506033e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001996514772533561, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.087657012683999e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020633253976207536, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004579222104468556, tolerance: 1.7581089675712314e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.866750917585598e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007285884478500362, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.91749183191321e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022128777297429625, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7785920164878977e-05, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013775312029679944, tolerance: 1.721488063506033e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016002269189548893, tolerance: 1.756648900012973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.370102270435775e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019344242411126547, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4401036428856743e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038137306187808334, tolerance: 1.7581089675712314e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.295e-03, tolerance: 2.156e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9597562044249353e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.205316312472275e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.196136635256699e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.518134307018525e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6397724399405964e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007520543517448644, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4398308494382945e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002584983353726613, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1739396001247205e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010607569679170481, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.892496826244293e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5685683912167467e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.431615206412602e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007363097243603661, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002731810255802991, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0697486459638785e-05, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.360210430678015e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.445059684282971e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.029688090050442e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1309496044087488e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006274586564595187, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.097475848756615e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001075804616871047, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.033823506403508e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020917114336130305, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028754934158108005, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018454948422249023, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.603568927121285e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.60369155737451e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3679505645954335e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005025040622564682, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.675618989088317e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6500481149266744e-05, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7454236478260258e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017530260833605038, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003307215226358226, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021865362061194646, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029196209738910524, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8926028094954153e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.040192846788097e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.776641201937934e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003922318593995288, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.154292589173656e-05, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.183373548364693e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004243597906541559, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1177234592333694e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.32676737885302e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3097326131340384e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019773697752784817, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.042216908834313e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029293004727048814, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6318845313598686e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.180202985290768e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.843757336537349e-05, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002935290748879429, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.121044404815707e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048390388750028327, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3523589882161466e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5888096732175174e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.344684870677835e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017632874522442808, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1722859689468164e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002308116860350838, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018614434010470532, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6998822250580423e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005385540058130463, tolerance: 1.794847134903301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8295655286410945e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3111662634748426e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.251931930251111e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6255508284320217e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.896071254371863e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011070928312645854, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001667516917412369, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.509006098445164e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.445257034681958e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.448249302076821e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.318e-03, tolerance: 2.180e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5335773414660954e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7697337660139978e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4996109979558666e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3370784165749736e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000172737373026614, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011331963930539049, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000263554572872627, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.44086510127247e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.284386094669234e-05, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.154804773724095e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.963659852846725e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4623946551467074e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1108561794992188e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4220578828979683e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3338449345391976e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2252149987424825e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041157654385978043, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010234616445341065, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3225407166795053e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0534313882224903e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016251335873889404, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021197501146784283, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.043588429685982e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5471810096471675e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3989119505263626e-05, tolerance: 1.7232693681029237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.649035928922131e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.135070566815488e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8602292597246287e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8753501693293965e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005247855907799179, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017449495920944004, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.78277893872903e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.187810523918235e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.960608031694773e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1911076185279765e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.538742988611903e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.331834638593353e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006479044136464794, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.369198736965327e-05, tolerance: 1.7694051078611623e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021405173676249806, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001454404277218893, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.061853510636825e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.270069337177119e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9931714159014524e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0918617130428335e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.391427451958413e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000826485523350907, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.950025990056912e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017567932177060992, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.508415531445786e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.595898695610115e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013125052326041272, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.375153824334875e-05, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.314482636024781e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2628422929414456e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0010297563369059863, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011064681410348672, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.299631219642808e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001472302188843819, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.420865284700341e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4976781984088932e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.268025715238477e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011750360796672701, tolerance: 1.7566331272722352e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.076239353529703e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0012518923565283235, tolerance: 1.7511187505926932e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.850694814290859e-05, tolerance: 1.7406922024267258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4816489085182497e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.9447949326399e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.346975664680513e-05, tolerance: 1.7601922329770254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.613812526667877e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012886323254695034, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.996699828866818e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001616635833079534, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7929806930312466e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.303788141369168e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.865906381944321e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.380243806570994e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002316799690536362, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.57898393158978e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.895724911423068e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7408450063422406e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013707781250799946, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.810603536152107e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9569587752147475e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.967931555825475e-05, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003125465642236473, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010413012032503902, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.699313846610845e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.951e-03, tolerance: 2.159e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.436438226192857e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.163778046569147e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.680967666033947e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010142689389957757, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.20095440843002e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.69637320257544e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004002187377208453, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010690030180213021, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.885959850895252e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.690335936079816e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2226410404072028e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.812922579890919e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.238655202897304e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003884059700020887, tolerance: 1.7340048076761244e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7888780543685867e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.571251845328504e-05, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.881862537771132e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.085468289566924e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.657935945283711e-05, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.84778072627123e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.817627152997177e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.040866972175824e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013106420377878222, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.442594453077763e-05, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.088882838067457e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.574766263661458e-05, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.417249482804262e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.124448929386959e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010193230877892073, tolerance: 1.7311570240200024e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8959836514300813e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.204558587629249e-05, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.053344318888288e-05, tolerance: 1.6914520909926502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.804724478294585e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017495535744189127, tolerance: 1.726464706731782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.464006225889544e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010444831912408657, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.793684204494909e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5775613419815394e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.343887189500056e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.896333696388083e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7625396238142863e-05, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0036647597139754e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0714579331458935e-05, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1642048031976502e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7919359548899715e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0772808021268554e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016134818552009534, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.428526065326451e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.014052143314767e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015116364546661695, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.975266677098751e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3335020440801157e-05, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.84169566260257e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8492524261460794e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.085470392331924e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0402497512179577e-05, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010402348794012056, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.335586079049722e-05, tolerance: 1.6674855961180187e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002713186328698932, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003481916358626033, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032929771746163794, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.32637868270007e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.156519799246484e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0279444172044414e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.056146666469966e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.77321618387949e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.806871962140778e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003207159297225256, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027489039224410684, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008495736994384648, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.752843254818412e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.582863349994353e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.307584195637008e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.783894527248208e-05, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.677525163605199e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035666778928539374, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003979708442177713, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008129085900018844, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.668356586679835e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.597660471775613e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0057094385298596e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2288547549906973e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.342197308117307e-05, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002599416625467671, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6318467325070136e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005357136531846125, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008640093301277418, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5155688789813636e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.97807054677915e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000279484098897876, tolerance: 1.7507992275244583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.232491472844815e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8107572461683805e-05, tolerance: 1.737340745273301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.97886057039759e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003969246750618327, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1848670520622244e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9559811670656216e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.926185694192499e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018207084520422984, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008477055364577019, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0747691449704156e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2515619702223886e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2215447796375e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000501434716596378, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.625059887982964e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.133812548343322e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000928233653844087, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004628278844707942, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0765277549683603e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001759473665114246, tolerance: 1.7002950825399627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5789940443828735e-05, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.742376981464776e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005720135870098259, tolerance: 1.7299371389939122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.335293693348257e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.337290146406777e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0720045923831744e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0010400028483548334, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005283661311156138, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.050463220460875e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7691906003664687e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022902356601115122, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.613243304077286e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000391947861815933, tolerance: 1.7002950825399627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.078458828593896e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.059941682117013e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.077011180961837e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.001084032757924882, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.801040241452244e-05, tolerance: 1.7700946425679045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005216589640341801, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.927264977986872e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003996141694191614, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001284854262910782, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0725083847254336e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.018738716543181e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000474851554210798, tolerance: 1.7002950825399627e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0010923048457073554, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012219441845635372, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.805496781224445e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000504615889485709, tolerance: 1.745130374553789e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6820608052453375e-05, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.722497752634104e-05, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005645891210997074, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.022524871426277e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0011699130845696515, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013317559821700645, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010263855825101698, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012670347005359558, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1418024066889585e-05, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005724110331048575, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.453528733383254e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001301294671368497, tolerance: 1.7680550789395077e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.18195104851205e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7236492898544823e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0012293406776220494, tolerance: 1.6982259661560816e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.700635601786294e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001448411397665366, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012897860353900878, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0375289751572174e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005110893496875829, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.875541844021099e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.10982146413215e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1634042762626315e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0884425737923386e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.427580581228564e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001736105729841066, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011714289511935774, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00044382790106852355, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.278048886053806e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0478952199483313e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5004540700566263e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001043890464978766, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.849650725399687e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020317786183671454, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5964370743098856e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.620677127048378e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000378392699916001, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015769213348778947, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2892640367285804e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4961649959394025e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.623635259471624e-05, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.19516066121995e-05, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.905028844091722e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022134887163795074, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9331853707237396e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9832957060979955e-05, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.076263430155506e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019810783252197268, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031262540004506085, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.794143818490898e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.210556663125295e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.93762278652412e-05, tolerance: 1.737340745273301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5664956590608703e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012060053039015736, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.039779347150187e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000250509714014413, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2481334881709224e-05, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.84994693883559e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026687550200395725, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.760142282267914e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7661260077402735e-05, tolerance: 1.737340745273301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.554525822654266e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.102566605261511e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022801465435828058, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002849444845500846, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.733258421370286e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.563269882218283e-05, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.416995686964628e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002627044919411821, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5446686197520014e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034588520728618885, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.151466710814982e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016562231006222615, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003264962058864597, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3609887992335514e-05, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011334262353658413, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001153647342789655, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1257470580279825e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.396220632142168e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002615387233358803, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.979844446266918e-05, tolerance: 1.737340745273301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.15433777102643e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012698355966450896, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029252398150067006, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.185436761994376e-05, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.669512705126298e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001528822078575411, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0545626440071976e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.525059981999162e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004062900545795398, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.5030719608292e-05, tolerance: 1.737340745273301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.847182781606538e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.342870927552227e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2474229108376126e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.878288425295504e-05, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011934908722665118, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7525694762172278e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000190854834917621, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000148277620262793, tolerance: 1.7677422070308303e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.485510441641475e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004985312303702749, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.243509259426088e-05, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013874796812747125, tolerance: 1.737340745273301e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.136227450759089e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.217920072754297e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3436669586381536e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9198713362938055e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.76315662083449e-05, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.097466872599614e-05, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005115354333955751, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.309713641769037e-05, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5380069000236274e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002536863606860916, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011384480121009663, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.43324891957194e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.527368165433774e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8069360647455886e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.051464778159498e-05, tolerance: 1.736604676162325e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001075747888341777, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.251994363208179e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.835573387673025e-05, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005053896099040112, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.612278307643361e-05, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.680529185820988e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.273623106168533e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020467693763694637, tolerance: 1.7622539646796384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.185110726275543e-05, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012876728534051175, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.392112048561829e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.21220417088624e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.304057358170329e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004952359115233508, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.46784472887826e-05, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.383041347568123e-05, tolerance: 1.702113533312458e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8996627445229337e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.620216455864407e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012956967857503162, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016275744614180788, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.326462369397623e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.052024170082824e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8886515523442587e-05, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004967604620894836, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.246647686069992e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.021417348375026e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011637813989533261, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021431783484987185, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010263204174396686, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.841239842152221e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.219651811301293e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.54048879427062e-05, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0113059718404255e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005346677502760799, tolerance: 1.693935944923494e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8753206325322156e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.49809714904129e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011891602613055968, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010585193759877608, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.21687809838899e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002216971690823851, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9510032346604922e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004445540318916818, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.783493428410634e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.346518965076847e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010688681205408678, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001237429529488852, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011994019839917623, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3104307114916317e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.268760549632801e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022333558404682204, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027321125689030307, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.438276454126055e-05, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.285017854564326e-05, tolerance: 1.72464082519429e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7419606930258076e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.032916398845007e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8557542147527167e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9840991381913356e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.1899316054328e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031863817610318194, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002385937439300951, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002305967155864794, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.267820134273021e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7846742335791976e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.306595489792392e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8689552446369178e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035016941487212277, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.939589733021134e-05, tolerance: 1.761141838283638e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026701651976239124, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003682833153729626, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2988049474889803e-05, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4654562710171736e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.132391864278365e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004256118013498799, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.408319189059095e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.153150337976106e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026944240663567733, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004871810153747923, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002185539788644407, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.108016672768223e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.94891224905843e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005470209928035898, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021380342036633835, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.142058670953021e-05, tolerance: 1.757740692526178e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000586560678976453, tolerance: 1.7461040518516782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.458599949762679e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.365886388996501e-05, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021976656460831814, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.033142760441916e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.322053473928937e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007696581972278177, tolerance: 1.7292387863012564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022394875925336967, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010681150851539825, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002725819225223268, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.395379150446416e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3498116513428974e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.877111918653181e-05, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.07816547525441e-05, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.448287067280009e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002258547684101603, tolerance: 1.6915864763426262e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.963845304576352e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025247836585042677, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.595744714703576e-05, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.729857399190726e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.712467902919087e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002897114669029007, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1745911544299394e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017032799485544679, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8925294746771626e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031603735815004946, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6865376876645904e-05, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0733009492332477e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6797246392075884e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.92043329705788e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027737030517359894, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.327764660807092e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6836523925339445e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8245934273489013e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2655080750187684e-05, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1269750768822546e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030383959257809096, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022718448822393444, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1799826826934066e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002673820512936378, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029531336584822775, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8559409609370317e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7801041247324686e-05, tolerance: 1.7089047071649992e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.697038320139457e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.116787136227695e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.608722273801574e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021407464241844643, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002938004839823302, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.553686418199988e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.01628453979346e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041495258148168887, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.596993377926426e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.274837728326119e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.883046349834276e-05, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3138119645212812e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004304204681063843, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.943e-03, tolerance: 2.210e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.300898921105826e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9831787819980388e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003516816853070849, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.341354927812029e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004984305179825902, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7290936586934516e-05, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6815517166680486e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.332605087333004e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.631866494436443e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000440630071164046, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.056032648605239e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004069645796407114, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.406819490141624e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1828422544155285e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005635874444689468, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.71766329394639e-05, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.094845096894561e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.803439990450382e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.479115781578114e-05, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023314072660001947, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004010102612686901, tolerance: 1.693541414359865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6658394301805333e-05, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004316926974159089, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4502404429824375e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8430147102169335e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2035649988243486e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015036729925571243, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.285e-03, tolerance: 2.123e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.072441974006108e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012483619220362945, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003131138400581518, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005577818870301848, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.430085231940498e-05, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3391919049702516e-05, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.727940895338969e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016786859903875073, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005167091831721248, tolerance: 1.7389106966616848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040712990648898283, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.135617614565255e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3507043826340455e-05, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011953297501805099, tolerance: 1.6851588470838067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002486492840405325, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048464682292783355, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010779741513059632, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0981651775680185e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002822746807807719, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002720399157593173, tolerance: 1.736604676162325e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00044730572422899996, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.44369139743517e-05, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.214989557592725e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041925706643697975, tolerance: 1.711695060615205e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012254179359822912, tolerance: 1.7259544944393462e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004064049979009797, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.222102270874019e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.759122584551122e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000436697493811704, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9482574866939995e-05, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003934258284563185, tolerance: 1.736604676162325e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5694381002612857e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014847698301248893, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6032128331866965e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003295865805764235, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038428267848025777, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.356677446525805e-05, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.388882365415672e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3048042032458582e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042710898880656857, tolerance: 1.736604676162325e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002030557957381652, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4312924453122496e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9289896376272278e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022574194398044482, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022638632803369425, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.733551922154501e-05, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4866885228854805e-05, tolerance: 1.78106679289383e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.807272905512724e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0357924742141374e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.49928616892472e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023997213614117795, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001292153112112649, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017651514200036147, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007874047295339994, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.432627481643642e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.378408709877605e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.162045902516049e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000259048112053354, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002451014027687803, tolerance: 1.7283612385519973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007475277450144273, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.746035347157838e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011172166034844125, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.50528176162216e-05, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.998854373458727e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008322656310285059, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026700474872461306, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021560019637053597, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.051691914986048e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6142861547873726e-05, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.851153295000899e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008873126817104462, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026997848682527165, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001831912558762066, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.996754675143123e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.728286054654247e-05, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008858566070775523, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.651070122498705e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027698493816190675, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.499156796728589e-05, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.739176178414085e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.163483511700287e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001163075327921155, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.897060678510286e-05, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.167743857331346e-05, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0010589174502335444, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001724925891531271, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.052700303290314e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7220800083983115e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.050e-03, tolerance: 2.169e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027344999528532143, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.470721704117174e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022333484024226156, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.591157719734378e-05, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001233307580887676, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0010753943204092863, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012821068939916636, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2461718425986765e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.881736277781618e-05, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4142380923230025e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.528517759739257e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4954091402663858e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.510887811773865e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002530832381704446, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029277267988749023, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.001111511104556354, tolerance: 1.7403849272947607e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8607401249788594e-05, tolerance: 1.76478230743802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3775917329571863e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2703390188465525e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010912875022796135, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002549853939103515, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3683220879621514e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019203989036228712, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.275217999890647e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.222408494670109e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.253833315760817e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043306954659602485, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013305398350119645, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037642851521862454, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013755787977918002, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.250459221725495e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0914701964490356e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.438006488324646e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.504391184174082e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039950883720359705, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.203283720884184e-05, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015449619397556874, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.163151959587195e-05, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2812673802604516e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4488337139284833e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.36891078990882e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005390332257342468, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.733684182466058e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003977641642340119, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1011520198130698e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.053863899201605e-05, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016662353623108674, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8678510121186935e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.966717893260849e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4016912517299538e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.923284742515798e-05, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011471675462247557, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.446120291608664e-05, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006083918431437074, tolerance: 1.7440856371827827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.086528943137829e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003899510219620419, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1207167498256286e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.498117324960513e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.965164150823147e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.164086914030824e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.441620538716208e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.383763099505931e-05, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.799742045215623e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010522062586655785, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.699320987707658e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002766576581755579, tolerance: 1.7370380877030586e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.953911630797489e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.546937718821631e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2619092797571475e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.613974127596094e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.282633208724628e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.326885853314267e-05, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0507160546689588e-05, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.495816646932186e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016732441378165298, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5013047949607823e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.936099636116374e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.691596056177749e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5006071499997055e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.724423240278115e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001846472263163472, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001468233196488108, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.038684563937439e-05, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019114821288155476, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.900906356776358e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.488070656097497e-05, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.436173928875839e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.685046804339023e-05, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.950333524307346e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.377555051835331e-05, tolerance: 1.7563958320006766e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.084068410162687e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4358132665297516e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001910993545517995, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002308618799953463, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001146968734687167, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.623965327472333e-05, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.248433666800833e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.118408931497226e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.108788344287656e-05, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034813234295883247, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014828630225949008, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.435786810004816e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022195406681267855, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8353064412612863e-05, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016212383861062205, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.569028821918135e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.764492799234777e-05, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015593640379545776, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.136371507164945e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017469617117757408, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5932042806705033e-05, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.490684671737462e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003219443909030992, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.691628112168638e-05, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016657676637522184, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.574796583202168e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021777616001093097, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021959953661691844, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.898613992949119e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7627222052243874e-05, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.18285104245584e-05, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2210837846605046e-05, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.303927126288667e-05, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021737869443990126, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018467412775581733, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018749065754894174, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.718595359439834e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001732193509020475, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.235896354621004e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.390178643598526e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.599374199833154e-05, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001601347419960446, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.404071749961578e-05, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0270050889048827e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021187880442235987, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016805099549001105, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010199556764705492, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.979507279535058e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.81550073826703e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024043438673756502, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.786472472812075e-05, tolerance: 1.732681950089463e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019680680848287863, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002601237078079374, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.444518981972661e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020583190772752863, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.329093285342824e-05, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.888569766832042e-05, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002101499676206535, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.13058617207669e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027219649490141384, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.24063158039039e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031693459551037674, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042416584558007363, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002173542024254643, tolerance: 1.756451270039519e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9484261516480514e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032759097713650886, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006359988658965112, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.09972870773235e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.321553034988517e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023706053276061848, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005784433451886213, tolerance: 1.7779393071296636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003372433493732969, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9123008511444896e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.652666326097633e-05, tolerance: 1.7334645261137576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003763667641206586, tolerance: 1.7421492696148984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010775463036871713, tolerance: 1.7531443885194882e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0009317289589172474, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.166377515833978e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003402362140459119, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.304605153056639e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7145483412042695e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002569502375224359, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029992769649146017, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011734261886456321, tolerance: 1.7334645261137576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021398511155862852, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.480607428834985e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008722836805720282, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5619949248989063e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.782816484102988e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027306193824888896, tolerance: 1.7535737386626668e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7881246360621573e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.796298544499934e-05, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030570338314039273, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0009097195685459714, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032986959591836153, tolerance: 1.7334645261137576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.80604228993935e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.822411702249749e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001349062690678129, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.585654969632916e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017947041338646323, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031969755695873334, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002125203879826806, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0009711034557058126, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.335641404205341e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004263214961675345, tolerance: 1.7334645261137576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2366121963026355e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017424530096358474, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.009789297733165e-05, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.479874528161287e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010787142554729082, tolerance: 1.77743120086355e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004558018773226794, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.778153235168164e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0010504543157310768, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004797147892473298, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004126644596029825, tolerance: 1.7334645261137576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011541909040864613, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.049957684279832e-05, tolerance: 1.6963021374230192e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010283621762813971, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011591349848620591, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000478123752840935, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.353376728539424e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0009916320169038127, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.157116580385303e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002468687846828889, tolerance: 1.7334645261137576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005832407805458038, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004880870923443456, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7687419609623695e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.645691135844691e-05, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4295828886098806e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.070381871184442e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.812122878446995e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.150733875445786e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0010590793610067862, tolerance: 1.7362309126751592e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004798175974069594, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0241348857695683e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012364984836971474, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000105730356593438, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010013837605747297, tolerance: 1.7260813223140528e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.617832658920448e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.766e-03, tolerance: 2.210e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006299912870334741, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.400589275052296e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6339121834331042e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004668979532490656, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.326625542505074e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017301592146196434, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006770877203673854, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.97887985719878e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.070722338778276e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.25580551765528e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.56432636824906e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002132082314831047, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003769778523192765, tolerance: 1.7355657383586286e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007172325886723192, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1372862691600852e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010948760296189668, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023719881856805285, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0444082776408714e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006812817114835671, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8560368694438625e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001294501392454621, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.955e-03, tolerance: 2.193e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022762143254039403, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5529853317713194e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005649432225184273, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.639105918520178e-05, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.80826052859261e-05, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024666604354169584, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019061484902374408, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00045194493020008636, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3724791826397126e-05, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023058405695045026, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.938015249423845e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.630e-03, tolerance: 2.164e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8595517980669824e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002525043283632209, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8093892806533945e-05, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000372609655878103, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003242099300177414, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0264275305769426e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.512111298324203e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.110127119840665e-05, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.719831199079434e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022313514773277584, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6546905393469546e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00047824114545053434, tolerance: 1.7576400759292985e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021451459392453527, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.329662810713755e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.960031507399981e-05, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.605405590588177e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.490501363759147e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015601144866051603, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.445138533373019e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3198017612423988e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012452787845536502, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7100090756902434e-05, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.01165313231464e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.601e-03, tolerance: 2.163e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4772874333139905e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.188854222021739e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.322053209866687e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.800636735210518e-05, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1425526220385238e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.491297553136218e-05, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.133480646846972e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.992844883260685e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.955052335612787e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.887177733583348e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8991727316071405e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8383447687474186e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.255533272370284e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002251182972292454, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.298509242568575e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.305232605500292e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.490477958149155e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.292961093989601e-05, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1563587376625254e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011984996784640198, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.499626739459938e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7625089395512106e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.229925159102531e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033821730791987973, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5194533182453594e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.968300429182386e-05, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.958349482124709e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.629655556068069e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8367288700787376e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.20621594323968e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7903718419354925e-05, tolerance: 1.7210293285069343e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043094254109770497, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.683034129135573e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9657015019921252e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.535171823995133e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.746604905127605e-05, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.286102991025171e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.675272429841351e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012683797383062669, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.084693996803771e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.991544007331271e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.177179427806722e-05, tolerance: 1.7210293285069343e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004210527981814878, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.626076124123954e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1326577719510635e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0463468204058528e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010229915260415168, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0451144690136576e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019722311270933476, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.279529992264359e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.717850195632097e-05, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042640894996865027, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011190044512914346, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3884440834166104e-05, tolerance: 1.7210293285069343e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.494805244598845e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.599969724583715e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4180351784554993e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.668e-03, tolerance: 2.193e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5413828296783356e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002415944709675405, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004289108161617764, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016243363144303156, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014027202184061383, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8873895622960324e-05, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4362372644637365e-05, tolerance: 1.7210293285069343e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011079268990070272, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.798274053716047e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.263447364089145e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.816580884792627e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013633275737131266, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003861257538256887, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4419359663831646e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025879782402133625, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016157206641264407, tolerance: 1.743546468793946e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014641124226052495, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6065122164333445e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.518237994578065e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0926058741107673e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001358912177432791, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036091673135124624, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.340122948864617e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026573788943845805, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001229129374216431, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5019155643561256e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.984848152297602e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.328601617229182e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033800170494548577, tolerance: 1.693205544150602e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013452304435710404, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.902160863030694e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.684165194119647e-05, tolerance: 1.7382318974770873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026604866831901955, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.879853174579989e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013328028237042516, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1997165427611876e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010205087947461229, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5390694089993948e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.607100822656777e-05, tolerance: 1.7210293285069343e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026431652004191807, tolerance: 1.692347649970425e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.514238506468863e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.830966846262069e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001669262339006189, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.355918931952819e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0021291367718014e-05, tolerance: 1.7218553407977345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9974616150164604e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012273196875075415, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.688041967279883e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7557125822896475e-05, tolerance: 1.7210293285069343e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.052719932320017e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018703200305083927, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2726710508836484e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.471270093284497e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.823080405541702e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025881587437122204, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.980159292174382e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6239690696105294e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4310270553131184e-05, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1928293913515973e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.5291181830485e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032624672785581434, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022460399435481927, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8858148222713686e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.340730288978852e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.234116350391534e-05, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.272127339141322e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0406810313879087e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3527091496892277e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003728337478725889, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4941133772532305e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.125962563676481e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025468641491822354, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010227590519137995, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.95083878875308e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.864139335516911e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000387299909581266, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010558700429352114, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002793311736404404, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.457535273771703e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001116823763916263, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.787365820618344e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.959913870783336e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038970365465893896, tolerance: 1.720235883539683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011580289296909095, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000299252729200135, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010282128838487795, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.33310057830349e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.331283135880462e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.841215223116122e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003149996296081016, tolerance: 1.662566652458845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010687513025031334, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4847708644215073e-05, tolerance: 1.7077240480989654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8397015445410026e-05, tolerance: 1.7548564006091585e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.394146785766597e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.358799456134776e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014942838674386442, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1220201186073215e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.851015793640249e-05, tolerance: 1.7077240480989654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001115515984117842, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.98356831359058e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6244296562453334e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.728924117072523e-05, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.872207121228966e-05, tolerance: 1.7077240480989654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.191097691702306e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.55094642588168e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.733246728218967e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4536375792672556e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.674943119843122e-05, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.125385405247823e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5055088718208977e-05, tolerance: 1.7077240480989654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.210968385311582e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8624491768445073e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.461566825901766e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3308484622604374e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.39789269993997e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e-03, tolerance: 2.163e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016774095155576776, tolerance: 1.7210293285069343e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5733201975658426e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.443886790221323e-05, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.934285838275674e-05, tolerance: 1.7077240480989654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.56529399898683e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2405384398981555e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.383158226075473e-05, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.731188741773906e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002585489135684361, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003036762420599755, tolerance: 1.7210293285069343e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.05247020976926e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.762489090973538e-05, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.305974419222889e-05, tolerance: 1.7077240480989654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8327235197199716e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002910398580880588, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6259641076770086e-05, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.036679669941795e-05, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000430750604046265, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.024188125626801e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0708694088258686e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.988391907627786e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.25452152435476e-05, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.444574736539719e-05, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.539393524014083e-05, tolerance: 1.7077240480989654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003422827905266903, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.450595827759773e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005618256401913784, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003440768790833118, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9679574345279546e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.842351340640486e-05, tolerance: 1.6881831361403076e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.436049610896062e-05, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.623257162024666e-05, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9035129633474266e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.927675055872032e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.180153646518377e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018042195276750383, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006592987673203904, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004993342756495229, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1353695322889185e-05, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7729097091131324e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001846935762346352, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001773292459473618, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032356006876227243, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.04150658933826e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.378327870047024e-05, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006385679410576351, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005719546852737825, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.238711927275705e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0288439666700867e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.890061048766053e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.897088148018031e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.483461870442902e-05, tolerance: 1.7398519350472792e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.300018676986402e-05, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014884814083037604, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002960593015158379, tolerance: 1.756041405451753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005515634865736856, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019330946447350394, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011805626945969594, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5806901015956035e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3049406460107164e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005736372870751381, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1137873301363226e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4624143598754875e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019110762711560016, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001219222846783781, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.706698192194706e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.951014245009028e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004603347646359862, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1588286920116934e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.074626711405259e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018556943197906017, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002292083756588086, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005683738308753473, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.017611988534452e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016551698511597934, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9604848114839965e-05, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.296143552303273e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7616163449293435e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.757520291553136e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003755313963725443, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.460e-03, tolerance: 2.163e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002664544999633157, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8038608334605974e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019041434748970788, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001747362527358045, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.361277093215733e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005545733296405606, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5809363262227084e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0215372452222352e-05, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.877238491960531e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9219379937996462e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028238789883255205, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002850956255407086, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3509139336045856e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.556750210025673e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001498197659267099, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011407634462096746, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7797288068820078e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021127906604963102, tolerance: 1.744479697988865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005465410127147583, tolerance: 1.735781981150952e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002636415961669407, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.22705969050081e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002067162591831125, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.242816551532774e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1801712422307024e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001227916176887254, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5924244903436158e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.923716210612828e-05, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026408585148652176, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.570e-03, tolerance: 2.120e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013611828452529326, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0608535714819e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.626987510899418e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1749512903911777e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4003395799729668e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012840151566390068, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.604111066262371e-05, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002837007593776272, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.278115402178961e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.081381974969974e-05, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.363885487143146e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.888566147925364e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2390629269395876e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.411219218642729e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030297434383783333, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.083432852340773e-05, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1851888035002986e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.481629499502801e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.01555720701258e-05, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.277114949574235e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7362243948651143e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.500848433089364e-05, tolerance: 1.7703605697170486e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0762591552307775e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.558631404945508e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032418466898869423, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.760335754257298e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001310934899210114, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.965542792532463e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.882846609471087e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.684070502845483e-05, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.267991014963215e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6990653663899684e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.290449763276512e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033044035060864497, tolerance: 1.7300279913279235e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.33673179408124e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8835445082673467e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018029081139024714, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.289501859108558e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7468884288194403e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019004010064564356, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.501949939448359e-05, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7836017379327845e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.791315511677892e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.391065023781962e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012725533420271298, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.001728882650513e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2529133610796337e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019997224955684966, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3566558367002005e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.992270519116378e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031192463479056635, tolerance: 1.7777732986950974e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.892024774743724e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015757167529966212, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.37651069795973e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002820512053286501, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8269307795505726e-05, tolerance: 1.7396936286606247e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.71461677611999e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.19789241219707e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.886979308071058e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6290792661840673e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2995212352303757e-05, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001954384954044484, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003405325656947013, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.739064348218216e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.246259731707688e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8193193138924266e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.254982241803622e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8081572200697935e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.0849119888583e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022068771219271519, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.266133298727338e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.649322272744544e-05, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.308961857873351e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1990707094694806e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.661743972347773e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.410005601971893e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9425233881266374e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3013966459279472e-05, tolerance: 1.6738769490706856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033906620414699343, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002607336964523931, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5881624983853033e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019220068219831868, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.891142600818658e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.344126640224189e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.382273194314054e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.563611968466189e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8747015293687297e-05, tolerance: 1.6738769490706856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.906207179273785e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032801487886818543, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032272500719766374, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2158667211502614e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.695788028559895e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002612343369430333, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.875184348517944e-05, tolerance: 1.7817538551827285e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.533992336694861e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4474435376968008e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003699395754928922, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6638626878821257e-05, tolerance: 1.6738769490706856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000305949999370992, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2041782584841102e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5901569526200555e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003112861230583578, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4761687616652733e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.977596917676714e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038722863061861103, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9645436582331477e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6751486223983156e-05, tolerance: 1.6738769490706856e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4759006965479474e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003363061538160482, tolerance: 1.707151417633384e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029384808036986634, tolerance: 1.6387349072509878e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.563460443242315e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.463825582050584e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1279187223086776e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.112073327832734e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039581755131222734, tolerance: 1.7461013473447212e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.584278045739488e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.549e-03, tolerance: 2.177e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.236282980488523e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0421712974377088e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3303205301587054e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.216520555131112e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.081185929740935e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3326391808973015e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9503891372188442e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8534483757835256e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7188758452220338e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.271e-03, tolerance: 2.194e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.501136263355669e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.104072351308579e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.818631985078781e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5191243236747513e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.000947119203485e-05, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.08860995579898e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.86971953303374e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.671124977063436e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.01770688318502e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014802810020564904, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.801528694379053e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.557824119936579e-05, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5204800695230274e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4279453758133405e-05, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.590854182698946e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.697198733640608e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.005220928793082e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.634158302447958e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011439212247135386, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.340247853984815e-05, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5947241791189624e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4540658996912573e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6176370967973964e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.810136212867513e-05, tolerance: 1.771399330357146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.155624749132685e-05, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024389048664026636, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.401705028850514e-05, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013122118089265757, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5751127393783962e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.074e-03, tolerance: 2.154e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.12509530752286e-05, tolerance: 1.7732233123922546e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010222186630278081, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7064496005445304e-05, tolerance: 1.771399330357146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012046312949265739, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6620200326024634e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.903985177900054e-05, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010857975968527923, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.524597528900553e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001678760627412748, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.431771206881997e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.81246639711976e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003030771341351636, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.783497052656248e-05, tolerance: 1.771399330357146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2304084233190635e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021056837857347938, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2164808498779942e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015289874043186713, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5557663855191654e-05, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2967780785828826e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.781648612929343e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001952189428098012, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8608004300958714e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00047464131036037416, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025481692544412904, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013989810952821924, tolerance: 1.771399330357146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018370203531295655, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010395317132175664, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.756774279277142e-05, tolerance: 1.7432402614078753e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.751783017380642e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006128299888194938, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9983523206020962e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023918051094063905, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017315509626785082, tolerance: 1.771399330357146e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8629817493255882e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033111229867526654, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020883264727143865, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014951242788229652, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.071065699237143e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002965854891236533, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.902006431436185e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018523137101826744, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.935454392762458e-05, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020737436655941105, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017299160192982103, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039199379004868987, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.108002541742544e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.237036646608135e-05, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024215035191285303, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.70697440797555e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012993996466323376, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5126253946033787e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016108744406568166, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021321791375322361, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018548907054315514, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0455050379137446e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.509715694894564e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004853173297082545, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3516860510807095e-05, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003173917052292242, tolerance: 1.67477491461813e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.812148569725838e-05, tolerance: 1.7587561070259605e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.044863845716767e-05, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9845594176457274e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019700284863951884, tolerance: 1.7176421286414245e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017667891238584005, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015258879032443735, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1860871639215948e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048242036210288143, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6773234291563534e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.660e-03, tolerance: 2.190e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.653780532013843e-05, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.652985851316636e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011638298105022971, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.295296285982588e-05, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004865267253573585, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015464121980061137, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010809766716061986, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.47214652375904e-05, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5326294109520586e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00046005742361839713, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010716951317027868, tolerance: 1.6805776948243564e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.333537664897707e-05, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.994930319819838e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9996310650480694e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1193938343775157e-05, tolerance: 1.703738231924986e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.312087486881694e-05, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013448754909142812, tolerance: 1.714285976876768e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004665523293574574, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1613522064721502e-05, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001305815276226776, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.421872336684852e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.345432291460148e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.249409119501488e-05, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048238709357001884, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1293698824797914e-05, tolerance: 1.703738231924986e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010745510274080307, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.808622896378725e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029488985388702923, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005051725443888399, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8025013812387504e-05, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.198061818043268e-05, tolerance: 1.703738231924986e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6428577105556267e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011387852127776443, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.155841948921997e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005225406122933277, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004509993739271398, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3637155437541274e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.792277714974741e-05, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7790900134686165e-05, tolerance: 1.703738231924986e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001380859226497045, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.758023383129347e-05, tolerance: 1.7303691192868782e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.363939958122818e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005213946297551991, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005740884146021117, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3243179542602188e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.274253968049221e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.255e-03, tolerance: 2.161e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0482207592646684e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7618398913987867e-05, tolerance: 1.703738231924986e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002089125115844609, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005161487195998039, tolerance: 1.7329781778698035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.420004197983491e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.339158975224036e-05, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006658612933015196, tolerance: 1.669504825796865e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.725834541801342e-05, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0567576235211438e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025199517211646136, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.533575895919941e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.064050457081505e-05, tolerance: 1.703738231924986e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.47502599382798e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.70340664571021e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001082232855181553, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.395975469548551e-05, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010440242577316235, tolerance: 1.7587561070259605e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028898414686378496, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0450562909980763e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.183916716994732e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7140157256032184e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2586445875673895e-05, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.457984527683674e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001277737454990109, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019585138087443305, tolerance: 1.7587561070259605e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.984081723856168e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030373332957902687, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.721927635430688e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.894165652549268e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001277074588924114, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.445992827185538e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037442455339158423, tolerance: 1.7587561070259605e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.846256408011282e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003049098096858452, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1357319539345998e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6601662758099498e-05, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0569187971226106e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.757784129054635e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3258845300841435e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.275166168643234e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004827364975881765, tolerance: 1.7587561070259605e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032345286677967853, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012022423816147008, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4116555798165545e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016612108647238652, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.10932699897734e-05, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.533006081081439e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.384704690658543e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.446698317361666e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034191058151822155, tolerance: 1.744220799957067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002755704527752003, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010432457232686584, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3069343066731834e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6675887122470464e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010869939873113695, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.980389013847959e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.009416274328547e-05, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003534616842325051, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.822557305874668e-05, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.73421850724766e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.683689522939655e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038358027439355617, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.40216142580772e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.395046583948475e-05, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.861713331465127e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.37362363593258e-05, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.936441203520555e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010584847586648117, tolerance: 1.7313781946851943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.888747605028652e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.661704486943295e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039025780503078485, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.619939631824287e-05, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.0625055317056e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.827768434198602e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012218318079756235, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0123534421716445e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.513725299305036e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003675890698345199, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.765477908143684e-05, tolerance: 1.7336073780212823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001001160313339744, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4505951015237286e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9947874359754064e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015869295027170702, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.29662461711661e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003315246533139027, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.267042183629462e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011524239061486488, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.043138055377608e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7156258177749087e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020759113835607662, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3623028548924035e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4993770643703074e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8221987398441344e-05, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030432274682353974, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014105260277414572, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011524484123925233, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0463589007799315e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9428613101986e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003003282628748945, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.328184264479901e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027505995776096887, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.721469171376202e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.000542364065781e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011259222940044819, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.109325741391036e-05, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.188955790542347e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.111164555467133e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015885826094856788, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.037121904394101e-05, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010744552166087364, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7438554108644214e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023696863428704883, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.64398764061899e-05, tolerance: 1.740993720633414e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.054436179854991e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010711580205310933, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.063737678492105e-05, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.091590514453686e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3528973204766865e-05, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.090830362790809e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.888032956636799e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.467594942804981e-05, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029478017982327055, tolerance: 1.740993720633414e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020028778770571527, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.322196358899765e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017350709913646623, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.457276546843616e-05, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.418668800050765e-05, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.680495386699465e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013365681045046998, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.943427454363579e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022055750506504157, tolerance: 1.6855029804344784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.104919215061243e-05, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0800936685792396e-05, tolerance: 1.738143182441704e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.02509138196748e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038208772632799745, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.980255724493069e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017638538231219778, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035558237726744484, tolerance: 1.740993720633414e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015838045333306516, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9618171335347817e-05, tolerance: 1.7020755670299735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016556945940724745, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3981635976297895e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004945953160986286, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.112857293365758e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020634013699330142, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018125611724362223, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021093396452748388, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.737038121443217e-05, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.737289094321859e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005480127755977914, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.344133151447076e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021546922133620222, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003882718304483966, tolerance: 1.740993720633414e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002358060812205319, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7278391396429067e-05, tolerance: 1.7182003036922617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001734758598331311, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011137467686232229, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015650161779383852, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002872233147515821, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005266162081354348, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.899056369415741e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012238832438191474, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004083022269020086, tolerance: 1.740993720633414e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023651453199532557, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001725721111672969, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.074047930542087e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014881268125789202, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004259526491083975, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.885258611125748e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033328623080178775, tolerance: 1.7628668033889722e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001414783004538827, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024079615538424512, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001937356473175767, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3562925086688218e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013723998603515537, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015770938938942589, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.770570593666616e-05, tolerance: 1.7182003036922617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7794982365054195e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001233151070921838, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030234262078220487, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003753780396912069, tolerance: 1.740993720633414e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016072630649926773, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0438559555555763e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.107617228703021e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012790852010194365, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026802535322310514, tolerance: 1.7598612472634045e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.364123841494322e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010553409513129218, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003101704832479315, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013034126754688131, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011874251395196239, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.062057569936608e-05, tolerance: 1.7182003036922617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.003137960925397e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.062624386259525e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033644789360152364, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016927774022332774, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.009551196565298e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1736171248109074e-05, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.30970577848753e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.080294493365248e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.943895486845065e-05, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.732750564447787e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.718358574122826e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.23158918219048e-05, tolerance: 1.7106587674926712e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.229239742707363e-05, tolerance: 1.7182003036922617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003555792774467162, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9884907141679114e-05, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017059064932003, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.907742461162558e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.980548684747674e-05, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.355222860013148e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032684944356215634, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.709238645582525e-05, tolerance: 1.7694913961630628e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003672489122465254, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.562594105198952e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1435168055499e-05, tolerance: 1.7182003036922617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2246297128708046e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012095605713655436, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3752396609153653e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.320301633005968e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010067075379963791, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3528443287165746e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037174527201841836, tolerance: 1.749251157096363e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.893156672455568e-05, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004954721253612807, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1969322877556956e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0089775874941188e-05, tolerance: 1.7182003036922617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3051275149978817e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5625842822714924e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.674947261934311e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013508412487784477, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.713873065569774e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019140102893544508, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006124234759624769, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5961009600244416e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8665185121016886e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.992076905662248e-05, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1450940153829035e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023918218388313366, tolerance: 1.7393912613066166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.61223124569961e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.746605797254169e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002391367891654804, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.116888092541582e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006603024350060292, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5362490795835524e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6032460254721973e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.560975723505282e-05, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.995095587043065e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.96412075090669e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026022019852414875, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.750646472278028e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.767249885099609e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.16341316002025e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.847824379248521e-05, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007772792113087366, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4108941891660268e-05, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.789965469293073e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.760062511027229e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005498077489645651, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.480431527677079e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013746110726734623, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.84749782614504e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.759058436049773e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008725657545423544, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.403192331164623e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0462879292353076e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006146348196001267, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4280722148330505e-05, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.72279940970569e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001583320949708495, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.80097973187032e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8098592161998554e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010943973807590272, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0009373099575283369, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.155518921754666e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005998770771539971, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.322404846890995e-05, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022841381343744957, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9355172475039872e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4076653557306843e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.894414608166235e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001115549958966653, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.241401573855917e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0009562243036393397, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005729983493017142, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.470167523653808e-05, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000264710956665545, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013299770084436273, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.392232950314878e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.852432217061377e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.238583062300213e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000950779577844011, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005571068130522617, tolerance: 1.754629124429021e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004027333867425379, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014259479917863756, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.858e-03, tolerance: 2.166e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002912488211694927, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.017941856227132e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.153463634095576e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.66966207463834e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.278703967730945e-05, tolerance: 1.7552024595640514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043366948201474, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008911702942589923, tolerance: 1.734211512334171e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1391795531592803e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014286815957239782, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031863708838706685, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4459646760940923e-05, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.082704491392922e-05, tolerance: 1.6844405746286857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.080332045083927e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.386006736966646e-05, tolerance: 1.6786225644519886e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2480287246950253e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043657661936031926, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013235111248478468, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4700575179436606e-05, tolerance: 1.7552024595640514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034075803252738967, tolerance: 1.7422147607758583e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4790142835637644e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4243478982723457e-05, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0362276384338083e-05, tolerance: 1.6790080451808864e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2591091156296678e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035206813495785444, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.899061795252568e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2257784158048743e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1098819663086313e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038419679623538563, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015924839255444453, tolerance: 1.7552024595640514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8745580959246555e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4165341116294536e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.40569907219309e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9579103063912043e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003823281675026111, tolerance: 1.678484937525842e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0542189814326765e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6541076031089128e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.974838597231665e-05, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.704132029218668e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.743647349820089e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.53048117546443e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0509206390414105e-05, tolerance: 1.7479493697764802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003526886821469434, tolerance: 1.7552024595640514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3966703914468646e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2511840639765496e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.492083787219347e-05, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.530322108345578e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1162486542492896e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0313391087332404e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2646739116841406e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.310422129272281e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.402764250887719e-05, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.633683210240145e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9558433443797596e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.672166864697069e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.754847495548833e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.247263425876611e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5028855742707454e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00051665608295463, tolerance: 1.7552024595640514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.530534201132625e-05, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0184971480138054e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.228080855454055e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011189301033888519, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.621087359414776e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.294865011422039e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.510e-03, tolerance: 2.163e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.405269596484366e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001424362964947655, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.935863822343566e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.933691723932459e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5246039017117264e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006453341805382137, tolerance: 1.7552024595640514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.221028423374677e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1362982528836125e-05, tolerance: 1.7210892355069916e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8245962437574148e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.90220580514071e-05, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.92324509896304e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021166005367174827, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.871963933303399e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1266483736308642e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.616232920562653e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2602590339592865e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4852929175163226e-05, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.747558406935655e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1224116201609394e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7600403082415556e-05, tolerance: 1.7210892355069916e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020912324336670035, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5005121935275116e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4494430208722717e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.093646065262169e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.550921936210411e-05, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024059750326159424, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.48606024470283e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.024057410280472e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8896026175486275e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7260846001745526e-05, tolerance: 1.7210892355069916e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.47964607993351e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042818816838047745, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8162524523936824e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.508392515808167e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.203099090466504e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.058148933981924e-05, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024957093159742264, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.351156543219061e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.302976650338302e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.899385684726168e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.916704490836224e-05, tolerance: 1.7210892355069916e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005845425116974435, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.129898667828199e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001694842761406322, tolerance: 1.724800454188142e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.991054208850116e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.931656420400932e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.117193939389823e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.595338806040252e-05, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3988239349614794e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9774017780381107e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.264551854140738e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002518247376102952, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006827142637114737, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.871944530737958e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.646781243373711e-05, tolerance: 1.7210892355069916e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000370013565835531, tolerance: 1.724800454188142e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.358858467542822e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014508527011530565, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.218977826267415e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.33071073520459e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.905157528469281e-05, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.653917071944803e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010117353890819886, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.385418665396918e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004270508511636361, tolerance: 1.724800454188142e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007564243634370083, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.024653982490089e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024593103339493744, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015304384042011052, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.014822498507557e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.192185815926874e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.506712087917671e-05, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011198215197156386, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001046544829786649, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004880118073514279, tolerance: 1.724800454188142e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.784161201264013e-05, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001176427396902581, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003996708938372607, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015206187829451025, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.828848229936414e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002403278893304949, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014730072574277442, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011985128173584714, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004977149284753849, tolerance: 1.724800454188142e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6808764748726764e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012826219293574775, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001106554566501234, tolerance: 1.7646389428499322e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018822619328166118, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014919453622600463, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.637913487706008e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6012846885952595e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001799661025984698, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001158038941619418, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011369096753085194, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000471080561756064, tolerance: 1.724800454188142e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.106294057377145e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023173465177708276, tolerance: 1.6846227292387576e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002673406759508678, tolerance: 1.7727319804612007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.858380037269563e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001482941583558989, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1423189568155485e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010092395788789069, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001762850063032279, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036800501876681994, tolerance: 1.724800454188142e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.65894427910596e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.150416863703266e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015732446229833972, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.509743635331992e-05, tolerance: 1.7544787373737817e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.427810561624933e-05, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.205048118476398e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000184635316518909, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.681636419704219e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6724058018125565e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8715984934783783e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014263133995956467, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014445955468402397, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.126123383220743e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000195727425644757, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.765896181399814e-05, tolerance: 1.728252178228147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6072021359447445e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5207506567720756e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.507900138014605e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.070255249349948e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016419563825237696, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8887185073510754e-05, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026395772780992387, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2799679587067606e-05, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.686e-04, tolerance: 2.131e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.449174131798071e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.66512250410367e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.918695795651337e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.580617732763721e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.189530810511661e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015834719465132943, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032707145779301613, tolerance: 1.719133534462439e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001545517908615613, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.192549181754752e-05, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8173758832853585e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014660252900662064, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.0775215788764e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.179007837906665e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.668024838568296e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015145107766134994, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000358258817828065, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.543017450910498e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4793596891240864e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.05188468384088e-05, tolerance: 1.746418995074904e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7093528078510343e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.852728037762227e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014552844350538673, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004885393399612576, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.169768349271372e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.523538599728947e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.511564564020162e-05, tolerance: 1.7280801190617685e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015160496697623742, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4366583307126183e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.272672917681914e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005697594834555269, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.873216559513795e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2101915441823106e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011317692508623113, tolerance: 1.7384667297079994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.692764354198412e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006315958702476261, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8035029670362968e-05, tolerance: 1.759999070660552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.181620376868429e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2375926092148973e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.079618703507759e-05, tolerance: 1.7635750717042516e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.782181000140181e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013883558982553945, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.647833045589062e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006851351362682848, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1627442508852235e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.764506909609931e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8831151536566684e-05, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.631197274317488e-05, tolerance: 1.7635750717042516e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.57393183130973e-05, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.209494300962165e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010616568246308092, tolerance: 1.759999070660552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006552350097065842, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3617947313126515e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010728323529904903, tolerance: 1.750976177612714e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.313276218807476e-05, tolerance: 1.7635750717042516e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7298244811119953e-05, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001773713217708469, tolerance: 1.759999070660552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006203372926467873, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.616710357311377e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.45095352328978e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.332995134144867e-05, tolerance: 1.7635750717042516e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005885569091154492, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.731034136287507e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.513154782817552e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029031488450972573, tolerance: 1.759999070660552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.599066737196727e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.467e-04, tolerance: 2.192e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019335304048849825, tolerance: 1.7635750717042516e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.537834204139819e-05, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00046319536289001016, tolerance: 1.7377668804712065e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.403871064765196e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.966831800849684e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.452513008300183e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1937142051588485e-05, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027745615387432185, tolerance: 1.7635750717042516e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004723519639081991, tolerance: 1.759999070660552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.978677680082545e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2110197065606605e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014925631183310487, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.077509710812387e-05, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.859724224421034e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003156905596956843, tolerance: 1.7635750717042516e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.770647326081309e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.006051652474072e-05, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005795456502344079, tolerance: 1.759999070660552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4265418510163954e-05, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1568689258770766e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.342851794384873e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.881188975490406e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.964114676422356e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.481771795385522e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2715858729118604e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.754509997044587e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010711410339805244, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3349623133865506e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006265239519964438, tolerance: 1.759999070660552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034943198651274897, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6702277892428715e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.582925909631669e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.001048307042057e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5760485760706504e-05, tolerance: 1.7243401029397784e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011707884208246241, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8981779426525897e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3832282449433306e-05, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.35378960272005e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005465208097538005, tolerance: 1.759999070660552e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.381481309196714e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005412245010372827, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.020244889506012e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.704240109260459e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.459066480260593e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011603225359812716, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.241790073718956e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9726934198749033e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.448357879017003e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00046060028873480107, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3046852154896296e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9092249549155305e-05, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.353932485652313e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014595504782989946, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7848250430790736e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001187768618559279, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1222949493205435e-05, tolerance: 1.718254571219452e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9254034359083086e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040640291454072195, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9510947043853934e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.947093392278264e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.413819937401208e-05, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7393880665437885e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.210520232002807e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.558768542248595e-05, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.30440407178254e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8268021432897227e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00044547531434098973, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9570683117504867e-05, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.052186950848907e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2175113770233424e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.338557677049508e-05, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.01872666414557e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.916518686772688e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.833048185391489e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003917577863850395, tolerance: 1.7253274606548474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1919925775811498e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8513220266093995e-05, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.785845121362854e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012663604486669658, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7537013288897166e-05, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.069405744588407e-05, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.906395068941335e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1703549180678017e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.898738898642541e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012740394269241187, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2127254292249894e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011918886564864228, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7395340230335135e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026283445168835164, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.707488234758766e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.988049247153784e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3486907320721673e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015420009424779637, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.221682988333255e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.490785709923388e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8875449301829503e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.197612423736916e-05, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.611943887804698e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001156702436358268, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004221806607612212, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.247246217199593e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5557053629354726e-05, tolerance: 1.7603552293964808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016005133260753573, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.745432595273241e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3662415811250698e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.664217642406809e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.860836429990481e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2611776455927837e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9128341227393764e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.960528710892508e-05, tolerance: 1.735304383357802e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.283616521728531e-05, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010890152495200527, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002407734629462279, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015669357831945077, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7841231390220156e-05, tolerance: 1.7603552293964808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.600508594531245e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.162640219921448e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.625712615699388e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.098868879316754e-05, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3285704715373355e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.230629657433772e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001860322506133591, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011776486256520093, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016663867355907244, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.620175496464942e-05, tolerance: 1.7603552293964808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.375101180459505e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.966948155781976e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.092464517034891e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012523465339409068, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030975360756790166, tolerance: 1.7476275562048565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014251771176320566, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016600152513630145, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.970265949056277e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.470643721621702e-05, tolerance: 1.7603552293964808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1467359317657933e-05, tolerance: 1.7267449306590606e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012222252227034932, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.245860267454795e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.114560650847823e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016347254013809268, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.188987811033773e-05, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9771432639790337e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020925817801724887, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014761264540300717, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2140089555467155e-05, tolerance: 1.7603552293964808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2820420283533455e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.791404564720967e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002754163895411395, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.957115334462916e-05, tolerance: 1.707230800121991e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011240008668119209, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.869600192033749e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001421414413573163, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002654864960267046, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.393524501163695e-05, tolerance: 1.7603552293964808e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0138624985132497e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3999021417381184e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8303658694032374e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.770835111836285e-05, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034848880218698794, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.894484592491183e-05, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8175515054912397e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013228720772669872, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028310883407641017, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.356522522381152e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8324635365731144e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.953078174608911e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011536268565143573, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.422062309372102e-05, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7545062799623028e-05, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031647658483443565, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.747145296657031e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014291863704965144, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028470892950081903, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.582708553118673e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000131012682669171, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.344218637526893e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0138753301668713e-05, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011464489843605267, tolerance: 1.7216870806747548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025817171699132196, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.928e-03, tolerance: 2.186e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9983306035055723e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027711520663804227, tolerance: 1.6614033861865122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016038877535028834, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.024151446967386e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002055843917051544, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0290966000283302e-05, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0408967359249126e-05, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026596316194977924, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7480650120983875e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.450483006211193e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.821395436308465e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011351036932646767, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1513623450260082e-05, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025480393162322784, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024679493422222545, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001561475034927342, tolerance: 1.7263125975488735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.003e-03, tolerance: 2.179e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.181068492906819e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4913683798515746e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.100013746291154e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013529345944128524, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005065521859046254, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004362927728266842, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4021616157223272e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9895510964327134e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001518306313646519, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2771874412024166e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00059375760931785, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005523983366706887, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028665917453031455, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6704975131509184e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1574429888030506e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012760744385282435, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.591647114048948e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036882627311190033, tolerance: 1.756395734212806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006011356111511879, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00045168857097185097, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.563336123702619e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.236559249132325e-05, tolerance: 1.7381776523822228e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010466667825866777, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.450365425327438e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004023974618867776, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005644670152674955, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.483443557241543e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.542653395516987e-05, tolerance: 1.7381776523822228e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.401968527879493e-05, tolerance: 1.701249972967131e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004241994497312788, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2854079730073394e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.175235559010948e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005036267732435248, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.321e-03, tolerance: 2.095e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004365495267530158, tolerance: 1.7140281028820744e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.207191771129527e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.130514294453576e-05, tolerance: 1.7381776523822228e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00044815776669474873, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9266849220472196e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5714711582885555e-05, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.126334813995419e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.198631354815138e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004054814597019944, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.203093620517659e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.101726572569616e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.56688038146008e-05, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6534245903859707e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016894339259563608, tolerance: 1.7381776523822228e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4287670611078906e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.891072402261754e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.472352782814188e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037443047559680524, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.826678802763481e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4094356456278235e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.819661721117667e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1745622284189146e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.518061899460584e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0639251753231816e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.873087961534221e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003469711410630334, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9055607496306785e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.64639819881264e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1657113458178195e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.654346532988624e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0659678080249197e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9473372586091666e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003755959429670901, tolerance: 1.7147011127009538e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.479583267513365e-05, tolerance: 1.7291389690525082e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.065625409956127e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9772263613278274e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.864895301987624e-05, tolerance: 1.746622793362029e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8923414181117038e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.116650501016436e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0868262005865196e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7114550606581987e-05, tolerance: 1.6806533534117587e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.856410609603608e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9981347493079962e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8165841475318786e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6748174764182854e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.325172506275827e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.042857892610664e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.16024985250532e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.717e-03, tolerance: 2.153e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.312843094392017e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1934728132737627e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2051053132970608e-05, tolerance: 1.710873630664742e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8714648389043486e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0108322994160108e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.345086524898829e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5104113561397884e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2956594212774663e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.670e-03, tolerance: 2.146e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.129544714364102e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1452693553430814e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.575015996882356e-05, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2248034246094154e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3142561066645702e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5528026509357565e-05, tolerance: 1.710873630664742e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.602861809865053e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.837669222096304e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001472804799457772, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.699235631962188e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.841843761161456e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.284652478988473e-05, tolerance: 1.710873630664742e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011004445695213423, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.928960109019427e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3418045912575753e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002164589368403736, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.605311602142066e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9141212173607106e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8448339831001835e-05, tolerance: 1.710873630664742e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020605447709328567, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.925881171389789e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.17334839574059e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.978657524337121e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.343545243929667e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.059859412674417e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.901820563797023e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.623e-03, tolerance: 2.148e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.675885522955169e-05, tolerance: 1.710873630664742e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011854801975166168, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.813838069703727e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4188695279434006e-05, tolerance: 1.6985983003889423e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1778056775906968e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016657480520360785, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.532474146027633e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.651466752087727e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.180571075415207e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.133678370604437e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.120168929359555e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018033646030365426, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.535286656288674e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.394050884365264e-05, tolerance: 1.710873630664742e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.324e-03, tolerance: 2.179e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027173563328696665, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010759906747571761, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010802570788031804, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.871e-03, tolerance: 2.148e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.432488138739801e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.348861601798348e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.910727434221054e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.216533401321359e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020678541764745928, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003695462915917422, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.201936481321034e-05, tolerance: 1.710873630664742e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1643442715359174e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011844803307039815, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012052293844839503, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.294208824771965e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001245809943658787, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.465244359483234e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.661844417891986e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00041835582661721264, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0406818020329148e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012550052293376288, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.461449096662001e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012663810179501783, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.537374274222853e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.776132344579346e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004716613211695718, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4778837682655486e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.703164525727385e-05, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.972125268057736e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017194801025980332, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.381161389551818e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.941526699231088e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.954523486318374e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017134898524302777, tolerance: 1.760643607893258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5359991925378665e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010391677873490286, tolerance: 1.7280136985402257e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018761836362194036, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.419218613638427e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7473304253765032e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010362503158105156, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0758574808059718e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.10044135303501e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.960592316667663e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015265395620968174, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010964048346881472, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.901182422716681e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.788329946288494e-05, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.773457381006569e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001528350785555601, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.247814841525435e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.698e-03, tolerance: 2.137e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011638833952182064, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.567387367514087e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.173662403299046e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.459871926026763e-05, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001296687407324245, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012976348091909423, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.281775105978344e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.540903705800998e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3717198177479124e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.265358283414526e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.212964486397525e-05, tolerance: 1.7288140362518007e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3897465425818344e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.601958777615729e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001928006119570249, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001952198133438436, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023038444588147555, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.301387955007272e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.116083940235374e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8882810034016873e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.212165638118118e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5509360819298e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9588092705054286e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021271950951452442, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000102477665659996, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020870051452308302, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.319496945127097e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.307927098806368e-05, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6682281176889158e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003583890132311104, tolerance: 1.747829266343442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5999185582145645e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002173127252315327, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010887294810302608, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.684759705876189e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5723932714090976e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.388101777803253e-05, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022014827970501943, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3879779931915146e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3883848520672305e-05, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.81359704251867e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002082429493652756, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.352614112307975e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002023132390294594, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011261714466385964, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.086979645304787e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2878836317010917e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.827297088920088e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.795672461826688e-05, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6385470480496873e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9489361046077314e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.134058964876946e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002387190049379269, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7380336139534254e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020400492729554486, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3860572837415203e-05, tolerance: 1.7436232741216457e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001633243267751671, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.535966500779926e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010504771124623773, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7798708026157146e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.410731220951593e-05, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7993003529998344e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.922282367969197e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.913262716336019e-05, tolerance: 1.7410810035794406e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002562178846304275, tolerance: 1.7759278428768404e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.600e-03, tolerance: 2.152e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4677427126237883e-05, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0483210625350506e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016306437016495867, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.052472358275286e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012281964035962614, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023678439206140274, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021357913368425397, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.006908121227364e-05, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2679143676976894e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001383843288713868, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001644551707866643, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036031936447178826, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.160011278321954e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2116632595973896e-05, tolerance: 1.7410810035794406e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8872632898783367e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e-03, tolerance: 2.169e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002610263462840814, tolerance: 1.7480298642157474e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.950072470904585e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014436397794305618, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034132065605332364, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2239918948079376e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001864748551185045, tolerance: 1.732777986663703e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.616370012368656e-05, tolerance: 1.7410810035794406e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.971867643998191e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.612865059641687e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015753596544859638, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.658986099629863e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3852594690736366e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0008871324569966e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2980476918687404e-05, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3380627053291164e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015514916596585417, tolerance: 1.7410810035794406e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6013313210521425e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038496217154532653, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001407624503129267, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.890077224874913e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.476821322000601e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.917617956999188e-05, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.019274431431725e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002466336191684409, tolerance: 1.7410810035794406e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3312112373583688e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1310372509930967e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011032376955097834, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6312365534457427e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001295228174876626, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.86054655366176e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026676198465684295, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6602631220926792e-05, tolerance: 1.7233161399452443e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027081226165928153, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9872562375800674e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038679918005856025, tolerance: 1.7410810035794406e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8871486486494536e-05, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9715655086674153e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.990757883026719e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.739028651819366e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013084003418732237, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003347715643549215, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0347805009066364e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010134338985278313, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4780722983085146e-05, tolerance: 1.783131842977531e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5515044313649824e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030007095353362743, tolerance: 1.7410810035794406e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012730228230780575, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.615698578168138e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6021038972666036e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003367916746654133, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000259140563564398, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.541126089995044e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010554350658626734, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.9115365989263e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012558686479423146, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033832635208894037, tolerance: 1.7410810035794406e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3077622036427395e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031710675625883783, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0830386461518496e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002651455335759851, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001307343735756552, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.234465850093456e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9335627350489798e-05, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001171401785824179, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.975665502187371e-05, tolerance: 1.7701555850730158e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002556129951614382, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7237245432448478e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017227714679323575, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013858764555605124, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002859760874210785, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1452286287196596e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012442532108599986, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1682560661755168e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001755675268561782, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0333464139740973e-05, tolerance: 1.7701555850730158e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.936208584619422e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014829510219651221, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029577576475840056, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.774489544802485e-05, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9864826498415005e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0927873761341503e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1723035328408136e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030617368011116863, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.027694289194042e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0832808069237994e-05, tolerance: 1.7701555850730158e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013634298345769052, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002884146251247426, tolerance: 1.7212317790380203e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2735935683936516e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5295774518570638e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0495166067451632e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.634193626586262e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.738472774269126e-05, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013182372863386124, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005763922321198479, tolerance: 1.780839229580695e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.022423344605646e-05, tolerance: 1.7701555850730158e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011652464772196613, tolerance: 1.7672668361249086e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.301698316647913e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.139650041787852e-05, tolerance: 1.783578086210308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.438968339557243e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9496714737500878e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001580049800211647, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.399371227131095e-05, tolerance: 1.755386826510018e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.042197060799476e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010843415831661256, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2462369603040017e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.701072084848003e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016082977370173758, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.788791897376599e-05, tolerance: 1.7592980115123004e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.1024171627566126e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.396692851938634e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015654376196179865, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6143338382855356e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.12505630637169e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002314396204327173, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.64608987422524e-05, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.615e-03, tolerance: 2.190e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013683581745012677, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.131922706771383e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8280017860329854e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3030511560863387e-05, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002772259120806409, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.373902842418677e-05, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012579462672027142, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5424135624575392e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.158577049063344e-05, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.426549425163128e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.137770674343891e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025805520760743654, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.735151958134304e-05, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011759817217107428, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9344570522147886e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.013041612277538e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013545414134675735, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8335093621018456e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3470753718371424e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022293220395964292, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.412687209713167e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.41553232497023e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011178702272152206, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8940132006888336e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001238152147965306, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010538246420106062, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.201207370608818e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.815349977511184e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.792738362175507e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.354605877005159e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017096746094437312, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.606407763647422e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.491091331125906e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.180631484165467e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.353497352281582e-05, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2189283790454853e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.773981671997476e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012146635056113977, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001640430765580828, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9480884075913815e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.452833897102728e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1439080926107662e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6818853125621456e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.236234094012829e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.754736863365945e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011203325554137028, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7412188426447284e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015284499270993863, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012534367807279234, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9457175897596115e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.130744695942436e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1030492924352384e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6831763674926845e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.847677410620807e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7406441432096798e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015865970846289622, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.746134118659335e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015322901621308074, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.961047418793868e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7460126677279354e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001773266056341559, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.574990032747093e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0620842573651774e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010340976330108428, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015737938371152092, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.608385803870225e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.057666652974346e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4803973895367454e-05, tolerance: 1.783131842977531e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016338524953143224, tolerance: 1.637028938393258e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019554268939610871, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.503633213634374e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001489624810154756, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001514950159562465, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.417967726179402e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.060887084289584e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.128739302972472e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.600425834306275e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021762760009024548, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011049791840727491, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.951020017352243e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.047578042057272e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.509068359070822e-05, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012469376234026594, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.464673083453454e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013934151372406618, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011904103427900353, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.488197118428856e-05, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.440534179528225e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7106640408761443e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.644159512839306e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.098634044242763e-05, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1655213037042225e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2861712478445566e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0925500351987036e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017347536089700258, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1833844716793657e-05, tolerance: 1.783131842977531e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.840683404452913e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.164655273589072e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011607422729458493, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.280636721007741e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001203800472933201, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.474864544384981e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.775990626038144e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00025944607675595574, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016480783448451046, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001789125187000619, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7098270289996687e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.858178933447814e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7624564029669722e-05, tolerance: 1.783131842977531e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.755441313294544e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015109112589126433, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.84527208645386e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.794379294604527e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.7140234702833e-05, tolerance: 1.700353987958871e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.808633327904599e-05, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001761406173921426, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017813292470293673, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004135217676367025, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.892345350731385e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7579546027202417e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4952934900394907e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8424759605041178e-05, tolerance: 1.783131842977531e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.512947691612951e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4577993470771025e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015988200653283859, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.955478026710344e-05, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001027193334591954, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018684766291822072, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003815336980639277, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.50942003408732e-05, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4095717722619203e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7626381190786614e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6031693609882348e-05, tolerance: 1.783131842977531e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.006702686719805e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2282195732757516e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0521998742993597e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001926302513996364, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.219211041210565e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010611402944044404, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026324897124590727, tolerance: 1.7335518365645505e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.667532298911238e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.079645042331768e-05, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013359076110532227, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.029369133173866e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001766991129854914, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.496096171389524e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.033499205390381e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002684585593317988, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.878731408096068e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010053751360890317, tolerance: 1.7285415537797237e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001864490262180763, tolerance: 1.7151406639343182e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5516185866733196e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018263799234113573, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011273314315601163, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016902138888323995, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8510492238560956e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003436572356379109, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0423618762535094e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001936111579683578, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7949014689037923e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.401455995287849e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024982289999353306, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8818527586214606e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002791552245930973, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3362626181838014e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.469822228952419e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034692391044017015, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9570101724201575e-05, tolerance: 1.756928635909579e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015490958001613056, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.860595632441342e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024298494180418836, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.88888316114229e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.292117335175135e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.796300332750446e-05, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9476396753898007e-05, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.067022626955528e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002874248279621447, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024975556078593586, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.383128744852872e-05, tolerance: 1.756928635909579e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013863127746953363, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003269364725428884, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023040143291490345, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5164069791402954e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6653993695305934e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.928329940731256e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001647796499068023, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001448754961718597, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4502168288555864e-05, tolerance: 1.756928635909579e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001050062087630619, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002943625845305449, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012655034799063068, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.914669802297389e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.853345471265408e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.881519490891009e-05, tolerance: 1.7393977036602943e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031224910432093526, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.488809179964976e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013618858589238732, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9656376936506154e-05, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.31403603381565e-05, tolerance: 1.756928635909579e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9125133069390213e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014119386135604851, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003329887509084502, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003921371875953688, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014771578223940138, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012530104433301596, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4239527399562995e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.274338359729283e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.002008727727128e-05, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.857239141074031e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002984745693110297, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.444045678592216e-05, tolerance: 1.7172894728628973e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003542820863968125, tolerance: 1.6941965213581057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001516521510284436, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.616870456773629e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017985122253459662, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9332429167712622e-05, tolerance: 1.7009748275553193e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1415238932654826e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5333380442508005e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002353372013465995, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6671845893169204e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3024259614916844e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003686754558664511, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.118815769600179e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015657428801658067, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5903307203379786e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001711164549657463, tolerance: 1.689769861808398e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.165565509941122e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0236831648834733e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011151222106160416, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00047754503292136985, tolerance: 1.769273828350252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.612600001051721e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.570212984758305e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013417225188941569, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0075628400651946e-05, tolerance: 1.6933609199103735e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3519987288184857e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001765748779260553, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.146130089885137e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000129446374431956, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4739149554000445e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9645881241847282e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.350234716504151e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011383985952892906, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017171488102126278, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.946861850511683e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.088869199024651e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8147680625124905e-05, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.986855251730692e-05, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0138207529749735e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.602756477454969e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0147245307728864e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016587605701265905, tolerance: 1.715475544386281e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.425030885266926e-05, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013160145782726423, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021356965105403386, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9071109982345125e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1910267385623176e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.900081457414509e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1338251486760375e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.022345586266775e-05, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.034085857104996e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.428118003936942e-05, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011596047981794755, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016698457482710628, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2076822449829808e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036498175391736203, tolerance: 1.7931240875912442e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.347324283127384e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9851969696435138e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.259386005974134e-05, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0734398647893892e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6549101445210175e-05, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.155036382421073e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001293832414975271, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017462506488270801, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.077156682375389e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0435827173509447e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.0359897543254e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015450171974130435, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6278978625414043e-05, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.775818506807858e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2878723487313943e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013372248877636158, tolerance: 1.7568247342943438e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015306837349003677, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0274219227046104e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001881621512008682, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.856894181467451e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.435786577376046e-05, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8410423252656835e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8506072895104614e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6377492120257555e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0510026105308513e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001231423160471777, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9695464810545677e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5367021231366234e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016333656124663948, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.575632136037371e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7597179764537322e-05, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015251644276039616, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011132026160324583, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7312185675534206e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.515480770613591e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.097427748021588e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.98840307557496e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7816599920984777e-05, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017401611683387844, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3903204191700472e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016263409202868333, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010545539806389344, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9304083605242023e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.867988415022608e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.735989872372941e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6431460576012596e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015841184096752198, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.865419103194016e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8964535880145705e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4077543209213267e-05, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.891990835721941e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.489022092199561e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8936244923209097e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6993733986643495e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.651562274954029e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.232555140218507e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.177132083819056e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.081831066346663e-05, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9003798421451885e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001227886573266293, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1987032481693364e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.612535005901324e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8384481159550385e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8595794220644703e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5877411575460016e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.58015592478168e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8673011633315324e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5281829987143104e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001342043371691786, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4006333010764824e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023077833457209318, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.840302761400847e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.783890413685016e-05, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.570073988949876e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7668917663436756e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7545131951467868e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.712700101914576e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.318949229281809e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.903350578215187e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.344719098359941e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.47090046535839e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9286551960356196e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003821218405661974, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.283229667259377e-05, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.775476799438014e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5240719322798125e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010322323651829231, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013175952137611975, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015045248160992265, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.134631067855612e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.020228829885222e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.908972108484631e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.57274939802853e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004328802364131825, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011947560889600042, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9203757542810354e-05, tolerance: 1.748880985246344e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010236392191435417, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.346742636140207e-05, tolerance: 1.7278855743677057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018007121567979213, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9953758674749397e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.178320351888026e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1225819056286565e-05, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014126933358318133, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.302581681219815e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015226717379269745, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032422352042435834, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010102088025059752, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.831618298358243e-05, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8491378929862134e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001792885258720036, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011798365052465357, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.724470576819532e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.179254098348988e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017269298496726954, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.727133598699928e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017786765053173434, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.956417672493299e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002512291072256749, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.667726708289111e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.174821310044528e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.097913455845998e-05, tolerance: 1.6521978186571565e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5025542644980733e-05, tolerance: 1.748880985246344e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024137866801081376, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002043525286084466, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001656363327777327, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019045707614510042, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4713671764670384e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1162763585273053e-05, tolerance: 1.740698077187718e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012837760161866184, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.39770557553559e-05, tolerance: 1.7618903919528298e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014061786999251293, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.456097410843532e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015355834955724582, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000254408080135775, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023583287757049376, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.918941589096855e-05, tolerance: 1.748880985246344e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.882392205308311e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.545949467225959e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7546652747023328e-05, tolerance: 1.740698077187718e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.318006732854419e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7871394925012916e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002063045939493819, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.437292540075953e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.686917379409557e-05, tolerance: 1.7153198652261114e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002798760805028822, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010838301066144144, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026259828580220586, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022859397053851503, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3761637313924472e-05, tolerance: 1.748880985246344e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.06604673430229e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.35618144831547e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.423476335341318e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.464461620127662e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.250607174141342e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006179295549261637, tolerance: 1.7721815542346815e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001479330312388876, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023653873466000005, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010979365413578239, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020365641400719482, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015239006419439857, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1892919982877124e-05, tolerance: 1.748880985246344e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.280588849184865e-05, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.303943723523018e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.436971533004064e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.153620259843721e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016567282553854416, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019242711253019198, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013622789838674612, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1195270948494438e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001134852468863256, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014529319440171815, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002476858149094985, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.33781531642184e-05, tolerance: 1.748880985246344e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4544518167635143e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017141703655721395, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7921019661831387e-05, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018074041928140515, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.402456392577775e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.389947895355994e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014529158512899439, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.599969426370611e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0386128401619907e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024239008647922167, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1400011105386987e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001988660803989694, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001732266080110914, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013558251699394286, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013682073564358875, tolerance: 1.767060376555955e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.123597903461917e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.920514685037141e-05, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003876004796851563, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013306240185694072, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0150519053411392e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.589584449556482e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000189535396097543, tolerance: 1.6879500452334967e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016760523190002038, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002293449027823656, tolerance: 1.743964919695692e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042619368327484475, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6885080893401335e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012758334703155162, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.650793250847134e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016125541160715885, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003347974443186075, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.805312679816276e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004307382104150985, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.665834074518444e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001827948817307093, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014287009864160337, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5889928915341642e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8699924471766993e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8561659928934687e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.081086677411102e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003267452591089485, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013009974200549768, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2901264930072375e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024773860372205407, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.206049996854309e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010694393491266288, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7549807400801115e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023582344601806235, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000124213657589611, tolerance: 1.771467779602437e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7532392587833153e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5310603234100894e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.981974828192353e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000302407343543757, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.844e-03, tolerance: 2.210e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000240561438160267, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.708701856135036e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034140356467773914, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.218198834137494e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6029787296700385e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.250698386581037e-05, tolerance: 1.739081410707963e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022237560688496798, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.881e-03, tolerance: 2.187e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9134910314917708e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6268905808814538e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036951056649015945, tolerance: 1.7221144843957683e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.503120927398368e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3531395903145175e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024375273709689553, tolerance: 1.7187220585509633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.477825558637632e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001533742562959562, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6588747385168235e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7793336921034797e-05, tolerance: 1.740698077187718e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002506660307026263, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.143e-03, tolerance: 2.182e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2481814373640575e-05, tolerance: 1.6924782341316485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.051312343741632e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9143767459544404e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.380809455865805e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.215550219600599e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2933066303167104e-05, tolerance: 1.740698077187718e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003187986394508624, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.621774973655608e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3886801167390115e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8045736026081616e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.374743958907452e-05, tolerance: 1.740698077187718e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000301478522936517, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8854804629746544e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9020227484537952e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.435901002428465e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.035582858715139e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002688924886588631, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.14543815439186e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1148665189681514e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015409857847747607, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7378346158868708e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.704980217549738e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.649685591704255e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.104270256115286e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001668966525501846, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0822997935055973e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.749525748530599e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002100787325909452, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6547718332856768e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.763883116842266e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.613534075066211e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8849358781769113e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.965172974460117e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001695594104363391, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.592003943217908e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.642064770691857e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.805529753047674e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019742514521958222, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.459684760104888e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.880748291187856e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.702821174775446e-05, tolerance: 1.7233357268256723e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4258194999286775e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002042704836970376, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.741674270543951e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2890659221917334e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016809863958228144, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8638112286448877e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010028188188501361, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.174241754411167e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9477887715909845e-05, tolerance: 1.740698077187718e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021632177750157376, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.460588340252398e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2184203992820645e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.328e-04, tolerance: 2.131e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013254193126393567, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.773275104924657e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.861515110000773e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3584723410626646e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8747829371085212e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.871078278940959e-05, tolerance: 1.740698077187718e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.594893866166719e-05, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016467166170988645, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.625612592590311e-05, tolerance: 1.728503049757372e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0002535753117084e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.68575270797961e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.143993566826931e-05, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.550150758586292e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0781198265498735e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.087392763678187e-05, tolerance: 1.740698077187718e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011960678298453698, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015878869693955293, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.259051701274556e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5184407750648086e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.264437315376583e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.567310549711284e-05, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.795374530933578e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018868539500730384, tolerance: 1.7047335172659197e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014443988427376847, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013905614641803767, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010061685964364922, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.227166783423968e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.043479049126858e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0542697644478775e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.058277287671044e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7747808300856644e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010634661323457792, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010977113374164085, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016168243599827894, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.371623510284744e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8255850644750826e-05, tolerance: 1.738869776813109e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.972482034346898e-05, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8071527286920028e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8786330085954492e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018504122060455583, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7722601979295802e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.917284845593674e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.655941756639419e-05, tolerance: 1.7170688705234196e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013156402812334808, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.31178949497685e-05, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.913567543369854e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9051499216297197e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016254822243109545, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.988480411353917e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.770049736146001e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.247711028925824e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011495121467799962, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.263494172311731e-05, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9540467867038697e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9191945505427424e-05, tolerance: 1.738869776813109e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7497172440330295e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.26114682753766e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015870300890115027, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.707985977945965e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.029316781268294e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001330231064864719, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4429907689381414e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.583386302430177e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7148238882676937e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013348780605480087, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001417903818682932, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.777939830173783e-05, tolerance: 1.738869776813109e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5313868787147156e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002209800498481542, tolerance: 1.7424334540998488e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0272166722233345e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.375065348657172e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013487293753509239, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.410731532651871e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9913453270823528e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016481758155928103, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.602400037303055e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.006917800883889e-05, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.970307341615714e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.979536953650694e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010987869298986937, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5159014468773286e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.121378054968301e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020271330260432548, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8669044830814264e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4776761430849586e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5650379354727237e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.638140689419359e-05, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010179229957578591, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9325407309030438e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1955932861245946e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.139384017435841e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.005992200618792e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.16633481870222e-05, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.321e-03, tolerance: 2.120e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013394288420711352, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.84061277069692e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.754841955303038e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.970108319288195e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.922790099328854e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.516425445276779e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.308698614918823e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001468989421569214, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3067327294492195e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.908921525480756e-05, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9449908503939634e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.774039599778017e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018740521405824148, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.527227328409664e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.784136675166015e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.678443486936517e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001530558229298571, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.815933601942958e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010467763253892148, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.671167162014291e-05, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001662695132537209, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.624590667450662e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5918053033886085e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.623191239827053e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2254984514512646e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6061630519533764e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011501297625776674, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.003805417273493e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001456755815552567, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.105318624868278e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.683364312538945e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.403445384125036e-05, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010987683363212868, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.712400703898347e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001963265663223945, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.358988026426571e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012806905343713034, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1930374232672495e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.084827706067994e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.067286002037117e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.560211826615604e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.6575836987241e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002146431701701198, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015660637631122014, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0409742057319116e-05, tolerance: 1.738869776813109e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.360081961801444e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.777393697765175e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012909084382115715, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.122230667342978e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.269451889686245e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.726951381903022e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020836525247174519, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010481114985378889, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6859219224140367e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.250637622862409e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021672949109822863, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.017995930058165e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.468384617827738e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.283205175491816e-05, tolerance: 1.7346065274089485e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.064730179632013e-05, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001243727901688793, tolerance: 1.7563056691639225e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.837825798920288e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.543491755249149e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.725932428573541e-05, tolerance: 1.738869776813109e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9240150842766918e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.614452780551346e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.193666007742437e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002643647212335395, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5018275683060957e-05, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.891216797147559e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001295996993549136, tolerance: 1.741030786581132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.585801788375895e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019047751478186437, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.539097407034882e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.431072348850401e-05, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.346318279135463e-05, tolerance: 1.738869776813109e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.414809215876289e-05, tolerance: 1.7578460369220962e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1736899278566174e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002951886990028036, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8659258087370835e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1654619446351275e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9141537631996616e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.405244896378047e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5192703305353054e-05, tolerance: 1.738869776813109e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012361802670451255, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.223118031633887e-05, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.667618741481308e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9899167528935474e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031974165209517746, tolerance: 1.718963782941067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017728687371828047, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.357058079722131e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6246650103742275e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.364629948051991e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.953626744097885e-05, tolerance: 1.738869776813109e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022171043044791946, tolerance: 1.6973898449571134e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.157209844778478e-05, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.774484989172919e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015618813648831352, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.726499543794652e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7871269039071436e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2421509850499445e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.476170167861797e-05, tolerance: 1.7120882877805984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.976469974169977e-05, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.270200147261059e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010432397049385533, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.137762307618626e-05, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014262297925797182, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.502774491568957e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018777862614460013, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1282628944443525e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001379973335497494, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001031447660899739, tolerance: 1.7523011053715453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013230951186416276, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.372357551018057e-05, tolerance: 1.7351049533937845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3720286824030316e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016904889556658073, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.631557116161004e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8675004032681934e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.973746321745547e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017351980814026002, tolerance: 1.725970649278601e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.613338231437657e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.83476560967899e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.627672206567116e-05, tolerance: 1.7351049533937845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002268048880202829, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.230951536672884e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0362586424196453e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.856385450064399e-05, tolerance: 1.758906696503089e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6526360931016244e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0107293851993197e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0109092009897023e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9302228329580217e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017248338549269698, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.018153524595585e-05, tolerance: 1.7351049533937845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.826471282369914e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.919158762894683e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.776498311231294e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.720873101099772e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.749591922131042e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8066900257733554e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014893795076569103, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2903673910570095e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9493064760098145e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7900725446908248e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5768566620790884e-05, tolerance: 1.7351049533937845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.613291636572023e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.857386695411009e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.104689749167764e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016622403660112878, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8749181533695035e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2900284824543468e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8698420541665012e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7286213590466124e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0660181304170576e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1544357008031774e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.643714843957178e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6142022327499057e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015733138246069, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017799105793694925, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.767537294863203e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.432904836598311e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.183659574019406e-05, tolerance: 1.7351049533937845e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.719346737196538e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4265391986133533e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002618430292255121, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.606293074481502e-05, tolerance: 1.7981140310567763e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.287913394751929e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015358557805007604, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002384406850503562, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1552338439473698e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.38111112634919e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0747523080985883e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003078386244491742, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4092024739727084e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.704496284073113e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8946765825185105e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016588002049002307, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e-03, tolerance: 2.185e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002633836651112255, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9620874101037786e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5278160169915224e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.90823336636377e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003146467637592451, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.580867582611874e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.822700539508542e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.012311522749254e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.369938471781223e-05, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002828391922105461, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.465894374591507e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.874950627137704e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003330534876910783, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5758286631798882e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.159968870217067e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011515148236086042, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028694936369914707, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.454806863614472e-05, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.049201490915767e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.070397537105341e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003519926757364622, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.592516244305214e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8522392525860756e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002921036662524006, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011339337803138554, tolerance: 1.701921875066123e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.436719307783377e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9332552513236928e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.293372429934605e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9048177525448143e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.222745804264658e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9318893645413277e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003006263620612898, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.275886905827405e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.525033430193351e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0815297365386204e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.54445271210716e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9123900943903913e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4893147231625987e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.53804169463558e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9898887598141756e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000308133275117352, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7098144995657547e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.457884405504418e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.787027143333134e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7825296388270656e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.318259512917354e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030459520370315976, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.251432607596539e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4043059297949865e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1205461561613417e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011375224101181636, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.774002226968233e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8435371835306275e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.763319284967858e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.95786089686904e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002864461394341683, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.690238327202463e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4329795568293552e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.493677108708462e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6784917688577274e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.682135185321996e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017122415695090994, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6488811425564264e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2748605112367717e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.179423122987162e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002743492798764017, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0586955135156356e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2119361238131606e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012138192797588303, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.830841373819951e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3267264193774975e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.392626327197652e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.298881308633512e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001590001944533955, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026736102877575676, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7608246574901676e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7493439109292804e-05, tolerance: 1.7979635552238104e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.84011112518975e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.798587675877147e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.873914969635989e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.08013626271224e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.545647825451778e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8003491301743155e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.983705206632142e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015110255300585483, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.751191342218468e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.920565328679331e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026219866868947093, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.530330597456815e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.092705764993919e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.521375210018262e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3467105729652215e-05, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.21313977586425e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.348850315814497e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016225390487264492, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002496467725369711, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.369255245820005e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8449699454161605e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3674220373722825e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.074074637325833e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.063649474432868e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.349337315227985e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.097235309854859e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1267154244997144e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001280737163138184, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020982833404889836, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4888286665151966e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.930671367091473e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.928920763931684e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.946483594190065e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.309652454203045e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015926228085225407, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6357293975019097e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.408357145677509e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010812368108626715, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3247861456456246e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001490109660850416, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010248606949273681, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3602026100173e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.726403131636003e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5075965658316246e-05, tolerance: 1.7686026129330067e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.539474454739675e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001501840307172658, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001049154545284571, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.269473058245593e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7256617993845142e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.973326084071374e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001735389725319083, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017167729816017566, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6453982545373714e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.914803656319067e-05, tolerance: 1.763176908752331e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.00600410958115e-05, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.830780491412809e-05, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.219590422120846e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5679107206301394e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1697106270927216e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001982266405303858, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7983568564495945e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6915844401429606e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001283961810993467, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.862014215176272e-05, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8521494511644054e-05, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2023802096468645e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.882206235865382e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.334168503944454e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018547762008108877, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.018769334988378e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011334528625776656, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012454282525440567, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.279014149239681e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.196678454523265e-05, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.212227277714751e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2927332302596793e-05, tolerance: 1.7332829764495855e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018521635025301516, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8866942455194915e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010425307580481848, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011203918200582185, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0576552669020424e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012221930317698332, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001546612345865372, tolerance: 1.6665479883104615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002005440261978594, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.88643736846158e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.661910735716257e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7596602886665054e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.820522517981959e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010287746954971803, tolerance: 1.7384776029276645e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014966153838910992, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020178910385293836, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.219703389810164e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016745852144032722, tolerance: 1.726124308091879e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3110490223489256e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.147356787660971e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1853365132835922e-05, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.616711681597197e-05, tolerance: 1.733913318137634e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5917829810317575e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0148700346989817e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018436297887923416, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.286411106984087e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020475394636589386, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.596694357589254e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.654528400612444e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022014229808767563, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.367766096765666e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.016e-03, tolerance: 2.200e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020718743692873295, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.616690909796511e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.120191019592895e-05, tolerance: 1.7186385190082675e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4173454999136187e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5996790132913443e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024996742532386144, tolerance: 1.681367883391132e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.151548104652526e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.601445257350345e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001969587729804918, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.840166609496559e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021204825808325122, tolerance: 1.7300852262473648e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.964143246974137e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0081070701441148e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4613038386897546e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018326978873012614, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3704626906324855e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.718199567542436e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.530058524992344e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5987372240552096e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001791030443243219, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8457382839152242e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.461630688311867e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1218743238265595e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8156878461312448e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.84640210326654e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1852536063553946e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1212618890601174e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.912841185224532e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001776047620625238, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.754907586446491e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.735024168077846e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.517603100115438e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.678769028316609e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.266021615031366e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.571722080559665e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.497100310748125e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0319321132859026e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001753186810802632, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.468574269158709e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.547386256736376e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.092150413077372e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.430780776623275e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0906114601643502e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.226707857552225e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015982086828735757, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.568e-03, tolerance: 2.160e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.312659379768948e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9297437290584536e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.267166560299607e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8175823608196015e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e-03, tolerance: 2.170e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0636396498079305e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011993625022861318, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4817185904463593e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1410421444574904e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.76204319934474e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4751421906590386e-05, tolerance: 1.7508130602444e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7510412737013065e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.464185661019922e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5894052453974588e-05, tolerance: 1.7717708888742035e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1731489337093535e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.322277698577508e-05, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.675211432635548e-05, tolerance: 1.7694910952203746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e-03, tolerance: 2.141e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6167744674691846e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9984834608417767e-05, tolerance: 1.671858584128514e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001590814026949071, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.123204066830866e-05, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.624e-03, tolerance: 2.166e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.645618289370607e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012436897853244921, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.317e-03, tolerance: 2.147e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010200967531053678, tolerance: 1.7381220241446207e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.542213965911702e-05, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8446101595528952e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.084472388255628e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.120663064365997e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001925559370096249, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5252073907851567e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.923801078757708e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023166727215099756, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.644876714925711e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001033656698702836, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.390186970716371e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011037142099270397, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7637842980849222e-05, tolerance: 1.727984215566921e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8733300346646803e-05, tolerance: 1.716333702550294e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030169904513814225, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8468441924391934e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.093354428957434e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.042482484556317e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.025402000949374e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.808176132639844e-05, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.276025938509392e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.306261860226047e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.729273743752816e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e-03, tolerance: 2.185e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.414959666418014e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.448300183887733e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.544862459830896e-05, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5239034227422796e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.758643795973444e-05, tolerance: 1.716333702550294e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.246834198905642e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.236533847231267e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6458901511534784e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000236858659871865, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4680503689502448e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.507725436841796e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1406090737759176e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.239705318889822e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9864965722922743e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.934218042663453e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026573456025260137, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5762078163744928e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0407965877155335e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2550279956949637e-05, tolerance: 1.764373501719345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.190685192079948e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8425044307043655e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.085865266271649e-05, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.321815422237037e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.500443596560904e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9486392685627106e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.123e-03, tolerance: 2.136e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.695614960852472e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.61088090927108e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.194117118706993e-05, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015282324752015353, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002846750169695246, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0524741013612513e-05, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.06177555791676e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9461660894674188e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1815222185292416e-05, tolerance: 1.764373501719345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.777717043256274e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001566474627568877, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.231094281174642e-05, tolerance: 1.716333702550294e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.502657569889531e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012551031540853318, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002756422218623336, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004329196474455767, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8685834054772352e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.910046672282291e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.291117606726111e-05, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.839330063801094e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0817004539387786e-05, tolerance: 1.764373501719345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0447046102974615e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.295363961946466e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004634381420247236, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8017081493350235e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005028299381066338, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003884561902512719, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.226199326892502e-05, tolerance: 1.764373501719345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.764229434013877e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7904974512125165e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.207814703312442e-05, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011378928540070957, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.28067221686824e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8867967096661562e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00056753302493231, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005080185469877293, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.165360240089e-05, tolerance: 1.764373501719345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034377290075027905, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9387930375368455e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.570092933800772e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.005784377924531e-05, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2992955590911413e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.857379057376013e-05, tolerance: 1.748959377900617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005659752688672772, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005651241270747667, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.82971009931667e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000289297757808598, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.397054992659073e-05, tolerance: 1.764373501719345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.737192590377644e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.155894675097067e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.480819363785875e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.839361210085124e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8216765999744255e-05, tolerance: 1.748959377900617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.470053473051421e-05, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000502265282357495, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006644992649023013, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036034632587487493, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5060231013087544e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.884368163677116e-05, tolerance: 1.764373501719345e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7305532713550393e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0268889731108602e-05, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.342792606926079e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.847280436112976e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.671688952905076e-05, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005565784706726903, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.153462935678037e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.742957294640078e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007747505926297936, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003629914254280192, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.200456531987858e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011110390818398937, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4928768413947505e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.542446437226817e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.729145535493101e-05, tolerance: 1.75026053175679e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002016281686490467, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.299944110937667e-05, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.214421091098831e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003459345630316187, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.915766082332982e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008284832290292579, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019610863069069277, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8856405015310607e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.903309918067705e-05, tolerance: 1.748959377900617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8985308460612096e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010026778692799523, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012921052012599375, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3596534232603328e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.194724102230337e-05, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003840385241580084, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.275656810751865e-05, tolerance: 1.7655809172632334e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008624726983195797, tolerance: 1.7201918648568254e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.219909056221797e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.870094374773419e-05, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4861115532883406e-05, tolerance: 1.748959377900617e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6340999172180914e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3928460505879406e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.591771646403209e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036146272262027775, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015118859517454508, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017143424115203393, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7578147357962962e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4245510913740997e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010190731134349616, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.31643393180841e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.510423946253809e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.796386701028631e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003858876105803082, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036077867706828855, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.73146949330818e-05, tolerance: 1.6874622607966862e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3411388241516284e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.980893257531052e-05, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.73412149193624e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1823983195629754e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.71836746783689e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003147472207942741, tolerance: 1.8192896769754403e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.999140167913883e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0890103028296803e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2368509635876943e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036446307236599057, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013538789254159602, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.233924373938907e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.24192182362956e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5465917192872556e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.137818218679588e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004200520340979881, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.27793668530342e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017357248481215427, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1488914514489885e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3885157282319565e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.198091321488441e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0773756007498763e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0945616907255826e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.189607900329217e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.096223880048182e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048419055659542796, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2019933036456574e-05, tolerance: 1.7926325619834743e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017176503981296363, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.692738784983866e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.185e-03, tolerance: 2.196e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8821261747521334e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6530074461010578e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0625084399150673e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005051323861846408, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9132111972599017e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.13807291044455e-05, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001819179141201912, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6590372896901724e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.124637551968917e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5282406167042443e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.776240673884033e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.205724180627625e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.878999431215167e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011749436886400108, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005256582348513467, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020241435387761356, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.910479342644513e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.594310411022405e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010782776459215132, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.819573063248635e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005378315571522244, tolerance: 1.7345026105491796e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019294449430815389, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.908657313067901e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2464939563859336e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.322071149990634e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.957861833800216e-05, tolerance: 1.7758456426226778e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.388508271282836e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018089503380273875, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.731679383296103e-05, tolerance: 1.6780904924946183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.621340250113114e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7790960474216413e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2916102415519256e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6395265166475005e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7765651651879474e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010373917440255169, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001059196147466853, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015153148384585736, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.913535064174959e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.055639860619381e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1987014023615876e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000124039878270088, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.013108608867809e-05, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029447709059935226, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.017609643720975e-05, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6265786949075544e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.682778994880398e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.462503390651167e-05, tolerance: 1.7288456203999112e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2764532720871986e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013495973846761739, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043340761683163115, tolerance: 1.7478905281207166e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.87251771390633e-05, tolerance: 1.6790486172573933e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012749415816476137, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010608779536186354, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.724328334947901e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.407678671406248e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.054803845965308e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010817749341134227, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0000804715665736e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012731113995078248, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7903414487125714e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.633967982882418e-05, tolerance: 1.6790486172573933e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029962120498445814, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.143045259237109e-05, tolerance: 1.75863143065453e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7157103040955852e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6162053181151956e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002952493367395434, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3261002272265416e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8522451210501237e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2475828420410494e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7649564520636624e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012017100872225954, tolerance: 1.7895470502402857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001593576334981521, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.416682438103045e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000391442546800189, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.275425640922875e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5008600451896512e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.961260775248553e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4899851562001163e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001619224152979576, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.876183758170105e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9600990001634316e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.198095611808114e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004310256487571468, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9712799106376003e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2756439239074444e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.598562009667113e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9668854487410112e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003852345832731259, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0364406336716616e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.464805759257223e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.436250574578956e-05, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.725096172226076e-05, tolerance: 1.6780904924946183e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005421035492635205, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0386507904632256e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011946855223897608, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.865109273160869e-05, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003270376250304836, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8257728097796286e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.058218684877256e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002451714147301224, tolerance: 1.708645968472994e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006288765549713662, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017711859154943138, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3322684970686633e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004802045890676416, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013749962782962782, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.518961168437004e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9017939792014688e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00068894036469919, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.512e-03, tolerance: 2.216e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012095901194877218, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005011646808437496, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.502893378230318e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003764744970294852, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.106986769891608e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4866868972526464e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007280479637468097, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004563300042040126, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.49148652623557e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.27165998486968e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005251827500733705, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.882726955331539e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007671493142643176, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.969747063469609e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.302827996516482e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8245757532984325e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004141948769092914, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006525701026248651, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.065211934151016e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.364850513133937e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2619099293391494e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007104961392433336, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.937724066763759e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3056482398858764e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035661897282368744, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007630267571510883, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0401770664670313e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000160423800992196, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.126430203532527e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012415355881473765, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.248924801947713e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003848330184239822, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2919712800387876e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.179179214696801e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008319901660488538, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.65886381377858e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.237902531320167e-05, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013669281983713123, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.73752666856347e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.640148560391063e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00043564457714049057, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008388672234313117, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8497060738588362e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.546485087048208e-05, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9496879289842275e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013877711448725266, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.037541353083516e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.068521367777244e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.354361209268353e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00044717762991700397, tolerance: 1.7412322446587492e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8513120017214432e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.933212386759722e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008536667449023988, tolerance: 1.670646705433984e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.790517724763982e-05, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014485103539376223, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.963575146548551e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3760785915363444e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5735545854592683e-05, tolerance: 1.7257308280507527e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.802518987249247e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.71233735892533e-05, tolerance: 1.7585839512412694e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.079109420142361e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.857244234921694e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1854779599258384e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.044550037445772e-05, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016948884043896904, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010976682697082154, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.93609151412011e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.44186276455788e-05, tolerance: 1.7585839512412694e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.735869580501604e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.520818149282012e-05, tolerance: 1.7533475027354633e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0110099670896803e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.341948830977049e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011226889870494076, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.010250527039307e-05, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8307405260397326e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.078553750847756e-05, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.761926165978031e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9007016229175694e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.731219850983615e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.946764514777725e-05, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.595341412898246e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.360364610043766e-05, tolerance: 1.8009895050615993e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.408811341393177e-05, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.468373757410965e-05, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.296805224689518e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.222221221303907e-05, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.790100738380875e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8165801830661438e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.07488159333368e-05, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.335037902768619e-05, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.959023413169064e-05, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010338134516300716, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.899505074008309e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010527406046209956, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1073075013744326e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.978472003983487e-05, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012504329526495835, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9886404305103087e-05, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.391739321448073e-05, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012822091681993948, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8247431282638824e-05, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012963123541423482, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9517966156928912e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018045498526487753, tolerance: 1.6872903970266636e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.471474552540308e-05, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001662530111422346, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8273821545993635e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.631254341472182e-05, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.234504046776803e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.499677693476598e-05, tolerance: 1.7424540280821395e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017795690698919897, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001337197532714704, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7946438582050472e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.670421783329286e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017315428930271028, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.080188214037732e-05, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.326551569084047e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.046418702609902e-05, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.003492210639565e-05, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.86536224318978e-05, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.815693548037553e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.392101902656441e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.998016371393193e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012496524568738482, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032262956252194445, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.610522244442473e-05, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.912045060045985e-05, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.401717065096768e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.366389270995044e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.076053634160387e-05, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.341876775584239e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.475735073635994e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015694072381855668, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.071621904913104e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004283245546683242, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.25504759684965e-05, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.580254090199345e-05, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8099179410097254e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.263856484192392e-05, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6942040438559957e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.835466219771072e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002343527762083894, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.886021792578683e-05, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8773855571272035e-05, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.624111393067634e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010626596161902521, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.71379703485463e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.987301523823093e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4951404972642305e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004885116536477267, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0128720041850262e-05, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010793559533481441, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015170276324757287, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003511227224498141, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.478441128954972e-05, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9503868718982742e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3610558395350274e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.462486956149241e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9823175402103894e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000519591915671561, tolerance: 1.7500818803873826e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.145831064584441e-05, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001301758383237435, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.512268401859562e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019421182344501586, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.88185534699091e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.412462035030499e-05, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.78701814423389e-05, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.49899898239273e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010088364188508801, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012436843210964638, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.214172904754153e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9281697540476234e-05, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.181690185529589e-05, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.997650103976236e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.866590844193098e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0609848212046625e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.783032253356494e-05, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3852413655930244e-05, tolerance: 1.727534823840964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010197268744092068, tolerance: 1.711857006221496e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.484630865803576e-05, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.42253759208866e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018602713189416753, tolerance: 1.660211200000003e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.084809811506136e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8391769346964154e-05, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.636357252781921e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.98609589509562e-05, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.685360370466039e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022889556293612633, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011813147722952375, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.525070902349499e-05, tolerance: 1.727534823840964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.863735727219035e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016432223393706563, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.020872916801581e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8290215382492075e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.158891690518901e-05, tolerance: 1.7882399049227526e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.40869986265309e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015824630560272966, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000502950757526377, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.980183080097219e-05, tolerance: 1.727534823840964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031268217876937766, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0004901812871456e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.500969829424612e-05, tolerance: 1.723244171299081e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1603605018905474e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012360588849759553, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.471916629433803e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027273188971760256, tolerance: 1.6657881598920588e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005996500973553826, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004332248834863599, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.159492756698654e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5762383303928882e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.394664906667416e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011782303464134142, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006680116040687365, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015186557663641673, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9328248222248244e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2968029300015486e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.588295961960973e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024365302369227897, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007306611351504599, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.928516247569917e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001206395218232275, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002545499466697961, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011372030522160091, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008110935131677684, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.104355063419898e-05, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.082680475826211e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.237334196007848e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033723381640986434, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.090023342106624e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015401070623284607, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008504416225759946, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.080610263459624e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.476140811977928e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8482079106482515e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015593784082693074, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037548249039249387, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2599645589250345e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030651784066319104, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008743968116729298, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0651209372909637e-05, tolerance: 1.7327981378042214e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.642378817390318e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.260716975021007e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013128370504411277, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004058697042710266, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00042671384038503457, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008886550490190288, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7846713818460304e-05, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.181071040691193e-05, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8246508935778477e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.163538402088886e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2089038936632874e-05, tolerance: 1.7327981378042214e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.259533586461507e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011821234044839726, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039865400401146826, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011400857017599547, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000505954735802264, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0009080349063469986, tolerance: 1.738447193519806e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1346131140506245e-05, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.281545784014544e-05, tolerance: 1.7327981378042214e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.412802211663884e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.09697592744317e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4794463818263168e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.994071155757772e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004544799626391618, tolerance: 1.7750206355445272e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.467506177004848e-05, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005549336218780808, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012136591696214512, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.173709048303122e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.39978375041676e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3657996234451194e-05, tolerance: 1.7327981378042214e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.947900436286725e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0304873428607365e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6170437132645025e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005774636864756865, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.890437719046151e-05, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3682638607935786e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6244158028767536e-05, tolerance: 1.7327981378042214e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8289805340733255e-05, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.397168470644981e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011094320621132109, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00062109209352826, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000130505214124629, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6005837690166317e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010373752690019146, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8261800611796388e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0093203165416048e-05, tolerance: 1.7327981378042214e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.720123866046535e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.491059056470047e-05, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011331641212115624, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015815443208086043, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9165153735018906e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.203488080216581e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.91071235395228e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005641877543597594, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.109048766905236e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000111423497941604, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.692541043997597e-05, tolerance: 1.7327981378042214e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001445357449946826, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3862152516380556e-05, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.844954610867252e-05, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017882951815734877, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0026702225962112e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011139295907133806, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7000673574960324e-05, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5769366409331834e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8069816073595395e-05, tolerance: 1.6794287595561524e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.935493396593729e-05, tolerance: 1.7526747762267604e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2267841086413484e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010691618255366086, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.923324113475252e-05, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.485132414118261e-05, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012103737153301014, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.814791090863296e-05, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016461566676842683, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010609300026837715, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011061483436172661, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0645135318277108e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.435738463264699e-05, tolerance: 1.6794287595561524e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.321747464066053e-05, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017013702450466815, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011411076967268209, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.007162100468485e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.701123453479823e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.538888166536828e-05, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011314496850917816, tolerance: 1.7671589519553884e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0083640921069882e-05, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8958732040635917e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.770473565390368e-05, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016503193187319097, tolerance: 1.7162642838119767e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3821327366432832e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.144100123866563e-05, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.905969417889338e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001153833044612463, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018050969980626125, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.455653645958767e-05, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002762845655666854, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.649638753138804e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.749696774251737e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.189196464465152e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011805381824395514, tolerance: 1.7032210631001402e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.383001871274399e-05, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013809267368876995, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010673155352480292, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001126735648405968, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002955153807956516, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.550650076178773e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4565396758688693e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014823302096217059, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7875965370705372e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.893704165019526e-05, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013561037038555858, tolerance: 1.7727929908525513e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5895394607452354e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028203197430890295, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3562444335550534e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2191798132444214e-05, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2625096288454716e-05, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.19944832934877e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.271745278525233e-05, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2638740337159804e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.417730263837945e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9203318936490688e-05, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.885812482743104e-05, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.036e-03, tolerance: 2.174e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003898597577106193, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.078944009655282e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.162266324504114e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001777767475443781, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.639289843700594e-05, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3770094187266708e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8576666609538585e-05, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013035882417268378, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.271921920457778e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027938614974960113, tolerance: 1.72071191952407e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9292113129735466e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.152802673186481e-05, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.26402025844041e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.395e-03, tolerance: 2.149e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004461673925150794, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5569139475342326e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003551988490671544, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017348261594863842, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.196312305756541e-05, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012128107097031729, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4351230568834164e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1344489247461518e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.146012938656366e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.072e-03, tolerance: 2.195e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027325687181484204, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016167023400930243, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00047367798259974597, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5884385878474055e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021400477445636043, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0550293934940524e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8652991123567844e-05, tolerance: 1.7692139435655953e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.331432171332099e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003636510232337183, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015872619114904764, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.420584529289984e-05, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002037877017728902, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004825973153572303, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.896668849012134e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013681780920220613, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.636622681979384e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002154832670826605, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8063626237137254e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.247e-03, tolerance: 2.111e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.124754596828098e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017975581140059968, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017044555895577834, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00048194948274733924, tolerance: 1.7524646542218052e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010104264193514824, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.899404326242103e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.933569463311821e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005074081523592268, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.227485186406645e-05, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.912939901236102e-05, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.612805992707495e-05, tolerance: 1.7738113135977063e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.064059443443726e-05, tolerance: 1.746187150393031e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.786274390250922e-05, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035908759564205316, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5693884062216038e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.199790113534028e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019622256226502124, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003558644461914909, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002696791415930516, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3856917450774257e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010550891726027536, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.241853733726001e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039287144586741367, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.049635430584779e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039158445946372786, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030735382629902726, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.125908927676489e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001867947825964153, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005410529463838378, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6283384978550983e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002873967267173431, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1296123100262316e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010835525138713255, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.363332779091939e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029064235104728567, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.647347564851848e-05, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006390843748766556, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003560852279588054, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.299125437206945e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002639679631466687, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.993959777181358e-05, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.53623135094204e-05, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006887613132145979, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0878731912714784e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6338613224889205e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003888850187806535, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.20183253157444e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036020644106986473, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021175532265443192, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019060848041194296, tolerance: 1.7404335162291615e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007269630335477346, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.90943403936356e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003622247385590433, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.91480001599772e-05, tolerance: 1.7086990512061964e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.906755815703475e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018471515575201047, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019720524645554238, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007605715186058213, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003843617735081184, tolerance: 1.789214121517912e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7589515444302075e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.968778055041776e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019601954795942823, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000178635258578334, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8148644827391545e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007737945077978606, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1865935889509884e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.322575966859282e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003137529057319795, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.632899015649468e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016769938099333616, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000781874915496238, tolerance: 1.695043803543151e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8085624021550728e-05, tolerance: 1.7591941330891566e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.352014819434703e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3020790605207554e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037482337490832036, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016262858995210253, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.966e-03, tolerance: 2.218e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.382641435039466e-05, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1237342498332118e-05, tolerance: 1.7591941330891566e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8395632147538457e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9877810665590466e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.321831386816389e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031459197075866044, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00040396473294137224, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.24717552544533e-05, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0213501497036435e-05, tolerance: 1.7591941330891566e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.489949957374532e-05, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.597773411095518e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.250700632902655e-05, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1434814698626185e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.709870868954905e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004162206000342646, tolerance: 1.74447402985873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.226053974312096e-05, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004090095551754873, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.807705998396502e-05, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000230572407561515, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010651779049117467, tolerance: 1.733571288721359e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7447061585043837e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.791416818159544e-05, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0483932154292542e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.410440619555787e-05, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.251483065101757e-05, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004804297340363847, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5152245726192966e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.544793241188915e-05, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002721210684118354, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e-03, tolerance: 2.137e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2612325082009494e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005761446805382202, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.376529869411789e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6944808142078175e-05, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016293355800964422, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.837110659147164e-05, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0775032840182617e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.842739321393597e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004927511696848595, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.939870294595535e-05, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9417112620163934e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010357322284896588, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2464046620235267e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017369912560836163, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8137511241038753e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005791353933941208, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001038158598676616, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7675459032237655e-05, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014259525624447413, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018365407029724383, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.946065626037937e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7613016823255986e-05, tolerance: 1.706516417353483e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006377150593115171, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001351065086715158, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012009182925097964, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013333425706557774, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.234564083993627e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019474112603590568, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.867958807069141e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018486913844877224, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9541631743827965e-05, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9993488308376233e-05, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019747567686165257, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001606269556408118, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.538181417716898e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001359824666578198, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.119161840668566e-05, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.281047511836515e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.894349882114945e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.389027581995416e-05, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9676870534317557e-05, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001653272637881873, tolerance: 1.7228971120521138e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2583827280305575e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.004942080607343e-05, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4240960473410085e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001859526929797843, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012694928713713992, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8854370616650656e-05, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3434739391868685e-05, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.858555747351903e-05, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9679937174869756e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.427922901137324e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.558554014922391e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.657921355928762e-05, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026019264133861353, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.25759840440547e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8296703957029065e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.242417869179712e-05, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001523087823316789, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.019920537703595e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4126393252713614e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2330265813854527e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6240760083273574e-05, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2665568582887208e-05, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.738989227710566e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022584598542407623, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016240124225724156, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.651522791607933e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9103880248761843e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.249551455525689e-05, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8958066816476306e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001532485986888068, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.985685972219191e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1988264637596714e-05, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8155929700324784e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0861899287959746e-05, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.355362482240388e-05, tolerance: 1.7591941330891566e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022307605609982704, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.62001406740026e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00035727694141476137, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0430193590544737e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011580570681313761, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.554298955043631e-05, tolerance: 1.789121936758057e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3116434276771415e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9693392523150175e-05, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013898909678684325, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.987942410058545e-05, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.417585050477124e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.652807011143895e-05, tolerance: 1.7591941330891566e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002148455506160293, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000127319205683726, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027358184946848704, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.185087627914691e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.134430010697924e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010750600028220161, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.604188587327418e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4535717833017454e-05, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012206323348814933, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.280774328267427e-05, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.937285617100025e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.262369412231728e-05, tolerance: 1.7591941330891566e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001997483446429093, tolerance: 1.7570163278546746e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014803377967775606, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002076881892381201, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8606240532973397e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.093387689599714e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.145612635382423e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.854339131469848e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.623435788986632e-05, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017069561105052464, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.715992694125011e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012255858808854925, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014125335492779292, tolerance: 1.7591941330891566e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002972768494976319, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018017452745264576, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5185637535470674e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.049037667656438e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4761875493151e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.622346959102865e-05, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012926270336596248, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.778886479635784e-05, tolerance: 1.7815012755003835e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029920814847195134, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011040806970073758, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00038532401212384645, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.684123753669964e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.808862354250322e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020197029856707915, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013642284176543056, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011383558796556389, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00044604003696296045, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039813320774743054, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.024299136776675e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019676438288085147, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.919195458220505e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7177549911233546e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.77707475085533e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001697865374649249, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003462837113568859, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005376906301419867, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014745222334237255, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001614546012272556, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.45115130589878e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002056177625302056, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4019332876510846e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.988297268160874e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8592427247038635e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7460980938285853e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0381980315791956e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00036811689736331726, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005954801133525684, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.048395751001966e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001703340537188222, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016050193777733448, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016900227757136402, tolerance: 1.692666427432417e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001729512974418257, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.936922284346344e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.160547941059947e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0459815534272507e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.45348422564618e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010519603227012245, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00046742259137699024, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.328107490099431e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0006730277133743644, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00017704048409001097, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016399876529901627, tolerance: 1.7704949721998122e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.418643082001374e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.704543480727816e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0375249444255202e-05, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2775637374822756e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.384111031721996e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004152782867894219, tolerance: 1.762300916812159e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5052197247264355e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007249235148993616, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016146439240692471, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.690412285559639e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.575088563616442e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014147150657439506, tolerance: 1.7618036259574143e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.572930782676017e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.065539039210849e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9686080492657445e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0007790680296071899, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.675317704718428e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015187901395772273, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.42318421830409e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.415643040490142e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.242708198582337e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9683770656177373e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1811636426951175e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.394415025986124e-05, tolerance: 1.7831615076897095e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014554282336317996, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008220942184235958, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.87656586029153e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.318783542192709e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4185404163172085e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011185606915328685, tolerance: 1.706516417353483e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003663218015281921, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0008401959510902736, tolerance: 1.693400330834251e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2611073628275857e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.134880383742991e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.203754825719748e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.697772698461107e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7980057815931228e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7474112959526296e-05, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000164662265880626, tolerance: 1.706516417353483e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8864430885608416e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00039460347435211, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.302667495358978e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.063576366113592e-05, tolerance: 1.7528311596828108e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.913116306774155e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.55391850307006e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001128200588551842, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.661994502669353e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015320804548853498, tolerance: 1.706516417353483e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.093349755914614e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003564469813129526, tolerance: 1.7279296296296324e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8221324062728597e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2087807771878527e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00034589739438286096, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.935183720656007e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.055263566734522e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0956907383603997e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7388239097845407e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.57608262702394e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.100868666214499e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004613962126313771, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9043212245826655e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.619971200785217e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.968205790285187e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5384690291830464e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.616e-03, tolerance: 2.222e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.045824410532287e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004446139472424268, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.898237795201589e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.704557442175133e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1597435018394506e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9681707109739377e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0304964364024902e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00047516742617772535, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.244300466608232e-05, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3886210167561178e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.355500647762518e-05, tolerance: 1.705479396433473e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e-03, tolerance: 2.192e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005283451852271491, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030317528271637736, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2734314413810908e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3664734043605043e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005639860080569829, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.344082025630429e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003112858247789523, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e-03, tolerance: 2.130e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8696912042361295e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0005685101808187657, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.568510530842662e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.138917206485902e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.066569896839749e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0914947294241662e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002978575085331187, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.850038909766762e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.911015800505622e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6790009008541855e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003012602294653029, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4878305667357676e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.250691560291105e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.495891346713217e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e-03, tolerance: 2.138e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.956852834896633e-05, tolerance: 1.823611093437619e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027793971356253853, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.18055044520134e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.132095787322924e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.797469693289564e-05, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.921551217636211e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.785892657246162e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.875383041822625e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010656759277451189, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00032445208811383505, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.053963462617369e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1868225037978704e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8453184149416994e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.75265750801598e-05, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.669729266381476e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.61298710328298e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0852366642581415e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9583120218573556e-05, tolerance: 1.7356832638936353e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.675677238826897e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.533035552985615e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011832989892253225, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.016741184798654e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.731229935606897e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.151657365424627e-05, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3440734097572233e-05, tolerance: 1.7112687573492873e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.919775414517381e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.631745599471479e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010937318056988572, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.869889534564903e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.3989351950778364e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8909814789815644e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021626229979447402, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.696442541204284e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.664183234306737e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8306337788995203e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000106946356322319, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e-03, tolerance: 2.130e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002172126885622216, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.221467936223332e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.14963121258141e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.545515314838139e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4634766758362472e-05, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9249656944730215e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010720059666694849, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.538502893805795e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.80399690658247e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.169791982107374e-05, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010782279032817664, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001943567546696702, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.326311791076273e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0540920362870945e-05, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6931517855480723e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010118717740951154, tolerance: 1.739266730198107e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.765826167529648e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.708075551975276e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.064523606370912e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.979e-03, tolerance: 2.178e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001931650010239443, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1738240493124504e-05, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4918760444132705e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.683662411142763e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.534583937441765e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.411169350477574e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.8308964219820685e-05, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.639916180434675e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021988946816219566, tolerance: 1.7329151224502646e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.039348286394954e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.39926300680864e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.404857677648989e-05, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6595997591468196e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.510271779901946e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.552180749313645e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.014293723275501e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023037131499094397, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.770565655577922e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.680521169552268e-05, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.059646786762146e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.513624532747142e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.137698574302559e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022500910486348072, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.3854113038523223e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016991116738536947, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2433460204241557e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.353101516058107e-05, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6843510501934993e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6051399659706856e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023103375292073298, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.622885084737762e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2116138469029266e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016858442761804975, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9148976564544182e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.763444365075021e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024474325672513113, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9773382164872556e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015716442672934067, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00018694473385718648, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.449218472136682e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.418516624815025e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.872177180677468e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019194232135387977, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.302047084683581e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013864270545837533, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.638852877475192e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1781449370700196e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.032311962451617e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016658151028304743, tolerance: 1.714031211456793e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.265079951576184e-05, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012506736172161227, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3307929778512236e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.818246615708043e-05, tolerance: 1.779500470570252e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7924171529856235e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.449662824149934e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.071299590431392e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.54889671382383e-05, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0970790586376755e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.7190244380756396e-05, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.97111047634783e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.80042074078369e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001289984513111811, tolerance: 1.7198372460230654e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.27864160733138e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0511323914728445e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.753e-03, tolerance: 2.208e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2218372970659274e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9644035721030845e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.960076596897093e-05, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.279855135532097e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5226033652409625e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7751668230793086e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6086470981290756e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0083802220260545e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.709582684322885e-05, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3180116860993406e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.958549214308932e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9330857619104005e-05, tolerance: 1.7316757661885598e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3478917060241554e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.81110253810007e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.488822795586944e-05, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.493818074049506e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2909664071421406e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.91651774210308e-05, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5146616644056326e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.305111021369462e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7413811099716014e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.690738903247743e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010344802174929064, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.116333794581829e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003651463555540673, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.53446504327351e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.641e-03, tolerance: 2.216e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.30483875359747e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004382199992930772, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.679660308698914e-05, tolerance: 1.765655194146848e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3799018538111766e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001060687738480906, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.067067420869072e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.673309779355e-05, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.154604269275119e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012687523660731906, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.8402117045889e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002750500623438148, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6411127321932186e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.933725087713514e-05, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5469100391629736e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003463065870817194, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.7450206330704355e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.210971790143207e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010579431443888572, tolerance: 1.717837488331857e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030249368350879387, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.746784877516624e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0359526308037718e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.4105436941162544e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003725221169073676, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3603842707547946e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.713793299770952e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4414225922623004e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0004110400651490386, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.413164416796657e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.65808810701973e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7231400322749733e-05, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037007198347420184, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.865701093503428e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0825814706452884e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.626433804352486e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.081494182614151e-05, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003777826573591039, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.382685853838864e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5558125541255166e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.504751171073614e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.557287335318354e-05, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037514527520526256, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9483446099870412e-05, tolerance: 1.767040323205121e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.535213624971447e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.444684533556244e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011396187740438969, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00037688140260802843, tolerance: 1.7442792625737502e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010874860990093584, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013302787088708204, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014180139593890362, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014744700759399682, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0754894378003513e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013597264163459598, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.652e-03, tolerance: 2.151e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00023621916038444463, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011291094303727059, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8390589397873515e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028042490686633163, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000104514856927365, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4362572232839233e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.093789250625944e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00030247704892985214, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010384862970993364, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.637911706746641e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.209393711702113e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029912348759064417, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011632115297076709, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.231826880401283e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1803579477162362e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00028912817894173257, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.8525277002630226e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011720298868783761, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.116668491673878e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000269150427383312, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010798711002571373, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6038208854969706e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8618085272682364e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6646247050683284e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019444336934893065, tolerance: 1.7376971313180136e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.720458076487373e-05, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5753330101535455e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014807016776190868, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.233265890500482e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001679738935349293, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.984982693922486e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.948319293386275e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0108154006208004e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3275187719361034e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015873641317590108, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.634320726681866e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014618497016636624, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8720017512237628e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001343133588545247, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.974997792526689e-05, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.70666320982957e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012289669498015323, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5389726089177292e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.36120974885612e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002959252651106876, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001242171610940689, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00011483722651193032, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2628922134143173e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00033495561911482414, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013685057114013258, tolerance: 1.715249993800836e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010855976193071496, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.115684698652398e-05, tolerance: 1.7416628033057885e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00029885930305059705, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2369431078804624e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010172965361648478, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002833236526602334, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.980583907539422e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001629328514938192, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.967623739668292e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.716634253549602e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.788725724235235e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.007531013872431e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013429665244232836, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.779714941129242e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.214051575747446e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.0344400097785134e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014175420696810846, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.42651535280457e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.591758354240873e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002147420885132336, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.36671147496237e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.651867348680344e-05, tolerance: 1.7300937187606238e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.675586856561775e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.456450812647758e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.662529730007191e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024604623070285155, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.392792152668485e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.146940132616702e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.392748524357926e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002623970254162552, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.691340046178392e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6607186594578003e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.314493689779695e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00027682120874192827, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.6709265606376116e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.735445727963977e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00026776640913862577, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9153166465725397e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4358608672751456e-05, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.589306276351352e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.971790740044765e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002489125385592775, tolerance: 1.7467866668509308e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.585296369754693e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.306291264576754e-05, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.5597395307855546e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1409424397992455e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0868516834544187e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5148966439532224e-05, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9771837611318516e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.403814673471917e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.810947350951736e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9070285018559525e-05, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.298320792318899e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.741066951889465e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.9656488234205154e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8649629360718437e-05, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.427057533392103e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.08179664684312e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0778388172549654e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.5046480156880324e-05, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.282360092608493e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.576892356019998e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.434338489053835e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4128121134501784e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.623541648060957e-05, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.09264145644888e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.291080092344579e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002764135714010329, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.308978622660816e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.127119988822821e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6353724219246e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002269258780994727, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.3517337236172876e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1315091683431374e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022887165383521715, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8649506400261057e-05, tolerance: 1.7987002677856827e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.127415190597651e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.297912975694291e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019880687559372355, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.917408065087433e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.180824502113614e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.491017828532536e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001698278218328058, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.386614072530691e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.410445849671463e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.897191869138173e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00016481381171203503, tolerance: 1.7285758449779284e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.246621775049828e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0001015261230360243, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.62783752818124e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2326281702391045e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.405459480716673e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.941501438741105e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8646019182394155e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.889121102220951e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.539390105134678e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.785941932116273e-05, tolerance: 1.7428306162562177e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2441332458558727e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010199987062644612, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.968925360269137e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.655117766910825e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010043114887207938, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.123398513663893e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.749146275432464e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.69980413482681e-05, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.2780354331406765e-05, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.826178785993356e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.33561075359837e-05, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012441786010819467, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.514686135059664e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.153628989824048e-05, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00015336763487202101, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014634246995030332, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.212757536723124e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0033537338849253e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000152646980398196, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022807083609304932, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.4136893825916235e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6056859129073947e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012744831765433215, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003002153694578397, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.469842464896191e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0720354992106597e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1944525866689907e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00012282509978859407, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.577e-03, tolerance: 2.188e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00031109285792404844, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7617857352382056e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.925620970426271e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00013007026533685614, tolerance: 1.7211017297596147e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0003170067631772192, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.0431900068200614e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.407321269149898e-05, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.629569824948082e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00014758524338396792, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.95815890339192e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.6510082321603594e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0195976561890195e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000263211783275449, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.9146074005940584e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002453711150217693, tolerance: 1.7810771214544263e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8705087314985145e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.371754118634135e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.8149058131579806e-05, tolerance: 1.7470558978907548e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2950030780596754e-05, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.579971739152115e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.83873038391131e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.235511608858232e-05, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.515402783484285e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.219159766421371e-05, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.0414738407129613e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.596913270157223e-05, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.445e-03, tolerance: 2.156e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.2259551836993856e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00021182365526116985, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.4024594469285116e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00020313613428697697, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.213851533989849e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00024058588607840572, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.9321420329440212e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00019979942172073002, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8359691308571522e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.684e-03, tolerance: 2.191e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0002231130034881148, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1091648443063125e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0236289203362512e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022780785319026237, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.4370651583829547e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.773397976125371e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00022740894279589356, tolerance: 1.7425422222222253e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.321693176989993e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.944084553971817e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.217595219026648e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.902730705433784e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2870616543310684e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.880047924271668e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.981479820854261e-05, tolerance: 1.7463191824613535e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.197895666655879e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.893453580681963e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.86484731097e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8121519361570526e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.243e-03, tolerance: 2.173e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.976451974744039e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.650809114972783e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.38144298558486e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.544928339380832e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.352661360700765e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.151743012906258e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.397437586491735e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.23770734166999e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.1242857752724705e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.1161312359561456e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.755810133178298e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.446252454348026e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5692807218866765e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.934676075663054e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.076070518554311e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010176243627751588, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.856827609420189e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.373329455854754e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010783732649883096, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.889943539591491e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010860326093253825, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.000106272103707181, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.86087449412327e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.00010047060099756516, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.265770127429361e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.351997987510925e-05, tolerance: 1.7263881714347852e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.357520747184449e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.257497146229218e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.2057248906443465e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.810865365269425e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.579918277156306e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.6752957214390355e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.708751825382689e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.807450079417081e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.735e-03, tolerance: 2.172e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.515746264849868e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.660321819768362e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.086820895098843e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.502642953678736e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.370746004445184e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.530298788578331e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.946349208421804e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.5759777831121404e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.52619766329932e-05, tolerance: 1.783896494919823e-05\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.305e-03, tolerance: 2.230e-05\n", - " model = cd_fast.enet_coordinate_descent(\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 2.3min finished\n" + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 9.4s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 11.5s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 52 tasks | elapsed: 1.4s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 2.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 1.7s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 4.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 5.4s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 13.9s finished\n" ] }, { @@ -16296,92 +93,59 @@ "Step 5\n", "Step 6\n", "Step 7\n", - "fitting_clusters {0: array([426, 948, 105, 720, 877, 84, 252, 385, 767, 912, 216, 350, 428,\n", - " 497, 423, 828, 596, 725, 856, 229, 142, 357, 867, 150, 31, 49,\n", - " 544, 407, 362, 898, 479, 669, 162, 643, 984, 319, 523, 997, 649,\n", - " 260, 328, 465, 291, 950, 662, 262, 484, 7, 949, 883, 790, 705,\n", - " 42, 911, 155, 805, 994, 633, 962, 70, 951, 116, 665, 976, 591,\n", - " 584, 121, 304, 277, 773, 632, 224, 450, 163, 772, 533, 470, 239,\n", - " 837, 695, 389, 566, 92, 125, 129, 507, 175, 623, 658, 490, 757,\n", - " 174, 785, 459, 896, 712, 943, 278, 782, 169, 127, 483, 244, 338,\n", - " 33, 37, 38, 191, 280, 967, 320, 585, 814, 809, 271, 135, 913,\n", - " 250, 452, 354, 181, 139, 433, 454, 728, 531, 573, 340, 963, 26,\n", - " 87, 19, 501, 689, 455, 959, 166, 580, 973, 791, 838, 259, 936,\n", - " 795, 136, 438, 21, 575, 861, 734, 849, 118, 917, 505, 583, 392,\n", - " 627, 258, 621, 157, 646, 709, 807, 396, 743, 655, 491, 449, 845,\n", - " 187, 768, 403, 360, 429, 284, 272, 543, 636, 637, 685, 164, 348,\n", - " 413, 503, 815, 329, 195, 215, 557, 400, 73, 536, 209, 969, 405,\n", - " 251, 927]), 1: array([138, 843, 369, 775, 677, 358, 887, 24, 80, 69, 590, 478, 600,\n", - " 61, 763, 416, 560, 133, 275, 988, 848, 234, 410, 179, 270, 766,\n", - " 387, 232, 355, 151, 850, 535, 295, 916, 527, 336, 904, 361, 418,\n", - " 377, 147, 642, 98, 938, 366, 227, 668, 652, 448, 367, 218, 765,\n", - " 781, 987, 292, 395, 204, 707, 955, 53, 415, 798, 509, 137, 474,\n", - " 493, 631, 966, 937, 379, 661, 477, 946, 495, 371, 864, 152, 200,\n", - " 82, 690, 930, 307, 447, 647, 203, 36, 126, 289, 592, 46, 673,\n", - " 770, 343, 441, 67, 735, 983, 140, 866, 571, 130, 567, 565, 680,\n", - " 863, 434, 14, 460, 719, 871, 692, 9, 605, 481, 562, 263, 607,\n", - " 897, 598, 297, 935, 248, 641, 577, 161, 475, 813, 512, 32, 881,\n", - " 372, 427, 41, 39, 359, 194, 294, 616, 847, 975, 420, 899, 625,\n", - " 288, 168, 520, 114, 65, 953, 926, 894, 660, 528, 701, 932, 748,\n", - " 321, 188, 980, 109, 971, 417, 939, 189, 108, 96, 442, 806, 119,\n", - " 253, 375, 903, 603, 676, 68, 784, 255, 714, 910, 801, 909, 587,\n", - " 606, 751, 12, 236, 786, 844, 160, 240, 310, 857, 862, 929, 90,\n", - " 808, 666, 445, 684, 145, 178, 740, 561, 957, 391, 254, 697, 693,\n", - " 223, 914, 700, 315, 799, 281, 968, 563, 593, 27, 249, 34, 6,\n", - " 212, 615, 219, 52, 873, 238, 952, 576, 316, 66, 888, 347, 231,\n", - " 609, 880, 906, 715, 777, 192, 656, 679, 124, 123, 834, 276, 804,\n", - " 170, 356, 156, 519, 300, 222, 653, 399, 612, 842, 925, 548, 312,\n", - " 635, 702, 522, 825, 444, 184, 431, 891, 941, 333, 115, 972, 882,\n", - " 811, 657, 858, 908, 833, 541, 802, 995, 721, 890, 388, 437, 923,\n", - " 670, 102, 88, 384, 706, 570, 492, 296, 167, 816, 467, 311, 812,\n", - " 436, 836, 269, 91])}\n", - "evaluation_clusters {0: array([341, 645, 639, 841, 196, 398, 830, 797, 746, 688, 893, 736, 796,\n", - " 346, 50, 243, 965, 620, 731, 342, 964, 711, 981, 201, 335, 308,\n", - " 368, 186, 23, 792, 774, 397, 539, 0, 752, 870, 183, 878, 977,\n", - " 273, 907, 451, 373, 958, 185, 380, 393, 513, 885, 839, 128, 290,\n", - " 629, 502, 464, 733, 11, 313, 305, 992, 233, 961, 461, 559, 853,\n", - " 217, 72, 221, 54, 664, 10, 540, 339, 60, 353, 435, 902, 473,\n", - " 302, 112, 330, 35, 928, 589, 206, 818, 876, 892, 704, 960, 213,\n", - " 332, 74, 172, 713, 617, 549, 230, 235, 306, 729, 107, 446, 851,\n", - " 202, 326, 268, 832, 394, 285, 63, 954, 778, 550, 749, 93, 831,\n", - " 264, 675, 947, 619, 327, 582, 905, 378, 569, 764, 900, 144, 404,\n", - " 482, 402, 846, 293, 852, 758, 104, 182, 919, 16, 131, 287, 207,\n", - " 529, 699, 581, 793, 257, 671, 741, 722, 282, 468, 100, 214, 261,\n", - " 94, 411, 638, 13, 854, 918, 739, 401, 628, 822, 726, 159, 30,\n", - " 672, 374, 750, 696, 681, 574, 241, 514, 829, 779, 810, 148, 586,\n", - " 1, 103, 537, 318, 48, 614, 510, 710, 85, 177, 352, 408, 462,\n", - " 663, 776]), 1: array([363, 742, 86, 794, 555, 496, 365, 274, 409, 22, 256, 286, 826,\n", - " 803, 608, 81, 737, 113, 97, 345, 820, 173, 4, 469, 17, 3,\n", - " 542, 279, 25, 708, 146, 498, 521, 266, 40, 827, 134, 439, 57,\n", - " 524, 974, 198, 667, 934, 78, 754, 486, 568, 18, 242, 817, 301,\n", - " 325, 265, 651, 819, 626, 730, 703, 650, 920, 622, 79, 821, 89,\n", - " 691, 640, 29, 551, 381, 738, 193, 800, 141, 99, 687, 337, 835,\n", - " 165, 456, 8, 515, 453, 143, 901, 996, 747, 610, 122, 76, 190,\n", - " 978, 634, 56, 895, 106, 424, 824, 504, 572, 211, 45, 28, 986,\n", - " 783, 247, 991, 554, 75, 552, 594, 225, 654, 5, 95, 682, 117,\n", - " 869, 472, 205, 789, 553, 597, 855, 298, 430, 245, 324, 999, 55,\n", - " 787, 674, 64, 344, 859, 506, 956, 771, 761, 432, 698, 525, 44,\n", - " 780, 879, 840, 376, 718, 717, 500, 538, 71, 595, 517, 322, 228,\n", - " 578, 931, 865, 15, 755, 77, 556, 58, 644, 83, 20, 47, 458,\n", - " 489, 860, 921, 414, 915, 383, 760, 611, 283, 110, 516, 601, 471,\n", - " 526, 753, 440, 443, 466, 732, 868, 323, 922, 210, 425, 547, 970,\n", - " 545, 386, 226, 511, 769, 349, 823, 457, 924, 508, 724, 176, 382,\n", - " 744, 762, 532, 993, 875, 564, 727, 686, 370, 624, 716, 303, 618,\n", - " 334, 51, 120, 933, 220, 331, 889, 132, 149, 659, 683, 982, 723,\n", - " 518, 944, 579, 985, 759, 2, 558, 604, 945, 463, 940, 756, 199,\n", - " 480, 530, 208, 485, 602, 406, 299, 171, 630, 419, 314, 499, 43,\n", - " 390, 422, 613, 979, 534, 317, 412, 476, 494, 421, 487, 351, 158,\n", - " 990, 998, 872, 874, 154, 101, 153, 788, 59, 745, 648, 111, 364,\n", - " 694, 588, 546, 237, 678, 197, 180, 599, 488, 886, 246, 989, 309,\n", - " 942, 62, 267, 884])}\n", + "Cluster 0 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 46 observations.\n", + "Cluster 1 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 32 observations.\n", + "Cluster 2 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 95 observations.\n", + "Cluster 3 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 82 observations.\n", + "Cluster 4 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 55 observations.\n", + "Cluster 5 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 8 observations.\n", + "Cluster 6 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 30 observations.\n", + "Cluster 7 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 26 observations.\n", + "Cluster 8 in variant lasso_l2_signed_nonnormed_noleafavg_rank has 10 observations.\n", "Step 8\n", - "Step 9\n" + "Step 9\n", + "Cluster 0 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 0.11628180543355045\n", + "model coef:\n", + "[-1.22974173e-01 -1.59912510e+11 7.91999974e+10 -2.73711338e+11\n", + " 2.89916992e-04 9.14573669e-04 -1.76807895e+12 1.88932419e-02]\n", + "Cluster 1 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 0.056045607091507334\n", + "model coef:\n", + "[ 1.12334090e-02 -1.10164620e-02 -2.22433099e-02 6.42496085e-33\n", + " 0.00000000e+00 3.55583381e-02 2.22138764e-01 -1.25431946e-02]\n", + "Cluster 2 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 0.24392440398660797\n", + "model coef:\n", + "[ 1.16262642e+13 2.32420669e+11 7.71361464e+12 1.59716174e+13\n", + " 1.62498372e+10 8.05664062e-03 2.23144531e-01 -2.51464844e-02]\n", + "Cluster 3 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 0.13858139204322678\n", + "model coef:\n", + "[-2.26481630e-01 3.61990675e-02 7.30894437e-02 -5.27355937e-16\n", + " 1.56125113e-17 3.34257771e-02 1.10454907e-01 -2.32625437e-02]\n", + "Cluster 4 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 0.0792404806256198\n", + "model coef:\n", + "[ 1.91560334e+12 -3.19952035e+10 2.78972473e+12 7.64653182e+12\n", + " -2.19278336e-01 1.96990967e-02 2.80479431e-01 -2.53963470e-02]\n", + "Cluster 5 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 0.5836482050932429\n", + "model coef:\n", + "[ 0.19981527 -0.12630984 0.33367424 -0.28432012 0. -0.0428721\n", + " 0. 0. ]\n", + "Cluster 6 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 0.3698331396436467\n", + "model coef:\n", + "[ 6.64820879e+10 3.53473279e+10 2.72602484e+10 5.64443879e+10\n", + " -8.96453857e-05 -1.05018616e-02 1.44816105e+11 1.60980225e-03]\n", + "Cluster 7 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 0.04957384552380952\n", + "model coef:\n", + "[-1.35129599e+11 -2.18542290e+11 3.99939026e+10 -2.00611786e+11\n", + " 1.22070312e-04 -1.19857788e-02 8.02447143e+11 -4.52423096e-02]\n", + "Cluster 8 in variant lasso_l2_signed_nonnormed_noleafavg_rank has RMSE 43772178280.70682\n", + "model coef:\n", + "[ 5.97355416e+11 3.36645305e+11 3.43886893e+10 -3.05175781e-05\n", + " 0.00000000e+00 -1.83105469e-03 0.00000000e+00 6.66538189e+11]\n" ] } ], "source": [ "# get data\n", - "X, y = get_openml_data(dataid)\n", + "X, y = get_openml_data(dataid, standardize)\n", "\n", "# split data\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5,\n", @@ -16396,15 +160,8 @@ " task = 'regression'\n", " \n", "# fit the prediction models\n", - "rf, rf_plus_baseline, rf_plus = fit_models(X_train, y_train, task)\n", - "\n", - "rf_plus_ridge = RandomForestPlusRegressor(rf_model=rf, prediction_model=RidgeCV(cv=5))\n", - "rf_plus_ridge.fit(X_train, y_train)\n", - "\n", - "rf_plus_lasso = RandomForestPlusRegressor(rf_model=rf,\n", - " prediction_model=LassoCV(cv=5,\n", - " max_iter=10000, random_state=0))\n", - "rf_plus_lasso.fit(X_train, y_train)\n", + " rf, rf_plus_baseline, rf_plus_ridge, rf_plus_lasso, rf_plus_elastic = \\\n", + " fit_models(X_train, y_train, task)\n", "\n", "print(\"Step 2\")\n", "\n", @@ -16424,10 +181,9 @@ "lmdi_variants = create_lmdi_variant_map()\n", "\n", "# obtain lmdi feature importances\n", - "lmdi_explainers = get_lmdi_explainers(rf_plus, lmdi_variants,\n", - " rf_plus_baseline = rf_plus_baseline,\n", - " rf_plus_lasso = rf_plus_lasso,\n", - " rf_plus_ridge = rf_plus_ridge)\n", + "lmdi_explainers = get_lmdi_explainers(rf_plus_baseline, rf_plus_ridge,\n", + " rf_plus_lasso, rf_plus_elastic,\n", + " lmdi_variants)\n", "\n", "print(\"Step 5\")\n", "\n", @@ -16469,19 +225,28 @@ " fitting_c_to_idxs = {}\n", " evaluation_c_to_idxs = {}\n", " for c, idxs in cluster_map.items():\n", + " if nclust == 9 and variant == \"lasso_l2_signed_nonnormed_noleafavg_rank\":\n", + " print(f\"Cluster {c} in variant {variant} has {len(idxs)} \" + \\\n", + " \"observations.\")\n", + " if len(idxs) < 3:\n", + " print(f\"For {nclust} clusters, cluster #{c} in \" + \\\n", + " f\"variant {variant} has fewer than 3 observations.\")\n", + " # # warning message that the cluster is too small\n", + " # warnings.warn(f\"For {nclust} clusters, cluster #{c} in \" + \\\n", + " # f\"variant {variant} has fewer than 3 observations.\",\n", + " # Warning)\n", + " # continue\n", " # shuffle the indices and split them in half\n", + " np.random.seed(1)\n", " np.random.shuffle(idxs)\n", " half = len(idxs) // 2\n", - " fitting_c_to_idxs[c] = idxs[:half]\n", - " evaluation_c_to_idxs[c] = idxs[half:]\n", + " fitting_c_to_idxs[c] = idxs[half:]\n", + " evaluation_c_to_idxs[c] = idxs[:half]\n", " fitting_nclust_to_c[nclust] = fitting_c_to_idxs\n", " evaluation_nclust_to_c[nclust] = evaluation_c_to_idxs\n", " fitting_clusters[variant] = fitting_nclust_to_c\n", " evaluation_clusters[variant] = evaluation_nclust_to_c\n", " \n", - "print(\"fitting_clusters\", fitting_clusters[\"shap\"][2])\n", - "print(\"evaluation_clusters\", evaluation_clusters[\"shap\"][2])\n", - " \n", "print(\"Step 8\")\n", " \n", "# obtain dataframes X_fit, y_fit, X_eval, y_eval\n", @@ -16523,474 +288,15 @@ { "data": { "text/plain": [ - "{'r2': {'lmdi_baseline': {2: 0.7992074117784433,\n", - " 3: 0.9390334652869762,\n", - " 4: 0.9713182320280844,\n", - " 5: 0.9764709176942732,\n", - " 6: 0.9728849782565656,\n", - " 7: 0.9832270126374433,\n", - " 8: 0.9834457237157822,\n", - " 9: 0.9767287097287944,\n", - " 10: 0.9899374987628033},\n", - " 'lmdi_lasso': {2: 0.9954593209188786,\n", - " 3: 0.994245609475536,\n", - " 4: 0.999196774577157,\n", - " 5: 0.9993400364677412,\n", - " 6: 0.9994040109911236,\n", - " 7: 0.9993889676458666,\n", - " 8: 0.999633892585917,\n", - " 9: 0.9996355579936119,\n", - " 10: 0.999851211258718},\n", - " 'lmdi_ridge': {2: 0.9943770624342212,\n", - " 3: 0.9944156870598685,\n", - " 4: 0.9993091622328684,\n", - " 5: 0.9993920493177577,\n", - " 6: 0.9992283505830387,\n", - " 7: 0.9995160553518698,\n", - " 8: 0.9996309700854302,\n", - " 9: 0.9992661374242299,\n", - " 10: 0.9998320381985973},\n", - " 'aloo_l2_signed_normed_noleafavg_rank': {2: 0.9939486598064695,\n", - " 3: 0.9979568983564288,\n", - " 4: 0.9978316129787653,\n", - " 5: 0.9980183933127245,\n", - " 6: 0.9987020488897803,\n", - " 7: 0.9988547522485848,\n", - " 8: 0.998719769441688,\n", - " 9: -961.5687107940583,\n", - " 10: -0.6835028193459798},\n", - " 'aloo_l2_signed_normed_noleafavg_norank': {2: 0.992002233801555,\n", - " 3: 0.9982625831902566,\n", - " 4: 0.998073291072019,\n", - " 5: 0.9975985005259651,\n", - " 6: 0.9986455919162363,\n", - " 7: 0.9986081953391683,\n", - " 8: 0.9987903079508085,\n", - " 9: -1.587962861100077,\n", - " 10: 0.9995792058270055},\n", - " 'aloo_l2_signed_nonnormed_noleafavg_rank': {2: 0.8908050897286542,\n", - " 3: 0.9974707686039803,\n", - " 4: 0.9987854153086664,\n", - " 5: 0.9993161061816395,\n", - " 6: 0.9996759787942365,\n", - " 7: 0.9996667888693644,\n", - " 8: 0.9992198537133096,\n", - " 9: -2.993037325080939,\n", - " 10: 0.9998550108490626},\n", - " 'aloo_l2_signed_nonnormed_noleafavg_norank': {2: 0.8896472073667256,\n", - " 3: 0.9976933180345346,\n", - " 4: 0.9987050002602568,\n", - " 5: 0.9993830185314746,\n", - " 6: 0.9995118946681871,\n", - " 7: 0.9997369796998866,\n", - " 8: 0.9982688796553323,\n", - " 9: 0.9947334948829839,\n", - " 10: 0.9995398879203523},\n", - " 'aloo_l2_unsigned_normed_noleafavg_rank': {2: 0.8661262389931274,\n", - " 3: 0.8498077837712443,\n", - " 4: 0.8668740626864297,\n", - " 5: 0.9369023035069594,\n", - " 6: 0.9134688313353981,\n", - " 7: 0.8910426463503699,\n", - " 8: 0.9567830487341037,\n", - " 9: 0.7480579974108599,\n", - " 10: 0.8874098985412395},\n", - " 'aloo_l2_unsigned_normed_noleafavg_norank': {2: 0.8506441844228535,\n", - " 3: 0.8733980683722395,\n", - " 4: 0.8500162257177427,\n", - " 5: 0.8699817760429966,\n", - " 6: 0.9144046200506485,\n", - " 7: 0.9156504164562336,\n", - " 8: 0.9330138653817049,\n", - " 9: -0.14230461919895634,\n", - " 10: 0.9622068764446859},\n", - " 'aloo_l2_unsigned_nonnormed_noleafavg_rank': {2: 0.8858834838909648,\n", - " 3: 0.9103465460035056,\n", - " 4: -1.3024065444568786,\n", - " 5: 0.9983236322510938,\n", - " 6: 0.9987611566643229,\n", - " 7: -2.6593395158552995,\n", - " 8: 0.9652472306686328,\n", - " 9: 0.9996963623689595,\n", - " 10: 0.8967719073039585},\n", - " 'aloo_l2_unsigned_nonnormed_noleafavg_norank': {2: 0.8847389433123944,\n", - " 3: 0.9886903458961938,\n", - " 4: -145.4833555323169,\n", - " 5: 0.9992710553774352,\n", - " 6: -0.49893647056277785,\n", - " 7: 0.7046225713928469,\n", - " 8: 0.9987788219367129,\n", - " 9: 0.9996169452320982,\n", - " 10: 0.9992121347295435},\n", - " 'aloo_nonl2_unsigned_nonnormed_noleafavg_rank': {2: 0.9941793511514432,\n", - " 3: 0.993484810661683,\n", - " 4: 0.9993564703587867,\n", - " 5: 0.9992478853150792,\n", - " 6: 0.999392741756706,\n", - " 7: 0.9994449187858558,\n", - " 8: 0.9997565629954285,\n", - " 9: 0.9997511835291804,\n", - " 10: 0.9993035002956973},\n", - " 'aloo_nonl2_unsigned_nonnormed_noleafavg_norank': {2: 0.9937066650130553,\n", - " 3: 0.9939889853937385,\n", - " 4: 0.9992805258758691,\n", - " 5: 0.9994198466725029,\n", - " 6: 0.9994262772615864,\n", - " 7: 0.9994624318439413,\n", - " 8: 0.9996247668425734,\n", - " 9: 0.9997465319108404,\n", - " 10: 0.999781648719509},\n", - " 'nonloo_l2_signed_normed_noleafavg_rank': {2: 0.9940947455002662,\n", - " 3: 0.9980794882663309,\n", - " 4: 0.9978843406756608,\n", - " 5: 0.9981425682891489,\n", - " 6: 0.9986794545389881,\n", - " 7: 0.9987895648738729,\n", - " 8: 0.9988431132802034,\n", - " 9: 0.9994665366239003,\n", - " 10: 0.2980498018943968},\n", - " 'nonloo_l2_signed_normed_noleafavg_norank': {2: 0.9931583936674367,\n", - " 3: 0.9978057214588435,\n", - " 4: 0.9979370541647333,\n", - " 5: 0.9981163031023724,\n", - " 6: 0.9987894724703887,\n", - " 7: 0.998918498315309,\n", - " 8: 0.9990225713638898,\n", - " 9: 0.17010753571678555,\n", - " 10: 0.12859148182907112},\n", - " 'nonloo_l2_signed_nonnormed_noleafavg_rank': {2: 0.8897604389450992,\n", - " 3: 0.9972612440406772,\n", - " 4: 0.9987090673992685,\n", - " 5: 0.9992499509123157,\n", - " 6: 0.9996527044593634,\n", - " 7: 0.99963671079779,\n", - " 8: -17.54266743160749,\n", - " 9: -0.04478312034257441,\n", - " 10: 0.7743791294546583},\n", - " 'nonloo_l2_signed_nonnormed_noleafavg_norank': {2: 0.894352708836842,\n", - " 3: 0.997766515118207,\n", - " 4: 0.9987888594226978,\n", - " 5: 0.999322992599998,\n", - " 6: 0.9995576189914414,\n", - " 7: 0.9996829125536119,\n", - " 8: 0.9993264843267945,\n", - " 9: 0.9995232526430999,\n", - " 10: -1.8369463309200587},\n", - " 'nonloo_l2_unsigned_normed_noleafavg_rank': {2: 0.8564275546273922,\n", - " 3: 0.8526227592011143,\n", - " 4: -2.2678422438174612,\n", - " 5: 0.9264408362623818,\n", - " 6: 0.9270572352743834,\n", - " 7: 0.9242922149467343,\n", - " 8: -0.8687844379353866,\n", - " 9: -3.732576248108366,\n", - " 10: -2.7463797113873243},\n", - " 'nonloo_l2_unsigned_normed_noleafavg_norank': {2: 0.8475963011911165,\n", - " 3: 0.8611823753920967,\n", - " 4: -12289.368615542078,\n", - " 5: 0.9128226067119851,\n", - " 6: 0.9297598127712292,\n", - " 7: 0.9316837748329028,\n", - " 8: 0.9691984862100427,\n", - " 9: 0.9356309355288155,\n", - " 10: 0.9644214224086288},\n", - " 'nonloo_l2_unsigned_nonnormed_noleafavg_rank': {2: 0.8803172574501336,\n", - " 3: 0.9718649606386375,\n", - " 4: 0.9978018780666189,\n", - " 5: -0.38762949772075256,\n", - " 6: 0.9885068416289065,\n", - " 7: -159.17549221118728,\n", - " 8: 0.126445764172041,\n", - " 9: 0.9996417235792462,\n", - " 10: 0.9992678646844985},\n", - " 'nonloo_l2_unsigned_nonnormed_noleafavg_norank': {2: 0.8850179021869514,\n", - " 3: 0.9900821687938526,\n", - " 4: 0.9941501646853954,\n", - " 5: 0.9988930438257083,\n", - " 6: 0.9992406173568779,\n", - " 7: 0.9025944197416402,\n", - " 8: -13.54481606625415,\n", - " 9: -1.8631571966090674,\n", - " 10: 0.9963549640233319},\n", - " 'nonloo_nonl2_unsigned_nonnormed_noleafavg_rank': {2: 0.9932684976291751,\n", - " 3: 0.9938155847630628,\n", - " 4: 0.999303038294349,\n", - " 5: 0.9992679035978962,\n", - " 6: 0.9993654422071376,\n", - " 7: 0.9993657747228811,\n", - " 8: 0.999654160067843,\n", - " 9: 0.9997236612321952,\n", - " 10: 0.9998431907941123},\n", - " 'nonloo_nonl2_unsigned_nonnormed_noleafavg_norank': {2: 0.9940250124352128,\n", - " 3: 0.9940349388583823,\n", - " 4: 0.9993015622994568,\n", - " 5: 0.9993008028518441,\n", - " 6: 0.9993563883751845,\n", - " 7: 0.9993769589012149,\n", - " 8: 0.9997340953472241,\n", - " 9: 0.999739889847395,\n", - " 10: 0.9997284745027807},\n", - " 'shap': {2: 0.9904360207598883,\n", - " 3: 0.9865032167296605,\n", - " 4: 0.9893041503842116,\n", - " 5: 0.9907938462043362,\n", - " 6: 0.9910130581931316,\n", - " 7: 0.9912146424898127,\n", - " 8: 0.9963742919680157,\n", - " 9: 0.9972467530970689,\n", - " 10: 0.9986157934977588},\n", - " 'rawdata': {2: 0.9947293807974643,\n", - " 3: 0.9941896594201596,\n", - " 4: 0.9993200568127751,\n", - " 5: 0.9993181693461165,\n", - " 6: 0.9991700652121769,\n", - " 7: 0.9993600312708869,\n", - " 8: 0.9997178862474441,\n", - " 9: 0.9997681382814312,\n", - " 10: 0.9998454277163481},\n", - " 'lime': {2: 0.9941271145086767,\n", - " 3: 0.9944134154003953,\n", - " 4: 0.9993791239089935,\n", - " 5: 0.9992433449991965,\n", - " 6: 0.9992126541568473,\n", - " 7: 0.9992032544493532,\n", - " 8: 0.9992622804683806,\n", - " 9: 0.9992859146995121,\n", - " 10: 0.9994811612581187}},\n", - " 'rmse': {'lmdi_baseline': {2: 0.0042797094273052455,\n", - " 3: 0.0017722203957726069,\n", - " 4: 0.001469199983774096,\n", - " 5: 0.0011201280795582183,\n", - " 6: 0.0010056310025173508,\n", - " 7: 0.0008096893385826307,\n", - " 8: 0.0006846562657706572,\n", - " 9: 0.000700202015001378,\n", - " 10: 0.0005093258965122081},\n", - " 'lmdi_lasso': {2: 0.0008752213632498653,\n", - " 3: 0.0008813992423642317,\n", - " 4: 0.0003305618952660956,\n", - " 5: 0.00027606774778831403,\n", - " 6: 0.00024677884413870687,\n", - " 7: 0.0002359656995506719,\n", - " 8: 0.0001754476079127756,\n", - " 9: 0.0001622409656063851,\n", - " 10: 0.00012167554176297746},\n", - " 'lmdi_ridge': {2: 0.0009296641743926467,\n", - " 3: 0.0008739381855548988,\n", - " 4: 0.0003123734303306203,\n", - " 5: 0.0002721970253343889,\n", - " 6: 0.00027569829306206447,\n", - " 7: 0.00021699438559721378,\n", - " 8: 0.0001858125837268016,\n", - " 9: 0.0002031663865173764,\n", - " 10: 0.00012442625845452006},\n", - " 'aloo_l2_signed_normed_noleafavg_rank': {2: 0.0009563186208116841,\n", - " 3: 0.0005607609670979686,\n", - " 4: 0.0005615345671630231,\n", - " 5: 0.0005294099304310374,\n", - " 6: 0.00041346518952780053,\n", - " 7: 0.0003916174572667223,\n", - " 8: 0.00036808118605486404,\n", - " 9: 0.1582581518277766,\n", - " 10: 0.006708998019398842},\n", - " 'aloo_l2_signed_normed_noleafavg_norank': {2: 0.00101993237780647,\n", - " 3: 0.000510076976967234,\n", - " 4: 0.0005330493088523873,\n", - " 5: 0.0005742259022830803,\n", - " 6: 0.0004008143633052793,\n", - " 7: 0.0004117832903043278,\n", - " 8: 0.0003719591741691266,\n", - " 9: 0.007949636193268998,\n", - " 10: 0.00020787719506128852},\n", - " 'aloo_l2_signed_nonnormed_noleafavg_rank': {2: 0.004594566109957665,\n", - " 3: 0.0007144501591848551,\n", - " 4: 0.00045087763379067904,\n", - " 5: 0.0002588349263726327,\n", - " 6: 0.00019287530118519567,\n", - " 7: 0.00018456883452665744,\n", - " 8: 0.0002600282595761296,\n", - " 9: 0.009741686326353222,\n", - " 10: 0.00012900800343362888},\n", - " 'aloo_l2_signed_nonnormed_noleafavg_norank': {2: 0.004619218798153428,\n", - " 3: 0.0006638778340917691,\n", - " 4: 0.0004777582093936439,\n", - " 5: 0.00025443029882040824,\n", - " 6: 0.00022876966484054636,\n", - " 7: 0.00017024343192998902,\n", - " 8: 0.0003290945141457101,\n", - " 9: 0.0005199722840810202,\n", - " 10: 0.00019698704833455698},\n", - " 'aloo_l2_unsigned_normed_noleafavg_rank': {2: 0.0051186343051927595,\n", - " 3: 0.0050294266339742525,\n", - " 4: 0.004864109529725477,\n", - " 5: 0.002724654789975776,\n", - " 6: 0.0037993248119507815,\n", - " 7: 0.003863377531484134,\n", - " 8: 0.002169981451747884,\n", - " 9: 0.004731333536327647,\n", - " 10: 0.003257067484668314},\n", - " 'aloo_l2_unsigned_normed_noleafavg_norank': {2: 0.0054180044904292264,\n", - " 3: 0.00466590115503036,\n", - " 4: 0.005045825234741264,\n", - " 5: 0.004624720900750133,\n", - " 6: 0.003216744023460478,\n", - " 7: 0.0033692991072397363,\n", - " 8: 0.0026403365694485106,\n", - " 9: 0.007688942524120231,\n", - " 10: 0.0017790292584658471},\n", - " 'aloo_l2_unsigned_nonnormed_noleafavg_rank': {2: 0.00466857250368707,\n", - " 3: 0.0037117283299062816,\n", - " 4: 0.008498830051007896,\n", - " 5: 0.0004341127599459379,\n", - " 6: 0.0003608897890286135,\n", - " 7: 0.009605210396724996,\n", - " 8: 0.0011862669998137917,\n", - " 9: 0.00018357679680548825,\n", - " 10: 0.0017684282529447399},\n", - " 'aloo_l2_unsigned_nonnormed_noleafavg_norank': {2: 0.004551101951582908,\n", - " 3: 0.0014222537964931868,\n", - " 4: 0.056203098527488234,\n", - " 5: 0.0002887316192666664,\n", - " 6: 0.0060762334033084495,\n", - " 7: 0.0028831297986558905,\n", - " 8: 0.0003333228007943033,\n", - " 9: 0.000202967933271009,\n", - " 10: 0.00026652742346743247},\n", - " 'aloo_nonl2_unsigned_nonnormed_noleafavg_rank': {2: 0.0009193378289345684,\n", - " 3: 0.0009464654522883662,\n", - " 4: 0.00030863389280197796,\n", - " 5: 0.00027676564089695535,\n", - " 6: 0.00024429028821393317,\n", - " 7: 0.00021396356207935188,\n", - " 8: 0.0001508108946831875,\n", - " 9: 0.00013720792509534058,\n", - " 10: 0.00017139320592746217},\n", - " 'aloo_nonl2_unsigned_nonnormed_noleafavg_norank': {2: 0.0009535480781104359,\n", - " 3: 0.0009357562315780182,\n", - " 4: 0.0003134197770542598,\n", - " 5: 0.0002633798433387263,\n", - " 6: 0.0002474449035656477,\n", - " 7: 0.00021103517183286024,\n", - " 8: 0.0001893468164436778,\n", - " 9: 0.00014132573621538556,\n", - " 10: 0.0001350285719863003},\n", - " 'nonloo_l2_signed_normed_noleafavg_rank': {2: 0.0009425457748569971,\n", - " 3: 0.0005280472510411637,\n", - " 4: 0.0005425426099868397,\n", - " 5: 0.0005125668852887581,\n", - " 6: 0.0004132979721094855,\n", - " 7: 0.0004018152510600007,\n", - " 8: 0.00035251781800883753,\n", - " 9: 0.00021468463788733403,\n", - " 10: 0.0043247204164001485},\n", - " 'nonloo_l2_signed_normed_noleafavg_norank': {2: 0.0009620221883107864,\n", - " 3: 0.000532432014452903,\n", - " 4: 0.0005419579750427502,\n", - " 5: 0.000505409770470908,\n", - " 6: 0.00040545557643871614,\n", - " 7: 0.0003811278076841294,\n", - " 8: 0.0003324393075328648,\n", - " 9: 0.004871971042452975,\n", - " 10: 0.004180057156876155},\n", - " 'nonloo_l2_signed_nonnormed_noleafavg_rank': {2: 0.004588736606633844,\n", - " 3: 0.0007247769111745693,\n", - " 4: 0.0004856136619494927,\n", - " 5: 0.00027559100673603187,\n", - " 6: 0.00020233083098440718,\n", - " 7: 0.0001919831775677218,\n", - " 8: 0.021168248394787516,\n", - " 9: 0.005062755554646501,\n", - " 10: 0.0026078638694440807},\n", - " 'nonloo_l2_signed_nonnormed_noleafavg_norank': {2: 0.004517614169818329,\n", - " 3: 0.0006575856336110999,\n", - " 4: 0.00045550419179737157,\n", - " 5: 0.000257352836921584,\n", - " 6: 0.00020896074478648284,\n", - " 7: 0.00017794422228700757,\n", - " 8: 0.00023174797947873887,\n", - " 9: 0.00020730371843071346,\n", - " 10: 0.008924297990551184},\n", - " 'nonloo_l2_unsigned_normed_noleafavg_rank': {2: 0.005220953311880016,\n", - " 3: 0.005094850913161665,\n", - " 4: 0.009468301461723335,\n", - " 5: 0.0029529194757440917,\n", - " 6: 0.0032601106731974445,\n", - " 7: 0.003296494594123509,\n", - " 8: 0.009509236164375294,\n", - " 9: 0.014040464465867422,\n", - " 10: 0.012947604010016456},\n", - " 'nonloo_l2_unsigned_normed_noleafavg_norank': {2: 0.0052423982301804785,\n", - " 3: 0.004831829381994728,\n", - " 4: 0.28310428660120956,\n", - " 5: 0.00327808472748255,\n", - " 6: 0.0028902471887398353,\n", - " 7: 0.0027205138185272997,\n", - " 8: 0.0016914704708196205,\n", - " 9: 0.00258346218777755,\n", - " 10: 0.0017850642275389944},\n", - " 'nonloo_l2_unsigned_nonnormed_noleafavg_rank': {2: 0.0048188435930881365,\n", - " 3: 0.002075113791527701,\n", - " 4: 0.0006312279347166497,\n", - " 5: 0.006160394061072743,\n", - " 6: 0.0007733440112223374,\n", - " 7: 0.06463466156244366,\n", - " 8: 0.00533632789258742,\n", - " 9: 0.00020220576321700363,\n", - " 10: 0.00027571303285865657},\n", - " 'nonloo_l2_unsigned_nonnormed_noleafavg_norank': {2: 0.0046961699145543,\n", - " 3: 0.0013500825536144536,\n", - " 4: 0.0008114701500936749,\n", - " 5: 0.000348944758076454,\n", - " 6: 0.00028038707657847284,\n", - " 7: 0.0017793374367975074,\n", - " 8: 0.01864053860068648,\n", - " 9: 0.008272805784878441,\n", - " 10: 0.00046141394525202426},\n", - " 'nonloo_nonl2_unsigned_nonnormed_noleafavg_rank': {2: 0.0009838117783347847,\n", - " 3: 0.0009390993359577543,\n", - " 4: 0.00031058503157093374,\n", - " 5: 0.00027633851224523424,\n", - " 6: 0.00024990051305388394,\n", - " 7: 0.0002413221052922631,\n", - " 8: 0.00019067643494173188,\n", - " 9: 0.00015209405459743267,\n", - " 10: 0.0001205959068949229},\n", - " 'nonloo_nonl2_unsigned_nonnormed_noleafavg_norank': {2: 0.0009398930631236299,\n", - " 3: 0.0009228243178235453,\n", - " 4: 0.00030592483844595585,\n", - " 5: 0.0002783628239257379,\n", - " 6: 0.0002562870152869035,\n", - " 7: 0.0002300275676378619,\n", - " 8: 0.00016653590666633434,\n", - " 9: 0.0001461983560152525,\n", - " 10: 0.00013356498413152654},\n", - " 'shap': {2: 0.0013785120298582018,\n", - " 3: 0.0013182486450693904,\n", - " 4: 0.0008882581863996872,\n", - " 5: 0.0007408758117560947,\n", - " 6: 0.0007006320079259191,\n", - " 7: 0.0006536002583904601,\n", - " 8: 0.00031598132922416535,\n", - " 9: 0.0003021261527674805,\n", - " 10: 0.00018420243972048936},\n", - " 'rawdata': {2: 0.0009192983291717425,\n", - " 3: 0.0008696468004552415,\n", - " 4: 0.000280201600796803,\n", - " 5: 0.0002811466452060042,\n", - " 6: 0.0002707524839733238,\n", - " 7: 0.0002442820363106109,\n", - " 8: 0.00016076035848765617,\n", - " 9: 0.0001411304709620455,\n", - " 10: 0.00011919018444485761},\n", - " 'lime': {2: 0.0009420212180003932,\n", - " 3: 0.00088744933690208,\n", - " 4: 0.00029762197919199786,\n", - " 5: 0.0003141735579687788,\n", - " 6: 0.000315690681490818,\n", - " 7: 0.000305333151117592,\n", - " 8: 0.0002959737458208915,\n", - " 9: 0.0002992396739847131,\n", - " 10: 0.00021901213496112337}}}" + "{2: 0.22644940353139587,\n", + " 3: 0.16843560066612098,\n", + " 4: 0.168237575339965,\n", + " 5: 0.1644902141996583,\n", + " 6: 0.1727456979236419,\n", + " 7: 0.17394522700718568,\n", + " 8: 0.17470725839037096,\n", + " 9: 1145868541.5440714,\n", + " 10: 1145868541.5421774}" ] }, "execution_count": 4, @@ -16999,7 +305,24 @@ } ], "source": [ - "metrics_to_scores" + "metrics_to_scores[\"rmse\"][\"lasso_l2_signed_nonnormed_noleafavg_rank\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# {2: 0.22644940353139587,\n", + "# 3: 0.19826779591905566,\n", + "# 4: 0.1953203953041628,\n", + "# 5: 0.18712289629518433,\n", + "# 6: 0.1799813913000766,\n", + "# 7: 0.18342879068896262,\n", + "# 8: 0.17688507989749783,\n", + "# 9: 0.18064867586887284,\n", + "# 10: 0.1718511382385029}" ] } ], diff --git a/feature_importance/subgroup/current/subgroup-debug.ipynb b/feature_importance/subgroup/current/subgroup-debug.ipynb index 76c720c..3da812a 100644 --- a/feature_importance/subgroup/current/subgroup-debug.ipynb +++ b/feature_importance/subgroup/current/subgroup-debug.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -33,71 +33,105 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "import lime\n", - "def get_lime(X, rf, task):\n", - " result = np.zeros((X.shape[0], X.shape[1]))\n", - " explainer = lime.lime_tabular.LimeTabularExplainer(X_train, verbose = False,\n", - " mode = task)\n", - " num_features = X.shape[1]\n", - " for i in range(X.shape[0]):\n", - " if task == 'classification':\n", - " exp = explainer.explain_instance(X[i, :], rf.predict_proba,\n", - " num_features = num_features)\n", - " else:\n", - " exp = explainer.explain_instance(X[i, :], rf.predict,\n", - " num_features = num_features)\n", - " original_feature_importance = exp.as_map()[1]\n", - " # print(\"----------------\")\n", - " # print(\"Original feature importance\")\n", - " # print(original_feature_importance)\n", - " sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0])\n", - " # print(\"----------------\")\n", - " # print(\"Sorted feature importance\")\n", - " # print(sorted_feature_importance)\n", - " # print(\"----------------\")\n", - " for j in range(num_features):\n", - " result[i, j] = sorted_feature_importance[j][1]\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# set inputs\n", "seed = 1\n", "dataids = [361247, 361243, 361242, 361251, 361253, 361260, 361259, 361256, 361254, 361622]\n", - "dataid = dataids[0]\n", - "clustertype = \"hierarchical\"" + "dataid = 361616\n", + "clustertype = \"kmeans\"" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3118253/2881731511.py:2: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.\n", - " X, y = get_openml_data(dataid)\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/openml/tasks/functions.py:442: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.\n", - " dataset = get_dataset(task.dataset_id, *dataset_args, **get_dataset_kwargs)\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/openml/tasks/task.py:150: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.\n", - " return datasets.get_dataset(self.dataset_id)\n", + "/tmp/ipykernel_407309/2136635497.py:2: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.\n", + " X, y = get_openml_data(dataid)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 7.5s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 10.4s finished\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 7.7s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 10.2s finished\n", "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 1.8min\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 4.6min finished\n" + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 3.2s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 8.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 52 tasks | elapsed: 1.5s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 2.5s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 1.6s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 4.4s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 2\n", + "Step 3\n", + "Step 4\n", + "Step 5\n", + "Step 6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/sklearn/metrics/_regression.py:1187: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n", + " warnings.warn(msg, UndefinedMetricWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 7\n" ] } ], @@ -106,7 +140,10 @@ "X, y = get_openml_data(dataid)\n", "\n", "# split data\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3,\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,\n", + " random_state=seed)\n", + "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train,\n", + " test_size = 0.5,\n", " random_state=seed)\n", "\n", "# check if task is regression or classification\n", @@ -115,901 +152,84 @@ "else:\n", " task = 'regression'\n", " \n", + "print(\"Step 1\")\n", + " \n", "# fit the prediction models\n", - "rf, rf_plus_baseline, rf_plus = fit_models(X_train, y_train, task)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ + "rf, rf_plus_baseline, rf_plus = fit_models(X_train, y_train, task)\n", + "\n", + "rf_plus_ridge = RandomForestPlusRegressor(rf_model=rf,\n", + " prediction_model=RidgeCV(cv=5))\n", + "rf_plus_ridge.fit(X_train, y_train)\n", + "\n", + "rf_plus_lasso = RandomForestPlusRegressor(rf_model=rf,\n", + " prediction_model=LassoCV(cv=5,\n", + " max_iter=10000, random_state=0))\n", + "rf_plus_lasso.fit(X_train, y_train)\n", + "\n", + "print(\"Step 2\")\n", + "\n", "# obtain shap feature importances\n", "shap_explainer = shap.TreeExplainer(rf)\n", - "shap_train_values, shap_train_rankings = get_shap(X_train, shap_explainer,\n", + "shap_train_values, shap_train_rankings = get_shap(X_val, shap_explainer,\n", " task)\n", "shap_test_values, shap_test_rankings = get_shap(X_test, shap_explainer,\n", " task)\n", "\n", + "print(\"Step 3\")\n", + "\n", "# get lime feature importances\n", - "lime_train_values = get_lime(X_train, rf, task)\n", - "lime_test_values = get_lime(X_test, rf, task)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ + "lime_train_values, lime_train_rankings = get_lime(X_val, rf, task)\n", + "lime_test_values, lime_test_rankings = get_lime(X_test, rf, task)\n", + "\n", + "print(\"Step 4\")\n", + "\n", "# create list of lmdi variants\n", - "lmdi_variants = create_lmdi_variant_map()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ + "lmdi_variants = create_lmdi_variant_map()\n", + "\n", "# obtain lmdi feature importances\n", "lmdi_explainers = get_lmdi_explainers(rf_plus, lmdi_variants,\n", - " rf_plus_baseline = rf_plus_baseline)\n", + " rf_plus_baseline = rf_plus_baseline,\n", + " rf_plus_lasso = rf_plus_lasso,\n", + " rf_plus_ridge = rf_plus_ridge)\n", "lfi_train_values, lfi_train_rankings = get_lmdi(X_train, y_train,\n", " lmdi_variants,\n", " lmdi_explainers)\n", + "lfi_train_values, lfi_train_rankings = get_lmdi(X_val, None,\n", + " lmdi_variants,\n", + " lmdi_explainers)\n", "lfi_test_values, lfi_test_rankings = get_lmdi(X_test, None,\n", " lmdi_variants,\n", " lmdi_explainers)\n", + "\n", + "print(\"Step 5\")\n", + "\n", "# add shap to the dictionaries\n", "lfi_train_values[\"shap\"] = shap_train_values\n", "lfi_train_rankings[\"shap\"] = shap_train_rankings\n", "lfi_test_values[\"shap\"] = shap_test_values\n", "lfi_test_rankings[\"shap\"] = shap_test_rankings\n", "\n", + "# add the raw data to the dictionaries as a baseline of comparison\n", + "lfi_train_values[\"rawdata\"] = X_val\n", + "lfi_test_values[\"rawdata\"] = X_test\n", + "\n", "# add lime to the dictionaries\n", "lfi_train_values[\"lime\"] = lime_train_values\n", "lfi_test_values[\"lime\"] = lime_test_values\n", - "\n", - "# add the raw data to the dictionaries as a baseline of comparison\n", - "lfi_train_values[\"rawdata\"] = X_train\n", - "lfi_test_values[\"rawdata\"] = X_test" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ + " \n", "# get the clusterings\n", "# method_to_labels, method_to_indices = get_train_clusters(lfi_train_values, clustertype)\n", "train_clusters = get_train_clusters(lfi_train_values, clustertype)\n", "cluster_centroids = get_cluster_centroids(lfi_train_values, train_clusters)\n", - "test_clusters = get_test_clusters(lfi_test_values, cluster_centroids)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ + "test_clusters = get_test_clusters(lfi_test_values, cluster_centroids)\n", + "\n", + "print(\"Step 6\")\n", + "\n", "# compute the performance\n", - "metrics_to_scores = compute_performance(X_train, X_test, y_train, y_test,\n", - " train_clusters, test_clusters, task)" - ] - }, - { - "cell_type": 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0.00027537694957089254},\n", - " 'nonloo_l2_signed_normed_noleafavg_norank': {2: 0.0010879612864375954,\n", - " 3: 0.0036167400643139723,\n", - " 4: 0.0006006888537509132,\n", - " 5: 0.0005893684216562623,\n", - " 6: 0.0004408831773342244,\n", - " 7: 0.0004024843822686487,\n", - " 8: 0.00039966015800607527,\n", - " 9: 0.0002986606276182197,\n", - " 10: 0.00027537694957089254},\n", - " 'nonloo_l2_signed_nonnormed_leafavg_rank': {2: 0.0279836998868036,\n", - " 3: 0.00073944949778832,\n", - " 4: 0.0005202420342194542,\n", - " 5: 0.00041023362613770974,\n", - " 6: 0.00018985624052391211,\n", - " 7: 0.0001713087984739208,\n", - " 8: 0.00015298904217795958,\n", - " 9: 0.00015059552571258455,\n", - " 10: 0.0001499934997089554},\n", - " 'nonloo_l2_signed_nonnormed_leafavg_norank': {2: 0.0279836998868036,\n", - " 3: 0.00073944949778832,\n", - " 4: 0.0005202420342194542,\n", - " 5: 0.00041023362613770974,\n", - " 6: 0.00018985624052391211,\n", - " 7: 0.0001713087984739208,\n", - " 8: 0.00015298904217795958,\n", - " 9: 0.00015059552571258455,\n", - " 10: 0.0001499934997089554},\n", - " 'nonloo_l2_signed_nonnormed_noleafavg_rank': {2: 0.02575387033969878,\n", - " 3: 0.00073944949778832,\n", - " 4: 0.0005202420342194542,\n", - " 5: 0.00040457636416023097,\n", - " 6: 0.0062988692593506646,\n", - " 7: 0.00016565153649644195,\n", - " 8: 0.00016071996534283567,\n", - " 9: 0.00013528144984887246,\n", - " 10: 0.00013381052483741983},\n", - " 'nonloo_l2_signed_nonnormed_noleafavg_norank': {2: 0.02575387033969878,\n", - " 3: 0.00073944949778832,\n", - " 4: 0.0005202420342194542,\n", - " 5: 0.00040457636416023097,\n", - " 6: 0.0062988692593506646,\n", - " 7: 0.00016565153649644195,\n", - " 8: 0.00016071996534283567,\n", - " 9: 0.00013528144984887246,\n", - " 10: 0.00013381052483741983},\n", - " 'nonloo_l2_unsigned_normed_leafavg_rank': {2: 0.0055943032683521045,\n", - " 3: 0.003472577376909516,\n", - " 4: 0.0031680714995054758,\n", - " 5: 0.0029349582466788398,\n", - " 6: 0.00250019236834227,\n", - " 7: 0.00249866693225432,\n", - " 8: 0.0023415115030626507,\n", - " 9: 0.0023415469728375036,\n", - " 10: 0.0025535683957374953},\n", - " 'nonloo_l2_unsigned_normed_leafavg_norank': {2: 0.0055943032683521045,\n", - " 3: 0.003472577376909516,\n", - " 4: 0.0031680714995054758,\n", - " 5: 0.0029349582466788398,\n", - " 6: 0.00250019236834227,\n", - " 7: 0.00249866693225432,\n", - " 8: 0.0023415115030626507,\n", - " 9: 0.0023415469728375036,\n", - " 10: 0.0025535683957374953},\n", - " 'nonloo_l2_unsigned_normed_noleafavg_rank': {2: 0.005614526535425095,\n", - " 3: 0.005332928990632896,\n", - " 4: 0.028294187390600585,\n", - " 5: 0.004412453814287961,\n", - " 6: 0.002714167904492364,\n", - " 7: 0.0027127830757169083,\n", - " 8: 0.0030465466626487282,\n", - " 9: 0.003045649686761254,\n", - " 10: 0.0023348061102357667},\n", - " 'nonloo_l2_unsigned_normed_noleafavg_norank': {2: 0.005614526535425095,\n", - " 3: 0.005332928990632896,\n", - " 4: 0.028294187390600585,\n", - " 5: 0.004412453814287961,\n", - " 6: 0.002714167904492364,\n", - " 7: 0.0027127830757169083,\n", - " 8: 0.0030465466626487282,\n", - " 9: 0.003045649686761254,\n", - " 10: 0.0023348061102357667},\n", - " 'nonloo_l2_unsigned_nonnormed_leafavg_rank': {2: 0.005130593677883271,\n", - " 3: 0.03517386424579513,\n", - " 4: 0.0005364384537087081,\n", - " 5: 0.00031920118398501773,\n", - " 6: 0.00031795492558976365,\n", - " 7: 0.0018775348592032426,\n", - " 8: 0.0018284834018166866,\n", - " 9: 0.0018284250172042231,\n", - " 10: 0.0018139028539246851},\n", - " 'nonloo_l2_unsigned_nonnormed_leafavg_norank': {2: 0.005130593677883271,\n", - " 3: 0.03517386424579513,\n", - " 4: 0.0005364384537087081,\n", - " 5: 0.00031920118398501773,\n", - " 6: 0.00031795492558976365,\n", - " 7: 0.0018775348592032426,\n", - " 8: 0.0018284834018166866,\n", - " 9: 0.0018284250172042231,\n", - " 10: 0.0018139028539246851},\n", - " 'nonloo_l2_unsigned_nonnormed_noleafavg_rank': {2: 0.005130593677883271,\n", - " 3: 0.03937189000818734,\n", - " 4: 0.003670886186315287,\n", - " 5: 0.003904949977206202,\n", - " 6: 0.0007719119261160723,\n", - " 7: 0.0007665859474074631,\n", - " 8: 0.007148775159455504,\n", - " 9: 0.007148636228474763,\n", - " 10: 0.0001956265173882158},\n", - " 'nonloo_l2_unsigned_nonnormed_noleafavg_norank': {2: 0.005130593677883271,\n", - " 3: 0.03937189000818734,\n", - " 4: 0.003670886186315287,\n", - " 5: 0.003904949977206202,\n", - " 6: 0.0007719119261160723,\n", - " 7: 0.0007665859474074631,\n", - " 8: 0.007148775159455504,\n", - " 9: 0.007148636228474763,\n", - " 10: 0.0001956265173882158},\n", - " 'nonloo_nonl2_unsigned_nonnormed_leafavg_rank': {2: 0.0010879612864375954,\n", - " 3: 0.016911718627357162,\n", - " 4: 0.00034649216072365395,\n", - " 5: 0.0003116400750925966,\n", - " 6: 0.00029443345726366745,\n", - " 7: 0.00027534224759073603,\n", - " 8: 0.008231668111239276,\n", - " 9: 0.0018335866123290515,\n", - " 10: 0.0018287134946921532},\n", - " 'nonloo_nonl2_unsigned_nonnormed_leafavg_norank': {2: 0.0010879612864375954,\n", - " 3: 0.016911718627357162,\n", - " 4: 0.00034649216072365395,\n", - " 5: 0.0003116400750925966,\n", - " 6: 0.00029443345726366745,\n", - " 7: 0.00027534224759073603,\n", - " 8: 0.008231668111239276,\n", - " 9: 0.0018335866123290515,\n", - " 10: 0.0018287134946921532},\n", - " 'nonloo_nonl2_unsigned_nonnormed_noleafavg_rank': {2: 0.0010879612864375954,\n", - " 3: 0.016742283540027783,\n", - " 4: 0.00034649216072365395,\n", - " 5: 0.0003116400750925966,\n", - " 6: 0.00029443345726366745,\n", - " 7: 0.00027534224759073603,\n", - " 8: 0.000176639100445493,\n", - " 9: 0.00014629567861526528,\n", - " 10: 0.0001407423024049934},\n", - " 'nonloo_nonl2_unsigned_nonnormed_noleafavg_norank': {2: 0.0010879612864375954,\n", - " 3: 0.016742283540027783,\n", - " 4: 0.00034649216072365395,\n", - " 5: 0.0003116400750925966,\n", - " 6: 0.00029443345726366745,\n", - " 7: 0.00027534224759073603,\n", - " 8: 0.000176639100445493,\n", - " 9: 0.00014629567861526528,\n", - " 10: 0.0001407423024049934},\n", - " 'shap': {2: 0.01680138440438891,\n", - " 3: 0.014817138920642287,\n", - " 4: 0.004177666799316172,\n", - " 5: 0.0003425645307131138,\n", - " 6: 0.0023973453655758434,\n", - " 7: 0.28696707240271746,\n", - " 8: 0.28693243003102975,\n", - " 9: 0.0054541403960435275,\n", - " 10: 0.00019771035596980648},\n", - " 'lime': {2: 0.002190967144310183,\n", - " 3: 0.0009919366358170653,\n", - " 4: 0.0009733105596059732,\n", - " 5: 0.0005691601809032432,\n", - " 6: 0.0005388001835438385,\n", - " 7: 0.0005382966471315097,\n", - " 8: 0.001113553112951133,\n", - " 9: 0.000715675200301912,\n", - " 10: 0.0007147476501163386},\n", - " 'rawdata': {2: 0.0010879612864375954,\n", - " 3: 0.001018818769185906,\n", - " 4: 0.00032848029220089805,\n", - " 5: 0.0003116400750925966,\n", - " 6: 0.0002925488654196652,\n", - " 7: 0.00027534224759073603,\n", - " 8: 0.00017008999868850522,\n", - " 9: 0.03837920776278292,\n", - " 10: 0.038363425470141814}}}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metrics_to_scores" + "metrics_to_scores = compute_performance(X_val, X_test, y_val, y_test,\n", + " train_clusters, test_clusters, task)\n", + "\n", + "print(\"Step 7\")\n" ] } ], diff --git a/feature_importance/subgroup/current/subgroup-runner.sh b/feature_importance/subgroup/current/subgroup-runner.sh index d128c40..f643b58 100644 --- a/feature_importance/subgroup/current/subgroup-runner.sh +++ b/feature_importance/subgroup/current/subgroup-runner.sh @@ -3,8 +3,19 @@ slurm_script="subgroup.sh" -ids=(361242 361251 361253 361260 361259 361256 361254 361622) +num_seeds=10 +# ids=(361234 361235 361236 361237 361241 361242 361243 361244 +# 361247 361249 361250 361251 361252 361253 361254 361255 +# 361256 361257 361258 361259 361260 361261 361264 361266 +# 361267 361268 361269 361272 361616 361617 361618 361619 +# 361621 361622 361623) +ids=(31 10101 3913 3 3917 9957 9946 3918 3903 37 + 9971 9952 3902 49 43 9978 10093 219 9976 14965 + 9977 15 29 14952 125920 3904 9910 3021 7592 + 146820 146819 14954 167141 167120 167125) -for id in "${ids[@]}"; do - sbatch $slurm_script $id # Submit SLURM job using the specified script +for seed in $(seq 1 $num_seeds); do + for id in "${ids[@]}"; do + sbatch $slurm_script $id $seed # Submit SLURM job using the specified script + done done diff --git a/feature_importance/subgroup/current/subgroup.ipynb b/feature_importance/subgroup/current/subgroup.ipynb index 191a3d7..34a39b0 100644 --- a/feature_importance/subgroup/current/subgroup.ipynb +++ b/feature_importance/subgroup/current/subgroup.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 97, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -15,30 +15,125 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['361272',\n", + " '7592',\n", + " '361237',\n", + " '9971',\n", + " '3903',\n", + " '14954',\n", + " '361261',\n", + " '361259',\n", + " '361253',\n", + " '3021',\n", + " '125920',\n", + " '361619',\n", + " '361254',\n", + " '9957',\n", + " '361621',\n", + " '3904',\n", + " '361266',\n", + " '9976',\n", + " '361247',\n", + " '3917',\n", + " '3913',\n", + " '9978',\n", + " '361249',\n", + " '361243',\n", + " '361234',\n", + " '10101',\n", + " '361617',\n", + " '14965',\n", + " '361250',\n", + " '146820',\n", + " '361257',\n", + " '361622',\n", + " '31',\n", + " '361244',\n", + " '167141',\n", + " '9977',\n", + " '361255',\n", + " '361618',\n", + " '361267',\n", + " '14952',\n", + " '3902',\n", + " '361260',\n", + " '49',\n", + " '43',\n", + " '219',\n", + " '361252',\n", + " '361258',\n", + " '167120',\n", + " '15',\n", + " '361241',\n", + " '361236',\n", + " '29',\n", + " '9946',\n", + " '361256',\n", + " '361623',\n", + " '361264',\n", + " '10093',\n", + " '9952',\n", + " '361269',\n", + " '37',\n", + " '146819',\n", + " '3',\n", + " '361251',\n", + " '361616',\n", + " '3918',\n", + " '361235',\n", + " '361242']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get all of the dataids in results/pipeline2\n", + "dataids = []\n", + "for filename in os.listdir('results/nonl2unsignedunnormed'):\n", + " # get last six characters of filename\n", + " dataids.append(filename[6:])\n", + "dataids" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# set the path we want to look at\n", "# dataids = [\"361242\", \"361251\", \"361253\", \"361259\", \"361260\"]\n", - "dataids = [\"361242\", \"361251\", \"361253\", \"361254\", \"361256\", \"361259\", \"361260\", \"361622\"]\n", + "# dataids = [\"361242\", \"361251\", \"361253\", \"361254\", \"361256\", \"361259\", \"361260\", \"361622\"]\n", "seed = \"1\"\n", "metric = \"rmse\"\n", "pipeline = 2\n", - "clustertype = \"kmeans\"\n", + "clustertype = \"hierarchical\"\n", "paths = []\n", "for dataid in dataids:\n", - " paths.append(oj(\"results\", f\"pipeline{pipeline}\", f\"dataid{dataid}\", f\"seed{seed}\", f\"metric{metric}\", str(clustertype)))" + " #paths.append(oj(\"results\", f\"pipeline{pipeline}\", f\"dataid{dataid}\", f\"seed{seed}\", f\"metric{metric}\", str(clustertype)))\n", + " paths.append(oj(\"results\", \"nonl2unsignedunnormed\", f\"dataid{dataid}\", f\"seed{seed}\", f\"metric{metric}\", str(clustertype)))" ] }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "data_results = []\n", "for path in paths:\n", + " # if path exists\n", + " if not os.path.exists(path):\n", + " continue\n", " files = os.listdir(path)\n", " method_results = []\n", " for file in files:\n", @@ -53,12 +148,53 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# in each data_result, keep only nclust, lmdi_lasso, lmdi_ridge, shap, lime, rawdata\n", + "for i in range(len(data_results)):\n", + " data_results[i] = data_results[i][[\"nclust\", \"lmdi_ridge\", \"shap\", \"lime\", \"rawdata\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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kfX2fr7fR19sHvt9GaV/f5642VlVVkZyc7Pgc70ifTlTs5Z7Q0FC3JCqBgYGEhob65BtQ2tf3+Xobfb194PttlPb1fe5uY1eGbchgWiGEEEJ4LUlUhBBCCOG1JFERQgghhNeSREUIIYQQXksSFSGEEEJ4LUlUhBBCCOG1JFERQgghhNeSREUIIYQQXksSFSGEEEJ4LUlUhBBCCOG1JFERQgghhNeSREUIIYQQXksSFSGEEO5hqgfV6ukoRB/Xp3dPFkII4aXqK9D9fRJnaWKBSz0djejDJFERQgjherk7UOpKiKYUc2Mt6MM9HZHoo6T0I4QQwvWKDgGgoKKUHPZwMKIvk0RFCCGE6xUfPHW7JMNzcYg+TxIVIYQQrtfUowKgFB/q4EQhOiaJihBCCNdSVSg+1YsiiYroCUlUhBBCuFblSWisdvyoSOlH9IAkKkIIIVyrqQdFDY4HQKnMAWN1R48Qol2SqAghhHCtIttAWjX5TBp0YbZjxdKrIrpHEhUhhBCuZe9RiR5GVUCS7VjRwQ4eIET7JFERQgjhWvYelZjhVPsPsB2TAbWimyRREUII4TpWq6PM0yJRKTrgwaBEXyaJihBCCNepzAFTLWj0EDGIKn976Ud6VET3SKIihBDCdeyDZqOHglZPtX+i7efqPKiv8FhYou+SREUIIYTr2JfOjxkOgFkXhBrSlKzIzB/RDR5NVAYOHIiiKK2+FixY4MmwhBBCdJe9xBM7wnFIbUpaWuz/I0QXeTRR2b59O/n5+Y6vNWvWADB//nxPhiWEEKK7HD0qwxyHVPttmaIsukHnySePiYlp8fPixYsZPHgw559/vociEkII0W3NZvwQ06xHJbqpR0USFdENHk1UmmtsbOS9997jgQceQFGUNs8xGo0YjUbHz1VVVQCYTCZMJpNL47Ffz9XX9RbSvr7P19vo6+0DH2xjxXH0pjpUrR/m0GRHu8yRQ9EBatFBzL7SVnzw99cGd7XRmespqqqqLn32bvroo4+4/vrrOXHiBImJiW2es3DhQhYtWtTq+LJlywgMDHR3iEIIIToQV/kTU4/9jUr/ZDaM+IvjuNbSwKV7fg3A12NexaQL8VSIwkvU1dVx/fXXU1lZSWhoaIfnek2iMmfOHPz8/Pjiiy/aPaetHpXk5GRKSko6baizTCYTa9asYdasWej1epde2xtI+/o+X2+jr7cPfK+Nmk0vo13/JNaRP8PyszdbtC/gjTNQKnMw3/Q5aspZng7VJXzt99cWd7WxqqqK6OjoLiUqXlH6OX78OGvXruXTTz/t8DyDwYDBYGh1XK/Xu+1N4s5rewNpX9/n62309faBD7Wx9DAAmrhRaJq1R6/Xo8SOgMocdGVHYLBvjUP0md9fB1zdRmeu5RXrqCxdupTY2Fjmzp3r6VCEEEJ0l33GT+zw1vfZpyjLCrXCSR5PVKxWK0uXLuXmm29Gp/OKDh4hhBDOslqh2Naj0nzGj0PsSNt32ZxQOMnjicratWs5ceIEt912m6dDEUII0V0V2WCuB60BIge1vj9WpiiL7vF4F8bs2bPxkvG8Qgghuste0olOB4229f3RwwAF6kqgphiCY1qfI0QbPN6jIoQQwgd0ND4FwC8QIlJbnitEF0iiIoQQoufsPSox7SQqcGqcigyoFU6QREUIIUTPOXpU2hhIayebE4pukERFCCFEz1gtUHLEdrvDHpWmJEYG1AonSKIihBCiZ8qzwdwAOn+IGNj+eTHNZv7IJArRRZKoCCGE6JniTmb82EWng6KBhgqoKeyV0ETfJ4mKEEKIninqwvgUAL0/RKa1fIwQnZBERQghRM8Ud2HGj51jQK3M/BFdI4mKEEKInunK1GQ7x4DaA+6LR/gUSVSEEEJ0n9UCJU17/LS32FtzsjmhcJIkKkIIIbqvLAssRtAFQPjAzs9vvjmhzPwRXSCJihBCiO6zL94Wkw6aLnykRA0BjQ6MVVCV597YhE+QREUIIUT3OcandDLjx07nB5GDmx4rM39E5yRREUII0X2dbUbYllhZSl90nSQqQgghus/ZHpXm58qAWtEFkqgIIYToHosZSpv2+HGqR6UpUZEeFdEFkqgIIYTonrJjYGkEfSCEpXT9cbHNelSsVvfEJnyGJCpCCCG6xzHjZ1jXZvzYRaaBRg+mWqjMcU9swmdIoiKEEKJ7ujM+BUCrh+ihttuylL7ohCQqQgghuqc7M37sHOUfGaciOiaJihBCiO7pbo9K88dIj4rohCQqQgghnGcxQelR2+1u9ajY9/yRzQlFxyRREUII4byyY2A1gV8whCU7/3hHj8phmfkjOiSJihBCCOcVNZvxoyjOPz5yEGgNYK6HimyXhiZ8iyQqQgghnFfcg/EpABqtbSNDkBVqRYckURFCCOG8oh7M+LFzLKUv41RE+yRREUII4TxHj0oPEhXH5oTSoyLaJ4mKEEII55gbT8346UmiIpsTii6QREUIIYRzyjLBaga/EAhL6v517Iu+lRwGq8U1sQmfI4mKEEII5/R0xo9deKptQ0OLEcqyXBOb8DmSqAghhHCOfUxJTwbSgm0jw2j7zB8ZUCvaJomKEEII5zh6VLo5Nbm5WFlKX3RMEhUhhBDOcVWPCpwajCubE4p2SKIihBCi68xGKM203XZJj8pI23fpURHt8Hiikpuby4033khUVBQBAQGMGTOGHTt2eDosIYQQbSk9CqoFDKEQmtjz69l7ZUqO2DY6FOI0Ok8+eXl5OWeffTYXXHABK1asICYmhiNHjhAREeHJsIQQQrTHMT5leM9m/NiFJds2NmyssfXUuKKcJHyKRxOVJUuWkJyczNKlSx3HBg0a5MGIhBBCdMiV41PAluzEDIPcnVB8UBIV0YpHSz+ff/45kydPZv78+cTGxjJhwgTefPNNT4YkhBCiI66c8WMXKyvUivZ5tEfl2LFjvPbaazzwwAP88Y9/ZPv27dxzzz34+flx8803tzrfaDRiNBodP1dVVQFgMpkwmVxb27Rfz9XX9RbSvr7P19vo6+2DvtlGXdFBFMAcORS1k7i72j5NVDpawFp4AEsfei364u/PWe5qozPXU1RVVV367E7w8/Nj8uTJbNq0yXHsnnvuYfv27WzevLnV+QsXLmTRokWtji9btozAwEC3xiqEEP2dxmri0t2/REFl1eiXaNC7ZjxhTNUezsr8K9WGBL4ZucQl1xTera6ujuuvv57KykpCQ0M7PNejiUpqaiqzZs3irbfechx77bXX+POf/0xubm6r89vqUUlOTqakpKTThjrLZDKxZs0aZs2ahV6vd+m1vYG0r+/z9Tb6evugD7axcD/6t85H9Q/D/MDRTgfTdrl9VXnoXxmLqmgx//4E6AwuDtw9+tzvrxvc1caqqiqio6O7lKh4tPRz9tlnk5GR0eLY4cOHSU1NbfN8g8GAwdD6DazX6932JnHntb2BtK/v8/U2+nr7oA+1sdy2Y7ISMwK9n1+XH9Zp+yJTwBCKYqxCX3Uc4kb1NNJe1Wd+fz3g6jY6cy2PDqa9//772bJlC08//TRHjx5l2bJl/OMf/2DBggWeDEsIIURb7ANpXT0zR1GaDaiVFWpFSx5NVKZMmcLy5ct5//33GT16NE899RQvvvgiN9xwgyfDEkII0Rb71GRXzvixk6X0RTs8WvoBuPTSS7n00ks9HYYQQojOOKYmD3P9tWVzQtEOjy+hL4QQog8wNUB5lu12rPSoiN4jiYoQQojOlRwG1Qr+4RAc5/rr2zcnLM+yJUVCNJFERQghROccS+ePcM0eP6cLjoWACFsyVHLY9dcXfZYkKkIIITrXfDNCd1CUU4N0pfwjmpFERQghROea96i4i33ac7EkKuIUSVSEEEJ0zt09KtCsR0Vm/ohTJFERQgjRscY6KM+23XZrj4p9irL0qIhTJFERQgjRsZLDgAoBkRAU477nsScq5dnQWOu+5xF9iiQqQgghOubuGT92QdEQGN30nBkdnyv6DUlUhBBCdKw3xqfYyQq14jSSqAghhOhYb8z4sZPNCcVpJFERQgjRMcdmhL3Qo2J/DulREU0kURFCCNG+xjooP267LT0qwgMkURFCCNG+kgxAtQ1yDYp2//PZe1Qqc8BY7f7nE15PEhUhhBDtK+rF8SkAgZGnNj2UmT8CSVSEEEJ0pLgXZ/zYSflHNCOJihBCiPY5elR6MVGRzQlFM5KoCCGEaJ9HelRkc0JxiiQqQggh2masgYoTttsxvTRGpflzyeaEAklUhBBCtKekaTBrUAwERfXe88YMs32vzoP6it57XuGVJFERQgjRtqJeXOituYBwCB1guy0zf/o9SVSEEEK0zT5GpLemJjdnT46KDvT+cwuvIomKEEKItnmqRwVkc0LhIImKEEKItvXmZoSnc/SoyMyf/k4SFSGEEK0Zq23L2IOHelRG2r5Lj0q/J4mKEEKI1uyDWIPjbMva9zb7zJ+aQqgr6/3nF15DEhUhhBCtFXlgobfmDMEQltIyFtEvSaIihBCiNU+OT7GTFWoFkqgIIYRoi6d7VJo/t6xQ269JoiKEEKI1+xgVj/aoNA2oldJPvyaJihBCiJYaqqDqpO22J3tUpPQjkERFCCHE6ey9KSEJtuXsPSV6GKBAXSnUFHsuDuFRkqgIIYRoqdgLxqcA+AVCRKrttvSq9FuSqAghhGipyAtm/Ng5xqnIgNr+ShIVIYQQLXlLj0rzGGRzwn7Lo4nKwoULURSlxdfw4V7whyGEEP2ZJzcjPJ1sTtjv6TwdwKhRo1i7dq3jZ53O4yEJIUT/VV8B1Xm22/Zl7D2p+eaEqgqK4tl4RK/zeFag0+mIj4/3dBhCCCGg2YyfRM/O+LGLTgdFAw0Vtn1/QuTzor/xeKJy5MgREhMT8ff3Z9q0aTzzzDOkpKS0ea7RaMRoNDp+rqqqAsBkMmEymVwal/16rr6ut5D29X2+3kZfbx94ZxuVgn3oAGvMMCw9jMs17dOiixiEUpaJOW8valpUj2JyJW/8/bmau9rozPUUVVVVlz67E1asWEFNTQ3Dhg0jPz+fRYsWkZuby759+wgJCWl1/sKFC1m0aFGr48uWLSMwMLA3QhZCCJ82+uR7DC5ezdGYOexPusHT4QAw5dhLJFbuZO+A6zkWe5GnwxEuUFdXx/XXX09lZSWhoaEdnuvRROV0FRUVpKam8sILL3D77be3ur+tHpXk5GRKSko6baizTCYTa9asYdasWej1epde2xtI+/o+X2+jr7cPvLON2mVXocn6FvPcF1HH39ija7mqfZoNz6D94Xms42/EMvfFHsXkSt74+3M1d7WxqqqK6OjoLiUqHi/9NBceHk56ejpHjx5t836DwYDBYGh1XK/Xu+1N4s5rewNpX9/n62309faBl7WxaYyKLn40uCimHrcv3raWiqbkMBpveZ2a8arfn5u4uo3OXMur1lGpqakhMzOThIQET4cihBD9T3051BTYbnvDjB87+6JvxYdsM39Ev+LRROV3v/sd3377LdnZ2WzatImf/exnaLVarrvuOk+GJYQQ/ZN9/ZTQJPB3bTm9R6KGgEYHxiqoyvV0NKKXebT0c/LkSa677jpKS0uJiYnhnHPOYcuWLcTExHgyLCGE6J/sK9LGesFCb83p/CByMJRk2JKpsCRPRyR6kUcTlQ8++MCTTy+EEKI5b1qR9nSxw22JSvFBGDrT09GIXuRVY1SEEEJ4ULEXbUZ4upimmGRzwn5HEhUhhBA29kQlxgsTFXvyJJsT9juSqAghhIC6MtsS9eBdM37sHJsTZoDV6tlYRK+SREUIIcSp3pSwFDAEezaWtkSmgUYPplqozPF0NKIXSaIihBDCtjsxeN+MHzutHqKH2m4XyziV/kQSFSGEEM3Gp3hpogLNxqkc9GwcoldJoiKEEKJZj4oXDqS1i5FEpT+SREUIIUSzHhUvHEhrZy9LFUui0p9IoiKEEP1dbSnUFttuR3txomLvUSk+LDN/+hFJVIQQor+z91CEe+mMH7vIQaA1gLkeKrI9HY3oJZKoCCFEf2cf8+GNC701p9FCTLrttoxT6TckURFCiP7OsXS+F8/4sZMBtf2OJCpCCNHfFXnx0vmncwyolbVU+gtJVIQQor8r9vLF3pqTzQn7HUlUhBCiP6sphrpSQPHuGT929nVeSg6DxezZWESvkERFCCH6M3tvSkQq+AV6NpauCE8FfSBYjFCe5eloRC+QREUIIfqzvjQ+BUCjgWiZ+dOfSKIihBD9WV+a8WNnL//IgNp+QRIVIYToz4r7WI8KnNo4UXpU+gVJVIQQor9S1WabEfalHpWRtu+SqPQLkqgIIUR/VVsM9WWgNBv30RfYk6rSo2AxeTYW4XaSqAghRH9l75GIGAj6AI+G4pSwZPALBqsJSjM9HY1wM0lUhBCiv+qL41MAFAVimtZ8KZbyj6+TREUIIfqrvjg+xS5WVqjtLyRREUKI/qqv9qhAs6X0D3g2DuF2kqgIIUR/1HzGT0wfWDr/dLI5Yb8hiYoQQvRHNYXQUNH3ZvzY2XtUSjPBbPRsLMKtJFERQoj+yDHjZxDo/T0bS3eEJoIhFFSLbZqy8FmSqAghRH/kWDq/D45PAdvMH8eAWpn548skURFCiP7IMT6lD874sZOl9PsFSVSEEKI/6us9KiCbE/YTkqgIIUR/o6qn1h+RHhXh5SRREUKI/qY6H4yVoGgheqino+k+++aE5VlgqvdsLMJtJFERQoj+xt4DEZkGOoNnY+mJ4FgIiADVCiWHPR2NcBOvSVQWL16Moijcd999ng5FCCF8m2N8Sh8u+0DTnj+ylL6v84pEZfv27bzxxhuMHTvW06EIIYTv68tL55/OsUKtjFPxVU4lKkVFRR3ebzab2bZtm1MB1NTUcMMNN/Dmm28SERHh1GOFEEJ0Q5GP9KiA9Kj0AzpnTk5ISCA/P5/Y2FgAxowZw9dff01ycjIApaWlTJs2DYvF0uVrLliwgLlz5zJz5kz+/Oc/d3iu0WjEaDy1VHJVVRUAJpMJk8nkTFM6Zb+eq6/rLaR9fZ+vt9HX2wceaqOqois+iAKYIoaCG5+7N9qnRA1FB6hFBzD38ntF3qM9v25XOJWoqKra4ufs7OxWT3b6OR354IMP+PHHH9m+fXuXzn/mmWdYtGhRq+OrV68mMDCwy8/rjDVr1rjlut5C2tf3+Xobfb190Ltt9G8sY46xGitaVmw7gqrJcvtzurN9fqYqLgaUiuOs+mI5Fm3vDw6W96jz6urqunyuU4lKVyiK0qXzcnJyuPfee1mzZg3+/l3bZ+KRRx7hgQcecPxcVVVFcnIys2fPJjQ0tFvxtsdkMrFmzRpmzZqFXq936bW9gbSv7/P1Nvp6+8AzbVQyv4H9oEQP5uJLL3Prc/VW+9RjC1HqSrho0kDUxAlue57TyXu0++wVka5weaLSVTt37qSoqIiJEyc6jlksFjZu3Mjf//53jEYjWq22xWMMBgMGQ+tsWa/Xu+1N4s5rewNpX9/n62309fZBL7ex7AgASuyIXntOt7cvdgRkf4eu/CiknuG+52mHvEe7d72ucipRURSF6upq/P39UVUVRVGoqalxZEbOZEgXXnghe/fubXHs1ltvZfjw4fzhD39olaQIIYRwAfvsGF+Y8WPXlKhQdMDTkQg3cHqMSnp6eoufJ0yY0OLnrpZ+QkJCGD16dItjQUFBREVFtTouhBDCRXxpxo+dYyl9mfnji5xKVNavX++uOIQQQribqkJxhu12X97j53SyOaFPcypROf/8890VBwAbNmxw6/WFEKJfqzwJjdWg0UHkYE9H4zr2pKsyB4zVYAjxbDzCpZxKVMxmMxaLpcWA1sLCQl5//XVqa2u57LLLOOecc1wepBBCCBew9zhEDQGdn2djcaXASAiOg5pCW49R0mRPRyRcyKmVaX/1q19xzz33OH6urq5mypQpvPrqq6xatYoLLriAr7/+2uVBCiGEcAH7ZoS+VPaxs5d/ZECtz3EqUfnhhx+46qqrHD+/8847WCwWjhw5wu7du3nggQd47rnnXB6kEEIIF3BsRuhDM37sZCl9n+VUopKbm8vQoUMdP69bt46rrrqKsLAwAG6++Wb279/v2giFEEK4hk/3qMjmhL7KqUTF39+f+vp6x89btmzhzDPPbHF/TU2N66ITQgjhGlbrqRk/0qMi+hCnEpXx48fz7rvvAvDdd99RWFjIjBkzHPdnZmaSmJjo2giFEP3Se1tP8H6mBqPZ6ulQfENlDphqQaOHyDRPR+N69h6V6jyor/BoKMK1nEpUHn/8cV566SUGDx7MnDlzuOWWW0hISHDcv3z5cs4++2yXBymE6F8q6008vSKDLUUa1h0s8nQ4vsE+PiV6KGh9cLl3/zAIHWC7Leup+BSn11HZuXMnq1evJj4+nvnz57e4f/z48ZxxRu/vsyCE8C1rDxRisth2Yl+xv5DLJyZ7OCIfYP/w9sXxKXYxw6Eq1zYWJ2Wqp6MRLuL0poQjRoxgxIi265u//vWvexyQEEJ8tTffcXvD4WLqGs0E+nlsD1XfUOTDM37sYkdA5jrpUfExTv3lb9y4sUvnnXfeed0KRgghKutNfHekGIBArUqdyco3h4q4dKyMf+uRYh+e8WPn2PNHZv74EqcSlenTpzs2HVRVtc1zFEXBYrH0PDIhRL+0pqnsMzQ2iFRdNWvzFL7aky+JSk/4+owfu9iRtu+SqPgUpxKViIgIQkJCuOWWW7jpppuIjo52V1xCiH7q66ayz8Wj4vErqWRtnob1GUXUGs0EGaT80y2VJ8BUB1o/iBjk6WjcJ2aY7XttEdSV2ZbWF32eU7N+8vPzWbJkCZs3b2bMmDHcfvvtbNq0idDQUMLCwhxfQgjRHc3LPheNjiMpCFIiA2hoKv+IbrKPT4lOB60PJ3uGYAhLsd2WXhWf4VSi4ufnxzXXXMOqVas4dOgQY8eO5e677yY5OZlHH30Us9nsrjiFEP2AveyTHhfM0NhgFMXWswKnelpEN/SH8Sl2skKtz3EqUWkuJSWFxx9/nLVr15Kens7ixYupqqpyZWxCiH7mqz15AMwdc2o8ysWj4wD45pCt/CO6wTHjpz8kKvYVaiVR8RXdSlSMRiPLli1j5syZjB49mujoaL766isiI6UeKITonso6E98fLQFg7th4x/GRCSEMjArEaJbyT7c5elR8eCCtnSyl73OcSlS2bdvGb37zG+Lj43nuuee47LLLyMnJ4aOPPuKiiy5yV4xCiH5g9YECTBaVYXEhDIkNcRxXFIVLxthWwP5qj5R/nGa1QvFh220p/Yg+yKlRVVOnTiUlJYV77rmHSZMmAfD999+3Ou+yyy5zTXRCiH7DPgZl7tiEVvfNHZvA/23IlNk/3VGRDeZ60Bog0odn/NhFDwMUqCuFmmIIjvF0RKKHnP5rP3HiBE899VS798s6KkIIZ1XWmfjuiK3sY+89aW5kQigDowLJLq1j3aEiLhsna6p0WfMZPxqtZ2PpDX6BEJEK5dm2XhVJVPo8p0o/Vqu106/q6mp3xSqE8FGrDhRgtqoMjw9hSGxwq/sVRXH0tNgH3IouspdA+sNAWjtZ+M2ndHvWz+mMRiMvvPACaWk+uH24EMKt7GWftnpT7Oz3bcgopkZm/3RdUT/YjPB0spS+T3EqUTEajTzyyCNMnjyZs846i88++wyAf/3rXwwaNIi//e1v3H///e6IUwjhoyrqGvm+g7KP3ciEUAZFB2E0W1l3sLC3wuv7HD0q/WDGj529rbI5oU9wKlF5/PHHee211xg4cCDZ2dnMnz+fX//617z44ou88MILZGdn84c//MFdsQohfNDqA4Udln3sbLN/ZPE3p1gtUHLEdru/9qi0sy+d6DucSlQ+/vhj3nnnHT755BNWr16NxWLBbDaze/durr32WrTafjBQSwjhUvYpx3M76E2xsy8Et17KP11Tng3mBtD5Q8RAT0fTe6LTQdFAQwVUF3g6GtFDTiUqJ0+edExLHj16NAaDgfvvv9+xo7IQQjijoq6RH5oWebukjWnJpxuREMKg6CAapfzTNcX9bMaPnd4fIpvGS8p6Kn2eU4mKxWLBz8/P8bNOpyM4uP2uWiGE6Mjq/afKPoNjOv+3RFEUR8+LLP7WBUX9cHyKnaP8I+NU+jqn1lFRVZVbbrkFg8EAQENDA3feeSdBQUEtzvv0009dF6EQwmd9tbfrZR+7S8Yk8Pf1R9lw2Fb+CZbF39pX3A9n/NjFjoBDX0qPig9w6i/85ptvbvHzjTfe6NJghBD9h7NlH7sRCSGkRQdxrKSWdQcLuXz8AHeF2Pc5NiOUHhXRdzmVqCxdutRdcQgh+hl72WdEQmiXyj529sXfXvnmKF/uyZdEpT1WC5T0oz1+Tmdf9K34kG3mj4yl7LNctuCbEEI440tH2Se+kzNbs6+38u3hYqobTC6Ny2eUZYHFCPpACE/1dDS9L2oIaHRgrIKqXE9HI3pAEhUhRK8rr21W9nFifIrd8PgQ0mLss3+KXB2eb7CPzYhOB00//Kde5weRg223pfzTp/XDd68QwtNWHyjA0lT2SXOi7GPXYvaPLP7Wtv48PsXOvr+RDKjt0yRREUL0ui+bphZf6sQg2tPZNymU8k877B/O/XF8ip1sTugTJFERQvSq8tpGNmWWAt0r+9gNi5PyT4ekR0U2J/QRHk1UXnvtNcaOHUtoaCihoaFMmzaNFStWeDIkIYSbrdpvK/vYNxnsLkVRuLQp0flSFn9ryWKGUvseP8M8G4snOTYnzACr1bOxiG7zaKKSlJTE4sWL2blzJzt27GDGjBlcfvnl7N+/35NhCSHcyLHIWw/KPnb29Vc2SvmnpbJjYGm0zfgJS/F0NJ4TmQYaPZhqoTLH09GIbvJoojJv3jwuueQShg4dSnp6On/5y18IDg5my5YtngxLCOEmZS4q+9gNiwthcEwQjRYra2Xvn1Mc41OG9c8ZP3ZaPUQPtd0ulpk/fZXXvIMtFgsffPABtbW1TJs2zdPhCCHcYLWLyj52Lff+kV1yHezjU2L68fgUO3v5p+iAZ+MQ3ebxTTL27t3LtGnTaGhoIDg4mOXLlzNy5Mg2zzUajRiNRsfPVVVVAJhMJkwm13b72q/n6ut6C2lf39cX2/jF7jwALh4V22ncXW3fnJExvPzNUb49XERZdR0h/nrXBNsL3PU71BbuRwNYooZi9eD7wxveo5qodLSAtfAAFvmccJq72ujM9RRVVVWXPruTGhsbOXHiBJWVlXzyySe89dZbfPvtt20mKwsXLmTRokWtji9btozAwMDeCFcI0U01JnhshxYrCn8abyYmwDXXVVV4ZreWwnqFG4dYmBLj0X/SvMIFBx8htCGXzWkPUhQ2ztPheFRCxQ7OyHqZioCBfDv8SU+HI5rU1dVx/fXXU1lZSWhoaIfnejxROd3MmTMZPHgwb7zxRqv72upRSU5OpqSkpNOGOstkMrFmzRpmzZqFXt93/ofWVdK+vq+vtfHDHSf50/8OMDIhhP/d1Xl515n2vbTuKH/fcIwZw2J448YJrgrZ7dzyO7SY0D2bgmI1Ybr7JwhLds11u8Er3qOlR9G/PhVVF4D5oWzQaF12aa9on5u5q41VVVVER0d3KVHxeOnndFartUUy0pzBYMBgMLQ6rtfr3fYmcee1vYG0r+/rK21cud+21sml4xKdircr7btsQhJ/33CM74+WUm+B0D5U/gEX/w4rjoHVBH7B6KMGecVmfB59j8amg9aAYq5HX5MLUYNd/hR95W+wJ1zdRmeu5dHBtI888ggbN24kOzubvXv38sgjj7BhwwZuuOEGT4YlhHCx0hojm4/ZZvvMdcFsn9Olx4UwJDbYNvvnQD+f/VPUbMaPFyQpHqfRQky67bbM/OmTPJqoFBUV8Ytf/IJhw4Zx4YUXsn37dlatWsWsWbM8GZYQwsVW7S/EYlUZPSCU1Kiez/Zpy6nZP/188bdimfHTiv21kBVq+ySPln7++c9/evLphRC95OumRd5csXZKe+aOTeCldUf47kgJlfUmwgJ8uyu+XfYP49h+vMfP6RybE0qPSl/kNeuoCCF8U2mNkU2ZJYB7yj526XEhDJXyj/SotEV6VPo0SVSEEG61an8hVhW3ln3s7D029h6cfsfcCKVHbbelR+UU+6JvJYdt+yCJPkUSFSGEW32117bI29wxiW5/Lvv+QRuPFFNZ77uLcLWrLBOsZjCEQugAT0fjPcJTbfseWRqhPMvT0QgnSaIihHCb0hojmzPdN9vndPbyj8mi9s/yj8z4aZtGA9FNM3+k/NPnSKIihHCblfsLsKowZkAYKVG9s3q0vVflq/5Y/nGMT5GyTyv28o8MqO1zJFERQriNfayIPXnoDfaem+/6Y/nHMeNHBtK2IpsT9lmSqAgh3KKkl8s+dkPjQkiPs5V/1vS38o/0qLTPMfNHelT6GklUhBBusXKfrewzNimM5Mje3TTUPnD3qz15vfq8HmU2Qmmm7bYkKq3ZZ0GVHgVLP+tp6+MkURFCuEVvLPLWnrlj4wH4/mgJlXX95EOp9CiolqYZP+6fYdXnhCWDX7BtHyR7Qif6BElUhBAuV1JjZIsb9/bpzJDYEIbFhWCyqKw+UNDrz+8Rjhk/w2XGT1sUxTYbCmScSh8jiYoQwuU8Wfax63eLv9nHp8hCb+2TmT99kiQqQgiXs28M6IneFLt+V/5x9KjIjJ92yVL6fZIkKkIIlyquNrI1y1b28cT4FLt+V/6RHpXOyeaEfZIkKkIIl7Iv8jbOg2Ufu36z+JupAcqO2W5Lj0r77K9NaaZtlpToEyRREUK41Nd7PDfb53T2GL4/4uPln9KjoFrBPwxC4j0djfcKTQRDmG12VMkRT0cjukgSFSGEy3hL2cduSGwww+NDMFtVVvly+cex0NsImfHTEUWR8k8fJImKEMJlvKnsY2dPmOwDfH2SY+l8GZ/SKftieDKgts+QREUI4TL2lWB7c2+fztgTlR+OllBR1+jhaNykeY+K6JhMUe5zJFERQrhEUXUDW7PKAO8o+9g1L/+s3u+je/9Ij0rXOXpUZNG3vkISFSGES6zaV4CqwrjkcJIivKPsY2dfz8UnZ/+YGqA8y3ZbelQ6FzvS9r0sC0z1no1FdIkkKkIIl/iyaQzIpV7Um2J3yVgfLv+UHLbN+AmIgOBYT0fj/YJjba8Vqu21E15PEhUhRI8VVTewLdtW9rl4jPdNjx0c48PlH5nx4xxFabZCrYxT6QskURFC9NjKprLPeC8s+9hd2tSr8qWvlX9kfIrzHFOUZeZPXyCJihCix7xhb5/O2Af4bjpaQnmtD5V/ZMaP8+zjVGSKcp8giYoQokeKqry77GOXFhPMiIRQW/nHlxZ/kx4V58laKn2KJCpCiB5Zud/7yz52c5sSqa/2+kii0lgH5dm22zGSqHSZfS2ViuPQWOvZWESnJFERQvSIY7aPFy3y1p7mi7/5RPmn5DCgQkAkBMV4Opq+IygaAqNtt4szPBuL6JQkKkKIbiuqamC7o+zj/YmKvfxjsaqs2u8DvSr28SmxMuPHafZeFSn/eD1JVIQQ3baiabbPhJRwBoQHeDqcLrH3/PjE4m/2D1kp+zjPsZS+JCreThIVIUS32T/svXm2z+kcs38ySynr6+Wf5j0qwjmOAbWyloq3k0RFCNEthc3KPt60t09nBkUHMbKp/LO6r5d/pEel+2Rzwj5DEhUhRLes2JuPqsLElHAS+0jZx26uL5R/Gmuh4oTttvSoOM+e3FXmQEOVZ2MRHZJERQjRLV83TfHtS70pdnN9ofxjn/ETGG2bxSKcExgJwXG22zLzx6tJoiKEcFphVQPbj/e9so/dwOggRiX28dk/RTI+pcdkQG2fIImKEMJpfbnsY2dPsL7uq+WfYhmf0mOyOWGf4NFE5ZlnnmHKlCmEhIQQGxvLFVdcQUaGdMEJ4e0cs33GJno4ku7r8+UfR4+KJCrdJpsT9gkeTVS+/fZbFixYwJYtW1izZg0mk4nZs2dTWytLGgvhrQoqG9hxvByAS7x4b5/O9Pnyj6NHRUo/3RYji771BTpPPvnKlStb/Pz2228TGxvLzp07Oe+88zwUlRCiIyv22co+k1IjSAjrm2Ufu7ljE9ifV8VXe/K57owUT4fTdcYamfHjCvYelep8qK+AgHBPRiPa4dFE5XSVlZUAREZGtnm/0WjEaDQ6fq6qsk0pM5lMmEwml8Ziv56rr+stpH19n6fa+NWePADmjIx163P3Rvtmj4jh2ZUZbMosoaCilqggP7c9V1u620alYD86QA2KxawPAS99n3v936E2EF1IIkp1Hub8fajJZzr1cK9vnwu4q43OXE9RVVV16bN3k9Vq5bLLLqOiooLvv/++zXMWLlzIokWLWh1ftmwZgYHevWurEL6gwghP/Gj7/82iiWbCDR4OyAWe26PlZK3CNWkWzorzin8OO5Vc+h0TT7xJcfBINg192NPh9GlTjz5HXPVediXfyvHoCzwdTr9RV1fH9ddfT2VlJaGhoR2e6zU9KgsWLGDfvn3tJikAjzzyCA888IDj56qqKpKTk5k9e3anDXWWyWRizZo1zJo1C71e79JrewNpX9/niTb+e/NxIIOJKeFc/7Mz3PpcvdW+nOAs/rrmCDnEcMklk932PG3pbhs167bCCYgcfjaXzLnEjRH2TF/4O9Ss3Qxb9zImXs+o2c69ln2hfT3lrjbaKyJd4RWJyt13382XX37Jxo0bSUpKavc8g8GAwdD6v3B6vd5tbxJ3XtsbSPv6vt5s48r9RQBcOjax157T3e2bN34Af11zhC1ZZVQZrUQF9343kdNtLDkMgDZ+JNo+8P726r/DuFEAaEsOdfu19Or2uYir2+hUYu6yZ+0GVVW5++67Wb58Od988w2DBg3yZDhCiA7kV9Y3m+3T9xZ5a09qVBBjBoRhVWFlX5n9Y9+fRmb89FzsSNt3WUvFa3k0UVmwYAHvvfcey5YtIyQkhIKCAgoKCqivr/dkWEKINqxoWjJ/cmoE8WH+Ho7GtfrU4m/Gatv+NAAxwzwbiy+wv4a1RVBX5tlYRJs8mqi89tprVFZWMn36dBISEhxfH374oSfDEkK04dQib77Tm2JnX/xtc2YpJTXGTs72MPu+NMFxtv1qRM8YgiGsaWq6rKfilTxe+mnr65ZbbvFkWEKI0+RX1rOzqexz8WjfS1RSogId5R+vX/ytSJbOdzn7eipFBzwbh2iT7PUjhOiUfafkKQN9r+xjZ+8p+mqPl5d/imUzQpdzbE4o41S8kSQqQohO2cdu+NIg2tPZyz9bjnl5+Ud6VFyvm5sT1m0pYMiBYCwVXvx+8QGSqAghOpRXYSv7KIpvln3skiMDGZvUNPtnnxeXf+xjVKRHxXW6sTlhw9Fyqr/KJqzSj4r/ZGBttLgpOCGJihCiQyv2+e5sn9PZe4y8tvzTUAVVJ223pUfFdaKHAQrUlUJNcaenW+vNlH982PGzuaCOik+P4CULvfscSVSEEB2y7+0z14fLPnb2Nm7NKqW42gu78+29KSEJsoGeK/kFQkSq7XYXBtRWfJ6JpbIRbaQ/R4ZXgwbqdhVT80OemwPtnyRREUK0K6+inh9PVNjKPv0gUWlR/vHG2T/FMj7FbewLv3UyoLZubzF1PxWBAqFXDaYqwkTIRbYkp/LrYzRkVrg50P5HEhUhRLvsg2inpEYSF+rbZR87e6/K195Y/imSGT9uY0/+OlhLxVLVSMXyowCETE/GLyUEgICp8QROiAUrlC07iLmiwe3h9ieSqAgh2uXLi7y15xJvLv9Ij4r7dDJFWVVVyv97GGudGX1iEKEXpjjuUxSFiCuHoE8MwlprpvTdg6gmGVzrKpKoCCHalFtRz0/2ss/oeE+H02uSIwMZ563lH+lRcZ+YZou+tTEotnZbAQ0Z5aBTiLxmGIqu5cenotcSddNINIE6TLk1lC8/KoNrXUQSFSFEm1bYyz4DI4ntJ2Ufu1OLv3nR4Mj6Cqhuikf2+HG96HRQNNBQCdUtE1RzaT2VXx0DIGzOQPRxQW1eQhfhT+T1I2yDa38sonaTF71/+jBJVIQQbXKUffrBINrT2deL2ZZVRlG1l4w3sM/4CR0A/mGejcUX6f0hMs12u9l6KqpVpeyjw6iNVgxpYQSfPaDDy/gPCSfsYtt1Kr46hvFYhbsi7jckURFCtNJfyz52yZGBjEsOt+394y2Lv8n4FPdzlH9OjVOp/vYkjcerUAxaIuano2iUTi8TfE4iAeNjwAql/zmEWVau7RFJVIQQrfTnso/d3DG2BM3es+RxMj7F/eyvbdNaKo15NVStPQ5A+LzB6CK69rdgG1w7FH1CENZaE6XvHUA1Wd0Scn8giYoQopUvm6bmXtqPZvuc7tTsHy8p/0iPivvZX9viQ6gmK2UfZoBFxX9kFIGTYp26lMav2eDakzWUfyaDa7tLEhUhRAsny+vYlWMr+1zUD8s+dkkRtvKP6i17/0iPivs5Fn3LoHJ1NubCOjTBeiKuHIKidF7yOZ0u0p/I64bbVuffWUjtFi/pnetjJFERQrSwYq/tQ/mMgZHEhvTPso/dpd6y9099OdQ0JUvR6Z6NxZdFDQGNDmN9CjXf5QIQceVQtMF+3b6k/9AIwi4eBEDFF8cwZle6JNT+RBIVIUQL/XGRt/Zc3DROZVt2GUVVHiz/2HtTQpPAP9Rzcfg6nR/W8JGUNd4PQODkOAJGRvX4ssHnDiBgbDRYVUrfO4ilUgbXOkMSFSGEQ06ZlH2aS4oIZLy9/OPJxd/s41NiZXyKu1UYb8ZCHNoAI+Hz0lxyTUVRiPh5Ovr4IKw1JkrfO4hqlsG1XSWJihDCYcU+W2/KmYOk7GNnH1D8pSfLP/YeFRlI61b1+0upKx0GWIkc9B0ag85l17YNrh2BEqCjMadaBtc6QRIVIYTDV03jU/rjIm/tse8avd2T5R9Hj4oMpHUXS00j5Z8eASBYuxxDw0aXP4cuKoAo++DaHYXUbvOCQdp9gCQqQgjAVvbZnVOBRoE5UvZxGBAewIQUW/lnhadm/9hXpY2RRMUdVFWl/NOjWGtN6KO1hOnetb3mVteXZ/zTIwidMxCAis8zMR6vcvlz+BpJVIQQwKmyzxlS9mnF3sPkkcXf6sqgptB2W/b4cYu6nYU0HCgFrULEtSNRtAqYaqHyhFueL+T8JALGRINFpfS9A1iqZHBtRyRREUIAp6bgzh2b6OFIvI9Hyz/FTeNTwlLAENy7z90PmMsaqPjCtuFg6KxU/JLCIXqo7c5mS+m7kn1wrS4uEGu1DK7tjCQqQghb2edkJRoFLholZZ/TebT8UyQzftxFtaqUfZyBarTglxpKyHlJtjvsY4GabU7oahqDluibRqL462g8UU3FF5lue66+ThIVIQRf77XP9okiJsTg4Wi801xPLf5WLDN+3KXm+1was6pQ/DREXt1sw0H7WCA39ajY6aIDiLxuGChQu7WAmm2ycm1bJFERQjgSlUtkkbd22ff+2X68jMLeLP8UyYwfdzAV1FK5KhuAsEvT0EUFnLrT3nvlxh4Vu4BhkYTOTgWg4n+ZGE/I4NrTSaIiRD8nZZ+uSQwPYKK9/NObg2qlR8XlVHOzDQeHRxI05bT3vb1HpTgDrJY2r3GyoZH/FlWwQR+Mydqz9VBCpicTMDrKNrj23YNYqhp7dD1fI4mKEP2clH267pLenv1TWwq1xbbbMuPHZarWnsCUX4smUEfEVUNbbzgYOQi0BjA3QHk2YEtMPioo476DJzhj8wEmbz7AfYdzeT8gigUZOT1KVhRFIWJ+OrrYQKzVjZT+RwbXNieJihD9nOzt03X2RGXH8XIKKnuh/GMvPYSngl+Q+5+vHzAer6L62xygacPBkDY2HNRoaYyyzfx566fvHInJPQdP8EFBGScaGtEqMDbYH52qsqK0mjsPZPcoWdEYdET9YiSKv5bG41VUfHms29fyNZKoCNGP5ZTVscde9pFF3jrVovyzrxd6VWR8iktZjRZbyUeFwImxBIyOdtx3ot7Ih/ll3HvwBFM2H+BzbElp0cm9jsRkYmggd6fEsmxsGhnnjOGr8YP5TX0RforCV8WVPU5W9NEBRF5rW7m2dks+tdtl5VoA121kIIToc+y9KVPToogOlrJPV8wdm8iPJyr4em8+t549yL1PJuNTXKryq2NYyhrQhhmomZXE6vxSNlXUsKmihpMNphbnHgkaCMBl5DN1bBpnhAURrNO2OMdksjLa3MBbI5L55cEcR7Ly+siB6DWnlZO6KGB4JKEzU6lac5zyz46ijw/CLzmkW9fyFdKjIkQ/dmqRNyn7dNUlY2w9T9uze6H8Y58eKz0qPaKqKsf3nNpb54+j/Dhj1xHuO5TDRwXlnGwwoVVgUmggv02J5f2xadw3ZToAo+uPMyMqtFWS0twFkSEsHTPIZT0rIRck4z/SPrj2AJbq/j24VhIVIfqpE6V17M21lX3myGyfLksIC2BSagTQC+Uf+xgV6VFxiqqqHK838n5+Kb89eJwLNuyj/BPbhoPLUvWsCAGdApNDA7knJZYPxqVx+JwxfDUpnUcHJ3JBVCgB8aNsFys5DBZzp895YVSoy5IVRaMQeXU6upgALFVNg2st/XdwrZR+hOin7GWfaYOl7OOsuWMS2Hm8nK/2uLH8U1MMdaWAAtHp7nkOH6GqKicaGvmhooZN5TVsrqgh12iy38ni3Q1EN6rkh2hRZyTxQUwoU0KDCOqgl4TwVNAHgqkOyrNOLavfAXuycuverB6XgTT+tsG1RX/fRWO2bXBtxOVDnL6OL/Boj8rGjRuZN28eiYmJKIrCZ5995slwhOhXHIu8jZGyj7Mubir/uHX2j703JSIV/ALd8xx9lKqqZNcbWZZXyt0HjjN58wHO3HKQBw7l8ElhOblGEzoFpoQG8aIpiJmFZtAojLtlLH8YlsT0yNCOkxQAjeZUgljU9YXfXNmzoo8JJPIa27T02s351O4o7NZ1+jqPJiq1tbWMGzeOV1991ZNhCNHvNC/7yCJvzksIC2ByU/nna3etqWIfnxIj41NOT0wmbT7A1C0HeSDjVGKiVxTOCAvivtQ4Pho3mIxzx7B8cArnbi4BIPTCFPwGOLmpo31skBOJCrg2WQkYGUXozBQAyj87QuPJ6m5dpy/zaOnn4osv5uKLL/ZkCEL0S83LPlFS9umWS8YksON4OV/tzee2c9xQ/rH3qPTDzQhtiUmjY0bO5ooa8owtZ+XoFYWJoYFMCw/mrPBgJoUFEqQ91UuiWlVKPs5AbbDglxxCyPRk5wPpweaEriwDhcxIoTG3hoaDZZS+e5DY345HG9zG+i8+SsaoCNEPfbU3D4C5YxI9HEnfdcmYBJ788gA7j5eTX1lPQlhA5w9yRnGG7Xs/6FFRVZWspsRkc1Nykt9OYnKWIzEJIlDbflGgZlMexsxKFL2GiKvTUbTdmC7cw80JXZWsKBqFyGuGUfTqLszF9ZT+5xAxvxyN0kH7fUmfSlSMRiNGo9Hxc1WVbfMmk8mEyWRq72HdYr+eq6/rLaR9fV9323i8rI59uVVoNQozhkV57Wvk7b/DqEAtk1LC2Xmigi925XLrWalOX6PdNqoquqKDKIApcgh46WvQmfbap6oq2Q2NbK6sZXNlHZsraylsbDmzRq8oTAgJYFpYENPCApkYEkhA8w9mqwVTO/vwmIvqqVyZBUDwnBQI13fvfRQ5BD2glh7B3FAL2pa9GF15j54XGtBinZVf7zvGq8OSne9Z0ULYdemUvbGPxqxKyr/MJOSSgU42yHnu+jt05nqKqqo9203JRRRFYfny5VxxxRXtnrNw4UIWLVrU6viyZcsIDJTBZkJ0xZpchS9PaEkPs7JgZP+d8ugK3+YrfJqtZVCIyn2j2/7Q7A6DqZKL9v0WFYUvx72JVdO3u/lVoEijI0Prz2GdP0e0Bio0Lf+frFNVBlqMDLM0kG42kmYx4kc3Pp6sMHxfKEG1OirDGjk6oga6t/YaqCpz99yBztrAN8OfpjogqZsXgn06f14LiMWsKEww1fKr+hI6Gc7bprAyPUMybAvAZQ2poSymb66xUldXx/XXX09lZSWhoaEdntunEpW2elSSk5MpKSnptKHOMplMrFmzhlmzZqHX6116bW8g7ev7utvGK17bzP68ap66bCTXTun+P7zu1hd+hwVVDZz73EYANv7uPBLC/J16fHttVLI3ovvPlagRgzDftd2lMfcWVVX5tKCM/xzK5HhgKEWmlomcX4sekyAmhAS07DHpppp1OdRuyEUJ0BJ19zi0oT1L8rRLZ6PJ+xHzz95CHXlFi/ucfY+uL6vmlwdzaFRVLo4K6V7PCqfaiE4h8lej0Se6bx8od/0dVlVVER0d3aVEpU+VfgwGAwZD64F/er3ebf+QufPa3kDa1/c508bsklr251Wj1ShcMjaxT7w23vw7TI7SM2VgBNuzy1lzqITbuzmotlUby44CoMSO9Nq2d6TMZOa+QzmsLq0CfRCYLPgpCpPCbGNMpoUHMyk0yCWJSXONOdXUbswFIOKKofhHueADPG4k5P2IruwInPa72Jy/mfUN6zlXPZdAfee9+rPjIlmq03Hr3ixWlFbz2yO53RqzEj57EJaCehoOlVH5/mFi73b/4FpX/x06cy2PjsSpqalh165d7Nq1C4CsrCx27drFiRMnPBmWED7LPtvnLJnt4zJzm9ah+WpPnusuWtR3Z/xsrahh5vYMVpdW4acoXGSs5KPRA23ThScM5aFBCZwTEeLyJMXa2LThoBUCxsUQOC7GNReOaT1FudHSyJJtS1iwfgHrGtZx/Yrr2VO8p0uXc8XUZfvgWl10AJYKI2XLDqFavKI44hYeTVR27NjBhAkTmDBhAgAPPPAAEyZM4PHHH/dkWEL4LFnkzfUuHpOAosCPJyrIq6h3zUWL+94aKlZV5aXsQq7cdZQ8o4m0AAOfjxvEz4wVTAt3fe/J6SpXZGEuqUcT6kfE5YNdd2F7stj0OzledZwbv76R9w6+B0CQEkRubS43r7iZf+79J1a183FfrkhWNAE6om4ageKnxXisksoVWc61qw/xaKIyffp0VFVt9fX22297MiwhfJKt7GOb7SN7+7hOXKg/U1IjARct/qaqfa5Hpcho4trdmTyTlY9FhaviIlg9OZ1RwS6est2OhsPl1G62vfaRP09HE+jCcpk9WSzN5Isjy7n6i6s5WHaQcEM4L57/IveF3MeslFmYVTMv/vgid6y5g+K64k4v64pkRR8XROTVttVza77PpW5XkdPN6wv6xyRsIUSLsk9kUN+eReJt7DsquyRRqSmEhgpQNBDV+f4ynraxrJoLd2SwsbyGAI2Gvw1P5u8jUjrcbdiVrHUmyj85DEDQtAT80yNc+wShidT5h/FoVBh/3PQ4deY6JsdN5pN5n3DegPMI0ASw+OzFLDprEQG6ALbkb+HnX/yc705+1+mlXZGsBIyOJuQC22J25f89QmNeTbea6c0kURGin/hqj+1DdK6UfVyuefknt6flH3tvSmQa6J2bRdSbzFaVxcfyuWZ3JsWNZoYH+bNycjrXJUShKN2dD+y88v9lYqlqRBcdQNjFrl8hOKP8MNfER/J5SDAaFO4afxdvzX6LuKA4xzmKonDl0Cv5YO4HpEekU9ZQxl3r7uK57c/RaOl4+vDpycod+51PVkJnpeI/LALVZKX0nQNYavvmujvtkURFiH4gq6SWA/m2ss9sKfu4XPPyz4qe9qo4xqd4b9knt6GRq3Yd5cXjhajATYlRrJiUzrCg3k2s6nYXU7+7GDQQec0wNH6u68VRVZX3D73P9V9dT7ZiIdZs5q2Y8/nNuN+g1bT9PGnhaSybu4zrh18PwDsH3uHGr2/keNXxDp/LnqwYNApflzifrNgH12qj/G2Da9/3rcG1kqgI0Q98LWUft5s7tmn2T08TFcf4FO8cSLu6pJKZ2zPYWllLsFbD6yNTeW5YstsHy57OUmmk/DPbNO6QC1LwSw5x2bUrjZXcv+F+nt76NI3WRs4PSuWT3AKmVJZ2+liD1sAjZz7CKzNeIdwQzsGyg8z/Yj6fZ37e4eMujApl6ejuJyuaQD3RN41E8dNgPFpB5arsLj/W20miIkQ/IGUf97t4dDyKAj/1tPzjpT0qjVYrTxzJ5Rd7syg3WxgbEsDaKcO4Is7FY0K6QFVVyv57BLXejH5AMKEzurHhYDt+KvqJ+V/MZ92Jdeg0Ov4w5Q+8Mv4BIqxWpzYnnJ48nU/mfcLkuMnUm+t59PtHeeS7R6g11bb7mBk9TFb08UFE/LxpcO3Gk9Tt9o3BtZKoCOHjmpd9ZLaP+8SG+jNlYA/LP6p6agM8L+pRya43Mu/HI7xx0jab5ddJMXwxcSgDAzyzFk/tlnyMh8tBpyHymmEu2ZzPYrXw5p43uXXlreTX5pMSksJ7l7zHjSNvRIkbZTupLAtMXU9C44LieGv2W9w9/m60ipYvj33J/C/ms79kf7uP6WmyEjg2hpDpthWnyz/xjcG1kqgI4eOal30ipOzjVvYeqy/3dDNRqc4HYyUoWoga4sLIuu9/ReXM2p7B7up6wnVa/j1mEE8OHYBB45mPD1NJPZVf29YMCbtoIPrYnu/zVlJfwh1r7+Dln17GolqYmzaXj+Z9xKiopgQlOBYCIgAVSg47dW2tRssd4+5g6UVLSQhKIKc6hxtX3Mi/9/+73TVXepqshM4eiGFouG1w7XsHsdb17cG1kqgI4ePsH5qXjpWyj7vZyz+7cio4WV7n/AWaz/jReXbl4HqLld9n5HDH/uNUW6ycERbE2inDmBMd5rGYVItK+YcZqCYrhiHhBJ+V2ONr/pD7A1d9fhVb87cSoAvgqbOf4plzniFI32z5fUVptkLtoW49z4TYCXw872Nmpc7CbDXz1x1/5a61d1FSX9Lm+T1JVhSNQtR1w9FG+mMpa6D0/UOoTs4ksqsqacDq4TxHEhUhfNix4hoO5leh0yjMHillH3eLDfXnDEf5p8D5C9jHp3h4obfDtQ1csvMw7+SVogD3psbx6fghJPl7tkeuekMOjTnVKP5aIn6ejtKNDf3sTFYTL+x8gTvX3klZQxnpEel8cOkHXDHkiranV9t/J0UHuv2cYYYwnj//eR6f9jgGrYEf8n7g55//nE25m9o8vyfJiiZQT9RNI1H0GoxHKqhycnBtXVUjGz84zEd/3kF1lmd/75KoCOHDHGWfIdFS9uklPZr94+Gl81VV5YP8UubsOMzB2gai9To+GDeYR9IS0PUgKXCFxpPVVK2z7QMXfvkQdOHd73E6WX2SW1bcwtJ9SwG4Ztg1/OeS/5AWltb+g2JH2r4Xd69HxU5RFOanz+eDuR8wJHwIpQ2l3LH2Dl7Y8QImS+uui54kK34JpwbXVn97kro9na+Ya6w3s+V/mbz72Gb2bjiJ1aJiqtGgqp6b7iyJihA+zFH2kdk+veainpR/ijzXo1JrtvDbgye471AO9VYr50YE882UYZwf6bppv92lmiyUfZQBVpWA0VEEju/+hoOrslcx/4v57CnZQ4hfCH+b/jf+NPVP+Os6WQPGPgurqOszfzoyJGII7899n2uGXQPA0v1L+cWKX5BTldPq3J4kK4HjYgg+r2lw7ceHMRW0PevI3Gjhp9UnePdPm9i54jhmo4XYgaHMvXsM0RMbenURv9NJoiKEj8osruFQQbWt7DMqrvMHCJeIDelm+UdVPdajsq+6jtk7DvNJYTka4OFB8XwwbjCxBhfumdMDlSuzMRfVownWE/6zod360GwwN/Dk5if53be/o8ZUw7iYcXwy7xNmps7s2gXss7AqjkNj+1OMneGv8+dPU//Ei9NfJNQvlH2l+5j/5Xy+OvZVq3N7kqyEzRmIYYhtcG3JuwdaDK61Wqzs/y6X9x7fwqZPj2KsNRMRH8jFd47h53+YxIBh4S5pa09IoiKEj/q6qTfl7CHRhAdK2ac32Qcuf+lM+acqD4xVoNH12owfVVVZmlvC3B+PkFlvJMGg59MJQ7hvYDxaD/4PurmGzApqfsgDIOLn6WiDnE+eMisyue6r6/j48McoKPxyzC9ZetFSEoOdGIwbFA2B0bbbPSz/nO7C1Av5ZN4nTIydSK2ploe/e5hHv3+UOlPLHrnuJiuKViHyuuFoIwxYShso/SADq9nK0Z1FvP/kNjb8J4PaCiPBkQZm/GIE1z5+JmnjYzzai9KcJCpC+Cj7GAlZ5K33zWkq/+zOqSCnrIvlH/tiYpGDQef+xLLSZOaX+7N55PBJjFaVWVGhrJ08jKnhwW5/7q6yNpgp/6hpw8Ez4gkYHunU41VV5b+H/8u1X17L0YqjRPlH8fqs17l34r3oNd3oLYrt2cyfjiQEJ/DPOf/krnF3oVE0fJ75OVd/eTUHSlsO3u1usqINsg2uRaeQs7+UD/+0iVVv7qOisA7/YD3nzB/KjYumMeKsBDQeHo90OklUhPBBUvbxrNgQf84c1FT+2dfFXpVeHJ/yY2UtM3cc5qviSvSKwqIhibwzZhBRfjq3P7czKj7PxFJpRBvlT9jcDga6tqG6sZrfb/w9CzcvpMHSwFmJZ/HJZZ9wVuJZ3Q/Inqg4sUKtM3QaHb8Z/xv+OfufxAXGcbzqODd8fQPvHni3xWDW7iYrZQ0Wtvrp2VxroayiEb1ewxnzBnHTn6cx7sJktHrvTAm8MyohRI9I2cfz7D1ZX3V18Tf7h58bx6dYVZX/O1HEZT8dIaehkRR/Pz6fOJQ7kmNd2s1vamhgz7pVfPr04+RvXM2xnduwWixOXaN+Xwl1PxaBApHz09EYur7h4L6SfVz9xdWszF6JTtFx/6T7eW3ma0QHRDvblJYcA2pd36PS3OT4yXwy7xNmJM/AbDXz7PZnWbBuAWUNZY5znElWSvNq+Pq1Pfz32Z0U5NWi0cBgg4aZYTrGT47Fz9+7EtTTeXd0QohucZR9ZJE3j5kzOp4nPt/P7pOV5JTVkRzZyQqqbu5RKW00c8/BE6wrqwJgXkw4zw9PJlTnuh2Hi49nsXvtSg5+t57G+lMlry//9gxBEZGMOv9Cxlwwm/D4jt+XlupGyj89AkDI+UkYBnZtkTmrauXdA+/y4s4XMatmBgQPYMl5SxgXM677jWrO0aPi3kQFINw/nBcveJEPMz7kue3P8V3ud/z885/z9LlPMzVhKnAqWbl1X5YjWXlj1ED0TaWbqpJ6tn+ZxaGtBaDa1q0bPi2ByRcPxPjZEYyZlZS+e5DYBePRBHhvOuC9kQkhuuVoUbOyz0gp+3hKbIg/ZwyKZMuxMr7em88d5w9u/2RVheIM22039KhsKq/hrgPHKWg0YdAoPDVkADclRrmkF8XUaOTw5u/ZvXYF+YdPfYCHxyUwcvpMDuzZjTE3m9ryMrZ99jHbPvuYlNFjGT1jDkOnTEPn17LHT1VVyv97BGudGX1CEKEzU7sUR1lDGX/6/k98l/sdALNSZ7HwrIWE+oX2uI0O9h6VyhxoqAJtgOuu3QZFUbh2+LVMjJvI77/9PZmVmfx69a+5fczt3DX+LvQafZvJyospiexeeZx9G3OxWmy9LIMnxHDGZWlEJthW3LVcP4KiV37CXFJP2QeHiLp5VI8W0HMnSVSE8DH2Rd7OGSplH0+bOzaxa4lKVS40VoNGD1EdnOcki6ryYnYhz2cXYAWGBhp4Y9RARgb3/AO29OQJdq9dwYGN32CstU3X1Wi1DJk8lbEzLyZl9FjMFgtFWn/m/OFPnNj9I3u/WU32np84sW8PJ/btwT8omBHnXcCYGXOISRkIQN32QhoOlYFWsW04qOt8hMK2/G08/N3DFNcXY9Aa+P2U3zM/fb7rZ60ERkJwPNQU2BLL+PGuvX470iPSef/S93l2+7N8cvgT3tr7FtsKtrHk3CUkhSQ5kpU7fsqkdkM+b2dkozXbEpSk4RFMvXwwcYNaJmz2wbVFr+2mIaOcqrXHCZs9sFfa4yxJVITwMfYxEZfIbB+Pu2hUPE/8b1+n5R/FXkqIGgJa16xdUmg0cdeB4/xQYds99+r4CJ4ZmkRQD0o95sZGjmz9gd1rV5J76NQOwKExcYy9cA6jL5hFUHjEqQc0jUvR6vSkTz2H9KnnUFVcxL4Na9i3fi3VpcX8tOILflrxBQlDhjF26hwittv+xx82ZyD6+CA6YraaeX336/xjzz9QUUkLS+O5858jPSK9223sVOzwpkTlYK8lKgABugCemPYEUxOmsmjTIvYU72H+F/N5YtoTzEyaReRPFTz4dTWWOjMAtbF+zL92OINGtj8ux29AMBFXDqH8o8NUf5OD34BgAkb1cByPG0iiIoQPOVpUTUZhNXqtwhzZ28fjYkIMnDkois3HSjvsVVFKXDs+ZX1pFXcfPEGpyUygVsOS9CTmxzs3tbe5srxc9qxbyf5v19FQbRvjomg0pE08g3GzLiZ17Hg0mq4lQKExsZw1/wamXnUtx/fsYu+6VWTu3ErB0cOMqJ2M6h9AraEaJbaaYFVtt1ekoLaAP2z8Az8W/QjAlUOv5A9T/kCgvue7KXcoZgQc2+D2AbXtmTNwDmOix/CHjX9gd9Ee/vnxfzlcYEVbZ1tZ1y/an4+G69ibqOeQpZrXrZH4dbDTddDEOEy5NdT8kEfZh4eJvTvQJTtSu5IkKkL4kK/22FZCPWdINGGB3rGqqLNqyko59tMOMnduI+9ENhvLCkgaPpLE9BGERHnf//Y6M3dsApuPlfJVR4mKi8anmKwqS7Ly+fuJIgBGBvnzj9EDGRLYyfLwbbCYTRzdvoXda1aQs3+P43hwVDRjZ8xh9IxZhES2/n00mCxsyixh3cEiNh4uxmrUckh/hFmjExiXFI62aRyERqNl0PhJDBo/idqKco4v20LoiWBMViPfHH2PuieqiE5OZcyM2Yw49wICQk6VLtafWM9jmx6j0lhJkD6Ix6c+ziVplzjdxm5xweaEPZUQlMBjcc+yeuMulArbnkf1/tVMvCSZ6ReOZ3BFjWPMyp37j/P6qNQOk5WwSwZhyq/FeKyS0ncOEHv3eDReNBPIeyIRQvSYfXxKXyr7qFYrhVmZHPtxG8d+3E7hsaMt7t+18gt2rfwCsH1IJg4dTmL6CBLThxM7KA2tzrsTsotGx/P4//axp6Pyjwt2Tc5paOQ3+7PZUWWbbXPLgGgWDk7EX+vcKhQVhQW23pMNa6mrrLAdVBTSJkxm7MyLGDR+Mhpty96TwqoGvjlUxLqDhXx/tIQGk7XZvQqvbczitY1ZRAX5ccHwWC4cHsu56TEEG2wfQfo6PaG5IYCK/pwIBp6YxOEtP1CSc5z1/36Tjf9ZypAzzmL49Av4uGEtyw4tA2BU1CieO+85kkOTu/OSdU9M7838aUvOwTK2fJZJ0fFqFAzoAuDHAWvYGrWSZYUKDx5+kOuHX99igG1nyYqi1RB5/XCKXtllG1z7YYZt52UvGVwriYoQPqJ52We2l5d9GhvqOb7nJ479uIOsn7ZTW1F+6k5FIX7wUAaOm8SxvHzigwMoOHKY4hNZ1JSWcLj0ew5v+R4And6P2LQhJKYPb/oa0XKMhBeIDjYwNS2KTZm2XpU7T+9VUa0oJbbVV7vbo7KiuIL7DuVQabYQotXwwvAU5sWGd/nxFrOZYzu3sXvtCo7v+clxPCgikjEXzGLMjDmExsQ6jlutKvvyKll3sIhvDhWxN7eyxfUSw/yZkR7BuAgLuw8dpSxkMN8dLae0tpFPdp7kk50n0WsVpqZFMXNoDDO2l6FYVPxHRBI1byRJyiQuuPUODn3/LXu+WUVx9jG27V7Hc8qHlIU1AnBd2tU8dNbD6F00pqfL7MlkdT40VHZ8rgsVZlex5bNMTh6y/a3oDFrGz0xmwswUrlYm8PgPNWw4uYHF2xazJW8LT579pFPJijbYj6ibRlD0+m4aDpZR/c2JLs+4cjdJVITwEd5e9qksKiBz53aO/biNkwf2YjGbHffp/QMYOG4CaRPPYND4SQSFR2AymSj5+mumX3IJer2exoZ6Co4eIf/IIfIOHyTv8CEaaqrJyzhAXsapbviw2DgS00eQ0JS4xKQMbNUD0NsuGZNgS1T2tE5UAhpLUUy1thk/kYOcuq7RauXJo3n8M7cEgAkhgbw+KpXUAEOXHl9VXMTeb1axd/0aastPLSaWOnYC42ZdTNrEM9DqbB8TdY1mfjhayrqDhXxzqIiiaqPjfEWBSQkBTIuzEq+tpbrkCIX7CtmvquiApIosfj80BU1ILJkNgaw/3khWWT3fHSlh9BFbz0CVovJxuMI5x8uZmBKBf1Aw4+fMZfycuby3+S2WZbxKo2LG0KjhnN1R+K/Yzpebn2HMjDmkTWjdy+M2/mEQOgCqck8Ngnajsvxatn5+jGM/FQOg0SmMPm8Aky4aSGCobVafHxG8PONllh1axvM7nmfDyQ38/POfs/i8xSwdPazLyYpfUggRPxtK+ceHqVp7An1iMLqhLpze3U2SqAjhI77aa9u4be5YJzZacyOrxULe4YMc+3E7x37cTunJEy3uD4uLZ/DEM0ibeAYDRoxCp+84ufLzDyBl9FhSRo8FmtbbyM8l7/Ah8g/bkpeSkyeoLCqksqiQg99vAEBv8Cd+SLqjxyVh6LAW4x16g738sze3khOldaREnSr/hDbk2m5ED3Vqxs+xOiN37s9mT009AHcmx/DHtIQOxyKA7feStWsHu9esIGvXTtsaLkBgWDijp89kzIUXER5n65HLq6hn3aFcvjlYyKbMUoxme0lHJd7PxLQ4lRRDPWp1MVVlFVSXQXWz5woLC6OmpobGxkYyjx4FbGW9WX5+xAwbgNkUwplZYFX1PK028P3mLF7anEVEoJ7pw2I5Jz2ErVX/ZEX2F6DA5NhJ3OY3j9yTW8ktPsCxnds4tnMbQRGRjJ4+k9HTZ3W6mJxLxAxvSlQOArGdnt4d1WUNbPsyi4zN+ahNi7UNOzOeKZcOIjS69fRyRVG4YcQNTIqbxEPfPkR2VTa3r7qdX439FW+NvIFfHsjpUrISNKlpcO2mPMo+zCD8V8OBere0saskURHCBxwprOZwYQ16rcIsDy7yVl9TTfaunRz7cTvZu3bSUFvjuE/RaBgwfCRpE88gbeIUIhOTerTOhaIoRCYmEZmYxOjpMwEw1tWSfySDvKbEJf9IBo31deTs39NiQGhEYlLTWBfbV1RSCkonH/A9cXr55zfTT/WqhNgTlZiuj0/5tLCchzJyqLVYidRreWl4CrOiO169tbqshL3rVrN3/WpqSkscx1NGj2XszEsYMuVMFI2O3ScrWLcqg3WHijiY3zTDBytRSh2Tgo0MDWrAr6EMk7EBiqF58SM+Pp6UlBTHV0BAAF999RWTJk3i5MmTZGdnc/z4cRoaGsg9ngXA5wbw0+iZGBNPojWULcUacupU/ndgO6srl6ExlICqMDXyOh6evIDBMaFw4VWU5uawb/0a9n+7jtryMrYu/4ityz/qcDE5l4kdAZnrmhbpc22iUl/dyM6Vx9n77UmsTWuhDBoXzZmXpxGV2PmGkcMjh/PhpR+yeNtilh9dzj/2/IPx+dt4fsxjPHisvsNkRVVV6uuPUz9pP5ZMBW1hOIVL1+I/YT1wlUvb6QxJVITwAfYl888dGkNYQO+VfVRVpSw3h8ydtoGweRkHUdVTAyn9g0MYNGEyaRMmM3DcJPyD3bszryEwiIHjJjJw3ERbfFYrpSdPkHfkEHkZh8g7cojyvJOOr/3frgXALyCQhKHDWvS6GAI7XsPDWXPH2so/X7eXqMR2Pj6lzmLlT0dOsizfVqaZGhbE/41MJdG/7Q9kq9XC8d0/sXvtSo79uA3Vavvd+IeE2npPZszBLyqO748U89byA6zPKKKkphEdFmI0NYzX1TAkoJ5gSxVYLWAGKsEE6HQ6BgwY4EhKkpOT8fdvObvIZDKhKArx8fEkJyczbdo0rFYrhYWFHPjfdo7nnqBAW0Gj1URjYQ6BwAyAIIV8v3yKGyIoNBsoLLqENYcGs2bTd6TFBHHh8FguHBHH2dfdwjnX3kTmjq1dWkzOZZp+V0rJIYg41yWXbGwws2ttDrvWnsDUYFt/ZkB6OFOvGEx8Wte2ELAL1Afy5NlPMi1xGk9ufpJdxbvI/P4W7h3zB14qHehIVl4aGkxDzR6qqvZQVbWbqqo9mM221FM7PJTUioX41SaQePAM1Fmdb3roLpKoCOEDenO2j9lk4uSBvU0lnW1UFhW2uD86OZW0iVNIm3gGCenDury+hjsoGg3RKQOJThnI2AsvAqC+uqqp18U2zqXg6GEa6+s4vuenUwNJFYXopBTHOJfE9OFEJAzoUQ/QnFHxPPZZ6/JPV3tUDtXW8+t9xzlc14AC3D8wjgdS49G1MTOjtqKcfevXsGfdKqqKT/1+kkaMZuzMiwgYOp5vMyt4a8VJth7bg9ZiJFZTTZqmhrMNNYQrtTiuarJ9CwgIaNFbkpCQgE7n/EeIRqMhvNyPYdkRDFMiiLp5FOWBDWRnZ3Pk2BGOZR1DsUBCfTwJ9bYSlKKrpjo8m4waf3JLQnjruxre/C6LUH8d04fFcuGIQcy87zGU2vJ2F5MbPWM2w886F78AF6wR0jToWSnOgB6O3baYrOzbmMuOFdk01Nhe7JiUEKZekUbyiMgevecuHnSxY82VPSV7WLrzT5wfM4H1hjv4ugSKilfxW/6GjlPjxayqnmJjKhllydQGZXKHMZwNxgEM8+AEIElUhOjjjhTWuL3sU1tRzrGftnNs53aO7/nJ1u3fRKvTkTx6nC05mTCFsFjv3l8oICS0KZGaAtjGbBSfyHaMc8k7cojKwgJKco5TknOcvetWAbZeiMShw5p6XIYTP2Qofv5dX4o+OtjAtMFR/HC0WflHtXbao6KqKu/nl/HokZPUW1Vi/XS8OiKVcyNDWp5ntXJi3x52r/2azB1bHbsVG4KCGHHuhehGTmNLuZ5/bSsk/6u1xGlqiFVquFRbTaje2Op5w8PDWyQm0dHRaFxQHrPUnNpwMPicAQQMiSQAKNQV8l7eexQkFxBljuKqqKuIqo/i+PHjGI1Ggs3FTNLBJB2oGj0F1mByTMF8v6eSL3bnotFomJQawcwRU7ng8Xlo8zLYt34NmTu3kn80g/yjGWz495sMO+s8xl44h/gh6d1PAmKGAaDUFuFnru7k5LZZrSoZWwrY9uUxaspsr394XCBnXpbG4AkxPZoabLWaqKnNcPSU/CaqhE+MZr6p1vJT8U8M1N3DiahH2WGYyvPmPzKtYA3HK5PIqkzlZE0iFtWWGmg1CpnRKjEGk+u3I3CCJCpC9HEr9ttm+7iy7KNarRRlH3OUdAqPHWlxf1BEJGkTJpM28QxSxoxz6gPb22i0WuIGDSZu0GDGz5kL2BIzW7noIPlHDlGQeYSG6irHwGCw9dbEpAwicdhwEocOJyF9BGGxcR3+g37JmISmRCXPlqhU5qCzNqJq/VAiWs/4qTZb+H1GDsuLKgCYHhHCKyNTiPE79Xuuq6pk3/o17F23iorCfMfxuCHD0YyYxg4lif8ePUnA0W3EaaoZr6lhmsF8+lMRFxfXIjEJC3Ou3NAVqqpS/ulRrDUmdHGBhM0eiMVq4Z/7/sn/7fo/LKqFlNAUnj3/WUZFjQLAYrFQUFBAdna2Y4xLY2MjCZSToLdN1TWjI88STMGJEF7PPsnTXwcwMCqIGcOv4LwLr8E/+ycObFhDeX4u+9avZt/61e0uJtclhmAIS4HKE4TU5zr9GmTtKmHL58coz7ftkRQUbuCMSwcxfFo8GifXvbGNK8mmqmpvU/lmN9U1B7BaWyaf88IhURPGx5VWKsyNhBY8RXX49ewJnck+fTrxFTWMjA9l1rgQhieEMjw+hCGxwWix8vXXXzsVk6tJoiJEH7din61rf24Pyz6mhgaO791lW3jtpx0tpqsCxA8e6hgIGzswza2DTz0tKDyCoVOmMXTKNMC2SmtR9jHHOJe8wwepKS2hKDuTouxMdq36CrDNnHGMc0kfTlzaEPR+p6YKX9RU/tmXW8Xx0loGOPb4GQralv8c76mu44792WTVN6JV4OFBCSxIiUWjKKiqyskDe9m9diVHtm7CarElHjr/AJQhkzjkn8QPVXVEZ2QTo9nP+YoVmuWwWq2WpKQkR1KSlJREQID7k826H4toOFDq2HCwxFTKI+sfYWv+VgDmps3lsamPEaQ/NT5Iq9UyYMAABgwYwNlnn91m4kJjIynaClK0FQAYVS2FVSFs3hLC8k2hmP1COG/yrzkrpIqgrB0c37G51WJyYy+cQ/LIMV1/X8cOtyUqDSe73P6TGeVsXp5JUbZtkLIhSMekOQMZM30AOr+ulUiNxmKqqk+NKWk+rqS5WlMAWZWptq+qFLIrU6hsDEPvV0t4yqc06PcTUvkOhsYDVEXfzvhRQ9ocYGtqsXifZ0iiIkQfll8HR4tr8dNqmNmNsk9lUaEjMcnZvweLyeS4T2/wJ3XsBNIm2Uo63raQWm/S6vQkDBlGwpBhTOJyAKpKipvWdLElLkVZx6irrODo9i0c3b4FAI1WR+ygNMc4l8T0EZw1OJrvj5bYltRXbEvnqzHDHGNCVFXln7klPHk0j0ZVZYBBz+ujBjIlLIj6mmoOfLuO3WtXUp5n+4C0anWYE4dQFBSPWVGIMNcTW3uU2Gafezo/AwNTUxiYmkpKSgqJiYndGl/SE+byBio+zwQgdGYq29RdPPrFo5Q1lBGgC+DRMx/lssGXdVpiaCtxyc/PdyQuJ06caDNxKcgI4T1rCIXqQIZOmcjZ2hwCjm2nKvc4GZs2krFpI2Fx8Yy5YDajzr+Q4MiojhsUOwKOrD41vbwDRcdti7XlHGxarM1Pw/iZKYyflYIhoP3fg9lcQ3X1PqqqdlNZtYfKyj00Nua1Os9k0XGi2la6OVaZQlZVKkV1McSG+DMiIZSpI0O4NT6U4QkhpEUHo9P+nP8c/A8v7HwB6ncQWXCMNaa7uBM6XW7fEyRREcJLWVWVCrOFMpOZ0kaz7bvp1M+ljY3srAwEGhmQFMInpRWE6bSE6rQtvofptARqNSiKYlvb5MghWwlj57bWa5vExjl6TZJGjul0bZP+LDQ6htDoGIZNs836MDUaKTqW6Rikm3f4IHWVFRQcPUzB0cP8+PX/AAiOnwIBk/nk+0NcFrOHUJMffmFDMTT9vh84lMOKEtv/kC+KDuWFYcnUHzvC1++uIGPL95jRYAkMxpyYRkNQODqd7UMljGbjhvyDSElJYeTQNFJTU102vqS7VKtKxceHUY0W9CnB/DP4Y/61dikA6RHpPHf+c6SFpXXr2vbeoaSkJM4555xWicvx48fBZCJVW0FqU+LSUKblJ2sIBQGTCBo+mYnmHAJz9lJZWMD3H7zDDx++x6CJkzteTK5pQG1IB4lKeUEtWz/PIvNH295LGq3CqPMGMPniU4u12VmtjdTUZFBVvZfKyt2Ulv9Eo/EYCi1n21hVhfzaeLIqUzjW1GNS3DCAwbHhjIgPZfbQUEbEhzAsPoSo4PYX/rtp5E1MipvE7zf+nuNVxwkr+gsbjFdwh/oL3hid5lXJiiQqQvQSo9XaOuGwJx1Nt21JiO2+crMZSyczAv2KrWiAjFANjx5p+x9Mg7GewTlHGJZzmOTjhzE01DnuUzUarCmD0Y0YS8joCYQPSMai11Gk09LQaCbMqhKq0xKo0Xh0MF1foPczMGD4SAYMHwnYekaqigvJyzjomB5dfCKLpKK9KCkTOVaj4Y2DJsLNZ8LRTSj//hl1fgHEGwK43hBASkQY0aYG3ikto17RYgkIxjJwJGqzvY10gApYDaHEJyYxeXQ6w4cMcsv4kp6o21yA8Vglql7hz7FvsOHADwBcO+xafjfldxi0XVtJtyvaS1yysrJsicuJE/iflrhU6XQcTTkLpdHIoPocQksyKd69ke/3rWdfWCBDx41m4PChBPlrwVgFxmooOwZARO1R+PpBCIwA/1AwhFJjjmT7j2Ec3K+xraenwLBJUZxx2RBCY4NQVZW6uiyqqvZQVPoTJeU/YW3MQMHUoi0KUFof4SjfZFWmYmQoaXGxjEgI4edjQxmREMLAqCB0To5tARgZNZIPL/2Qp7c+zeeZnxNUuZxNBw5wq+n3LJ0w2WuSFa9IVF599VWee+45CgoKGDduHK+88gpnnHGGp8MSol2qqlJltrRMOJolHc0TDvvPNZbu1XpDdRoi9Tqi9DrH9yg/HaaKBt6pzUWjUZg/JpE6DVSZLVSazKhF+YRn7ich6yAD8k+gaba2Sb0hgKzkoWSmDiM7eSgN/k3TNauBQzltxqBTaNVT06rnRq9r9/4AjdLvEh1FUQiLjScsNp4R514A4NgGYOvn2eytghMhA4ks34UVDarZjL+lFj1g0Wqprm2gIiAI4k8b6Gm1om2oRVtXg7auBp2xjoDAQPyLDpN5dBt5wSH4O76CHbcDgoKbHQ/BEBjoknFGZouVBrOV+kYLDSYL9SYL9Y227zX1Ro4VatFtO4YB+DxiDd9U7CfYEMcT0x7morQL0bhq4ztVBVMdNDQlEsYqMFahbagiyVhNkq6ac5OrsMbUUFdWQH1FEY3VJaj1lRgUIwZdIwadEUOgCU6v+pz8HtoZiqJVzfDTvwFosIaws+ZK9tZdggXbazvQsI2pwf8h6uQJ1P8Dk06HWati0YFBp5Cg1RCjUzDrFOoVPwpNkeQ1xpLbmITRMJTQyEGkx8cza2w8g5MTCQsNtS1T6yJB+iD+cs5fmJY4jYWbngRjBrsO3MM1dffy4dnX4A1/tR5PVD788EMeeOABXn/9dc4880xefPFF5syZQ0ZGBrGx7lmaWIjTNVqtlJssjqSixNHz0bLc0jzxMHdj/SOtwqlkw554+OmI1Gsdx6Icx2zH2/tfzV9XHgRg+tBonh8xgJMH93Fsl22WTmVhQYtzwwYkEz12IsGjJ2BNTmOECpVmiy2x6eS7WQWzCmUmC2Umi/ONBvSK0mmiE6pv+XMgVsoULflGE3qrigYF+2ea/bYGW0Jg+w4Kttu2+xSUZuf0FlVVUVUTqmrGajW3uh2R6s8VZ8Wwd2UZ+dHJ3DNkFW9EzSequprQunpQNaBq0KgasFqwWMwYG1T0FghTTYRY6lDra6mvqXaMKaqvqqS+quWASisKZkWLWdFj1ugwKzpMis5x26zxg4AgNP5BqH6BqH4BqH4BWHQGLFo/LFo9ZkWPSdFiQkOjVaHBCkazSr3JQoPJSoPJitna2R+CAthmt1B0FhSdRS1w134j8DV+OoUQnYVonZEovZFIbQMRugYiNEbCNPWEKA2EKHUEU0+QWkegWkeAtRZ/ay0GSy1+5hr05lp05hoUteP3p6qCYoVAq0KAFaxWBawKqhVUq4LVolBv1aNaFcxWDY1Wve3LosNs1mA2KVhMoKpa/EOiCDWeRDE1Ytb4cSRwDof852JSbIl/rPUgE6zvE2c5jNIAtRo/FA0oGhVFC34aMGhUFI351HFNA0O1VShKNrAN6oByIPO011NnAK3B9l0fAPoArLoALPogLLogTPoQTLpgTPoQGvWhGHUhNGjDqNeH0qALxaj406hqMalajKqWRlWDyTqY+VF/55PMVVQ2lLO/4Ecm/1TFjLhJ6CsULunKm99NFFVVPbfcHHDmmWcyZcoU/v73vwNgtVpJTk7mt7/9LQ8//HCHj62qqiIsLIzKykpCQ123d8eH/3yZkxkHsFhUtFoFvCKndDW1Wfvc/lQtv7e82f3rdXI11aqiaDSogIrt92j7roKqQUVBQbH966UqtjPUpjPVpuMoaJq+K6rtnaBRQbFdtOncpg9DVXWco2D7B1HTFJvS9BjF/l5SwfG+UhUUVFs8zY83scXY7GfV9gy1qg4LEEgjOky0eKGVputoNIAGFKXZU6r2Czc9vXrq8sqp+9Sm69jvsyqg2n/WKKgKWGk61nR9qy1bwKJVsKLajjuarGBteoEsGtv5qsZ2vrXpulb7teyPU60oGtuLqcG2lLsCKIrVdlu1NiUogP0+rCiK2vTbVh01fttjVMf9YPv9KI7XzGqLUjn1PBrHa2L/hTe/rv137XiFbLeV019itdVti1VPRv4gUBUGxuSiVUyoqsbWs2JVsDa9GKqqQVU1WFCwNr3gqqrBqmpQVQXV2tQiq4LjFbIqtvtOvduaYjz1JlJO++5433Dqr6T5Mfu5p9qqnvZYGy0qGqy2tx5WdCr4oeJnNaKzGjGojRisFvSqGT/VhN5qwc9qRquqtj/LpqTB1k7bG85xTLXfpzSdCyoa27n2Y6oCFts4DlW1vX62azl+fa2opzWkRYuV04+dejXtp1QHJ1EUMwGLzpag+NeXklC4lZDqnFavkeORLf8ET93f9HGs2t9cGtWWxChq023V9t62/YNj+9PW2N5z9u9obec1/aNkO1ertrieRVEwaXQYNTqMih6jRk+DRk+9xo8GxY86jYE6jYFaxUCtJgCznx8v/+Vv6F04Zs2Zz2+P9qg0Njayc+dOHnnkEccxjUbDzJkz2bx5c6vzjUYjRuOpueFVVbYpXiaTCZPJ1Or87srftAtqivDsfqvu58n29Ubq1/Y/xs7z/OS8jtV1fopXUDj1nvPEe6/lR7U7OFdGicW2101dkf1I195pzV9Hb2YFGpu+TtHTYp50VzTl2t6rFIxroemjyQRUxwAxvbA5ojNUwNL01SkLgdQRSB0xlBNkrqG+2SKPruDMZ7ZHE5WSkhIsFgtxcS2nVcbFxXHoUOvts5955hkWLVrU6vjq1asJDHTBssh2Gugb/xQI7+HRjkkP8MVeRuEeSie3ldPeTm39F6OD20rzY637MFp8V7pwzun3K+3E3MSqWKn1q6ReV9PyHvX0Ryitj7XoKT3V62rrnbX38p5+Py16c0+/lr1Xtvl5nH671bGO/55VDaxf902H5zirrq7r/8Xy+BgVZzzyyCM88MADjp+rqqpITk5m9uzZLi39cMklmEwm1qxZw6xZs1za3eUtpH19n6+30dfbB77fRmlf35dVU8fBjRtc3kZ7RaQrPJqoREdHo9VqKSxsualZYWEh8fHxrc43GAwYDK2nsen1ere9Sdx5bW8g7ev7fL2Nvt4+8P02Svv6rkHBgRzE9W105loerfz5+fkxadIk1q1b5zhmtVpZt24d06ZN82BkQgghhPAGHi/9PPDAA9x8881MnjyZM844gxdffJHa2lpuvfVWT4cmhBBCCA/zeKJyzTXXUFxczOOPP05BQQHjx49n5cqVrQbYCiGEEKL/8XiiAnD33Xdz9913ezoMIYQQQngZr56dLoQQQoj+TRIVIYQQQngtSVSEEEII4bUkURFCCCGE15JERQghhBBeSxIVIYQQQngtSVSEEEII4bUkURFCCCGE15JERQghhBBeyytWpu0uVVUB57aL7iqTyURdXR1VVVU+uSumtK/v8/U2+nr7wPfbKO3r+9zVRvvntv1zvCN9OlGprq4GIDk52cORCCGEEMJZ1dXVhIWFdXiOonYlnfFSVquVvLw8QkJCUBTFpdeuqqoiOTmZnJwcQkNDXXptbyDt6/t8vY2+3j7w/TZK+/o+d7VRVVWqq6tJTExEo+l4FEqf7lHRaDQkJSW59TlCQ0N99g0I0j5f4Ott9PX2ge+3UdrX97mjjZ31pNjJYFohhBBCeC1JVIQQQgjhtSRRaYfBYOCJJ57AYDB4OhS3kPb1fb7eRl9vH/h+G6V9fZ83tLFPD6YVQgghhG+THhUhhBBCeC1JVIQQQgjhtSRREUIIIYTXkkRFCCGEEF5LEpVmnnnmGaZMmUJISAixsbFcccUVZGRkeDosl3rttdcYO3asY/GeadOmsWLFCk+H5TaLFy9GURTuu+8+T4fiEgsXLkRRlBZfw4cP93RYLpebm8uNN95IVFQUAQEBjBkzhh07dng6LJcYOHBgq9+hoigsWLDA06G5hMVi4bHHHmPQoEEEBAQwePBgnnrqqS7t6dKXVFdXc99995GamkpAQABnnXUW27dv93RY3bJx40bmzZtHYmIiiqLw2WeftbhfVVUef/xxEhISCAgIYObMmRw5cqTX4pNEpZlvv/2WBQsWsGXLFtasWYPJZGL27NnU1tZ6OjSXSUpKYvHixezcuZMdO3YwY8YMLr/8cvbv3+/p0Fxu+/btvPHGG4wdO9bTobjUqFGjyM/Pd3x9//33ng7JpcrLyzn77LPR6/WsWLGCAwcO8PzzzxMREeHp0Fxi+/btLX5/a9asAWD+/Pkejsw1lixZwmuvvcbf//53Dh48yJIlS3j22Wd55ZVXPB2aS/3yl79kzZo1vPvuu+zdu5fZs2czc+ZMcnNzPR2a02praxk3bhyvvvpqm/c/++yzvPzyy7z++uts3bqVoKAg5syZQ0NDQ+8EqIp2FRUVqYD67bffejoUt4qIiFDfeustT4fhUtXV1erQoUPVNWvWqOeff7567733ejokl3jiiSfUcePGeToMt/rDH/6gnnPOOZ4Oo9fce++96uDBg1Wr1erpUFxi7ty56m233dbi2JVXXqnecMMNHorI9erq6lStVqt++eWXLY5PnDhRffTRRz0UlWsA6vLlyx0/W61WNT4+Xn3uueccxyoqKlSDwaC+//77vRKT9Kh0oLKyEoDIyEgPR+IeFouFDz74gNraWqZNm+bpcFxqwYIFzJ07l5kzZ3o6FJc7cuQIiYmJpKWlccMNN3DixAlPh+RSn3/+OZMnT2b+/PnExsYyYcIE3nzzTU+H5RaNjY2899573HbbbS7fWNVTzjrrLNatW8fhw4cB2L17N99//z0XX3yxhyNzHbPZjMViwd/fv8XxgIAAn+vhzMrKoqCgoMW/pWFhYZx55pls3ry5V2Lo05sSupPVauW+++7j7LPPZvTo0Z4Ox6X27t3LtGnTaGhoIDg4mOXLlzNy5EhPh+UyH3zwAT/++GOfrRd35Mwzz+Ttt99m2LBh5Ofns2jRIs4991z27dtHSEiIp8NziWPHjvHaa6/xwAMP8Mc//pHt27dzzz334Ofnx8033+zp8Fzqs88+o6KigltuucXTobjMww8/TFVVFcOHD0er1WKxWPjLX/7CDTfc4OnQXCYkJIRp06bx1FNPMWLECOLi4nj//ffZvHkzQ4YM8XR4LlVQUABAXFxci+NxcXGO+9xNEpV2LFiwgH379vlcdgwwbNgwdu3aRWVlJZ988gk333wz3377rU8kKzk5Odx7772sWbOm1f92fEHz/5WOHTuWM888k9TUVD766CNuv/12D0bmOlarlcmTJ/P0008DMGHCBPbt28frr7/uc4nKP//5Ty6++GISExM9HYrLfPTRR/znP/9h2bJljBo1il27dnHfffeRmJjoU7+/d999l9tuu40BAwag1WqZOHEi1113HTt37vR0aD5HSj9tuPvuu/nyyy9Zv349SUlJng7H5fz8/BgyZAiTJk3imWeeYdy4cbz00kueDssldu7cSVFRERMnTkSn06HT6fj22295+eWX0el0WCwWT4foUuHh4aSnp3P06FFPh+IyCQkJrZLmESNG+FyJ6/jx46xdu5Zf/vKXng7FpR566CEefvhhrr32WsaMGcNNN93E/fffzzPPPOPp0Fxq8ODBfPvtt9TU1JCTk8O2bdswmUykpaV5OjSXio+PB6CwsLDF8cLCQsd97iaJSjOqqnL33XezfPlyvvnmGwYNGuTpkHqF1WrFaDR6OgyXuPDCC9m7dy+7du1yfE2ePJkbbriBXbt2odVqPR2iS9XU1JCZmUlCQoKnQ3GZs88+u9WyAIcPHyY1NdVDEbnH0qVLiY2NZe7cuZ4OxaXq6urQaFp+tGi1WqxWq4cicq+goCASEhIoLy9n1apVXH755Z4OyaUGDRpEfHw869atcxyrqqpi69atvTa2UUo/zSxYsIBly5bxv//9j5CQEEf9LSwsjICAAA9H5xqPPPIIF198MSkpKVRXV7Ns2TI2bNjAqlWrPB2aS4SEhLQaUxQUFERUVJRPjDX63e9+x7x580hNTSUvL48nnngCrVbLdddd5+nQXOb+++/nrLPO4umnn+bqq69m27Zt/OMf/+Af//iHp0NzGavVytKlS7n55pvR6Xzrn+F58+bxl7/8hZSUFEaNGsVPP/3ECy+8wG233ebp0Fxq1apVqKrKsGHDOHr0KA899BDDhw/n1ltv9XRoTqupqWnRK5uVlcWuXbuIjIwkJSWF++67jz//+c8MHTqUQYMG8dhjj5GYmMgVV1zROwH2ytyiPgJo82vp0qWeDs1lbrvtNjU1NVX18/NTY2Ji1AsvvFBdvXq1p8NyK1+annzNNdeoCQkJqp+fnzpgwAD1mmuuUY8ePerpsFzuiy++UEePHq0aDAZ1+PDh6j/+8Q9Ph+RSq1atUgE1IyPD06G4XFVVlXrvvfeqKSkpqr+/v5qWlqY++uijqtFo9HRoLvXhhx+qaWlpqp+fnxofH68uWLBAraio8HRY3bJ+/fo2P/tuvvlmVVVtU5Qfe+wxNS4uTjUYDOqFF17Yq+9dRVV9bLlAIYQQQvgMGaMihBBCCK8liYoQQgghvJYkKkIIIYTwWpKoCCGEEMJrSaIihBBCCK8liYoQQgghvJYkKkIIIYTwWpKoCCFayc7ORlEUdu3a5elQHA4dOsTUqVPx9/dn/PjxPbqWoih89tlnLolLCOFekqgI4YVuueUWFEVh8eLFLY5/9tlnKIrioag864knniAoKIiMjIwW+46crqCggN/+9rekpaVhMBhITk5m3rx5HT6mJzZs2ICiKFRUVLjl+kL0d5KoCOGl/P39WbJkCeXl5Z4OxWUaGxu7/djMzEzOOeccUlNTiYqKavOc7OxsJk2axDfffMNzzz3H3r17WblyJRdccAELFizo9nP3BlVVMZvNng5DCK8jiYoQXmrmzJnEx8fzzDPPtHvOwoULW5VBXnzxRQYOHOj4+ZZbbuGKK67g6aefJi4ujvDwcJ588knMZjMPPfQQkZGRJCUlsXTp0lbXP3ToEGeddRb+/v6MHj2ab7/9tsX9+/bt4+KLLyY4OJi4uDhuuukmSkpKHPdPnz6du+++m/vuu4/o6GjmzJnTZjusVitPPvkkSUlJGAwGxo8fz8qVKx33K4rCzp07efLJJ1EUhYULF7Z5nbvuugtFUdi2bRtXXXUV6enpjBo1igceeIAtW7a0+Zi2ekR27dqFoihkZ2cDcPz4cebNm0dERARBQUGMGjWKr7/+muzsbC644AIAIiIiUBSFW265xdGmZ555hkGDBhEQEMC4ceP45JNPWj3vihUrmDRpEgaDge+//57du3dzwQUXEBISQmhoKJMmTWLHjh1txi5EfyCJihBeSqvV8vTTT/PKK69w8uTJHl3rm2++IS8vj40bN/LCCy/wxBNPcOmllxIREcHWrVu58847ueOOO1o9z0MPPcSDDz7ITz/9xLRp05g3bx6lpaUAVFRUMGPGDCZMmMCOHTtYuXIlhYWFXH311S2u8e9//xs/Pz9++OEHXn/99Tbje+mll3j++ef561//yp49e5gzZw6XXXYZR44cASA/P59Ro0bx4IMPkp+fz+9+97tW1ygrK2PlypUsWLCAoKCgVveHh4d356UDbDurG41GNm7cyN69e1myZAnBwcEkJyfz3//+F4CMjAzy8/N56aWXAHjmmWd45513eP3119m/fz/3338/N954Y6tk7+GHH2bx4sUcPHiQsWPHcsMNN5CUlMT27dvZuXMnDz/8MHq9vtuxC9Hn9dr2h0KILrv55pvVyy+/XFVVVZ06dap62223qaqqqsuXL1eb/9k+8cQT6rhx41o89m9/+5uampra4lqpqamqxWJxHBs2bJh67rnnOn42m81qUFCQ+v7776uqqqpZWVkqoC5evNhxjslkUpOSktQlS5aoqqqqTz31lDp79uwWz52Tk9NiV+Dzzz9fnTBhQqftTUxMVP/yl7+0ODZlyhT1rrvucvw8btw49Yknnmj3Glu3blUB9dNPP+30+QB1+fLlqqqe2jm2vLzccf9PP/2kAmpWVpaqqqo6ZswYdeHChW1eq63HNzQ0qIGBgeqmTZtanHv77ber1113XYvHffbZZy3OCQkJUd9+++1O2yBEf6HzWIYkhOiSJUuWMGPGjDZ7Ebpq1KhRaDSnOlDj4uIYPXq042etVktUVBRFRUUtHjdt2jTHbZ1Ox+TJkzl48CAAu3fvZv369QQHB7d6vszMTNLT0wGYNGlSh7FVVVWRl5fH2Wef3eL42Wefze7du7vYQtsYD3e55557+M1vfsPq1auZOXMmV111FWPHjm33/KNHj1JXV8esWbNaHG9sbGTChAktjk2ePLnFzw888AC//OUveffdd5k5cybz589n8ODBrmuMEH2MlH6E8HLnnXcec+bM4ZFHHml1n0ajafUBbTKZWp13eulAUZQ2j1mt1i7HVVNTw7x589i1a1eLryNHjnDeeec5zmurDOMOQ4cORVEUDh065NTj7Alc89fx9Nfwl7/8JceOHeOmm25i7969TJ48mVdeeaXda9bU1ADw1VdftXhtDhw40GKcCrR+fRYuXMj+/fuZO3cu33zzDSNHjmT58uVOtUkIXyKJihB9wOLFi/niiy/YvHlzi+MxMTEUFBS0+JB15donzQegms1mdu7cyYgRIwCYOHEi+/fvZ+DAgQwZMqTFlzPJSWhoKImJifzwww8tjv/www+MHDmyy9eJjIxkzpw5vPrqq9TW1ra6v73pwzExMYBtHIxdW69hcnIyd955J59++ikPPvggb775JgB+fn4AWCwWx7kjR47EYDBw4sSJVq9NcnJyp21JT0/n/vvvZ/Xq1Vx55ZVtDnQWor+QREWIPmDMmDHccMMNvPzyyy2OT58+neLiYp599lkyMzN59dVXWbFihcue99VXX2X58uUcOnSIBQsWUF5ezm233QbYBpiWlZVx3XXXsX37djIzM1m1ahW33npriw/trnjooYdYsmQJH374IRkZGTz88MPs2rWLe++91+l4LRYLZ5xxBv/9/3btWDWRIIDD+F+CZSBFwMIiKERYcN1nsFDiC1jmBSKyyVrYCBoDboo0iqa2skqzZMEUwRRJk8C2EZYg1pJOSCV31cl5d4EL4WDv7vvVM7PDVh8zc3WlMAz1/Pysbre7cY31vW/x0Gw2FYahfN/XxcXFxhjbtnVzc6PZbKYgCDSZTNbBtre3p1gspuvray0WCy2XS21vb6tWq+n4+FjD4VAvLy8KgkC9Xk/D4fDd/b+9valSqeju7k7z+VwPDw96enpafwv4HxEqwF/i9PT0p6sZwzA0GAzU7/dlWZYeHx8/9ZblR67rynVdWZal+/t7eZ6n3d1dSVqfgqxWKxWLRZmmKdu2tbOzs/Ee5ndUq1WdnJzIcRyZpqnxeCzP87S/v/+hddLptIIgUD6fl+M4ymazKhQKur291eXl5S/nxONxjUYjTadT5XI5nZ+f6+zsbGPMarXS0dGRDMPQwcGBMpmMBoOBJCmZTKrVaqleryuRSKhSqUiS2u22Go2GOp3Oep7v+0qlUu/uf2trS6+vrzo8PFQmk1G5XFapVFKr1frQfwD+JbEvf/IFGgAAwCdwogIAACKLUAEAAJFFqAAAgMgiVAAAQGQRKgAAILIIFQAAEFmECgAAiCxCBQAARBahAgAAIotQAQAAkUWoAACAyCJUAABAZH0FGYXCjSjRCXIAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] @@ -152,14 +518,16 @@ " # move legend to side\n", " # plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", " # remove legend\n", - " plt.legend().remove()\n", + " #plt.legend().remove()\n", + " # show legend\n", + " plt.legend()\n", " plt.show()\n", " i+=1\n" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -185,419 +553,102 @@ " \n", " nclust\n", " lmdi_ridge\n", - " aloo_l2_signed_normed_noleafavg_norank\n", - " nonloo_l2_unsigned_nonnormed_noleafavg_norank\n", - " aloo_l2_unsigned_normed_noleafavg_rank\n", - " nonloo_nonl2_unsigned_nonnormed_noleafavg_rank\n", - " aloo_l2_signed_normed_noleafavg_rank\n", - " aloo_l2_signed_nonnormed_noleafavg_rank\n", - " nonloo_l2_unsigned_normed_noleafavg_norank\n", - " rawdata\n", - " ...\n", - " nonloo_l2_signed_normed_noleafavg_rank\n", - " nonloo_l2_signed_nonnormed_noleafavg_norank\n", - " aloo_l2_signed_nonnormed_noleafavg_norank\n", - " aloo_nonl2_unsigned_nonnormed_noleafavg_rank\n", - " nonloo_l2_signed_nonnormed_noleafavg_rank\n", - " lmdi_baseline\n", - " aloo_l2_unsigned_nonnormed_noleafavg_rank\n", - " nonloo_l2_unsigned_normed_noleafavg_rank\n", + " shap\n", " lime\n", - " nonloo_l2_signed_normed_noleafavg_norank\n", + " rawdata\n", " \n", " \n", " \n", " \n", " 0\n", " 2\n", - " 2.311079e+01\n", - " 6.252605e+05\n", - " 9.296644e+01\n", - " 5.120805e+01\n", - " 4.649235e+01\n", - " 1.819256e+01\n", - " 1.881731e+01\n", - " 5.232357e+01\n", - " 1.955093e+01\n", - " ...\n", - " 2.469885e+05\n", - " 3.141752e+01\n", - " 2.229413e+01\n", - " 2.276840e+01\n", - " 4.010341e+01\n", - " 2.810067e+01\n", - " 4.072127e+01\n", - " 3.158104e+01\n", - " 1.036050e+02\n", - " 3.002511e+01\n", + " 1.866249e+09\n", + " 2.048186e+09\n", + " 5.276825e+07\n", + " 3.437753e+08\n", " \n", " \n", " 1\n", " 3\n", - " 1.013604e+02\n", - " 2.955016e+02\n", - " 1.109200e+02\n", - " 7.542218e+01\n", - " 5.660524e+01\n", - " 1.671550e+02\n", - " 5.090679e+01\n", - " 6.066720e+01\n", - " 2.893648e+01\n", - " ...\n", - " 2.106720e+02\n", - " 1.007541e+02\n", - " 6.703516e+01\n", - " 5.655978e+01\n", - " 6.152765e+01\n", - " 1.771106e+05\n", - " 2.416361e+02\n", - " 1.114383e+02\n", - " 1.296980e+02\n", - " 2.202045e+02\n", + " 4.688789e+09\n", + " 4.815873e+08\n", + " 3.146967e+09\n", + " 1.938586e+09\n", " \n", " \n", " 2\n", " 4\n", - " 1.360637e+05\n", - " 1.570083e+04\n", - " 2.726269e+02\n", - " 1.273834e+05\n", - " 1.760142e+02\n", - " 7.962072e+08\n", - " 3.431625e+02\n", - " 1.284827e+07\n", - " 2.613066e+01\n", - " ...\n", - " 4.170622e+02\n", - " 5.815618e+04\n", - " 3.539975e+02\n", - " 9.366919e+01\n", - " 1.772032e+02\n", - " 1.442584e+05\n", - " 5.696785e+02\n", - " 3.948486e+03\n", - " 2.739302e+05\n", - " 6.644389e+01\n", + " 2.136069e+08\n", + " 8.668755e+08\n", + " 2.952833e+09\n", + " 4.719427e+09\n", " \n", " \n", " 3\n", " 5\n", - " 8.719107e+03\n", - " 2.074473e+03\n", - " 6.067783e+01\n", - " 2.481538e+04\n", - " 3.116779e+07\n", - " 5.748171e+06\n", - " 2.393367e+02\n", - " 2.271490e+04\n", - " 7.885906e+02\n", - " ...\n", - " 2.068101e+03\n", - " 2.498104e+05\n", - " 3.367272e+03\n", - " 5.554219e+04\n", - " 2.601168e+03\n", - " 5.130713e+08\n", - " 4.271206e+06\n", - " 8.360917e+08\n", - " 7.184998e+08\n", - " 2.958252e+04\n", + " 2.741610e+09\n", + " 2.237728e+09\n", + " 3.717341e+08\n", + " 3.805131e+08\n", " \n", " \n", " 4\n", " 6\n", - " 5.728780e+06\n", - " 1.116723e+06\n", - " 1.537322e+02\n", - " 1.048529e+04\n", - " 1.553114e+09\n", - " 1.132142e+09\n", - " 1.701368e+05\n", - " 4.770950e+05\n", - " 1.341969e+02\n", - " ...\n", - " 6.098385e+05\n", - " 9.864178e+02\n", - " 3.123022e+05\n", - " 3.326280e+08\n", - " 3.715059e+11\n", - " 7.229768e+07\n", - " 1.297426e+07\n", - " 2.602657e+06\n", - " 4.457127e+06\n", - " 1.772270e+04\n", + " 2.834394e+07\n", + " 7.772313e-01\n", + " 5.253683e+08\n", + " 1.671354e+09\n", " \n", " \n", " 5\n", " 7\n", - " 4.780905e+05\n", - " 6.001165e+04\n", - " 6.366777e+06\n", - " 3.455281e+03\n", - " 1.074896e+08\n", - " 4.164254e+05\n", - " 3.397791e+07\n", - " 2.427479e+07\n", - " 1.980644e+03\n", - " ...\n", - " 3.111292e+08\n", - " 2.060083e+09\n", - " 2.412734e+09\n", - " 3.754691e+08\n", - " 2.412763e+05\n", - " 4.783604e+08\n", - " 3.455062e+05\n", - " 8.364442e+03\n", - " 9.500430e+06\n", - " 1.644328e+06\n", + " 1.587444e+09\n", + " 6.329133e+08\n", + " 8.666135e-01\n", + " 6.533625e+09\n", " \n", " \n", " 6\n", " 8\n", - " 3.707724e+10\n", - " 1.601995e+05\n", - " 2.601497e+09\n", - " 5.495632e+04\n", - " 8.808882e+07\n", - " 5.008406e+10\n", - " 8.276637e+06\n", - " 1.257992e+07\n", - " 1.031778e+04\n", - " ...\n", - " 3.481067e+08\n", - " 3.130310e+10\n", - " 9.971316e+06\n", - " 5.491333e+06\n", - " 4.791161e+09\n", - " 7.936494e+08\n", - " 2.910856e+06\n", - " 4.223033e+04\n", - " 1.457063e+05\n", - " 2.054288e+04\n", + " 9.548187e+08\n", + " 3.338103e+07\n", + " 9.061299e-01\n", + " 2.926234e+09\n", " \n", " \n", " 7\n", " 9\n", - " 1.289424e+04\n", - " 9.360585e+04\n", - " 3.713166e+04\n", - " 6.285434e+08\n", - " 2.901749e+06\n", - " 1.504306e+09\n", - " 1.547156e+08\n", - " 4.251629e+08\n", - " 1.422652e+04\n", - " ...\n", - " 1.800623e+11\n", - " 1.651193e+08\n", - " 3.464653e+05\n", - " 7.142204e+03\n", - " 3.938059e+06\n", - " 7.799954e+09\n", - " 8.002621e+04\n", - " 3.047389e+09\n", - " 6.768241e+07\n", - " 4.300903e+08\n", + " 4.199973e+09\n", + " 1.828156e+08\n", + " 1.022672e+00\n", + " 3.783191e+09\n", " \n", " \n", " 8\n", " 10\n", - " 2.239441e+10\n", - " 1.110225e+08\n", - " 6.233501e+06\n", - " 2.967493e+04\n", - " 2.696858e+08\n", - " 1.712099e+05\n", - " 3.035351e+03\n", - " 2.369144e+05\n", - " 2.612402e+10\n", - " ...\n", - " 2.447210e+10\n", - " 2.035806e+07\n", - " 9.873820e+03\n", - " 3.927941e+08\n", - " 3.227150e+09\n", - " 3.882526e+08\n", - " 9.882694e+07\n", - " 4.852223e+05\n", - " 6.585757e+10\n", - " 2.870660e+04\n", + " 1.342997e+06\n", + " 8.751049e-01\n", + " 1.085139e+00\n", + " 1.871336e+09\n", " \n", " \n", "\n", - "

9 rows × 27 columns

\n", "" ], "text/plain": [ - " nclust lmdi_ridge aloo_l2_signed_normed_noleafavg_norank \\\n", - "0 2 2.311079e+01 6.252605e+05 \n", - "1 3 1.013604e+02 2.955016e+02 \n", - "2 4 1.360637e+05 1.570083e+04 \n", - "3 5 8.719107e+03 2.074473e+03 \n", - "4 6 5.728780e+06 1.116723e+06 \n", - "5 7 4.780905e+05 6.001165e+04 \n", - "6 8 3.707724e+10 1.601995e+05 \n", - "7 9 1.289424e+04 9.360585e+04 \n", - "8 10 2.239441e+10 1.110225e+08 \n", - "\n", - " nonloo_l2_unsigned_nonnormed_noleafavg_norank \\\n", - "0 9.296644e+01 \n", - "1 1.109200e+02 \n", - "2 2.726269e+02 \n", - "3 6.067783e+01 \n", - "4 1.537322e+02 \n", - "5 6.366777e+06 \n", - "6 2.601497e+09 \n", - "7 3.713166e+04 \n", - "8 6.233501e+06 \n", - "\n", - " aloo_l2_unsigned_normed_noleafavg_rank \\\n", - "0 5.120805e+01 \n", - "1 7.542218e+01 \n", - "2 1.273834e+05 \n", - "3 2.481538e+04 \n", - "4 1.048529e+04 \n", - "5 3.455281e+03 \n", - "6 5.495632e+04 \n", - "7 6.285434e+08 \n", - "8 2.967493e+04 \n", - "\n", - " nonloo_nonl2_unsigned_nonnormed_noleafavg_rank \\\n", - "0 4.649235e+01 \n", - "1 5.660524e+01 \n", - "2 1.760142e+02 \n", - "3 3.116779e+07 \n", - "4 1.553114e+09 \n", - "5 1.074896e+08 \n", - "6 8.808882e+07 \n", - "7 2.901749e+06 \n", - "8 2.696858e+08 \n", - "\n", - " aloo_l2_signed_normed_noleafavg_rank \\\n", - "0 1.819256e+01 \n", - "1 1.671550e+02 \n", - "2 7.962072e+08 \n", - "3 5.748171e+06 \n", - "4 1.132142e+09 \n", - "5 4.164254e+05 \n", - "6 5.008406e+10 \n", - "7 1.504306e+09 \n", - "8 1.712099e+05 \n", - "\n", - " aloo_l2_signed_nonnormed_noleafavg_rank \\\n", - "0 1.881731e+01 \n", - "1 5.090679e+01 \n", - "2 3.431625e+02 \n", - "3 2.393367e+02 \n", - "4 1.701368e+05 \n", - "5 3.397791e+07 \n", - "6 8.276637e+06 \n", - "7 1.547156e+08 \n", - "8 3.035351e+03 \n", - "\n", - " nonloo_l2_unsigned_normed_noleafavg_norank rawdata ... \\\n", - "0 5.232357e+01 1.955093e+01 ... \n", - "1 6.066720e+01 2.893648e+01 ... \n", - "2 1.284827e+07 2.613066e+01 ... \n", - "3 2.271490e+04 7.885906e+02 ... \n", - "4 4.770950e+05 1.341969e+02 ... \n", - "5 2.427479e+07 1.980644e+03 ... \n", - "6 1.257992e+07 1.031778e+04 ... \n", - "7 4.251629e+08 1.422652e+04 ... \n", - "8 2.369144e+05 2.612402e+10 ... \n", - "\n", - " nonloo_l2_signed_normed_noleafavg_rank \\\n", - "0 2.469885e+05 \n", - "1 2.106720e+02 \n", - "2 4.170622e+02 \n", - "3 2.068101e+03 \n", - "4 6.098385e+05 \n", - "5 3.111292e+08 \n", - "6 3.481067e+08 \n", - "7 1.800623e+11 \n", - "8 2.447210e+10 \n", - "\n", - " nonloo_l2_signed_nonnormed_noleafavg_norank \\\n", - "0 3.141752e+01 \n", - "1 1.007541e+02 \n", - "2 5.815618e+04 \n", - "3 2.498104e+05 \n", - "4 9.864178e+02 \n", - "5 2.060083e+09 \n", - "6 3.130310e+10 \n", - "7 1.651193e+08 \n", - "8 2.035806e+07 \n", - "\n", - " aloo_l2_signed_nonnormed_noleafavg_norank \\\n", - "0 2.229413e+01 \n", - "1 6.703516e+01 \n", - "2 3.539975e+02 \n", - "3 3.367272e+03 \n", - "4 3.123022e+05 \n", - "5 2.412734e+09 \n", - "6 9.971316e+06 \n", - "7 3.464653e+05 \n", - "8 9.873820e+03 \n", - "\n", - " aloo_nonl2_unsigned_nonnormed_noleafavg_rank \\\n", - "0 2.276840e+01 \n", - "1 5.655978e+01 \n", - "2 9.366919e+01 \n", - "3 5.554219e+04 \n", - "4 3.326280e+08 \n", - "5 3.754691e+08 \n", - "6 5.491333e+06 \n", - "7 7.142204e+03 \n", - "8 3.927941e+08 \n", - "\n", - " nonloo_l2_signed_nonnormed_noleafavg_rank lmdi_baseline \\\n", - "0 4.010341e+01 2.810067e+01 \n", - "1 6.152765e+01 1.771106e+05 \n", - "2 1.772032e+02 1.442584e+05 \n", - "3 2.601168e+03 5.130713e+08 \n", - "4 3.715059e+11 7.229768e+07 \n", - "5 2.412763e+05 4.783604e+08 \n", - "6 4.791161e+09 7.936494e+08 \n", - "7 3.938059e+06 7.799954e+09 \n", - "8 3.227150e+09 3.882526e+08 \n", - "\n", - " aloo_l2_unsigned_nonnormed_noleafavg_rank \\\n", - "0 4.072127e+01 \n", - "1 2.416361e+02 \n", - "2 5.696785e+02 \n", - "3 4.271206e+06 \n", - "4 1.297426e+07 \n", - "5 3.455062e+05 \n", - "6 2.910856e+06 \n", - "7 8.002621e+04 \n", - "8 9.882694e+07 \n", - "\n", - " nonloo_l2_unsigned_normed_noleafavg_rank lime \\\n", - "0 3.158104e+01 1.036050e+02 \n", - "1 1.114383e+02 1.296980e+02 \n", - "2 3.948486e+03 2.739302e+05 \n", - "3 8.360917e+08 7.184998e+08 \n", - "4 2.602657e+06 4.457127e+06 \n", - "5 8.364442e+03 9.500430e+06 \n", - "6 4.223033e+04 1.457063e+05 \n", - "7 3.047389e+09 6.768241e+07 \n", - "8 4.852223e+05 6.585757e+10 \n", - "\n", - " nonloo_l2_signed_normed_noleafavg_norank \n", - "0 3.002511e+01 \n", - "1 2.202045e+02 \n", - "2 6.644389e+01 \n", - "3 2.958252e+04 \n", - "4 1.772270e+04 \n", - "5 1.644328e+06 \n", - "6 2.054288e+04 \n", - "7 4.300903e+08 \n", - "8 2.870660e+04 \n", - "\n", - "[9 rows x 27 columns]" + " nclust lmdi_ridge shap lime rawdata\n", + "0 2 1.866249e+09 2.048186e+09 5.276825e+07 3.437753e+08\n", + "1 3 4.688789e+09 4.815873e+08 3.146967e+09 1.938586e+09\n", + "2 4 2.136069e+08 8.668755e+08 2.952833e+09 4.719427e+09\n", + "3 5 2.741610e+09 2.237728e+09 3.717341e+08 3.805131e+08\n", + "4 6 2.834394e+07 7.772313e-01 5.253683e+08 1.671354e+09\n", + "5 7 1.587444e+09 6.329133e+08 8.666135e-01 6.533625e+09\n", + "6 8 9.548187e+08 3.338103e+07 9.061299e-01 2.926234e+09\n", + "7 9 4.199973e+09 1.828156e+08 1.022672e+00 3.783191e+09\n", + "8 10 1.342997e+06 8.751049e-01 1.085139e+00 1.871336e+09" ] }, - "execution_count": 101, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -608,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -633,12 +684,22 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -656,50 +717,184 @@ " auroc_ranks.append(auroc_rank)\n", "# merge series in auroc_ranks by averaging the ranks corresponding to the same names\n", "auroc_ranks = pd.concat(auroc_ranks, axis=1)\n", - "auroc_ranks = auroc_ranks.mean(axis=1)\n", - "auroc_ranks = auroc_ranks.sort_values()\n", - "auroc_ranks.plot(kind=\"bar\")\n", + "auroc_ranks_mean = auroc_ranks.mean(axis=1)\n", + "auroc_ranks_median = auroc_ranks.median(axis=1)\n", + "auroc_ranks_mean = auroc_ranks_mean.sort_values()\n", + "auroc_ranks_median = auroc_ranks_median.sort_values()\n", + "auroc_ranks_mean.plot(kind=\"bar\")\n", + "plt.show()\n", + "auroc_ranks_median.plot(kind=\"bar\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "aloo_l2_signed_normed_noleafavg_norank 8.875\n", - "nonloo_l2_signed_normed_noleafavg_norank 10.125\n", - "shap 10.875\n", - "lmdi_ridge 11.000\n", - "nonloo_nonl2_unsigned_nonnormed_noleafavg_rank 11.375\n", - "lmdi_baseline 12.125\n", - "aloo_l2_signed_normed_noleafavg_rank 12.250\n", - "nonloo_nonl2_unsigned_nonnormed_noleafavg_norank 12.250\n", - "aloo_nonl2_unsigned_nonnormed_noleafavg_rank 12.750\n", - "nonloo_l2_signed_nonnormed_noleafavg_rank 13.000\n", - "lmdi_lasso 13.000\n", - "aloo_l2_signed_nonnormed_noleafavg_norank 13.125\n", - "nonloo_l2_signed_normed_noleafavg_rank 13.375\n", - "aloo_l2_signed_nonnormed_noleafavg_rank 13.875\n", - "aloo_nonl2_unsigned_nonnormed_noleafavg_norank 13.875\n", - "aloo_l2_unsigned_nonnormed_noleafavg_rank 14.000\n", - "nonloo_l2_unsigned_nonnormed_noleafavg_rank 14.375\n", - "lime 14.500\n", - "nonloo_l2_unsigned_normed_noleafavg_norank 14.625\n", - "nonloo_l2_unsigned_normed_noleafavg_rank 15.125\n", - "rawdata 15.500\n", - "nonloo_l2_signed_nonnormed_noleafavg_norank 15.750\n", - "nonloo_l2_unsigned_nonnormed_noleafavg_norank 15.875\n", - "aloo_l2_unsigned_normed_noleafavg_norank 16.250\n", - "aloo_l2_unsigned_nonnormed_noleafavg_norank 16.500\n", - "aloo_l2_unsigned_normed_noleafavg_rank 16.625\n", - "dtype: float64" - ] - }, - "execution_count": 104, + " 0 1 2 3 4 5 6 7 8 9 ... 24 25 \\\n", + "lmdi_ridge 3.0 2.0 3.0 2.0 1.0 4.0 2.0 4.0 2.0 2.0 ... 3.0 2.0 \n", + "shap 1.0 1.0 2.0 1.0 3.0 1.0 4.0 1.0 4.0 3.0 ... 2.0 1.0 \n", + "lime 2.0 3.0 4.0 3.0 4.0 3.0 3.0 3.0 3.0 4.0 ... 4.0 3.0 \n", + "rawdata 4.0 4.0 1.0 4.0 2.0 2.0 1.0 2.0 1.0 1.0 ... 1.0 4.0 \n", + "\n", + " 26 27 28 29 30 31 32 33 \n", + "lmdi_ridge 2.0 4.0 2.0 1.0 3.0 4.0 3.0 2.0 \n", + "shap 3.0 1.0 3.0 3.0 1.0 1.0 1.0 1.0 \n", + "lime 4.0 2.0 4.0 2.0 2.0 3.0 2.0 4.0 \n", + "rawdata 1.0 3.0 1.0 4.0 4.0 2.0 4.0 3.0 \n", + "\n", + "[4 rows x 34 columns]" + ] + }, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -707,6 +902,62 @@ "source": [ "auroc_ranks" ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# for each row in auroc_ranks, plot a histogram of the ranks\n", + "for i in range(auroc_ranks.shape[0]):\n", + " auroc_ranks.iloc[i].plot(kind=\"hist\")\n", + " plt.title(\"Method: \" + auroc_ranks.index[i])\n", + " plt.show()\n", + "\n", + " " + ] } ], "metadata": { diff --git a/feature_importance/subgroup/current/subgroup.py b/feature_importance/subgroup/current/subgroup.py index 4378a86..8d4503a 100644 --- a/feature_importance/subgroup/current/subgroup.py +++ b/feature_importance/subgroup/current/subgroup.py @@ -17,9 +17,12 @@ # sklearn imports from sklearn.model_selection import train_test_split -from sklearn.linear_model import LogisticRegression, LinearRegression, LassoCV +from sklearn.linear_model import LogisticRegression, LinearRegression, LassoCV,\ + RidgeCV, ElasticNetCV, LogisticRegressionCV from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \ accuracy_score, r2_score, f1_score, log_loss, root_mean_squared_error +from sklearn.cluster import KMeans +from sklearn.preprocessing import StandardScaler # for reading data import openml @@ -32,9 +35,14 @@ # for function headers from typing import Tuple, Dict +# because openml package has pesky FutureWarnings +import warnings +warnings.filterwarnings(action='ignore', category=FutureWarning, + module='openml') -def get_openml_data(id: int, - num_samples: int = 2000) -> Tuple[np.ndarray, np.ndarray]: + +def get_openml_data(id: int, standardize: bool, + num_samples: int = 5000) -> Tuple[np.ndarray, np.ndarray]: """ Get benchmark datasets from OpenML. @@ -48,20 +56,43 @@ def get_openml_data(id: int, """ # check that the dataset_id is in the set of tested datasets - known_ids = {361247, 361243, 361242, 361251, 361253, 361260, 361259, 361256, - 361254, 361622} - # error if not recognized, since we do not know if we need to transform - if id not in known_ids: - raise ValueError(f"Data ID {id} is not in the set of known datasets.") + regr_ids = {361234, 361235, 361236, 361237, 361241, 361242, 361243, 361244, + 361247, 361249, 361250, 361251, 361252, 361253, 361254, 361255, + 361256, 361257, 361258, 361259, 361260, 361261, 361264, 361266, + 361267, 361268, 361269, 361272, 361616, 361617, 361618, 361619, + 361621, 361622, 361623} + binary_class_ids = {31, 10101, 3913, 3, 3917, 9957, 9946, 3918, 3903, 37, + 9971, 9952, 3902, 49, 43, 9978, 10093, 219, 9976, 14965, + 9977, 15, 29, 14952, 125920, 3904, 9910, 3021, 7592, + 146820, 146819, 14954, 167141, 167120, 167125} + # error if not recognized, since we do not know if we need to transform + if id not in regr_ids and id not in binary_class_ids: + raise ValueError(f"Data ID {id} is not a benchmark dataset.") + # get the dataset from openml task = openml.tasks.get_task(id) dataset = task.get_dataset() X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute) + # if the dataset is categorical, we may have to convert its type + if id in binary_class_ids: + # check if it is categorical, convert to 1/0 + if pd.api.types.is_categorical_dtype(y): + y = pd.get_dummies(y, drop_first=True, dtype=float) + + + # one-hot encode the categorical variables + X = pd.get_dummies(X, dtype=float) + + # remove rows with missing values + missing_rows = X.isnull().any(axis=1) + X = X[~missing_rows] + y = y[~missing_rows] + # subsample the data if necessary if num_samples is not None and num_samples < X.shape[0]: - X = X.sample(num_samples) + X = X.sample(num_samples, random_state=1) y = y.loc[X.index] # reset the index of X and y @@ -72,10 +103,28 @@ def get_openml_data(id: int, X = X.to_numpy() y = y.to_numpy() + # if y is not a 1D array, convert it to one + if len(y.shape) > 1: + y = y.reshape(-1) + + # convert y to float (sometimes, it is int, e.g. abalone dataset) + y = y.astype(float) + # perform transformations if needed - log_transform = {361260, 361622} + log_transform = {361236, 361242, 361244, 361252, 361257, 361260, 361261, + 361264, 361266, 361267, 361272, 361618, 361622} if id in log_transform: - y = np.log(y) + if np.min(y) > 0: + y = np.log(y) + if np.min(y) <= 0: + y = np.log(y - np.min(y) + 1) + + if standardize: + scaler = StandardScaler() + X = scaler.fit_transform(X) + # if the dataset is a regression dataset, standardize y + if id in regr_ids: + y = scaler.fit_transform(y.reshape(-1, 1)).flatten() return X, y @@ -93,7 +142,12 @@ def fit_models(X_train: np.ndarray, y_train: np.ndarray, task: str): - rf_plus_baseline (RandomForestPlusClassifier/RandomForestPlusRegressor): The baseline RF+ (no raw feature, only on in-bag samples, regular linear/logistic prediction model). - - rf_plus (RandomForestPlusClassifier/RandomForestPlusRegressor): The RF+. + - rf_plus_ridge (RandomForestPlusClassifier/RandomForestPlusRegressor): + The RF+ with a ridge prediction model. + - rf_plus_lasso (RandomForestPlusClassifier/RandomForestPlusRegressor): + The RF+ with a lasso prediction model. + - rf_plus_elastic (RandomForestPlusClassifier/RandomForestPlusRegressor): + The RF+ with an elastic net prediction model. """ # if classification, fit classifiers @@ -109,10 +163,30 @@ def fit_models(X_train: np.ndarray, y_train: np.ndarray, task: str): include_raw=False, fit_on="inbag", prediction_model=LogisticRegression()) rf_plus_baseline.fit(X_train, y_train) + + # ridge rf+ + rf_plus_ridge = RandomForestPlusClassifier(rf_model=rf, + prediction_model=LogisticRegressionCV(penalty='l2', + cv=5, max_iter=10000, random_state=42)) + rf_plus_ridge.fit(X_train, y_train) + + # lasso rf+ + rf_plus_lasso = RandomForestPlusClassifier(rf_model=rf, + prediction_model=LogisticRegressionCV(penalty='l1', + solver = 'saga', cv=3, n_jobs=-1, tol=5e-4, + max_iter=5000, random_state=42)) + rf_plus_lasso.fit(X_train, y_train) + + # elastic net rf+ + rf_plus_elastic = RandomForestPlusClassifier(rf_model=rf, + prediction_model=LogisticRegressionCV(penalty='elasticnet', + l1_ratios=[0.1,0.5,0.9,0.99], solver='saga', cv=3, + n_jobs=-1, tol=5e-4, max_iter=5000, random_state=42)) + rf_plus_elastic.fit(X_train, y_train) # standard rf+ uses default values - rf_plus = RandomForestPlusClassifier(rf_model=rf) - rf_plus.fit(X_train, y_train) + # rf_plus = RandomForestPlusClassifier(rf_model=rf) + # rf_plus.fit(X_train, y_train) # if regression, fit regressors elif task == "regression": @@ -128,16 +202,34 @@ def fit_models(X_train: np.ndarray, y_train: np.ndarray, task: str): prediction_model=LinearRegression()) rf_plus_baseline.fit(X_train, y_train) + # ridge rf+ + rf_plus_ridge = RandomForestPlusRegressor(rf_model=rf, + prediction_model=RidgeCV(cv=5)) + rf_plus_ridge.fit(X_train, y_train) + + # lasso rf+ + rf_plus_lasso = RandomForestPlusRegressor(rf_model=rf, + prediction_model=LassoCV(cv=5, + max_iter=10000, random_state=42)) + rf_plus_lasso.fit(X_train, y_train) + + # elastic net rf+ + rf_plus_elastic = RandomForestPlusRegressor(rf_model=rf, + prediction_model=ElasticNetCV(cv=5, + l1_ratio=[0.1,0.5,0.7,0.9,0.95,0.99], + max_iter=10000,random_state=42)) + rf_plus_elastic.fit(X_train, y_train) + # standard rf+ uses default values - rf_plus = RandomForestPlusRegressor(rf_model=rf) - rf_plus.fit(X_train, y_train) + # rf_plus = RandomForestPlusRegressor(rf_model=rf) + # rf_plus.fit(X_train, y_train) # otherwise, throw error else: raise ValueError("Task must be 'classification' or 'regression'.") # return tuple of models - return rf, rf_plus_baseline, rf_plus + return rf, rf_plus_baseline, rf_plus_ridge, rf_plus_lasso, rf_plus_elastic def create_lmdi_variant_map() -> Dict[str, Dict[str, bool]]: """ @@ -148,7 +240,7 @@ def create_lmdi_variant_map() -> Dict[str, Dict[str, bool]]: """ # enumerate the different options when initializing a LMDI+ explainer. - loo = {True: "aloo", False: "nonloo"} + glm = ["ridge", "lasso", "elastic"] l2norm = {True: "l2", False: "nonl2"} sign = {True: "signed", False: "unsigned"} normalize = {True: "normed", False: "nonnormed"} @@ -157,12 +249,12 @@ def create_lmdi_variant_map() -> Dict[str, Dict[str, bool]]: # create the mapping of variants to argument mappings lmdi_variants = {} - for l in loo: - for n in l2norm: + for g in glm: + for l2 in l2norm: for s in sign: - for nn in normalize: + for n in normalize: # sign and normalize are only relevant if l2norm is True - if (not n) and (s or nn): + if (not l2) and (s or n): continue for la in leaf_average: for r in ranking: @@ -170,16 +262,57 @@ def create_lmdi_variant_map() -> Dict[str, Dict[str, bool]]: if la: continue # create the name the variant will be stored under - variant_name = f"{loo[l]}_{l2norm[n]}_{sign[s]}" + \ - f"_{normalize[nn]}_{leaf_average[la]}_{ranking[r]}" + variant_name = f"{g}_{l2norm[l2]}_{sign[s]}" + \ + f"_{normalize[n]}_{leaf_average[la]}_{ranking[r]}" # store the arguments for the lmdi+ explainer - arg_map = {"loo": l, "l2norm": n, "sign": s, - "normalize": nn, "leaf_average": la, + arg_map = {"glm": g, "l2norm": l2, "sign": s, + "normalize": n, "leaf_average": la, "ranking": r} lmdi_variants[variant_name] = arg_map return lmdi_variants +# def create_lmdi_variant_map() -> Dict[str, Dict[str, bool]]: +# """ +# Create a mapping of LMDI+ variants to argument mappings. + +# Outputs: +# - lmdi_variants (Dict[str, Dict[str, bool]]): The LMDI variants to use. +# """ + +# # enumerate the different options when initializing a LMDI+ explainer. +# loo = {True: "aloo", False: "nonloo"} +# l2norm = {True: "l2", False: "nonl2"} +# sign = {True: "signed", False: "unsigned"} +# normalize = {True: "normed", False: "nonnormed"} +# leaf_average = {True: "leafavg", False: "noleafavg"} +# ranking = {True: "rank", False: "norank"} + +# # create the mapping of variants to argument mappings +# lmdi_variants = {} +# for l in loo: +# for n in l2norm: +# for s in sign: +# for nn in normalize: +# # sign and normalize are only relevant if l2norm is True +# if (not n) and (s or nn): +# continue +# for la in leaf_average: +# for r in ranking: +# # ranking is only relevant if leaf_average is False +# if la: +# continue +# # create the name the variant will be stored under +# variant_name = f"{loo[l]}_{l2norm[n]}_{sign[s]}" + \ +# f"_{normalize[nn]}_{leaf_average[la]}_{ranking[r]}" +# # store the arguments for the lmdi+ explainer +# arg_map = {"loo": l, "l2norm": n, "sign": s, +# "normalize": nn, "leaf_average": la, +# "ranking": r} +# lmdi_variants[variant_name] = arg_map + +# return lmdi_variants + def get_shap(X: np.ndarray, shap_explainer: shap.TreeExplainer, task: str): """ Get the SHAP values and rankings for the given data. @@ -257,26 +390,28 @@ def get_lime(X: np.ndarray, rf, task: str): return lime_values, lime_rankings -def get_lmdi_explainers(rf_plus, lmdi_variants: Dict[str, Dict[str, bool]], - rf_plus_baseline = None, rf_plus_lasso = None, - rf_plus_ridge = None): +def get_lmdi_explainers(rf_plus_baseline, rf_plus_ridge, + rf_plus_lasso, rf_plus_elastic, + lmdi_variants: Dict[str, Dict[str, bool]]): """ Create the LMDI explainer objects for the subgroup experiments. Inputs: - - rf_plus (RandomForestPlusClassifier/RandomForestPlusRegressor): The RF+. + - rf_plus_baseline (RandomForestPlusClassifier/RandomForestPlusRegressor): + The baseline RF+ (no raw feature, only on in-bag samples, + regular linear/logistic prediction model). + - rf_plus_ridge (RandomForestPlusClassifier/RandomForestPlusRegressor): + The RF+ with a ridge prediction model. + - rf_plus_lasso (RandomForestPlusClassifier/RandomForestPlusRegressor): + The RF+ with a lasso prediction model. + - rf_plus_elastic (RandomForestPlusClassifier/RandomForestPlusRegressor): + The RF+ with an elastic net prediction model. - lmdi_variants (Dict[str, Dict[str, bool]]): The LMDI variants to use. Stored as a dictionary with keys corresponding to the name of the lmdi+ variant and the value correponding to the argument mapping. In the argument mapping, keys are strings corresponding to elements of the variant (e.g. "loo") and the values are bools to indicate if they have that property. - - rf_plus_baseline (RandomForestPlusClassifier/RandomForestPlusRegressor): - The baseline RF+ (no raw feature, only on in-bag samples, - regular linear/logistic prediction model). - - rf_plus_lasso (RandomForestPlusClassifier/RandomForestPlusRegressor): - The version of RF+ that worked best for Zhongyuan's feature - selection experiments. Outputs: - lmdi_explainers (Dict[str, RFPlusMDI/AloRFPlusMDI]): The LMDI+ explainer @@ -295,24 +430,82 @@ def get_lmdi_explainers(rf_plus, lmdi_variants: Dict[str, Dict[str, bool]], lmdi_explainers["lmdi_baseline"] = RFPlusMDI(rf_plus_baseline, mode = "only_k", evaluate_on = "inbag") - if rf_plus_lasso is not None: - lmdi_explainers["lmdi_lasso"] = RFPlusMDI(rf_plus_lasso, - mode="only_k", - evaluate_on="all") - if rf_plus_ridge is not None: - lmdi_explainers["lmdi_ridge"] = RFPlusMDI(rf_plus_ridge, - mode="only_k", - evaluate_on="all") + for variant_name in lmdi_variants.keys(): + lmdi_explainers + # create the explainer objects for each variant, using AloRFPlusMDI if loo # is True and RFPlusMDI if loo is False for variant_name in lmdi_variants.keys(): - if lmdi_variants[variant_name]["loo"]: - lmdi_explainers[variant_name] = AloRFPlusMDI(rf_plus, - mode = "only_k") + if lmdi_variants[variant_name]["glm"] == "ridge": + lmdi_explainers[variant_name] = RFPlusMDI(rf_plus_ridge, + mode = "only_k") + elif lmdi_variants[variant_name]["glm"] == "lasso": + lmdi_explainers[variant_name] = RFPlusMDI(rf_plus_lasso, + mode = "only_k") + elif lmdi_variants[variant_name]["glm"] == "elastic": + lmdi_explainers[variant_name] = RFPlusMDI(rf_plus_elastic, + mode = "only_k") else: - lmdi_explainers[variant_name] = RFPlusMDI(rf_plus, mode = "only_k") + raise ValueError("Invalid GLM type.") return lmdi_explainers + +# def get_lmdi_explainers(rf_plus, lmdi_variants: Dict[str, Dict[str, bool]], +# rf_plus_baseline = None, rf_plus_lasso = None, +# rf_plus_ridge = None): +# """ +# Create the LMDI explainer objects for the subgroup experiments. + +# Inputs: +# - rf_plus (RandomForestPlusClassifier/RandomForestPlusRegressor): The RF+. +# - lmdi_variants (Dict[str, Dict[str, bool]]): The LMDI variants to use. +# Stored as a dictionary with keys corresponding to the name +# of the lmdi+ variant and the value correponding to the +# argument mapping. In the argument mapping, keys are strings +# corresponding to elements of the variant (e.g. "loo") and +# the values are bools to indicate if they have that property. +# - rf_plus_baseline (RandomForestPlusClassifier/RandomForestPlusRegressor): +# The baseline RF+ (no raw feature, only on in-bag samples, +# regular linear/logistic prediction model). +# - rf_plus_lasso (RandomForestPlusClassifier/RandomForestPlusRegressor): +# The version of RF+ that worked best for Zhongyuan's feature +# selection experiments. + +# Outputs: +# - lmdi_explainers (Dict[str, RFPlusMDI/AloRFPlusMDI]): The LMDI+ explainer +# objects. The keys correspond to the variant names, and the +# values are the explainer objects, where AloRFPlusMDIs are +# used if "loo" is True and RFPlusMDIs are used if "loo" is +# False. Unique objects are used for each variant to prevent +# saved fields from interfering with results. +# """ + +# lmdi_explainers = {} + +# # if a baseline is provided, we need to treat it separately +# if rf_plus_baseline is not None: +# # evaluate on inbag samples only +# lmdi_explainers["lmdi_baseline"] = RFPlusMDI(rf_plus_baseline, +# mode = "only_k", +# evaluate_on = "inbag") +# if rf_plus_lasso is not None: +# lmdi_explainers["lmdi_lasso"] = RFPlusMDI(rf_plus_lasso, +# mode="only_k", +# evaluate_on="all") +# if rf_plus_ridge is not None: +# lmdi_explainers["lmdi_ridge"] = RFPlusMDI(rf_plus_ridge, +# mode="only_k", +# evaluate_on="all") +# # create the explainer objects for each variant, using AloRFPlusMDI if loo +# # is True and RFPlusMDI if loo is False +# for variant_name in lmdi_variants.keys(): +# if lmdi_variants[variant_name]["loo"]: +# lmdi_explainers[variant_name] = AloRFPlusMDI(rf_plus, +# mode = "only_k") +# else: +# lmdi_explainers[variant_name] = RFPlusMDI(rf_plus, mode = "only_k") + +# return lmdi_explainers def get_lmdi(X: np.ndarray, y: np.ndarray, @@ -366,20 +559,30 @@ def get_lmdi(X: np.ndarray, y: np.ndarray, # if the explainer mapping has a lasso variant, we treat it differently if "lmdi_lasso" in lmdi_explainers: # print("Computing LMDI+ for Lasso variant...") + # lmdi_values["lmdi_lasso"] = \ + # lmdi_explainers["lmdi_lasso"].explain_linear_partial(X, y, + # l2norm=True, sign=True, + # normalize=True, leaf_average=False, + # ranking=True) lmdi_values["lmdi_lasso"] = \ lmdi_explainers["lmdi_lasso"].explain_linear_partial(X, y, - l2norm=True, sign=True, - normalize=True, leaf_average=False, + l2norm=False, sign=False, + normalize=False, leaf_average=False, ranking=True) # print("Done, values are:") # print(lmdi_values["lmdi_lasso"]) if "lmdi_ridge" in lmdi_explainers: # print("Computing LMDI+ for Ridge variant...") + # lmdi_values["lmdi_ridge"] = \ + # lmdi_explainers["lmdi_ridge"].explain_linear_partial(X, y, + # l2norm=True, sign=True, + # normalize=True, leaf_average=False, + # ranking=True) lmdi_values["lmdi_ridge"] = \ lmdi_explainers["lmdi_ridge"].explain_linear_partial(X, y, - l2norm=True, sign=True, - normalize=True, leaf_average=False, + l2norm=False, sign=False, + normalize=False, leaf_average=False, ranking=True) # print("Done, values are:") # print @@ -478,13 +681,21 @@ def get_train_clusters(lfi_train_values: Dict[str, np.ndarray], method: str): num_cluster_map = {} # number of clusters varies from 2 to 10 for num_clusters in range(2, 11): + + ### kmeans - recommended because different random inits + kmeans = KMeans(n_clusters=num_clusters, random_state=42, + n_init=10) + kmeans.fit(values) + num_cluster_map[num_clusters] = kmeans.labels_ + + ### scipy - shouldn't use # obtain the cluster centroids first (this is how they suggest # doing it, although it feels weird) - centroids, _ = cluster.vq.kmeans(obs=values, - k_or_guess=num_clusters) + # centroids, _ = cluster.vq.kmeans(obs=values, + # k_or_guess=num_clusters) # assign the values to the clusters using centroids - kmeans, _ = cluster.vq.vq(values, centroids) - num_cluster_map[num_clusters] = kmeans + # kmeans, _ = cluster.vq.vq(values, centroids) + # num_cluster_map[num_clusters] = kmeans # store the mapping for the variant method_to_labels[variant] = num_cluster_map @@ -695,9 +906,11 @@ def compute_performance(X_train: np.ndarray, X_test: np.ndarray, # create a mapping of metrics to measure if task == "classification": - metrics = {"accuracy": accuracy_score, "roc_auc": roc_auc_score, - "average_precision": average_precision_score, - "f1": f1_score, "log_loss": log_loss} + # metrics = {"accuracy": accuracy_score, "roc_auc": roc_auc_score, + # "average_precision": average_precision_score, + # "f1": f1_score, "log_loss": log_loss} + metrics = {"accuracy": accuracy_score} # forget others since they aren't + # defined if only one class is present else: metrics = {"r2": r2_score, "rmse": root_mean_squared_error} @@ -729,6 +942,18 @@ def compute_performance(X_train: np.ndarray, X_test: np.ndarray, # fit regression model to the cluster's training data if task == "classification": + # check if the train cluster has only one class + if len(np.unique(y_cluster_train)) == 1: + print(f"For {nclust} clusters, cluster #{c} in variant {variant} has only " + \ + "one class. Predicting that class for all " + \ + "test points.") + # if so, predict that class for all test points + y_cluster_pred = np.ones(X_cluster_test.shape[0])* \ + y_cluster_train[0] + cluster_scores.append(metric_func(y_cluster_test, + y_cluster_pred)) + cluster_sizes.append(X_cluster_test.shape[0]) + continue model = LogisticRegression() else: model = LinearRegression() @@ -736,6 +961,11 @@ def compute_performance(X_train: np.ndarray, X_test: np.ndarray, # store the cluster scores and sizes for weighted average y_cluster_pred = model.predict(X_cluster_test) + if task == "regression" and metric_name == "rmse" and variant == "lasso_l2_signed_nonnormed_noleafavg_rank" and nclust == 9: + print(f"Cluster {c} in variant {variant} has RMSE {metric_func(y_cluster_test, y_cluster_pred)}") + # print coefficents of the model + print("model coef:") + print(model.coef_) cluster_scores.append(metric_func(y_cluster_test, y_cluster_pred)) cluster_sizes.append(X_cluster_test.shape[0]) @@ -752,6 +982,7 @@ def compute_performance(X_train: np.ndarray, X_test: np.ndarray, return metrics_to_variants def write_results(result_dir: str, dataid: int, seed: int, clustertype: str, + task: str, metrics_to_scores: Dict[str, Dict[str, Dict[int, float]]]): """ Writes the results to a csv file. @@ -761,6 +992,7 @@ def write_results(result_dir: str, dataid: int, seed: int, clustertype: str, - dataid (int): The OpenML dataset ID. - seed (int): The random seed used. - clustertype (str): The clustering method used. + - task (str): The task type, either 'classification' or 'regression'. - metrics_to_scores (Dict[str, Dict[str, Dict[int, float]]]): Results calculated from compute_performance. @@ -779,12 +1011,12 @@ def write_results(result_dir: str, dataid: int, seed: int, clustertype: str, columns=["nclust", f"{metric_name}"] ) # if the path does not exist, create it - if not os.path.exists(oj(result_dir, f"dataid{dataid}/seed{seed}"+ \ - f"/metric{metric_name}/{clustertype}")): - os.makedirs(oj(result_dir, f"dataid{dataid}/seed{seed}" + \ - f"/metric{metric_name}/{clustertype}")) + if not os.path.exists(oj(result_dir, f"{task}/dataid{dataid}/seed{seed}"+ \ + f"/{metric_name}/{clustertype}")): + os.makedirs(oj(result_dir, f"{task}/dataid{dataid}/seed{seed}" + \ + f"/{metric_name}/{clustertype}")) # save the dataframe to a csv file - df.to_csv(oj(result_dir, f"dataid{dataid}/seed{seed}/metric" + \ + df.to_csv(oj(result_dir, f"{task}/dataid{dataid}/seed{seed}/" + \ f"{metric_name}/{clustertype}", f"{variant}.csv")) return @@ -891,7 +1123,7 @@ def run_pipeline1(seed: int, dataid: int, clustertype: str): return -def run_pipeline2(seed: int, dataid: int, clustertype: str): +def run_pipeline2(seed: int, dataid: int, clustertype: str, standarize: bool): """ Run pipeline 2 for the subgroup experiments. @@ -899,13 +1131,14 @@ def run_pipeline2(seed: int, dataid: int, clustertype: str): - seed (int): The random seed to use. - dataid (int): The OpenML dataset ID. - clustertype (str): The clustering method to use. + - standardize (bool): Whether to standardize the data. Outputs: - None """ # get data - X, y = get_openml_data(dataid) + X, y = get_openml_data(dataid, standardize) # split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, @@ -918,16 +1151,8 @@ def run_pipeline2(seed: int, dataid: int, clustertype: str): task = 'regression' # fit the prediction models - rf, rf_plus_baseline, rf_plus = fit_models(X_train, y_train, task) - - rf_plus_ridge = RandomForestPlusRegressor(rf_model=rf, - prediction_model=RidgeCV(cv=5)) - rf_plus_ridge.fit(X_train, y_train) - - rf_plus_lasso = RandomForestPlusRegressor(rf_model=rf, - prediction_model=LassoCV(cv=5, - max_iter=10000, random_state=0)) - rf_plus_lasso.fit(X_train, y_train) + rf, rf_plus_baseline, rf_plus_ridge, rf_plus_lasso, rf_plus_elastic = \ + fit_models(X_train, y_train, task) # obtain shap feature importances shap_explainer = shap.TreeExplainer(rf) @@ -941,10 +1166,13 @@ def run_pipeline2(seed: int, dataid: int, clustertype: str): lmdi_variants = create_lmdi_variant_map() # obtain lmdi feature importances - lmdi_explainers = get_lmdi_explainers(rf_plus, lmdi_variants, - rf_plus_baseline = rf_plus_baseline, - rf_plus_lasso = rf_plus_lasso, - rf_plus_ridge = rf_plus_ridge) + # lmdi_explainers = get_lmdi_explainers(rf_plus, lmdi_variants, + # rf_plus_baseline = rf_plus_baseline, + # rf_plus_lasso = rf_plus_lasso, + # rf_plus_ridge = rf_plus_ridge) + lmdi_explainers = get_lmdi_explainers(rf_plus_baseline, rf_plus_ridge, + rf_plus_lasso, rf_plus_elastic, + lmdi_variants) # we don't actually want to use the training values, but for leaf averaging # variants, we need to have the training data to compute the leaf averages lfi_train_values, lfi_train_rankings = get_lmdi(X_train, y_train, @@ -978,11 +1206,17 @@ def run_pipeline2(seed: int, dataid: int, clustertype: str): fitting_c_to_idxs = {} evaluation_c_to_idxs = {} for c, idxs in cluster_map.items(): + if len(idxs) < 3: + # warning message that the cluster is too small + warnings.warn(f"For {nclust} clusters, cluster #{c} in " + \ + f"variant {variant} has fewer than 3 observations.", + Warning) # shuffle the indices and split them in half np.random.shuffle(idxs) half = len(idxs) // 2 - fitting_c_to_idxs[c] = idxs[:half] - evaluation_c_to_idxs[c] = idxs[half:] + # NOTE: half: and :half were switched below. + fitting_c_to_idxs[c] = idxs[half:] + evaluation_c_to_idxs[c] = idxs[:half] fitting_nclust_to_c[nclust] = fitting_c_to_idxs evaluation_nclust_to_c[nclust] = evaluation_c_to_idxs fitting_clusters[variant] = fitting_nclust_to_c @@ -994,9 +1228,12 @@ def run_pipeline2(seed: int, dataid: int, clustertype: str): evaluation_clusters, task) # save the results + # result_dir = oj(os.path.dirname(os.path.realpath(__file__)), + # 'results/pipeline2/') result_dir = oj(os.path.dirname(os.path.realpath(__file__)), - 'results/pipeline2/') - write_results(result_dir, dataid, seed, clustertype, metrics_to_scores) + 'results/') + write_results(result_dir, dataid, seed, clustertype, task, + metrics_to_scores) print("Results saved!") @@ -1011,7 +1248,7 @@ def run_pipeline2(seed: int, dataid: int, clustertype: str): parser.add_argument('--dataid', type=int, default=None) parser.add_argument('--pipeline', type=int, default=None) parser.add_argument('--clustertype', type=str, default=None) - parser.add_argument('--njobs', type=int, default=1) + parser.add_argument('--standardize', type=int, default=None) args = parser.parse_args() # convert namespace to a dictionary @@ -1022,7 +1259,15 @@ def run_pipeline2(seed: int, dataid: int, clustertype: str): dataid = args_dict['dataid'] pipeline = args_dict['pipeline'] clustertype = args_dict['clustertype'] - njobs = args_dict['njobs'] + standardize = args_dict['standardize'] + + # enforce that standardize is either 0 or 1 + if standardize not in [0, 1]: + raise ValueError("Standardize must be 0 or 1.") + if standardize == 1: + standardize = True + else: + standardize = False # enforce that pipeline either needs to in [1, 2] if pipeline not in [1, 2]: @@ -1031,8 +1276,8 @@ def run_pipeline2(seed: int, dataid: int, clustertype: str): ### PIPELINE 1 ### if pipeline == 1: - print("Running pipeline 1...") + print(f"Running pipeline 1 with data ID {dataid} ...") run_pipeline1(seed, dataid, clustertype) else: - print("Running pipeline 2...") - run_pipeline2(seed, dataid, clustertype) \ No newline at end of file + print(f"Running pipeline 2 with data ID {dataid}...") + run_pipeline2(seed, dataid, clustertype, standardize) \ No newline at end of file diff --git a/feature_importance/subgroup/current/subgroup.sh b/feature_importance/subgroup/current/subgroup.sh index 3842f2a..91adbe5 100644 --- a/feature_importance/subgroup/current/subgroup.sh +++ b/feature_importance/subgroup/current/subgroup.sh @@ -2,15 +2,15 @@ #SBATCH --partition=yss #SBATCH --cpus-per-task=8 -seed=1 -# dataid=361247 -pipeline=1 -clustertype="kmeans" -njobs=32 +# seed=1 +# dataid=361268 +pipeline=2 +clustertype="hierarchical" +standardize=1 source activate mdi -command="subgroup.py --seed $seed --dataid ${1} --pipeline $pipeline --clustertype $clustertype --njobs $njobs" -# command="subgroup-incase.py --seed $seed --dataid $dataid --clustertype $clustertype --njobs $njobs" +command="subgroup.py --seed ${2} --dataid ${1} --pipeline $pipeline --clustertype $clustertype --standardize $standardize" +# command="subgroup.py --seed $seed --dataid $dataid --pipeline $pipeline --clustertype $clustertype" # Execute the command python $command \ No newline at end of file