10-classification-nanotube-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c772a64f-823c-4973-bf41-3c9a239c601a",
   "metadata": {},
   "source": [
    "# Group-based Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc25fc3-e84a-4d87-ace3-e947218d58aa",
   "metadata": {},
   "source": [
    "### Importing libraries and define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e31b3ce-df35-4b10-a99a-2f753f523fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import math\n",
    "import os\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8375cb30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Managing Warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    os.environ[\"PYTHONWARNINGS\"] = \"ignore\" # Also affect subprocesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d2c745b-9e8f-415c-a43c-f08fb5937b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import preprocessing\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b9378dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick.classifier import ROCAUC\n",
    "from yellowbrick.classifier import ClassPredictionError\n",
    "from yellowbrick.classifier import PrecisionRecallCurve\n",
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "from yellowbrick.classifier import DiscriminationThreshold\n",
    "from yellowbrick.model_selection import FeatureImportances\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03a6e29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.classification import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d5a3c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import functions as f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d563849c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ab55689",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0b351a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = ['NP_type', 'Coating','shape','Cell_name','Cell_species','cell_Organ','Cell_morphology',\n",
    "                       'Cell_age','cell_type','sex','Assay','Test_indicator']\n",
    "numerical_columns = ['core_size_nm','hydro_size_nm','Surf_charge_mV','Surface_area_m2_g','Dose_microg_mL','Duration_h',\n",
    "                    'Viability']\n",
    "\n",
    "numerical_columns_no_target = ['core_size_nm','hydro_size_nm','Surf_charge_mV','Dose_microg_mL','Duration_h']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5e030a",
   "metadata": {},
   "source": [
    "# Nanotubes classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a92e9968",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_group_name = \"nanotube\"\n",
    "group_name = \"nanotubes\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6d7f7e",
   "metadata": {},
   "source": [
    "### Load the data and filter instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da49b7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_df = pd.read_csv(\"output/datasets/universal/data_classification_train_df.csv\", encoding=\"utf-8\")\n",
    "data_test_df = pd.read_csv(\"output/datasets/universal/data_classification_test_df.csv\", encoding=\"utf-8\")\n",
    "data_df = pd.concat([data_train_df,data_test_df])\n",
    "\n",
    "del data_train_df\n",
    "del data_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f38a5c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4656, 20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4e127fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_df = data_df[data_df['nanomaterial_group'] == group_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "037994ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(207, 20)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_group_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea457428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NP_type</th>\n",
       "      <th>Coating</th>\n",
       "      <th>core_size_nm</th>\n",
       "      <th>hydro_size_nm</th>\n",
       "      <th>Surf_charge_mV</th>\n",
       "      <th>Surface_area_m2_g</th>\n",
       "      <th>shape</th>\n",
       "      <th>Dose_microg_mL</th>\n",
       "      <th>Duration_h</th>\n",
       "      <th>Cell_name</th>\n",
       "      <th>Cell_species</th>\n",
       "      <th>cell_Organ</th>\n",
       "      <th>Cell_morphology</th>\n",
       "      <th>Cell_age</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>sex</th>\n",
       "      <th>Assay</th>\n",
       "      <th>Test_indicator</th>\n",
       "      <th>nanomaterial_group</th>\n",
       "      <th>viability_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MWCNT</td>\n",
       "      <td>None</td>\n",
       "      <td>17.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needle</td>\n",
       "      <td>32.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HUVEC</td>\n",
       "      <td>Human</td>\n",
       "      <td>Umbilical</td>\n",
       "      <td>Endothelial</td>\n",
       "      <td>fetus</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>toluylene red</td>\n",
       "      <td>nanotubes</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>MWCNT</td>\n",
       "      <td>None</td>\n",
       "      <td>10.33</td>\n",
       "      <td>292.0</td>\n",
       "      <td>-9.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needle</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HUVEC</td>\n",
       "      <td>Human</td>\n",
       "      <td>Umbilical</td>\n",
       "      <td>Endothelial</td>\n",
       "      <td>fetus</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CCK_8</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>nanotubes</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>MWCNT</td>\n",
       "      <td>COOH</td>\n",
       "      <td>25.00</td>\n",
       "      <td>38.8</td>\n",
       "      <td>-10.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needle</td>\n",
       "      <td>200.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>A549</td>\n",
       "      <td>Human</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTS</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>nanotubes</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>MWCNT</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needle</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NR8383</td>\n",
       "      <td>Rat</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Macrophage</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>male</td>\n",
       "      <td>Alamar_Blue</td>\n",
       "      <td>AlamarBlue</td>\n",
       "      <td>nanotubes</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>SWCNT</td>\n",
       "      <td>None</td>\n",
       "      <td>112.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needle</td>\n",
       "      <td>50.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>mDF</td>\n",
       "      <td>Mouse</td>\n",
       "      <td>Skin</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>nanotubes</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    NP_type Coating  core_size_nm  hydro_size_nm  Surf_charge_mV  \\\n",
       "30    MWCNT    None         17.60            NaN            0.90   \n",
       "42    MWCNT    None         10.33          292.0           -9.09   \n",
       "58    MWCNT    COOH         25.00           38.8          -10.30   \n",
       "114   MWCNT    None           NaN            NaN          -13.00   \n",
       "152   SWCNT    None        112.90            NaN           12.90   \n",
       "\n",
       "     Surface_area_m2_g   shape  Dose_microg_mL  Duration_h Cell_name  \\\n",
       "30                 NaN  Needle            32.0        24.0     HUVEC   \n",
       "42                 NaN  Needle             8.0        24.0     HUVEC   \n",
       "58                 NaN  Needle           200.0        24.0      A549   \n",
       "114                NaN  Needle             6.0         4.0    NR8383   \n",
       "152                NaN  Needle            50.0        24.0       mDF   \n",
       "\n",
       "    Cell_species cell_Organ Cell_morphology Cell_age cell_type   sex  \\\n",
       "30         Human  Umbilical     Endothelial    fetus    Normal   NaN   \n",
       "42         Human  Umbilical     Endothelial    fetus    Normal   NaN   \n",
       "58         Human       Lung      Epithelial    Adult    cancer  male   \n",
       "114          Rat       Lung      Macrophage    Adult    Normal  male   \n",
       "152        Mouse       Skin      Fibroblast    Adult    Normal   NaN   \n",
       "\n",
       "           Assay   Test_indicator nanomaterial_group viability_class  \n",
       "30            NR    toluylene red          nanotubes        NonToxic  \n",
       "42         CCK_8  TetrazoliumSalt          nanotubes        NonToxic  \n",
       "58           MTS  TetrazoliumSalt          nanotubes        NonToxic  \n",
       "114  Alamar_Blue       AlamarBlue          nanotubes        NonToxic  \n",
       "152          MTT  TetrazoliumSalt          nanotubes        NonToxic  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_group_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ca8bfa",
   "metadata": {},
   "source": [
    "### Create training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bcf76419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_64f9c_row5_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_64f9c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_64f9c_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_64f9c_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_64f9c_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_64f9c_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_64f9c_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_64f9c_row1_col1\" class=\"data row1 col1\" >viability_class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_64f9c_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "      <td id=\"T_64f9c_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_64f9c_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "      <td id=\"T_64f9c_row3_col1\" class=\"data row3 col1\" >NonToxic: 0, Toxic: 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_64f9c_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "      <td id=\"T_64f9c_row4_col1\" class=\"data row4 col1\" >(207, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_64f9c_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "      <td id=\"T_64f9c_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_64f9c_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_64f9c_row6_col1\" class=\"data row6 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_64f9c_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_64f9c_row7_col1\" class=\"data row7 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_64f9c_row8_col0\" class=\"data row8 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_64f9c_row8_col1\" class=\"data row8 col1\" >(165, 18)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_64f9c_row9_col0\" class=\"data row9 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_64f9c_row9_col1\" class=\"data row9 col1\" >(42, 18)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_64f9c_row10_col0\" class=\"data row10 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_64f9c_row10_col1\" class=\"data row10 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_64f9c_row11_col0\" class=\"data row11 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_64f9c_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_64f9c_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_64f9c_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_64f9c_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
       "      <td id=\"T_64f9c_row13_col1\" class=\"data row13 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_64f9c_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_64f9c_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_64f9c_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
       "      <td id=\"T_64f9c_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_64f9c_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_64f9c_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_64f9c_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_64f9c_row17_col1\" class=\"data row17 col1\" >splitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_64f9c_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
       "      <td id=\"T_64f9c_row18_col1\" class=\"data row18 col1\" >3a3f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_64f9c_row19_col0\" class=\"data row19 col0\" >Fix Imbalance</td>\n",
       "      <td id=\"T_64f9c_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_64f9c_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_64f9c_row20_col0\" class=\"data row20 col0\" >Fix Imbalance Method</td>\n",
       "      <td id=\"T_64f9c_row20_col1\" class=\"data row20 col1\" >SMOTE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f32f0aa1040>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "splitter = setup(data = data_group_df, target = 'viability_class',\n",
    "                 preprocess = False,\n",
    "                 train_size=0.8,\n",
    "                 data_split_stratify=True,\n",
    "                 # don't change the following\n",
    "                 session_id = 123,\n",
    "                 use_gpu=False,\n",
    "                 log_experiment = False,\n",
    "                 silent=True,\n",
    "                 experiment_name = 'splitter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3898aa43",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_train_df = get_config(variable=\"X_train\").copy()\n",
    "data_group_train_df['viability_class'] = get_config(variable=\"y_train\").copy()\n",
    "\n",
    "data_group_test_df = get_config(variable=\"X_test\").copy()\n",
    "data_group_test_df['viability_class'] = get_config(variable=\"y_test\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0804450",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(165, 19)\n",
      "(42, 19)\n"
     ]
    }
   ],
   "source": [
    "print(data_group_train_df.shape)\n",
    "print(data_group_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e53cd4f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x3000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f.plot_distribution_train_test(data_group_train_df, data_group_test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9b00e8",
   "metadata": {},
   "source": [
    "### Save cleaned datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d2f791ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_train_df.to_csv('output/datasets/'+model_group_name+'/data_classification_train_df.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28b9aa49",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_test_df.to_csv('output/datasets/'+model_group_name+'/data_classification_test_df.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffe023c-bf03-4ba8-a278-8b1261da6572",
   "metadata": {},
   "source": [
    "### Use the best scenario identified in step 01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2cf8d7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_ignore_features = None\n",
    "sc_bin_numeric_features = None\n",
    "sc_high_cardinality_features = None\n",
    "\n",
    "sc_imputation_type = 'simple'\n",
    "sc_numeric_imputation = 'mean'\n",
    "\n",
    "sc_normalize = True\n",
    "sc_remove_outliers = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0fb96c",
   "metadata": {},
   "source": [
    "### Define seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7db1802e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_inner_seed = 123456\n",
    "cv_outer_seed = 123"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b866744f",
   "metadata": {},
   "source": [
    "### Data Preprcoessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb2a02f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9eeee_row5_col1, #T_9eeee_row29_col1, #T_9eeee_row36_col1, #T_9eeee_row44_col1, #T_9eeee_row58_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9eeee\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9eeee_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_9eeee_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_9eeee_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_9eeee_row0_col1\" class=\"data row0 col1\" >123456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_9eeee_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_9eeee_row1_col1\" class=\"data row1 col1\" >viability_class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_9eeee_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "      <td id=\"T_9eeee_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_9eeee_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "      <td id=\"T_9eeee_row3_col1\" class=\"data row3 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_9eeee_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "      <td id=\"T_9eeee_row4_col1\" class=\"data row4 col1\" >(165, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_9eeee_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "      <td id=\"T_9eeee_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_9eeee_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_9eeee_row6_col1\" class=\"data row6 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_9eeee_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_9eeee_row7_col1\" class=\"data row7 col1\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_9eeee_row8_col0\" class=\"data row8 col0\" >Ordinal Features</td>\n",
       "      <td id=\"T_9eeee_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_9eeee_row9_col0\" class=\"data row9 col0\" >High Cardinality Features</td>\n",
       "      <td id=\"T_9eeee_row9_col1\" class=\"data row9 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_9eeee_row10_col0\" class=\"data row10 col0\" >High Cardinality Method</td>\n",
       "      <td id=\"T_9eeee_row10_col1\" class=\"data row10 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_9eeee_row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_9eeee_row11_col1\" class=\"data row11 col1\" >(165, 32)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_9eeee_row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_9eeee_row12_col1\" class=\"data row12 col1\" >(42, 32)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_9eeee_row13_col0\" class=\"data row13 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_9eeee_row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_9eeee_row14_col0\" class=\"data row14 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_9eeee_row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_9eeee_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_9eeee_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_9eeee_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
       "      <td id=\"T_9eeee_row16_col1\" class=\"data row16 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_9eeee_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_9eeee_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_9eeee_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
       "      <td id=\"T_9eeee_row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_9eeee_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_9eeee_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_9eeee_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_9eeee_row20_col1\" class=\"data row20 col1\" >group_based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_9eeee_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
       "      <td id=\"T_9eeee_row21_col1\" class=\"data row21 col1\" >3c0e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_9eeee_row22_col0\" class=\"data row22 col0\" >Imputation Type</td>\n",
       "      <td id=\"T_9eeee_row22_col1\" class=\"data row22 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_9eeee_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Iteration</td>\n",
       "      <td id=\"T_9eeee_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_9eeee_row24_col0\" class=\"data row24 col0\" >Numeric Imputer</td>\n",
       "      <td id=\"T_9eeee_row24_col1\" class=\"data row24 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_9eeee_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Numeric Model</td>\n",
       "      <td id=\"T_9eeee_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_9eeee_row26_col0\" class=\"data row26 col0\" >Categorical Imputer</td>\n",
       "      <td id=\"T_9eeee_row26_col1\" class=\"data row26 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_9eeee_row27_col0\" class=\"data row27 col0\" >Iterative Imputation Categorical Model</td>\n",
       "      <td id=\"T_9eeee_row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_9eeee_row28_col0\" class=\"data row28 col0\" >Unknown Categoricals Handling</td>\n",
       "      <td id=\"T_9eeee_row28_col1\" class=\"data row28 col1\" >least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_9eeee_row29_col0\" class=\"data row29 col0\" >Normalize</td>\n",
       "      <td id=\"T_9eeee_row29_col1\" class=\"data row29 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_9eeee_row30_col0\" class=\"data row30 col0\" >Normalize Method</td>\n",
       "      <td id=\"T_9eeee_row30_col1\" class=\"data row30 col1\" >zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_9eeee_row31_col0\" class=\"data row31 col0\" >Transformation</td>\n",
       "      <td id=\"T_9eeee_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_9eeee_row32_col0\" class=\"data row32 col0\" >Transformation Method</td>\n",
       "      <td id=\"T_9eeee_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_9eeee_row33_col0\" class=\"data row33 col0\" >PCA</td>\n",
       "      <td id=\"T_9eeee_row33_col1\" class=\"data row33 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_9eeee_row34_col0\" class=\"data row34 col0\" >PCA Method</td>\n",
       "      <td id=\"T_9eeee_row34_col1\" class=\"data row34 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_9eeee_row35_col0\" class=\"data row35 col0\" >PCA Components</td>\n",
       "      <td id=\"T_9eeee_row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_9eeee_row36_col0\" class=\"data row36 col0\" >Ignore Low Variance</td>\n",
       "      <td id=\"T_9eeee_row36_col1\" class=\"data row36 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_9eeee_row37_col0\" class=\"data row37 col0\" >Combine Rare Levels</td>\n",
       "      <td id=\"T_9eeee_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_9eeee_row38_col0\" class=\"data row38 col0\" >Rare Level Threshold</td>\n",
       "      <td id=\"T_9eeee_row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_9eeee_row39_col0\" class=\"data row39 col0\" >Numeric Binning</td>\n",
       "      <td id=\"T_9eeee_row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_9eeee_row40_col0\" class=\"data row40 col0\" >Remove Outliers</td>\n",
       "      <td id=\"T_9eeee_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_9eeee_row41_col0\" class=\"data row41 col0\" >Outliers Threshold</td>\n",
       "      <td id=\"T_9eeee_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_9eeee_row42_col0\" class=\"data row42 col0\" >Remove Multicollinearity</td>\n",
       "      <td id=\"T_9eeee_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_9eeee_row43_col0\" class=\"data row43 col0\" >Multicollinearity Threshold</td>\n",
       "      <td id=\"T_9eeee_row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_9eeee_row44_col0\" class=\"data row44 col0\" >Remove Perfect Collinearity</td>\n",
       "      <td id=\"T_9eeee_row44_col1\" class=\"data row44 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_9eeee_row45_col0\" class=\"data row45 col0\" >Clustering</td>\n",
       "      <td id=\"T_9eeee_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_9eeee_row46_col0\" class=\"data row46 col0\" >Clustering Iteration</td>\n",
       "      <td id=\"T_9eeee_row46_col1\" class=\"data row46 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_9eeee_row47_col0\" class=\"data row47 col0\" >Polynomial Features</td>\n",
       "      <td id=\"T_9eeee_row47_col1\" class=\"data row47 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_9eeee_row48_col0\" class=\"data row48 col0\" >Polynomial Degree</td>\n",
       "      <td id=\"T_9eeee_row48_col1\" class=\"data row48 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_9eeee_row49_col0\" class=\"data row49 col0\" >Trignometry Features</td>\n",
       "      <td id=\"T_9eeee_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_9eeee_row50_col0\" class=\"data row50 col0\" >Polynomial Threshold</td>\n",
       "      <td id=\"T_9eeee_row50_col1\" class=\"data row50 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_9eeee_row51_col0\" class=\"data row51 col0\" >Group Features</td>\n",
       "      <td id=\"T_9eeee_row51_col1\" class=\"data row51 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_9eeee_row52_col0\" class=\"data row52 col0\" >Feature Selection</td>\n",
       "      <td id=\"T_9eeee_row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_9eeee_row53_col0\" class=\"data row53 col0\" >Feature Selection Method</td>\n",
       "      <td id=\"T_9eeee_row53_col1\" class=\"data row53 col1\" >classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_9eeee_row54_col0\" class=\"data row54 col0\" >Features Selection Threshold</td>\n",
       "      <td id=\"T_9eeee_row54_col1\" class=\"data row54 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_9eeee_row55_col0\" class=\"data row55 col0\" >Feature Interaction</td>\n",
       "      <td id=\"T_9eeee_row55_col1\" class=\"data row55 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_9eeee_row56_col0\" class=\"data row56 col0\" >Feature Ratio</td>\n",
       "      <td id=\"T_9eeee_row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_9eeee_row57_col0\" class=\"data row57 col0\" >Interaction Threshold</td>\n",
       "      <td id=\"T_9eeee_row57_col1\" class=\"data row57 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "      <td id=\"T_9eeee_row58_col0\" class=\"data row58 col0\" >Fix Imbalance</td>\n",
       "      <td id=\"T_9eeee_row58_col1\" class=\"data row58 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9eeee_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
       "      <td id=\"T_9eeee_row59_col0\" class=\"data row59 col0\" >Fix Imbalance Method</td>\n",
       "      <td id=\"T_9eeee_row59_col1\" class=\"data row59 col1\" >SMOTE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f32cf912e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stup = setup(data = data_group_train_df, target = 'viability_class', \n",
    "              \n",
    "        test_data = data_group_test_df,\n",
    "             \n",
    "        categorical_features=categorical_columns,\n",
    "        numeric_features=numerical_columns_no_target,\n",
    "\n",
    "        imputation_type = sc_imputation_type, \n",
    "        numeric_imputation = sc_numeric_imputation, #default: mean\n",
    "\n",
    "        ignore_features = sc_ignore_features,\n",
    "        bin_numeric_features = sc_bin_numeric_features,\n",
    "        high_cardinality_features = sc_high_cardinality_features,\n",
    "\n",
    "        normalize = sc_normalize, #default: False\n",
    "        remove_outliers= sc_remove_outliers, #default: False\n",
    "\n",
    "        transformation= False, #default\n",
    "        transformation_method = 'yeo-johnson', #default\n",
    "        combine_rare_levels = False,\n",
    "        rare_level_threshold = 0.1, #default: 0.1\n",
    "\n",
    "        remove_multicollinearity = False, #default \n",
    "        multicollinearity_threshold = 0.9, #default\n",
    "\n",
    "        ignore_low_variance = True, \n",
    "\n",
    "        feature_interaction = False, \n",
    "        feature_ratio = False,\n",
    "        polynomial_features = False, \n",
    "        trigonometry_features = False,\n",
    "\n",
    "        fix_imbalance = True,\n",
    "        fold = 5,\n",
    "\n",
    "        # don't change the following\n",
    "        session_id = cv_inner_seed,\n",
    "        use_gpu=False,\n",
    "        log_experiment = False,\n",
    "        silent=True,\n",
    "        experiment_name = 'group_based')\n",
    "\n",
    "set_config('seed', cv_inner_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ebe27ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(165, 32)\n",
      "(165,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X_train\").shape)\n",
    "print(get_config(variable=\"y_train\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "34f3d922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(165, 32)\n",
      "(165,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X\").shape)\n",
    "print(get_config(variable=\"y\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f8e4ec4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set_config('X_train', get_config(variable=\"X\").copy()) \n",
    "#set_config('y_train', get_config(variable=\"y\").copy()) \n",
    "#print(get_config(variable=\"X_train\").shape)\n",
    "#print(get_config(variable=\"y_train\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1b417175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 32)\n",
      "(42,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X_test\").shape)\n",
    "print(get_config(variable=\"y_test\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4387c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load_config('output/models/universal_classification_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6fff2182",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_transformed_df = get_config(variable=\"X\").copy()\n",
    "data_train_transformed_df['Viability'] = get_config(variable=\"y\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cf253b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test_transformed_df = get_config(variable=\"X_test\").copy()\n",
    "data_test_transformed_df['Viability'] = get_config(variable=\"y_test\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "43f293b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_transformed_df.to_csv('output/datasets/'+model_group_name+'/transformed/data_classification_train_transformed_df.csv', encoding='utf-8', index=False)\n",
    "data_test_transformed_df.to_csv('output/datasets/'+model_group_name+'/transformed/data_classification_test_transformed_df.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe54e2d4",
   "metadata": {},
   "source": [
    "## Double cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db53e62",
   "metadata": {},
   "source": [
    "### Inner CV: 5-fold cross-validation for model selection / hyperparameter tuning for 7 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8922c9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = ['lr', 'knn', 'svm', 'rf', 'et', 'gbc', 'lightgbm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "69c30960",
   "metadata": {},
   "outputs": [],
   "source": [
    "inner_results = dict()\n",
    "inner_results_tuned = dict()\n",
    "outer_results = dict()\n",
    "predicted_results = dict()\n",
    "tuned_models = dict()\n",
    "final_models = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c2a73417",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table id=\"T_4eefb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4eefb_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_4eefb_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_4eefb_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_4eefb_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_4eefb_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_4eefb_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_4eefb_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4eefb_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_4eefb_row0_col0\" class=\"data row0 col0\" >0.94</td>\n",
       "      <td id=\"T_4eefb_row0_col1\" class=\"data row0 col1\" >0.98</td>\n",
       "      <td id=\"T_4eefb_row0_col2\" class=\"data row0 col2\" >0.83</td>\n",
       "      <td id=\"T_4eefb_row0_col3\" class=\"data row0 col3\" >0.83</td>\n",
       "      <td id=\"T_4eefb_row0_col4\" class=\"data row0 col4\" >0.83</td>\n",
       "      <td id=\"T_4eefb_row0_col5\" class=\"data row0 col5\" >0.80</td>\n",
       "      <td id=\"T_4eefb_row0_col6\" class=\"data row0 col6\" >0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eefb_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_4eefb_row1_col0\" class=\"data row1 col0\" >0.91</td>\n",
       "      <td id=\"T_4eefb_row1_col1\" class=\"data row1 col1\" >0.90</td>\n",
       "      <td id=\"T_4eefb_row1_col2\" class=\"data row1 col2\" >0.83</td>\n",
       "      <td id=\"T_4eefb_row1_col3\" class=\"data row1 col3\" >0.71</td>\n",
       "      <td id=\"T_4eefb_row1_col4\" class=\"data row1 col4\" >0.77</td>\n",
       "      <td id=\"T_4eefb_row1_col5\" class=\"data row1 col5\" >0.71</td>\n",
       "      <td id=\"T_4eefb_row1_col6\" class=\"data row1 col6\" >0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eefb_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_4eefb_row2_col0\" class=\"data row2 col0\" >0.85</td>\n",
       "      <td id=\"T_4eefb_row2_col1\" class=\"data row2 col1\" >0.88</td>\n",
       "      <td id=\"T_4eefb_row2_col2\" class=\"data row2 col2\" >0.86</td>\n",
       "      <td id=\"T_4eefb_row2_col3\" class=\"data row2 col3\" >0.60</td>\n",
       "      <td id=\"T_4eefb_row2_col4\" class=\"data row2 col4\" >0.71</td>\n",
       "      <td id=\"T_4eefb_row2_col5\" class=\"data row2 col5\" >0.61</td>\n",
       "      <td id=\"T_4eefb_row2_col6\" class=\"data row2 col6\" >0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eefb_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_4eefb_row3_col0\" class=\"data row3 col0\" >0.94</td>\n",
       "      <td id=\"T_4eefb_row3_col1\" class=\"data row3 col1\" >0.97</td>\n",
       "      <td id=\"T_4eefb_row3_col2\" class=\"data row3 col2\" >0.86</td>\n",
       "      <td id=\"T_4eefb_row3_col3\" class=\"data row3 col3\" >0.86</td>\n",
       "      <td id=\"T_4eefb_row3_col4\" class=\"data row3 col4\" >0.86</td>\n",
       "      <td id=\"T_4eefb_row3_col5\" class=\"data row3 col5\" >0.82</td>\n",
       "      <td id=\"T_4eefb_row3_col6\" class=\"data row3 col6\" >0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eefb_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_4eefb_row4_col0\" class=\"data row4 col0\" >0.91</td>\n",
       "      <td id=\"T_4eefb_row4_col1\" class=\"data row4 col1\" >0.94</td>\n",
       "      <td id=\"T_4eefb_row4_col2\" class=\"data row4 col2\" >0.86</td>\n",
       "      <td id=\"T_4eefb_row4_col3\" class=\"data row4 col3\" >0.75</td>\n",
       "      <td id=\"T_4eefb_row4_col4\" class=\"data row4 col4\" >0.80</td>\n",
       "      <td id=\"T_4eefb_row4_col5\" class=\"data row4 col5\" >0.74</td>\n",
       "      <td id=\"T_4eefb_row4_col6\" class=\"data row4 col6\" >0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eefb_level0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "      <td id=\"T_4eefb_row5_col0\" class=\"data row5 col0\" >0.91</td>\n",
       "      <td id=\"T_4eefb_row5_col1\" class=\"data row5 col1\" >0.93</td>\n",
       "      <td id=\"T_4eefb_row5_col2\" class=\"data row5 col2\" >0.85</td>\n",
       "      <td id=\"T_4eefb_row5_col3\" class=\"data row5 col3\" >0.75</td>\n",
       "      <td id=\"T_4eefb_row5_col4\" class=\"data row5 col4\" >0.79</td>\n",
       "      <td id=\"T_4eefb_row5_col5\" class=\"data row5 col5\" >0.74</td>\n",
       "      <td id=\"T_4eefb_row5_col6\" class=\"data row5 col6\" >0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eefb_level0_row6\" class=\"row_heading level0 row6\" >Std</th>\n",
       "      <td id=\"T_4eefb_row6_col0\" class=\"data row6 col0\" >0.03</td>\n",
       "      <td id=\"T_4eefb_row6_col1\" class=\"data row6 col1\" >0.04</td>\n",
       "      <td id=\"T_4eefb_row6_col2\" class=\"data row6 col2\" >0.01</td>\n",
       "      <td id=\"T_4eefb_row6_col3\" class=\"data row6 col3\" >0.09</td>\n",
       "      <td id=\"T_4eefb_row6_col4\" class=\"data row6 col4\" >0.05</td>\n",
       "      <td id=\"T_4eefb_row6_col5\" class=\"data row6 col5\" >0.07</td>\n",
       "      <td id=\"T_4eefb_row6_col6\" class=\"data row6 col6\" >0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f33a70c1d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    default_model = create_model(model_name, round=2)\n",
    "\n",
    "    results_df = pull()\n",
    "    results_df = results_df.reset_index(level=0)\n",
    "    \n",
    "    inner_results[model_name] = results_df.copy()\n",
    "    results_df.to_csv('output/models_results/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_inner_cross_validation_results.tsv\", \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")\n",
    "\n",
    "\n",
    "    tuned_model = tune_model(default_model, choose_better = True, search_library = 'optuna', n_iter = 50, round=2)\n",
    "\n",
    "    results_df = pull()\n",
    "    results_df = results_df.reset_index(level=0)\n",
    "    \n",
    "    inner_results_tuned[model_name] = results_df.copy()\n",
    "    results_df.to_csv('output/models_results/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_tuned_inner_cross_validation_results.tsv\", \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")\n",
    "    \n",
    "    tuned_models[model_name] = deepcopy(tuned_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2543bed8",
   "metadata": {},
   "source": [
    "### Outer CV: 10-fold cross-validation for the tuned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7d9c9302",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = get_config(variable=\"X_train\").copy()\n",
    "y = get_config(variable=\"y_train\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3c21760e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(165, 32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5a9c93d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(165,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f052eebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_outer = StratifiedKFold(n_splits=10, shuffle=True, random_state=cv_outer_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "564a8934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr scores: 0.8, 0.4, 0.86, 0.4, 0.67, 1.0, 0.86, 1.0, 0.8, 0.5\n",
      "lr mean/std: 0.73 / 0.22\n",
      "knn scores: 0.8, 0.29, 0.86, 0.57, 0.67, 0.8, 0.4, 1.0, 0.8, 0.5\n",
      "knn mean/std: 0.67 / 0.21\n",
      "svm scores: 0.33, 0.33, 0.57, 0.4, 0.4, 1.0, 0.86, 0.86, 0.4, 0.4\n",
      "svm mean/std: 0.56 / 0.24\n",
      "rf scores: 1.0, 0.44, 0.86, 0.67, 0.75, 0.57, 0.57, 1.0, 0.75, 0.4\n",
      "rf mean/std: 0.7 / 0.2\n",
      "et scores: 0.86, 0.44, 0.86, 0.75, 0.57, 1.0, 0.57, 1.0, 0.67, 0.5\n",
      "et mean/std: 0.72 / 0.19\n",
      "gbc scores: 0.86, 0.57, 0.86, 0.67, 0.57, 1.0, 0.4, 1.0, 0.86, 0.4\n",
      "gbc mean/std: 0.72 / 0.22\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "lightgbm scores: 0.8, 0.4, 0.67, 0.4, 0.86, 1.0, 0.67, 0.67, 0.8, 0.5\n",
      "lightgbm mean/std: 0.68 / 0.19\n"
     ]
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "    scores = cross_validate(tuned_models[model_name], X, y, cv=cv_outer, scoring=('f1','accuracy','roc_auc'))\n",
    "    scores_f1 = np.round(scores['test_f1'], 2);\n",
    "    scores_accuracy = np.round(scores['test_accuracy'], 2);\n",
    "    scores_roc_auc = np.round(scores['test_roc_auc'], 2);\n",
    "    outer_results[model_name] = scores.copy()\n",
    "    \n",
    "    with open('output/models_results/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_tuned_outer_cross_validation_results.tsv\", 'w', newline='') as file:\n",
    "        file.write('F1\\tAccuracy\\tAUC' + \"\\n\")\n",
    "        for idx, x in enumerate(scores_f1):\n",
    "            file.write(str(scores_f1[idx]) + '\\t' + str(scores_accuracy[idx]) + '\\t' + str(scores_roc_auc[idx]) + \"\\n\")\n",
    "\n",
    "    print(model_name + \" scores: \" + ', '.join(scores_f1.astype('str')))\n",
    "    print(model_name + \" mean/std: \" + str(np.round(scores_f1.mean(),2)) + \" / \" + str(np.round(scores_f1.std(), 2)))\n",
    "    \n",
    "    #with open(the_filename, 'r') as f:\n",
    "    #    my_list = [line.rstrip('\\n') for line in f]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567af0bf",
   "metadata": {},
   "source": [
    "### Finalize the models and evaluate on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bfae263b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_94f0a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_94f0a_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_94f0a_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_94f0a_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_94f0a_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_94f0a_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_94f0a_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_94f0a_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_94f0a_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_94f0a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_94f0a_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_94f0a_row0_col1\" class=\"data row0 col1\" >0.95</td>\n",
       "      <td id=\"T_94f0a_row0_col2\" class=\"data row0 col2\" >1.00</td>\n",
       "      <td id=\"T_94f0a_row0_col3\" class=\"data row0 col3\" >0.89</td>\n",
       "      <td id=\"T_94f0a_row0_col4\" class=\"data row0 col4\" >0.89</td>\n",
       "      <td id=\"T_94f0a_row0_col5\" class=\"data row0 col5\" >0.89</td>\n",
       "      <td id=\"T_94f0a_row0_col6\" class=\"data row0 col6\" >0.86</td>\n",
       "      <td id=\"T_94f0a_row0_col7\" class=\"data row0 col7\" >0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x7f3329ead070>"
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_90141_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_90141_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_90141_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_90141_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_90141_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_90141_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_90141_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_90141_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_90141_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_90141_row0_col0\" class=\"data row0 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_90141_row0_col1\" class=\"data row0 col1\" >0.98</td>\n",
       "      <td id=\"T_90141_row0_col2\" class=\"data row0 col2\" >0.99</td>\n",
       "      <td id=\"T_90141_row0_col3\" class=\"data row0 col3\" >1.00</td>\n",
       "      <td id=\"T_90141_row0_col4\" class=\"data row0 col4\" >0.90</td>\n",
       "      <td id=\"T_90141_row0_col5\" class=\"data row0 col5\" >0.95</td>\n",
       "      <td id=\"T_90141_row0_col6\" class=\"data row0 col6\" >0.93</td>\n",
       "      <td id=\"T_90141_row0_col7\" class=\"data row0 col7\" >0.93</td>\n",
       "    </tr>\n",
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       "</table>\n"
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       "<table id=\"T_8bd8f\">\n",
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       "      <th id=\"T_8bd8f_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_8bd8f_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_8bd8f_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_8bd8f_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_8bd8f_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_8bd8f_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_8bd8f_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_8bd8f_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8bd8f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8bd8f_row0_col0\" class=\"data row0 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_8bd8f_row0_col1\" class=\"data row0 col1\" >0.93</td>\n",
       "      <td id=\"T_8bd8f_row0_col2\" class=\"data row0 col2\" >0.91</td>\n",
       "      <td id=\"T_8bd8f_row0_col3\" class=\"data row0 col3\" >0.89</td>\n",
       "      <td id=\"T_8bd8f_row0_col4\" class=\"data row0 col4\" >0.80</td>\n",
       "      <td id=\"T_8bd8f_row0_col5\" class=\"data row0 col5\" >0.84</td>\n",
       "      <td id=\"T_8bd8f_row0_col6\" class=\"data row0 col6\" >0.80</td>\n",
       "      <td id=\"T_8bd8f_row0_col7\" class=\"data row0 col7\" >0.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "</style>\n",
       "<table id=\"T_fe95d\">\n",
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fe95d_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_fe95d_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_fe95d_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_fe95d_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_fe95d_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_fe95d_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_fe95d_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_fe95d_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fe95d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_fe95d_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_fe95d_row0_col1\" class=\"data row0 col1\" >0.98</td>\n",
       "      <td id=\"T_fe95d_row0_col2\" class=\"data row0 col2\" >1.00</td>\n",
       "      <td id=\"T_fe95d_row0_col3\" class=\"data row0 col3\" >1.00</td>\n",
       "      <td id=\"T_fe95d_row0_col4\" class=\"data row0 col4\" >0.90</td>\n",
       "      <td id=\"T_fe95d_row0_col5\" class=\"data row0 col5\" >0.95</td>\n",
       "      <td id=\"T_fe95d_row0_col6\" class=\"data row0 col6\" >0.93</td>\n",
       "      <td id=\"T_fe95d_row0_col7\" class=\"data row0 col7\" >0.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x7f32d1996c70>"
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     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e95c5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e95c5_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_e95c5_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_e95c5_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_e95c5_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_e95c5_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_e95c5_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_e95c5_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_e95c5_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e95c5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_e95c5_row0_col0\" class=\"data row0 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_e95c5_row0_col1\" class=\"data row0 col1\" >0.95</td>\n",
       "      <td id=\"T_e95c5_row0_col2\" class=\"data row0 col2\" >0.99</td>\n",
       "      <td id=\"T_e95c5_row0_col3\" class=\"data row0 col3\" >0.89</td>\n",
       "      <td id=\"T_e95c5_row0_col4\" class=\"data row0 col4\" >0.89</td>\n",
       "      <td id=\"T_e95c5_row0_col5\" class=\"data row0 col5\" >0.89</td>\n",
       "      <td id=\"T_e95c5_row0_col6\" class=\"data row0 col6\" >0.86</td>\n",
       "      <td id=\"T_e95c5_row0_col7\" class=\"data row0 col7\" >0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f33a5fdfa00>"
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     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_bf8ed\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bf8ed_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_bf8ed_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_bf8ed_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_bf8ed_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_bf8ed_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_bf8ed_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_bf8ed_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_bf8ed_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bf8ed_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_bf8ed_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_bf8ed_row0_col1\" class=\"data row0 col1\" >0.98</td>\n",
       "      <td id=\"T_bf8ed_row0_col2\" class=\"data row0 col2\" >1.00</td>\n",
       "      <td id=\"T_bf8ed_row0_col3\" class=\"data row0 col3\" >1.00</td>\n",
       "      <td id=\"T_bf8ed_row0_col4\" class=\"data row0 col4\" >0.90</td>\n",
       "      <td id=\"T_bf8ed_row0_col5\" class=\"data row0 col5\" >0.95</td>\n",
       "      <td id=\"T_bf8ed_row0_col6\" class=\"data row0 col6\" >0.93</td>\n",
       "      <td id=\"T_bf8ed_row0_col7\" class=\"data row0 col7\" >0.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_769a0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_769a0_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_769a0_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_769a0_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_769a0_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_769a0_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_769a0_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_769a0_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_769a0_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_769a0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_769a0_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_769a0_row0_col1\" class=\"data row0 col1\" >0.98</td>\n",
       "      <td id=\"T_769a0_row0_col2\" class=\"data row0 col2\" >1.00</td>\n",
       "      <td id=\"T_769a0_row0_col3\" class=\"data row0 col3\" >0.89</td>\n",
       "      <td id=\"T_769a0_row0_col4\" class=\"data row0 col4\" >1.00</td>\n",
       "      <td id=\"T_769a0_row0_col5\" class=\"data row0 col5\" >0.94</td>\n",
       "      <td id=\"T_769a0_row0_col6\" class=\"data row0 col6\" >0.93</td>\n",
       "      <td id=\"T_769a0_row0_col7\" class=\"data row0 col7\" >0.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x7f33a484ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    final_model = finalize_model(tuned_models[model_name])\n",
    "    \n",
    "    final_models[model_name] = deepcopy(final_model)\n",
    "    \n",
    "    hyperparams = pd.DataFrame.from_dict(final_model.get_params(), orient='index', columns=['parameter_value'])\n",
    "    hyperparams = hyperparams.reset_index(level=0)\n",
    "    hyperparams.to_csv('output/models_parameters/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_hyperparams.tsv\", \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")\n",
    "    \n",
    "    model_predict = predict_model(final_model, round=2)\n",
    "    \n",
    "    results_df = pull()\n",
    "    results_df = results_df.reset_index(level=0)\n",
    "    \n",
    "    predicted_results[model_name] = results_df.copy()\n",
    "    results_df.to_csv('output/models_results/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_test_set_validation_results.tsv\", \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cef3c2f",
   "metadata": {},
   "source": [
    "### Save models and configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f7dbf7e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_config('output/models/'+model_group_name+'/classification_config.cfg')\n",
    "\n",
    "for model_name in model_names:\n",
    "    save_model(tuned_models[model_name], 'output/models/'+model_group_name+'/classification_model_'+model_name+'_tuned.mdl')\n",
    "    save_model(final_models[model_name], 'output/models/'+model_group_name+'/classification_model_'+model_name+'_final.mdl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8dee9d",
   "metadata": {},
   "source": [
    "### Create Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "521ec3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = get_config(variable=\"X_test\").copy()\n",
    "y_test = get_config(variable=\"y_test\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "44efd5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in ['rf', 'et', 'gbc', 'lr']:\n",
    "    \n",
    "    #import matplotlib.pyplot as plt\n",
    "    #plt.rcParams[\"figure.figsize\"] = [16,9]\n",
    "\n",
    "    visualizer1 = FeatureImportances(final_models[model_name], size=(800,550), topn=10)\n",
    "    visualizer1.fit(X, y)\n",
    "    visualizer1.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_feature_importance_plot.png\",dpi = 300)\n",
    "\n",
    "    plt.clf() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b4489866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer2 = PrecisionRecallCurve(final_models[model_name], hist=False, qqplot=True, size=(800,550))\n",
    "    visualizer2.fit(X, y)\n",
    "    visualizer2.score(X_test, y_test)\n",
    "    visualizer2.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_PrecisionRecallCurve_plot.png\",dpi = 300)\n",
    "    \n",
    "    plt.clf() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1a279e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer3 = ClassPredictionError(final_models[model_name], size=(800,550))\n",
    "    visualizer3.fit(X, y)\n",
    "    visualizer3.score(X_test, y_test)\n",
    "    visualizer3.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_error_plot.png\",dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "23777c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer4 = ConfusionMatrix(final_models[model_name], size=(800,550), percent=True)\n",
    "    visualizer4.fit(X, y)\n",
    "    visualizer4.score(X_test, y_test)\n",
    "    for label in visualizer4.ax.texts:\n",
    "        label.set_size(18)\n",
    "    visualizer4.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_ConfusionMatrix_plot.png\",dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ef7e1f19",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7830536962204743, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7830536962204743\n",
      "[LightGBM] [Warning] feature_fraction is set=0.49627434232637735, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.49627434232637735\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer5 = DiscriminationThreshold(final_models[model_name], size=(800,550))\n",
    "    visualizer5.fit(X, y)\n",
    "    visualizer5.score(X_test, y_test)\n",
    "    visualizer5.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_DiscriminationThreshold_plot.png\",dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cf552eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_name in ['lr', 'knn', 'rf', 'et', 'gbc', 'lightgbm']:\n",
    "    \n",
    "    visualizer6 = ROCAUC(final_models[model_name], size=(800,550))\n",
    "    visualizer6.fit(X, y)\n",
    "    visualizer6.score(X_test, y_test)\n",
    "    visualizer6.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_ROCAUC_plot.png\",dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2eafd5",
   "metadata": {},
   "source": [
    "### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2406f629",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = ['lr', 'knn', 'svm', 'rf', 'et', 'gbc', 'lightgbm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6f5acd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_config = load_config('output/models/'+model_group_name+'/classification_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7503a083",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_models_load = dict()\n",
    "final_models_load = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "af29b475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n",
      "Transformation Pipeline and Model Successfully Loaded\n"
     ]
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "    tuned_models_load[model_name] = load_model('output/models/'+model_group_name+'/classification_model_'+model_name+'_tuned.mdl')\n",
    "    final_models_load[model_name] = load_model('output/models/'+model_group_name+'/classification_model_'+model_name+'_final.mdl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "acba0082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boosting_type': 'gbdt',\n",
       " 'objective': None,\n",
       " 'num_leaves': 40,\n",
       " 'max_depth': -1,\n",
       " 'learning_rate': 0.04111253544522762,\n",
       " 'n_estimators': 244,\n",
       " 'subsample_for_bin': 200000,\n",
       " 'min_split_gain': 0.08592536102546655,\n",
       " 'min_child_weight': 0.001,\n",
       " 'min_child_samples': 32,\n",
       " 'subsample': 1.0,\n",
       " 'subsample_freq': 0,\n",
       " 'colsample_bytree': 1.0,\n",
       " 'reg_alpha': 0.736203375365871,\n",
       " 'reg_lambda': 2.667919040243529e-10,\n",
       " 'random_state': 123456,\n",
       " 'n_jobs': -1,\n",
       " 'silent': 'warn',\n",
       " 'importance_type': 'split',\n",
       " '_Booster': <lightgbm.basic.Booster at 0x7f33a4b89b50>,\n",
       " '_evals_result': None,\n",
       " '_best_score': defaultdict(collections.OrderedDict, {}),\n",
       " '_best_iteration': None,\n",
       " '_other_params': {'feature_fraction': 0.49627434232637735,\n",
       "  'bagging_fraction': 0.7830536962204743,\n",
       "  'bagging_freq': 6},\n",
       " '_objective': 'binary',\n",
       " 'class_weight': None,\n",
       " '_class_weight': None,\n",
       " '_class_map': {0: 0, 1: 1},\n",
       " '_n_features': 32,\n",
       " '_n_features_in': 32,\n",
       " '_classes': array([0, 1]),\n",
       " '_n_classes': 2,\n",
       " 'feature_fraction': 0.49627434232637735,\n",
       " 'bagging_fraction': 0.7830536962204743,\n",
       " 'bagging_freq': 6,\n",
       " '_le': LabelEncoder(),\n",
       " '_fobj': None,\n",
       " 'fitted_': True}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(final_models_load['lightgbm']._final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2fc4157c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERETkAWMymdgRHc/Xe4+zIzoek8lk7ZIyKWLtAiTv/P39iY+Px8bmZmYuU6YMDRo0oEePHlSvXt3K1eVcYGAggYGB1i7jnq1Zs4Zp06bxyy+/WLsUERERkYfe2shYhq3fQ/TFa+Zl1Uq7MLmNL+283a1Y2f/Rma0H3MiRI4mMjGTPnj0sWLCAkiVL8vLLL/Pbb79ZuzQREREREYtYGxlLx8U7MgUtgOiL1+i4eAdrI2OtVFlmOrP1kChatCjVqlVj2LBh2NraMnLkSL777jvOnz/PRx99xJ49e0hNTcXPz48xY8ZQokQJjEYjH374ITt27CAxMRFPT09GjhxJ7dq1Adi0aRPz5s3jxIkTlC5dmrfffptXX301R/X4+/vTs2dPNm3axP79+/Hy8mL69OlMnTqV7du3U7ZsWaZNm0bt2rWznBE6ePAgY8eO5ciRI5QrV44BAwbw4osvcvLkSVq0aMGYMWOYMWMGo0ePpnXr1mzbto3Q0FBiY2MpVaoUb775Jl27dgXAaDQyfPhwfvjhBypUqMCHH35Ijx49WLRoEQ0aNLjjPmT0N2/ePCZPnszp06d58cUX6dOnD0OHDuXw4cN4e3vz2Wef4erqeg+jZ11XsOOsMQ0HU4q1S5ECkJiYpjEvRDTehYvGu3ApzONtMpkYvG436dlMGUw3mRi+YQ+Btd0wGAwFXF1mClsPoTfffJP58+dz8OBBPvzwQ6pXr873339PYmIiAwYMYMyYMXz66acsXryYCxcusHXrVuzs7Jg/fz6jRo1i7dq1REZG8sEHHzBz5kwaNWrE3r176dmzJ4899hi+vr45qmP58uWEhobi4uJCYGAgnTt3JiQkhAkTJvDOO+/w2WefMXfu3EzPMRqN9OrVi7feeoslS5bwxx9/0Lt3bzw9PbG3twdg165dbN++nWLFinH48GEGDBjAp59+SrNmzfjzzz/p3bs3lStXplmzZkybNo0jR47w3XffYWNjw/Dhw0lNTc3V8QwPDycsLIzDhw/TpUsXTpw4wZQpU3BwcKBdu3Z88803dO/ePVfbvBuj0Ziv27tTP7tsKrArNglIKpA+5T6gMS9cNN6Fi8a7cCmk4308/hInLl2/Y5t/LlxjW1QcjT3K5Hv/ufmcprD1ECpTpgzFixfn999/5+DBg8ybNw9nZ2ecnZ15++23eeedd0hOTubq1asULVoUBwcHihQpQnBwMMHBwcDN64+eeeYZmjRpAsCTTz7JCy+8wLfffpvjsPXMM89QpUoVAOrUqcONGzdo3LgxAE2aNOHrr7/O8pydO3eSkpLCm2++ia2tLY0bN2bGjBk4ODiYL3gMDAzE2dkZgG+++YZGjRrRsmVLABo1asQzzzzDpk2baNasGTt27OD111+nfPnyAHTv3j3X11S9/PLLuLi48NRTT+Hi4kLjxo1xc3Mz71dMTEyutpcTlthmtmwqF1xfIiIiIvfomjExR+12H/6HUsbzFq7mzhS2HlKpqam4ubnh6urKI488Yl7u7u5OSkoK8fHxvP766wQFBdGsWTOaNm1Ky5YtadGiBQCxsbH89ttveHt7m59rMpnM4SsnMgIOgL29vTkgZTxOTk7O8pzY2FjKly+Pra2teVlGTSdPngSgYsWK5nUnT56kWrVqmbZRuXJl9uzZA8D58+epVKmSed2t+5NTFSpUyFR3uXLl7rof98rDwwNHR8d83+5/GY1G6secpkKFCuYzh/JwS0pK4syZMxrzQkLjXbhovAuXwjzeHriwcufd29WrWR0vC53ZyukfxhW2HkInTpwgISGB48ePZ9vGYDBQqVIlNm3aREREBNu3b2f06NGsW7eO0NBQHBwc6NSpE6NGjcpzHRl3SczucXbPSU9Pv2ObW4NYdkEnY35ueno6RYr838s8JzVkt6172UZuOTo64uTkZPF+AFxJpnIJpwLrT6wrIcGWhDMa88JC4124aLwLl8I83lVKOTN6819Zbo5xq+plXGjpZf1rtnQ3wofQzJkzqVGjBk2aNOHKlStcuHDBvO7YsWPmszM3btwgLS2Np59+mpEjR7Jq1Sq2bNnC5cuXcXd358iRI5m2e/bsWdLS0ixau5ubG6dOncoUosLDw4mKirpte3d3d44dO5Zp2bFjx8zT/EqXLs2pU6fM6yIjIy1QtYiIiIgUFIPBwOQ2vthkE6RsDAYmtfa1etACha2HSnx8PBMnTuT7779n/PjxeHt7U61aNaZNm0ZCQgLx8fHMmTOHl156iaJFi9K/f38mT57M9evXSU9PZ+/evZQoUQJXV1c6dOjAnj17+Oabb0hOTiYqKopXXnmFLVu2WHQf/Pz8cHJyYu7cuSQlJbFr1y7GjBmT6WzWrdq2bcsvv/zCDz/8QGpqKj///DM//vij+Xu7GjRowNdff825c+c4d+4cX375pUXrFxERERHLa+ftTlg3P6qXccm0vHoZF8K6+d0337OlaYQPuHHjxjFhwgRMJhPFihWjUaNGrFq1yvylxrNnzyYkJIRnnnkGR0dHWrZsyXvvvQdASEgIo0ePxs/PD4PBwGOPPcasWbOwsbExh7TQ0FA++ugjypYtS1BQEC+++KJF98fOzo4vvviC4cOHs2DBAipUqMCECROoUaOG+ZqtW/n4+DB+/HimTZvGoEGDqFSpElOnTqV+/foADB06lIEDB9KiRQuqVq3KiBEj+Omnnyw2FfDChQtZrgtr2LAh8+fPt0h/IiIiIoVVO293Amu78fOxc5y5aqSiqyNNqpS9L85oZTCYTNncoF7kIZGcnIydnR0AcXFxtGzZkq1bt+Lufn/8xeNWCQkJREVF4eXlVSDzrwu6P7E+jXnhovEuXDTehYvG23pyc+w1jVAearNmzaJdu3acO3eOxMREPv/8c6pVq5bpDoUiIiIiIpagaYSSa23btr3jnQ4XLVrEU089VYAVZS8oKIizZ88SEBBASkoKtWrVIjQ0lEuXLtG8efM7Plc30xARERGRe6GwJbm2bt06a5eQYw4ODoSEhBASEpJlncKUiIiIiFiSphGKiIiIiIhYgMKWiIiIiIiIBShsiYiIiIiIWIDCloiIiIiIiAUobImIiIiIiFiAwpaIiIiIiIgFKGyJiIiIiIhYgMKWiIiIiIiIBShsiYiIiIiIWIDCloiIiIiIiAUobImIiIiIiFiAwpaIiIiIiIgFKGyJiIiIiIhYgMKWiIiIiIiIBShsiYiIiIiIWIDCloiIiIiIiAUobImIiIiIiFiAwpaIiIiIiIgFKGyJiIiIiIhYgMKWiIiIiIiIBShsiYiIiIiIWIDCloiIiIiIiAUobImIiIiIiFiAwpaIiIiIiIgFFLF2ASIiIiIPI5PJxM/HznH6agIVizvRtGpZDAaDtcsSkQKksCX5bufOnQQFBfH6668zZswYa5eTrTVr1vD+++/zzjvv0L9//0zrUlNTadKkCU5OTmzfvp3u3bvzxx9/AJCWlkZ6ejpFixY1t1+4cCFBQUHmx8nJyRQtWtT8j2pAQADjxo0rgL0SEZH7wdrIWIat30P0xWvmZdVKuzC5jS/tvN2tWJmIFCSFLcl3q1at4qWXXmLjxo0MHz4ce3t7a5eUrdKlS7Nhw4YsYWvnzp2Z/vq4aNEi8+8zZ87k559/JiwsLNNzIiMjzb97enoye/Zs/Pz8LFS5iIjcr9ZGxtJx8Q7STaZMy6MvXqPj4h2EdfNT4BIpJBS2JF9dvnyZ7du3s379eg4ePMjWrVtp3bo1AD/++CPTpk3j5MmTFCtWjICAAAYPHoyNjQ1r1qxh3rx5xMfHU7JkSd544w26d+8O3AwxEydO5O+//8bOzo5WrVoxcuRIUlNTady4MVOnTsXf399cQ7du3XjiiScYNGjQXeutVq0ap06dYt++fdStW9e8fMOGDTRr1oxdu3bl7wG6D13BjrPGNBxMKdYuRQpAYmKaxrwQ0XgXPJPJxOB1u7MErQzpJhPDN+whsLabphSKFAIKW5Kvvv32W7y8vPDw8KBNmzasXr2a1q1bk5KSwsCBA5k1axaNGjXixIkT9OjRAx8fH2rXrs3YsWNZuXIlnp6eHDhwgKCgIBo2bEitWrUYOHAgbdu2ZcmSJcTHx/Paa69RvXp13njjDZ577jnWr19vDluXL1/mjz/+YNSoUTmu+fnnn2f9+vXmsGU0Gvn5558ZN26c1cKW0WgssH522VRgV2wSkFQgfcp9QGNeuGi8C9Tx+EucuHT9jm3+uXCNbVFxNPYok699Z/zbUVD/hoh1abytJzfHXGFL8tXq1avp1KkTcPM6pVmzZnHy5ElKlChBYmIiTk5OGAwGPDw8+O6777CxseGff/4hPT0dJycnAGrXrs1vv/2Gjc3Nm2WGh4djZ2eHra0tFStW5KmnnuLAgQPmPnr16sX169dxdnbm+++/p0aNGlSvXj3HNbdt25a33nqL999/nyJFirB9+3bq1atH8eLF8/no5FxMTEzBdWZTueD6EhF5yF0zJuao3e7D/1DKeN4iNRTovyFidRrv+5vCluSbffv2ERMTwwsvvACAm5sbdevWZc2aNfTv35933nmHLl26UKdOHRo3bkz79u2pUKEC1apVIyAggBdeeIH69evTpEkT2rVrR8mSJQH4/fffmTVrFjExMaSmppKamsrzzz8PQIMGDShVqhTbtm0jMDCQrVu30qZNm1zVXbNmTcqWLcsvv/xCs2bN2LBhA23bts3fg5NLHh4eODo6Wrwfo9FI/ZjTVKhQ4b6+tk7yT1JSEmfOnNGYFxIa74LngQsrd969Xb2a1fGywJmtmJiYAvs3RKxL4209Gcc+JxS2JN+sWrWK1NRUWrRoYV6WkpJCfHw8ffv2pW/fvrzyyits27aNbdu2sWDBAhYvXkydOnUICQmhR48ebNu2jf/973/Mnz+fsLAwkpOTGTBgAMOGDaNjx444ODgwZMgQUlNTATAYDLRt25b169fTsmVLIiIiGDt2bK5rDwgIYN26ddStW5e9e/cyY8YM9u3bl1+HJtccHR3NZ/oszZVkKpdwKrD+xLoSEmxJOKMxLyw03gWvSilnRm/+K9NdCP+rehkXWnpZ7pqtgvw3RKxP431/05caS764ceMGmzZt4qOPPiI8PNz8s3r1as6dO8dvv/3Gv//+S7ly5ejcuTNffPEFzz//PN9++y3p6elcvXqVypUrExQURFhYGNWrV2fr1q1ERUVhZ2dH165dcXBwwGQyERUVlanvgIAAIiIiWLNmDU888QTlypXLdf2tW7dmx44dbN68mebNm+svwCIikicGg4HJbXyxySZI2RgMTGrtq5tjiBQSCluSLzZt2oS9vT3t2rWjcuXK5p+aNWvi7+/PokWLeOGFF9i/fz8mk4mLFy9y/Phx3N3d2bRpE6+88grHjh0D4NSpU8THx+Pu7s6jjz5KYmIiUVFRXLlyhSlTpmBnZ8e5c+cw/f87PVWtWhUvLy8+/fTTXE8hzFC2bFnq1KnD559/nudtiIiIALTzdiesmx/Vy7hkWl69jItu+y5SyGgaoeSLb775hjZt2mBnZ5dl3csvv0zfvn3p1asX7777LhcuXKBEiRK88MILdO7cGVtbW44ePUq3bt24evUqZcqU4ZVXXqFly5YAdO7cmS5duuDo6EifPn0YMWIEffr0YeDAgcyYMQOAwMBAJk2axHPPPZfnfQgICODjjz+mYcOGed6GiIgI3AxcgbXd+PnYOc5cNVLR1ZEmVcrqjJZIIWMwmbL5IgiRB0hoaChxcXFMmTLF2qXck4SEBKKiovDy8iqQ+dcF3Z9Yn8a8cNF4Fy4a78JF4209uTn2OrMlD7x9+/axZMkSlixZYu1SRERERETMFLbkgRYUFMSRI0cYNmwYNWvWNC9fuHCheYrh7QQEBDBu3LgCqFBERERECiuFLXmgLVy48LbLg4KCCAoKKuBqRERERET+j+5GKCIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJaxZs4bGjRsDEBERgaenJ0lJSVauKnve3t788ssv1i5DRMSqTCYTO6Lj+XrvcXZEx2MymaxdkoiI/EcRaxcg+ePYsWPMmjWL3377jRs3blC6dGn8/f3p27cvJUqUsHZ5+SoyMtLifURERNC1a1fs7OzMy4oUKUKVKlXo2bMnL7zwQo62ExcXx8GDB3n++ectVaqIFEJrI2MZtn4P0RevmZdVK+3C5Da+tPN2t2JlIiJyK53ZeghERUXRoUMHypcvz7p169izZw+zZs3iyJEjdOrUicTERGuX+MD6888/iYyMJDIykoiICLp168Z7773H7t27c/T87777ji1btli4ShEpTNZGxtJx8Y5MQQsg+uI1Oi7ewdrIWCtVJiIi/6UzWw+BsWPH0qRJE4YMGWJe5uXlxZw5cxg/fjznzp3D1taWkJAQ9u7dS3p6Os2bN2f06NE4OzvnuV+j0ciHH37Ijh07SExMxNPTk5EjR1K7dm1mzpzJ7t27qV+/PosXLyY1NZWuXbsyYMAAANLT0/nss89Yt24d58+fp3r16owYMYJ69eoBcOnSJT788EN++eUXHB0dad++PQMHDsRgMODp6cn8+fPx8/MjMTGRjz/+mO3bt/Pvv//i7e3NmDFjqF69OgCff/45K1as4NKlS5QvX57g4GACAgLytL92dnYEBASwdu1avv/+e3OtX375JUuXLuXixYuUL1+egQMH8uyzz7Jw4UKmTp0KwLZt29i3bx+2trZ5Pt6WcgU7zhrTcDClWLsUKQCJiWka8weYyWRi8LrdpGczZTDdZGL4hj0E1nbDYDAUcHUiIvJfClsPuIsXL7Jnzx6WLFmSZZ2zszMTJ07EZDIRGBiIr68v06dPJyEhgUGDBjF58mRCQkLy3PfixYu5cOECW7duxc7Ojvnz5zNq1CjWrl0LwF9//YWPjw8///wzkZGRBAUF8fjjj9OyZUsWL17Mxo0bWbBgARUrVmTlypX06dOHH3/8EScnJ0aOHEmRIkX46aef+Pfff+nSpQuVKlWiY8eOmWqYOnUqhw4dYuXKlbi6uhIaGkrfvn3ZvHkze/fu5auvviIsLIwKFSrwyy+/0K9fP5o0aULp0qXzvN8pKf/3AfWPP/5g2rRpfPPNNzz22GOsXbuW9957jx9//JGgoCCOHj1KUlIS06dPz1UfRqMxz/Xltp9dNhXYFZsE3L/X6Uk+05g/sI7HX+LEpet3bPPPhWtsi4qjsUcZ83tJQb2niHVpvAsXjbf15OaYK2w94OLi4gCoUqVKtm0iIyM5evQoK1aswNHREUdHR/r160dQUBBjx47Nc99Xr16laNGiODg4UKRIEYKDgwkODjavt7Gx4Z133qFIkSLUq1ePJk2a8OOPP9KyZUtWr17Nm2++iYeHBwBvvPEGixcv5scff6RRo0b88MMPfPPNNzg7O+Ps7Mz06dMpUiTzyzU9PZ01a9YwY8YMypUrB8C7777L0qVL2b9/P9euXcPGxgYHBwcMBgNNmjRh9+7d2Njkbfas0Whk48aN7Nu3j/fffx+AevXq8csvv1C8eHEAWrduzfvvv8/ff/9Nw4YN89QPQExMTJ6fm2s2lQuuLxG5J9eMOZsWvvvwP5Qynjc/LtD3FLE6jXfhovG+vylsPeAypomkp6dn2yYuLo60tDQaNGiQaXlaWhqXL1/Oc9+vv/46QUFBNGvWjKZNm9KyZUtatGhhXu/u7p4pIFWsWNH8hhAbG8v48eOZMGGCeX16ejpnzpzh5MmTpKenU6lSJfM6Hx+fLP1fvHiRGzduEBwcnGm6TMZ2/P39qVWrFv7+/jRq1Ag/Pz8CAgJwcnLK8T4++eST5t9TUlKoWbMmc+bMoXbt2sDNYzhr1iz+97//cenSJXPb5OTkHPdxOx4eHjg6Ot7TNnLCaDRSP+Y0FSpUwN7e3uL9ifUlJSVx5swZjfkDygMXVu68e7t6Navj9f/PbMXExBTYe4pYl8a7cNF4W0/Gsc8Jha0HnLv7zbtOHT161Hx257/s7e1xcnJi7969+dp3pUqV2LRpExEREWzfvp3Ro0ezbt06QkNDgZtB5FYmk8kcihwcHBg3bhzPPfdclu0ePHgQuHOAzNgGwNdff20OP/81d+5cDh8+zPfff8+yZctYtGgRa9aswcXFJUf7+Oeff5o/kA4ePJhLly7h5+dnXj9r1iw2b97M3LlzqVmzJiaTiVq1auVo23fi6OiYq1B4L1xJpnIJpwLrT6wrIcGWhDMa8wdVlVLOjN78V5abY9yqehkXWnplvmarIN9TxPo03oWLxvv+prsRPuBKlixJ/fr1+eKLL7KsMxqNtG/fnuLFi5OQkGCecghw/fr1ezqrBXDjxg3S0tJ4+umnGTlyJKtWrWLLli3m7Z45c4bU1FRz+9OnT5sDoZubG0eOHMm0vZMnTwLw6KOPYmNjw/Hjx83rfv/9d7Zv356pvYuLCyVKlMh2OykpKVy/fp2aNWvyzjvvEB4ejsFg4Ndff83T/r7//vscOHCAb775xrwsMjKSFi1aUKtWLWxsbMxBUUTEEgwGA5Pb+GKTzc0vbAwGJrX21c0xRETuEwpbD4EPPviAffv2MWjQIM6ePUt6ejpRUVH06NEDBwcHfHx88PHxYfz48Vy6dImrV68yZswYhg4dek/99u/fn8mTJ3P9+nXS09PZu3cvJUqUwNXVFYDU1FQWLFhAcnIyf/75J7/88gv+/v4AvPbaayxbtox9+/aRlpbGpk2baN26NadPn6ZEiRK0aNGCWbNm8e+//3L69GlGjRpFfHx8lhpee+015syZQ3R0NCkpKXz55Zd06NABo9HIokWL6NmzJ2fPngUgOjqaK1eumM8G5laZMmUYPHgwkydP5vz5m9dCPProoxw+fBij0cg///zDggULcHFxMddqb2/PmTNnuHr1aqbgKSKSV+283Qnr5kf1MpnP0Fcv40JYNz99z5aIyH1E0wgfAjVr1iQsLIyZM2fSrl07EhISKF++PK1bt6Znz54ULVqUadOmMXbsWFq0aIGdnR2NGjVi0qRJ99RvSEgIo0ePxs/PD4PBwGOPPcasWbPMN6B47LHHSE1NpWnTpqSmphIUFMQzzzwDQIcOHThz5gx9+/bl+vXrVK1alc8++4yKFSsCMHHiRD744AOaN2+Os7MzgYGBvPrqq1lqCA4O5urVq7z++uukpKTg5eXF/PnzcXR05K233uL06dMEBgaSmJhIhQoVeO+99/Dy8srzPr/66quEh4czduxYZs6cSa9evRg4cCANGzbkscceY+LEiZQrV45x48ZRqlQp2rRpw//+9z+aN2/O+vXrzfsnInIv2nm7E1jbjZ+PnePMVSMVXR1pUqWszmiJiNxnDCZTNl/WIXIPZs6cyc8//0xYWJi1S3mgJCQkEBUVhZeXV4HMvy7o/sT6NOaFi8a7cNF4Fy4ab+vJzbHXNEIREREREREL0DRCua39+/fTuXPnbNdXrFiRLVu2FGBF+Wfz5s13vF7tqaeeYtGiRQVYkYiIiIg8jBS25Lbq1KlDZGRknp/fr18/+vXrl48V5Z8XXniBF154wdpliIiIiMhDTtMIRURERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAopYuwAREZHCyGQy8fOxc5y+mkDF4k40rVoWg8Fg7bJERCQfKWzJQ8/b25u5c+fSuHFji/Yzc+ZMfv75Z8LCwizaj4g8+NZGxjJs/R6iL14zL6tW2oXJbXxp5+1uxcpERCQ/KWyJRfn7+xMfH4+Nzc0Zq2XKlKFBgwb06NGD6tWrW6TPgwcPcuXKFZ5++mkAIiMjLdKPiEherI2MpePiHaSbTJmWR1+8RsfFOwjr5qfAJSLykFDYEosbOXIknTp1IiUlhdjYWFavXs3LL7/M3LlzadSoUb7398033+Dk5GQOW3JnV7DjrDENB1OKtUuRApCYmKYxtyKTycTgdbuzBK0M6SYTwzfsIbC2m6YUiog8BBS2pMAULVqUatWqMWzYMGxtbRk5ciRffPEFrVq1YtOmTVSrVg2AqVOn8tdff7FkyRIiIiLo3bs3AwYMIDQ0lIULF1K3bl2mTZvG+vXruXr1Kh4eHowYMYKnnnqKkJAQli9fjo2NDVu2bGHr1q14enoyf/58/Pz8SEpKYsqUKWzbto3Lly9Tu3ZtRo4ciZeXFwCenp7MnDmTL774gqioKNzc3Jg8eTK1atXK8X6uWLGCWbNmcf36dTp16sSwYcNyfayMRmOun5MXRqORXTYV2BWbBCQVSJ9yH9CYW83x+EucuHT9jm3+uXCNbVFxNPYoc8/9ZbyXFNR7iliXxrtw0XhbT26OucKWWMWbb77J/Pnz+ffff+/aNiUlhRMnTvDrr79ib2/Pt99+S3h4OKtXr+aRRx5hzpw59O/fn507dzJq1Cj+/vtvnnjiCd57770s25o+fTp//PEHS5cupUyZMkybNo1evXqxbds27OzsAFiwYAGTJk2iQoUK9O3bl+nTpzN//vwc7deJEye4cuUK27dv57fffuPtt9+mdevWPP7447k6PjExMblqf09sKhdcXyKF3DVjYo7a7T78D6WM5/Ot3wJ9TxGr03gXLhrv+5vCllhFmTJlKF68OL///vtd26akpPD666/j4OAAQJs2bWjRogUuLi4AvPTSS8ycOZPTp0/j5uZ2x22tXr2asWPHUqlSJQDeffddli5dyp49e2jYsCEAAQEBVK1aFbh5zdnChQtzvF9FihTh7bffxsbGhmbNmuHs7Mzx48dzHbY8PDxwdHTM1XPywmg0Uj/mNBUqVMDe3t7i/Yn1JSUlcebMGY25lXjgwsqdd29Xr2Z1vPLpzFZMTEyBvaeIdWm8CxeNt/VkHPucUNgSq0lNTcXW1jZHbStWrGj+3Wg0MmHCBHbs2MGVK1fMy5OTk++4jStXrnDt2jVzkAIoVqwYpUuX5tSpU+ZlGUEMwNHRkaSknE+1qlixovlmIAAODg53ret2HB0dcXJyyvXz8sKVZCqXcCqw/sS6EhJsSTijMbeWKqWcGb35r0x3Ifyv6mVcaOmVv9dsFeR7ilifxrtw0Xjf3/SlxmIVJ06cICEhgaZNm2ZZl5aWlmVZkSL/93eBjz76iIMHD7Js2TIiIyPZtGlTjvq8U+i59UPNvXzA0QXtInInBoOByW18scnmvcLGYGBSa1+9l4iIPCQUtsQqZs6cSY0aNShZsiQAiYn/dx1DXFzcHZ+7f/9+2rZti4eHBwaDgYMHD+aoz9KlS1OsWDGOHTtmXnblyhUuXryIu7tusywiBaOdtzth3fyoXsYl0/LqZVx023cRkYeMphFKgYqPj2fRokV8//33LF68mFKlSuHi4sJ3331HzZo1+e2339i3bx9VqlTJdhuVKlUiMjKS5ORkDh06xMaNGwE4d+4c1apVw97enpMnT3LlyhVcXV3Nz7OxsaF169Z8/vnn+Pr6Urx4caZOnYqbmxs+Pj4W33cRkQztvN0JrO3Gz8fOceaqkYqujjSpUlZntEREHjI6syUWN27cOLy9valduzZt27YlPj6eVatWUadOHWxtbRkzZgxr167lySefJDw8nM6dO99xe4MHDyY6Opr69eszffp0Ro0aRatWrQgODubgwYO0b9+eHTt28Oyzz2aZkjh8+HC8vLx45ZVXaN68OefPn+eLL77I8bVjIiL5xWAw4FetHK/6eNC0ajkFLRGRh5DBZMrmmxVFpMAlJCQQFRWFl5dXgVzsWtD9ifVpzAsXjXfhovEuXDTe1pObY68zWyIiIiIiIhaga7ZEcmDhwoXMmDEj2/UBAQGMGzeu4AoSERERkfuewpZIDgQFBREUFGTtMkRERETkAaJphCIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWkOewtXPnTvPvBw8eZPz48Xz99df5UpSIiIiIiMiDLk9ha968eQwfPhyAS5cu8eabb3L48GEWLFjAZ599lq8FioiIiIiIPIjyFLZWrVrFvHnzAFi3bh1ubm4sWbKEBQsWsG7dunwtUERECjeTycSO6Hi+3nucHdHxmEwma5ckIiKSI0Xy8qSLFy/y+OOPA/Drr7/y/PPPA+Dh4cH58+fzrzqRbPzxxx90796d3bt3Y2dnZ+1yRMRC1kbGMmz9HqIvXjMvq1bahcltfGnn7W7FykRERO4uT2e2XFxcuHTpEtevX+ePP/7g6aefBm5OKdQHXykITz31FJGRkXq9iTzE1kbG0nHxjkxBCyD64jU6Lt7B2shYK1UmIiKSM3k6s9WyZUveeustbGxsqFy5MrVr1yYpKYnx48fToEGD/K5RRCzoCnacNabhYEqxdilSABIT0x6IMTeZTAxet5v0bKYMpptMDN+wh8DabhgMhgKuTkREJGfyFLaGDx/Ol19+ybVr1+jcuTMA6enpXL58mUmTJuVrgWIdcXFxjBkzhr1791KiRAneeustunbtytmzZ/noo4/Ys2cPqamp+Pn5MWbMGEqUKEFERAS9e/dmwIABhIaGsnDhQnx8fFi6dCnLli3j9OnTVKpUiYEDB9KyZcsc1fH555+zYsUKLl26RPny5QkODiYgIICIiAi6du3K/v372bx5M6NGjTI/x2QykZKSwpIlS6hfvz6nTp0iJCSEvXv3kp6eTvPmzRk9ejTOzs537X/mzJkcOnQIX19fvvzyS5KTkwkICGDkyJEAvPHGGzRq1IhDhw6xc+dOKlWqRGhoKMuXLyc8PJxixYoREhKCn59fro6/0WjMVfu8MhqN7LKpwK7YJCCpQPqU+8ADMObH4y9x4tL1O7b558I1tkXF0dijTAFV9eDJeC8pqPcUsS6Nd+Gi8bae3BzzPIUtOzs73n777UzLHB0dWbRoUV42J/ehvn37Ur9+fWbNmkVMTAydO3emWrVqTJs2jerVq/P999+TmJjIgAEDGDNmDJ9++ikAKSkpnDhxgl9//RV7e3u+++47PvvsMxYsWEDNmjXZvn077777Lt999x0VK1a8Yw179uzhq6++IiwsjAoVKvDLL7/Qr18/mjRpkqldYGAggYGB5sdTp05l586d1K1bF5PJRHBwML6+vkyfPp2EhAQGDRrE5MmTCQkJydGx2LNnD3Xq1OGHH35g9+7dvPnmm7Rt25Y6deoAEBYWRmhoKJMmTaJz5850796d/v37M3ToUEJCQpgyZUquw1ZMTEyu2t8Tm8oF15dIDl0zJuao3e7D/1DKqGuF76ZA31PE6jTehYvG+/6Wp7AF8M033xAeHs7p06f5/vvvSU5O5ssvv8wSwuTBc+jQIY4cOcLixYtxdHTEy8uLzz77DEdHRw4ePMi8efNwdnbG2dmZt99+m3feeYfk5GTgZth6/fXXcXBwAGD16tV06NCB2rVrA/Dss89Sr149NmzYcNfXyrVr17CxscHBwQGDwUCTJk3YvXs3NjY2/PPPP7d9zs8//8yKFSv45ptvsLOzY//+/Rw9epQVK1bg6OiIo6Mj/fr1IygoiLFjx+Zo+pGtrS29evXCxsaGRo0aUapUKaKjo81hy9fX1/x7/fr1+eGHH2jfvj0AzZo1Izw8/O4H/T88PDxwdHTM9fNyy2g0Uj/mNBUqVMDe3t7i/Yn1JSUlcebMmft+zD1wYeXOu7erV7M6XjqzlS2j0UhMTEyBvaeIdWm8CxeNt/VkHPucyFPYWrJkCdOnT6ddu3b89ddfAFy+fJnly5cDKHA94GJjY3F2dqZEiRLmZU8//TRbt27F1dWVRx55xLzc3d2dlJQU4uPjzctuPWMVGxvLL7/8wuLFi83LTCYT1atXv2sdjRo1olatWvj7+9OoUSP8/PwICAjAycnptu3Pnz/PsGHDGDNmDB4eHsDN6ZBpaWlZriVMS0vj8uXLlCpV6q51VKxYERub/7uXjKOjI4mJ//dX9/Lly5t/t7e3p1y5cubHdnZ25iCaG46OjtnuZ35zJZnKJZwKrD+xroQEWxLO3P9jXqWUM6M3/5Xl5hi3ql7GhZZeumYrJwryPUWsT+NduGi87295CltLly5l9uzZNGzYkNWrVwNQrlw5Zs6cyYABAxS2HnA2Njakp6dnWX6n0HDrh50iRf7vZeXg4MDgwYPp3r17ruuws7Nj7ty5HD58mO+//55ly5axaNEi1qxZk6Vteno6Q4YM4ZlnnqFt27bm5fb29jg5ObF3795c95/h1qCVk/V3ay8id2cwGJjcxpeOi3fc9iYZNgYDk1r7KmiJiMh9LU+fCs+ePXvbuw4+/vjj+p6th4Cbmxs3btzg3Llz5mXbtm3jkUce4cqVK1y4cMG8/NixY1nO5tzK3d2dI0eOZFp2+vTpHH0paUpKCtevX6dmzZq88847hIeHYzAY+PXXX7O0nTt3LufPn890o4yM/hMSEoiLizMvu379OpcvX75r/yJiXe283Qnr5kf1Mi6Zllcv40JYNz99z5aIiNz38hS2ypYtS2xs1u83OXDgAK6urvdclFiXl5cXtWrVYsaMGdy4cYO///6bDz74AKPRaL5JRkJCAvHx8cyZM4eXXnqJokWL3nZbr776Kps2beLHH38kNTWV33//ndatW5unn97JokWL6NmzJ2fPngUgOjqaK1eu4O6e+QPWn3/+ycKFC5kxY0aWOcs1atTAx8eH8ePHc+nSJa5evcqYMWMYOnRoHo+OiBSkdt7uHB4ewA/Bz7K8S1N+fOdZDg8PUNASEZEHQp6/Z+vdd99lwIABmEwmDh48yIEDB5g9ezYvvfRSftcoVjB37lyGDh3K008/TenSpQkODqZZs2ZUrlyZkJAQnnnmGRwdHWnZsiXvvfdetttp3Lgxw4YNY+zYsVy4cIFKlSrx4YcfUrdu3bvW8NZbb3H69GkCAwNJTEykQoUKvPfee3h5eREREWFu980335CQkGC+KUWGPn36EBwczLRp0xg7diwtWrTAzs6ORo0a6SsKRB4gBoMBv2q3P3suIiJyPzOYcjKf6z+Sk5MZNWoU69evN1/bU6RIETp27Mjw4cOxs7PL90JFCoOEhASioqLw8vIqkItdC7o/sT6NeeGi8S5cNN6Fi8bbenJz7PP8PVuTJ09mxIgRnDhxAnt7e9zd3XXbSRERERERkf8vT2Grffv2rFmzBldXV/P3C4nk1pNPPklSUlK26//3v//x6KOPWqz/jOu8shMQEMC4ceMs1r+IiIiIPNzyFLaSkpL4+++/qVGjRn7XI4XIn3/+adX+g4KCCAoKsmoNIiIiIvLwylPY6tixIwMHDqRJkya4ublluhOdwWCgY8eO+VagiIiIiIjIgyhPYWvixInAzVtx/5fCloiIiIiISB7D1uHDh/O7DhERERERkYdKnr7UWERERERERO4sT2e2atasicFgyHZ9VFRUngsSERERERF5GOQpbI0ZMyZT2EpLS+P48eP89NNPBAcH51txIiIiIiIiD6o8ha1OnTrddvmzzz7LypUradeu3T0VJSIiIiIi8qDL12u2nnrqKX766af83KSIiIiIiMgDKV/D1vfff0+RInk6WSYiIiIiIvJQyVMyatKkSZZliYmJ3LhxI9sphiIiIiIiIoVJnsLWa6+9lmWZvb091apVw9/f/56LEhERERERedDlKWzVq1ePRo0aZVmemJjIxo0beemll+65MBERERERkQdZnq7Z6t27922XJyYm8sEHH9xTQSIiIiIiIg+DXJ3ZWrVqFatXryY5Ofm2UwnPnTtH8eLF8604ERERERGRB1Wuwpafnx+JiYlERkZSpUqVLOtr1apFQEBAvhUnIiIiIiLyoMpV2CpXrhxvvPEGZ86cYejQobdt8/fff+dLYSIiIiIiIg+yPF2zlRG00tPTSU5ONv/ExMTo1u8iIiIiIiLk8W6EcXFxDBkyhAMHDpCWlpZp3WOPPZYvhYmIiIiIiDzI8nRmKyQkBCcnJ0aOHImtrS0hISG8/PLL+Pj4sHTp0vyuUURERERE5IGTp7D1119/8emnn/Laa69ha2tLhw4dGDduHC+99BILFizI7xpFREREREQeOHkKW0lJSbi4uNzcgI0NSUlJAAQEBLBmzZr8q05EREREROQBlaewVaNGDRYtWkRaWhqVKlVi8+bNAFy6dAmj0ZivBYqIiIiIiDyI8hS2+vbtyyeffMKNGzd47bXXGDFiBK1bt6Z9+/Y0bdo0v2sUERERERF54OQpbPn5+fHDDz9QvHhxOnfuzMSJE6lXrx7BwcFMnDgxv2uUfLZmzRoaN24MQEREBJ6enuapoHJvbj22IoWdyWRiR3Q8X+89zo7oeEwmk7VLEhERKVB5uvU7wCOPPAJAamoqAQEBBAQE5FtRcn/btGkTS5cu5fDhw5hMJjw8POjQoQOdOnXCxiZP+d2iZs+ezZw5c4CbH/5SUlKws7Mzrw8JCSEwMDDP2//iiy944403KFIkz/87iTx01kbGMmz9HqIvXjMvq1bahcltfGnn7W7FykRERApOnj4Zp6enExoaSvPmzfH19QXAaDQyZswYkpOT87VAub989tlnhISE0LVrV3777Tf++OMPhg4dyldffcXw4cOtXd5tBQcHExkZSWRkJAsXLgTgzz//NC+7l6B16dIlJk+enOX75kQKs7WRsXRcvCNT0AKIvniNjot3sDYy1kqViYiIFKw8/Sl+5syZrFmzhm7dujFjxgwAEhIS2LdvH59++ilDhgzJzxrlDuLi4hgzZgx79+6lRIkSvPXWW3Tt2pVTp04REhLC3r17SU9Pp3nz5owePRpnZ+c89xUbG8usWbOYO3cuzZo1My9v1KgRs2fPpnXr1rRp04amTZsyc+ZMDhw4gKOjIzt27GDPnj1cunSJgQMHsnfvXqpUqcKgQYN4++23+f7776lUqRKRkZFMnDiRv//+Gzs7O1q1asXIkSMpWrQoERERBAcHM336dCZMmMDZs2epV68en3zyCa6urvd0DBMTE/n444/Zvn07//77L97e3owZM4bq1asDZHssExMTeeaZZzCZTDz55JN89NFH5m1u3bqVSZMmce7cOV544QXGjx9P0aJF76lOS7mCHWeNaTiYUqxdihSAxMQ0i465yWRi8LrdpGczZTDdZGL4hj0E1nbDYDDke/8iIiL3kzyFrW+//ZY5c+ZQq1YtPv30UwBKly7N9OnT6dq1q8JWAerbty/169dn1qxZxMTE0LlzZ6pVq8bHH3+Mr68v06dPJyEhgUGDBjF58mRCQkLy3NfWrVtxc3PLFLQyVKtWjcaNG7NlyxbzTVL27dvHgAEDmDZtGgAffPABKSkp7Nixg8uXLzN48OBM2xg4cCBt27ZlyZIlxMfH89prr1G9enXeeOMN4ObZ040bN7Jy5UqMRiMdOnQgLCyMnj175nmfAKZOncqhQ4dYuXIlrq6uhIaG0rdvX/NdNoODg7M9lgsXLqRr1678+eef2Nvbs2bNGm7cuMHu3btZv349MTExdOzYkZYtW/Lss8/muKaCuqun0Whkl00FdsUmAbpur9Cw4Jgfj7/EiUvX79jmnwvX2BYVR2OPMvnev2SW8V6iOwUXDhrvwkXjbT25OeZ5CluXLl2iVq1aWZZXrlyZK1eu5GWTkgeHDh3iyJEjLF68GEdHR7y8vPjss8+ws7Pj6NGjrFixAkdHRxwdHenXrx9BQUGMHTs2z/3FxsZSuXLlbNdXqVKFI0eOmB/b2trSqVMnDAYD6enp/Pzzz8yYMYMSJUpQokQJXn31VUaPHm1uHx4ejp2dHba2tlSsWJGnnnqKAwcOmNenpaXRo0cPXF1dcXV1pV69ehw7dizP+wM3p8SuWbOGGTNmUK5cOQDeffddli5dyv79+zEYDLk+lklJSfTr1w8nJydq1apF1apVOX78eK7qiomJuaf9yhWb7MdUJLeuGRNz1G734X8oZTxv4WokQ4G+p4jVabwLF433/S1PYatixYpERUXh5eWV6e5Sv/76q/nGGWJ5sbGxODs7U6JECfOyp59+mo0bN5KWlkaDBg0ytU9LS+Py5cv31Oedrk0ymUyZpgWVL1/e/Pjff/8lJSWFRx991Lze29s70/N///138xm61NRUUlNTef755zO1qVSpkvl3R0dHEhNz9sEuOxcvXuTGjRsEBwdnqj09PZ0zZ86QlpaW62NZsmRJihUrZn7s4OCQ62sZPTw8cHR0zNVz8sJoNFI/5jQVKlTA3t7e4v2J9SUlJXHmzBmLjbkHLqzcefd29WpWx0tntizOaDQSExNTYO8pYl0a78JF4209Gcc+J/IUttq2bcs777xDUFAQJpOJ7777jgMHDrBixQreeuutvGxS8sDGxob09PQsy+3t7XFycmLv3r352l+VKlXYsWNHllCV4fjx41StWtX8+Na782WE8luX3XrnwujoaAYMGMCwYcPo2LEjDg4ODBkyhNTU1Ex95PfdDh0cHAD4+uuvqV27dpb127Zty/WxzI/rUBwdHXFycrrn7eSEK8lULuFUYP2JdSUk2JJwxnJjXqWUM6M3/5Xl5hi3ql7GhZZeumarIBXke4pYn8a7cNF439/y9Mm1V69eBAYGEhoaSkpKCv3792fNmjX07t2b3r1753eNkg03Nzdu3LjBuXPnzMu2bdtG8eLFSUhIIC4uzrz8+vXr93xWq1WrVsTHx7Nt27Ys62JiYvjtt9948cUXb/vcEiVKYGtry+nTp83LIiMjzb9HRUVhZ2dH165dcXBwwGQyERUVdU/15oSLiwslSpTINP0R4OTJkwC4u7tb5FiKPKwMBgOT2/hik02QsjEYmNTaV0FLREQKhVyFrYEDBwI3/zHt378/v/32G3379uXPP/9k586dBAUF3Zffs/Sw8vLyolatWsyYMYMbN27w999/88EHH5CYmIiPjw/jx4/n0qVLXL16lTFjxjB06NB76u/RRx+lX79+DBs2jLVr15KYmEhqaiq//vorb731Fq+88gpPPfXUbZ9ra2vLk08+yRdffMG1a9c4fvw4q1atyrTtxMREoqKiuHLlClOmTMHOzo5z585Z/ItQX3vtNebMmUN0dDQpKSl8+eWXdOjQAaPRSI0aNe54LDPOjB0/fpyEhASL1inyoGjn7U5YNz+ql3HJtLx6GRfCuvnpe7ZERKTQyNU0wu3bt2d6bGNjw/z58+nbt2++FiU5N3fuXIYOHcrTTz9N6dKlCQ4Oxs/Pj2rVqjF27FhatGiBnZ0djRo1YtKkSffcX58+fXB3d2fJkiXmG0RUqVKF3r1707Fjxzs+d/z48QwYMIDGjRtTq1YtevXqRXBwMDY2Nvj4+NC5c2e6dOmCo6Mjffr0YcSIEfTp04eBAwfSqVOne649O8HBwVy9epXXX3+dlJQUvLy8mD9/vnn+87Rp07I9ll5eXvj4+NChQwcGDhxIyZIlLVanyIOknbc7gbXd+PnYOc5cNVLR1ZEmVcrqjJaIiBQqBlMuThvUqVOH/fv333WZSHaSk5Oxs7MDbt4Q46233uKvv/4yLyvsEhISzDefKYj51wXdn1ifxrxw0XgXLhrvwkXjbT25Ofa5mvN3u79I6q+UklMjRoygZ8+eXL16lWvXrvHFF1/w9NNPK2iJiIiIyEMpT3cjlIfP/v376dy5c7brK1asyJYtW+6pjyFDhjBmzBhatmyJwWCgXr16fPTRR/e0TYDevXvzyy+/ZLs+JCSEwMDAe+5HRERERCQ3FLYEuDkd9Na7A1pCyZIlCQ0Nzfftzp07N9+3KSIiIiJyr3IVtlJSUhg8ePBdl02bNu3eKxMREREREXmA5Sps1atXL9N3OmW3TEREREREpLDLVdhasmSJpeoQERERERF5qOgbiEVERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsoIi1CxARkfubyWTi52PnOH01gYrFnWhatSwGg8HaZYmIiNz3dGbrAbdmzRoaN24MQEREBJ6eniQlJVm5KhF5WKyNjMVz4rc0n/0dnZfupPns7/Cc+C1rI2OtXZqIiMh9T2HrPnDs2DEGDx7M008/zRNPPIG/vz/jxo3j33//tXZp94WZM2fi6enJtm3bsqzz9/cnIiLC3K5mzZp4e3ubfxo0aEBQUBCHDh0yPycxMZGxY8fi5+eHr68vr7zyCr/++qt5/cmTJwkODqZ+/fo0aNCAnj17cvz4cfP63377jVdeeQVfX1/8/Pz46KOPMBqN5vXLli3jueeeo27durRq1YqFCxda4rCIWNzayFg6Lt5B9MVrmZZHX7xGx8U7FLhERETuQtMIrSwqKorOnTvTqVMn1q1bR8mSJfn777+ZMGECnTp1Yu3atTg4OFi7TKsrWbIkEydOpGnTptjb22fbrk6dOoSFhZkfX716ldDQUHr06MH//vc/ihcvTmhoKH/++SdhYWGUKVOGsLAwgoOD+f777yldujTvvPMOTzzxBD/88AMmk4lRo0YxcOBAwsPDuXTpEsHBwQwZMoSOHTty4cIF3n77bUJDQxk2bBjbtm0jNDSU+fPnU7t2bfbs2UP37t2pXLkyLVu2LIhDlWtXsOOsMQ0HU4q1S5ECkJiYlqMxN5lMDF63m3ST6bbr000mhm/YQ2BtN00pFBERyYbClpWNHTuWJk2aMGTIEPMyLy8v5syZw/jx4zl37hy2traEhISwd+9e0tPTad68OaNHj8bZ2TnP/UZERBAcHMz06dOZMGECZ8+epV69enzyySe4urpiMpmYNm0a69ev5+rVq3h4eDBixAieeuopAN544w0aNWrEoUOH2LlzJ5UqVSI0NJTly5cTHh5OsWLFCAkJwc/PD4DDhw8zceJEDh48SJEiRWjdujXDhg2jaNGiOaq3WbNmnDhxgs8//5x+/frleD+LFy/O8OHDWblyJXv37qVZs2YcPHiQpk2bUr58eQBefvllPvroI44fP46LiwtdunTh2WefpVixYgC0bt2aAQMGYDKZOHbsGAkJCbRv354iRYpQvnx5/Pz8OHDgAABly5Zl+vTp1KlTB4Ann3ySatWqcfTo0VyFrVvPlFmS0Whkl00FdsUmAZp+WmjkYMyPx1/ixKXrd9zMPxeusS0qjsYeZfK5QMkvGe8lBfWeItal8S5cNN7Wk5tjrrBlRRcvXmTPnj0sWbIkyzpnZ2cmTpyIyWQiMDAQX19fpk+fTkJCAoMGDWLy5MmEhITcU/9Go5GNGzeycuVKjEYjHTp0ICwsjJ49e/Ltt98SHh7O6tWreeSRR5gzZw79+/dn586d2NraAhAWFkZoaCiTJk2ic+fOdO/enf79+zN06FBCQkKYMmUKfn5+GI1GevTowRtvvMH8+fOJj48nODiYhQsX0rt37xzVajAYGD16NJ07dyYwMBA3N7cc72d6ejqmW/4637x5c1auXMmrr75KuXLlWL16NWXLlqVWrVrY2dnxyiuvmNueOXOG5cuX8/zzz2MwGPDy8qJs2bIsX76czp07c/78eX766SdefvllAHPIAkhJSWHbtm3ExcXRvHnzHNcLEBMTk6v298SmcsH1JQ+Ma8bEHLXbffgfShnPW7gauVcF+p4iVqfxLlw03vc3hS0riouLA6BKlSrZtomMjOTo0aOsWLECR0dHHB0d6devH0FBQYwdO/ae+k9LS6NHjx64urri6upKvXr1OHbsGABt2rShRYsWuLi4APDSSy8xc+ZMTp8+bQ46vr6+5nBRv359fvjhB9q3bw/cPBMVHh4OwI8//ojJZKJXr14AuLm5ERQUxLx583IctgBq1apFYGAgEyZMYM6cOTl6zuXLl5k+fTolSpSgQYMGALz55ptERUXRqlUrAEqUKMGsWbNwcnLK9NzatWuTkpJCq1atzMe6WLFizJo1i7fffpvJkyebj023bt0yPXf27NnMnDmTEiVKMGnSJGrWrJnj/QTw8PDA0dExV8/JC6PRSP2Y01SoUOGO0zPl4ZGUlMSZM2fuOuYeuLBy5923V69mdbx0Zuu+ZTQaiYmJKbD3FLEujXfhovG2noxjnxMKW1aUcZ1Denp6tm3i4uJIS0szB4UMaWlpXL58+Z5rqFSpkvl3R0dHEhNv/jXbaDQyYcIEduzYwZUrV8xtkpOTzb9nTMMDsLe3p1y5cubHdnZ25rZxcXFcvHgRb29v83qTyYSdnV2u63333Xd57rnn+Omnn2jWrFmW9fv378/UT3JyMi1atGDJkiXma99mz57N4cOH2bx5MxUqVGDTpk307t2bdevWUbFiRfNzDxw4wNmzZ5k8eTJBQUEsW7aMf//9l+DgYIKDg+nQoQMXLlxg6NChTJo0iffff9/83ODgYHr06MHOnTt5//33KVq06G3rzY6jo2OW8GcpriRTuYRTgfUn1pWQYEvCmbuPeZVSzoze/FeWm2PcqnoZF1p66ZqtB0FBvqeI9Wm8CxeN9/1NdyO0Ind3dwCOHj2abRt7e3ucnJyIjIzM9HPo0CFKlSp1zzXY2Nz+JfDRRx9x8OBBli1bRmRkJJs2bbrrc7Pblr29PY899lim+g8cOMCePXtyXa+rqyuDBg1i/PjxmYJfhjp16pj7+OOPP3Bzc6N27dqZzh4uWbKEHj16ULVqVRwdHXn55ZepVKkSW7ZsybK98uXL8/7777Nnzx4OHjzI5s2bKVasGF27dsXJyQl3d3d69OjBqlWrsjzXzs4Of39/nnvuOZYvX57rfRWxJoPBwOQ2vthkE6RsDAYmtfZV0BIREbkDhS0rKlmyJPXr1+eLL77Iss5oNNK+fXuKFy9OQkKCecohwPXr1/PlrNad7N+/n7Zt2+Lh4YHBYODgwYN53pa7uztxcXHcuHHDvOzy5ctcv37ni++z06FDB1xcXO56S3UHBwfGjBnDnDlziI6ONi9PT08nLS0tU9uM4Hbs2DGaNWuW6fhmhMiiRYuSnp6e5UxkcnKy+QPnhx9+yNSpUzOtNxgMFCmik8jy4Gnn7U5YNz+ql3HJtLx6GRfCuvnRztvdSpWJiIg8GBS2rOyDDz5g3759DBo0iLNnz5Kenk5UVBQ9evTAwcEBHx8ffHx8GD9+PJcuXeLq1auMGTOGoUOHWrSuSpUqERkZSXJyMvv27WPjxo0AnDt3LtfbatKkCaVKlWLy5Mlcv36d8+fPM2DAgCyhJKdsbGwYM2YMn3/+OVevXr1j26ZNm9KqVStGjhxpDkn+/v4sXryYuLg4kpOTCQ8PJzY2lmbNmlG5cmVcXFwYN24cV69e5fr160ybNg13d3eqVq1KkyZNOHv2LMuXLyc5OZmzZ8+yePFi850G69evz/Lly4mIiCAtLY09e/awcePGXN8gQ+R+0c7bncPDA/gh+FmWd2nKj+88y+HhAQpaIiIiOaCwZWU1a9YkLCyM9PR02rVrh4+PD++++y4NGzZk0aJFFC1alGnTpmEymWjRogWtWrUiLS2NSZMmWbSuwYMHEx0dTf369Zk+fTqjRo2iVatWBAcH5/osV9GiRZk9ezbHjh2jcePGBAYG4uHhwbBhw/JcX506dXjxxRe5di3760kyvP/++/zzzz8sW7YMuBlwfXx86NKlCw0bNmTJkiXMmjWLqlWrYmtry7x580hISKBZs2a0aNGCCxcuMHfuXOzs7KhSpQpz585l7dq1NGzYkI4dO+Lp6cnIkSMBePHFFxkyZAjvv/8+Pj4+DB8+nN69e9OhQ4c876uItRkMBvyqleNVHw+aVi2nqYMiIiI5ZDCZsvnGShEpcAkJCURFReHl5VUgF7sWdH9ifRrzwkXjXbhovAsXjbf15ObY68yWiIiIiIiIBeiq/YfQ/v376dy5c7brK1aseNs771lDSEgIYWFh2a7v06cPwcHBBViRiIiIiEj+UNh6CGXc/vxBMGrUKEaNGmXtMkRERERE8p2mEYqIiIiIiFiAwpaIiIiIiIgFKGyJiIiIiIhYgMKWiIiIiIiIBShsiYiIiIiIWIDCloiIiIiIiAUobImIiIiIiFiAwpaIiIiIiIgFKGyJiIiIiIhYgMKWiIiIiIiIBShsiYiIiIiIWIDCloiIiIiIiAUobImIiIiIiFiAwpaIiIiIiIgFKGyJiIiIiIhYgMKWiIiIiIiIBShsiYiIiIiIWIDCloiIiIiIiAUobImIiIiIiFiAwpaIiIiIiIgFKGyJiIiIiIhYgMKWiIiIiIiIBRSxdgEiIpI7JpOJn4+d4/TVBCoWd6Jp1bIYDAZrlyUiIiL/8UCf2VqzZg2NGzcGICIiAk9PT5KSkqxc1d3dWndeeHp6smPHjnysqHDR8ZMH2drIWDwnfkvz2d/ReelOms/+Ds+J37I2MtbapYmIiMh/WD1sHTt2jMGDB/P000/zxBNP4O/vz7hx4/j333+tXZrcwZo1a/D09CQ0NDTLutTUVBo2bIi/v78VKhN5eK2NjKXj4h1EX7yWaXn0xWt0XLxDgUtEROQ+Y9VphFFRUXTu3JlOnTqxbt06SpYsyd9//82ECRPo1KkTa9euxcHBwZolyh2ULl2aDRs20L9//0zLd+7cqSlND5Ar2HHWmIaDKcXapcgdmEwmBq/bTbrJdNv16SYTwzfsIbC2m/7/ExERuU9YNWyNHTuWJk2aMGTIEPMyLy8v5syZw/jx4zl37hy2traEhISwd+9e0tPTad68OaNHj8bZ2TnP/UZERNCnTx+mTJnC+PHjuXz5Mt26daNFixZ88MEHxMXF0bhxY6ZPn07RokVJT09nzpw5hIeHEx8fT7Vq1Rg6dCiNGjUCwN/fn1deeYXVq1fTpEkTXnzxRbp3785nn33GuHHjOH/+PH5+fkyePJlixYqZ69i6dSuTJk3i3LlzvPDCC4wfPz5H/d0qKSmJKVOmsG3bNi5fvkzt2rUZOXIkXl5eAOzfv58hQ4Zw9uxZGjVqRJMmTVi0aBEbN26kcePGTJ06NdMZqG7duvHEE08waNCgux7HatWqcerUKfbt20fdunXNyzds2ECzZs3YtWuXeVlkZCQTJ07k77//xs7OjlatWjFy5EiKFi0K3AxoH3/8MbGxsVSuXJnhw4fTqFEjIiIi6N27NwMGDCA0NJSFCxfi4+PD119/zeLFizl9+jQVKlSgf//+vPjii+bx6Nq1Kz/99BO7d++mfPnyTJo0CV9fX3M958+fp1u3buzdu5dKlSoxY8YMatSoAcC6deuYO3cuZ86coWTJkvTo0YPXX38dgLS0NMaPH8/atWtxdXVlyJAhfPrpp/Tu3Zv27duTmJjIxx9/zPbt2/n333/x9vZmzJgxVK9e/a7H81ZGozFX7fPKaDSyy6YCu2KTgPt/Cm5hdjz+EicuXb9jm38uXGNbVByNPcpk2ybjtVVQrzGxLo134aLxLlw03taTm2NutbB18eJF9uzZw5IlS7Ksc3Z2ZuLEiZhMJgIDA/H19WX69OkkJCQwaNAgJk+eTEhIyD31bzQa+e2339i4cSNbtmxh+PDhHDlyhC+//JIrV67Qtm1btm/fznPPPceyZctYtWoV8+bNo0qVKixdupTg4GC2bdtG6dKlAdi4cSOLFi3C3d2dXbt2kZqaSnh4OGvWrCExMZG33nqLTz/9lBEjRgBw48YNdu/ezfr164mJiaFjx460bNmSZ599Nkf9ZZg+fTp//PEHS5cupUyZMkybNo1evXqxbds2AHr37k3btm159913iYiIYMSIEdjb2+Po6Mhzzz3H+vXrzWHr8uXL/PHHH4waNSrHx/H5559n/fr15rBlNBr5+eefGTduXKawNXDgQNq2bcuSJUuIj4/ntddeo3r16rzxxhvEx8fTr18/xo8fT6tWrVi/fj3vvPMO27dvByAlJYUTJ07w66+/Ym9vz/bt25kyZQrz5s3jiSeeYOvWrQwZMoRq1arh6ekJwBdffMGnn36Kl5cXoaGhvPPOO/z8888UKXLzJb9y5UomT57MI488QnBwMJ988glz584lLi6OYcOGsXDhQho1asTvv/9O9+7d8fX1pWbNmixZsoTNmzcTFhZGuXLlGDFiBOfOnTPv59SpUzl06BArV67E1dWV0NBQ+vbty+bNm3N1tiEmJibHbe+ZTeWC60vy7JoxMUftdh/+h1LG83dtV6CvMbE6jXfhovEuXDTe9zerha24uDgAqlSpkm2byMhIjh49yooVK3B0dMTR0ZF+/foRFBTE2LFj76n/9PR0Xn/9dRwdHfH398dkMvHcc89RqlQpSpUqRdWqVTlx4gQAq1ev5vXXXzd/kO/evTsLFizgxx9/5OWXXwagadOmVK6c+UNrUFAQrq6uuLq68tprr7F06VJz2EpKSqJfv344OTlRq1YtqlatyvHjx3PcX4bVq1czduxYKlWqBMC7777L0qVL2bNnD0WLFuXSpUv06dMHBwcHmjVrRsOGDdm7dy8AAQEB9OrVi+vXr+Ps7Mz3339PjRo1cnUWpm3btrz11lu8//77FClShO3bt1OvXj2KFy+eqV14eDh2dnbY2tpSsWJFnnrqKQ4cOADA5s2bcXNzM5+Zat++Pfb29qSnpwM3w9brr79unlK6evVqWrduzZNPPgnAiy++yKJFi9iyZYv5mPn7+5sDYK9evVi4cCF//fUX9erVM+97xmvP39+fFStWAFCpUiV+//13XF1dAWjUqBGlS5fm4MGD1KxZk59++onWrVvz2GOPATB48GC2bt0K3HxNrVmzhhkzZlCuXLlM47F//36eeOKJHB9XDw8PHB0dc9w+r4xGI/Vjbp4dtLe3t3h/knceuLBy593b1atZHa+7nNmKiYkpsNeYWJfGu3DReBcuGm/ryTj2OWG1sJXxV/6MD9S3ExcXR1paGg0aNMi0PC0tjcuXL99zDRUqVAAwf8jM+ICcsSzjzoYnT56kWrVqmZ7r7u7OqVOnzI8fffTRLNuvWrWq+feKFStmOgNSsmTJTFMKHRwcSE5OznF/AFeuXOHatWuZ+ilWrBilS5fm1KlTFCtWDGdnZ3NwAPD29jaHrQYNGlCqVCm2bdtGYGAgW7dupU2bNlkP1B3UrFmTsmXL8ssvv9CsWTM2bNhA27Zts7T7/fffmTVrFjExMaSmppKamsrzzz8PQGxsrDksZnjppZcyPa5YsaL595MnT9KwYcNM6ytXrpzp+Nwa4osXL46Li0um439rf/b29qSk3LxeyWAwsGLFClavXs25c+cwmUwkJyebx+b8+fM0a9YsUz8ZU1ovXrzIjRs3CA4OznQWKz09nTNnzuQqbDk6OuLk5JTj9vfClWQql3AqsP4kb6qUcmb05r+y3BzjVtXLuNDSK2fXbBXka0ysT+NduGi8CxeN9/3NamHL3d0dgKNHj2YKObeyt7fHycnJHA7ym42NzR0fZ8j4oP1ft36gsbW1zbI+LS0t2/Z3+jCUk/7u1C6jbXp6unnaXHY1tG3blvXr19OyZUsiIiLydMYwICCAdevWUbduXfbu3cuMGTPYt2+feX10dDQDBgxg2LBhdOzYEQcHB4YMGUJqaipw87jfKXQDmfYjJ8fnv9szmUw5Ov6rVq3i888/Z/bs2Tz11FPY2tpmClfp6enm68wyZLxuMs68ff3119SuXfuO+yOSWwaDgcltfOm4eMdtb5JhYzAwqbWvbo4hIiJyH7Hard9LlixJ/fr1+eKLL7KsMxqNtG/fnuLFi5OQkGCecghw/fr1fDmrlRvu7u4cO3bM/Dg1NZUTJ07g5uZ2x+fFxv7fbZhPnTqVbajMa3+lS5emWLFimdpeuXKFixcv4u7uTunSpbly5QrXr//fRfWRkZGZthEQEEBERARr1qzhiSeeyHGNt2rdujU7duxg8+bNNG/ePMt0tKioKOzs7OjatSsODg6YTCaioqLM6ytVqmSeQplh6dKlmcb9Vv89PnDzKwRuPT63HvuMY1C+fPm77ktkZCRPPvkkDRs2xNbWlvPnz2c6I5Zx1jDDiRMnuHr1KgAuLi6UKFGCI0eOZNrmyZMn79qvSE6083YnrJsf1cu4ZFpevYwLYd38aOftbqXKRERE5Has+j1bH3zwAfv27WPQoEGcPXuW9PR0oqKi6NGjBw4ODvj4+ODj48P48eO5dOkSV69eZcyYMQwdOrRA6wwICGD58uVER0eTnJzM3LlzSUtLu+v3SH355Zdcu3aNs2fPsnLlSpo3b56v/dnY2NC6dWs+//xzzp49S0JCAlOnTsXNzQ0fHx9q166No6Mj8+fPJzk5mR07dmS6aQXcnOro5eXFp59+musphBnKli1LnTp1+Pzzz2+7jUcffZTExESioqK4cuUKU6ZMwc7OzjxNr3Xr1pw5c4awsDCSk5PZuHEjn3zySaZplv89PuvXr2ffvn2kpKSwZs0ajh49mmnq4Q8//MDBgwdJSkpi3rx5lClTBm9v77vuy6OPPsqxY8e4cuUKp06dYty4cVSsWJH4+Hjg5tTL9evXc/z4ca5du8b06dMznbp/7bXXmDNnDtHR0aSkpPDll1/SoUMH3SlI8k07b3cODw/gh+BnWd6lKT++8yyHhwcoaImIiNyHrHrr95o1axIWFsbMmTNp164dCQkJlC9fntatW9OzZ0+KFi3KtGnTGDt2LC1atMDOzo5GjRoxadKkAq2ze/fuXL58mZ49e3L16lW8vLz46quvstwE4r9atGhBYGAg586do1mzZlm+jyo/+hs+fDghISG88sorJCcn4+PjwxdffIGtrS3FihVjxowZjBo1iq+++gp/f3/efPNNli1blmkbgYGBTJo0ieeeey7nB+U/AgIC+Pjjj7NcSwXg4+ND586d6dKlC46OjvTp04cRI0bQp08fBg4cyIwZM1i4cCFjxoxh/PjxeHh4MGvWLEqVKnXbvl566SVOnTrF0KFDuXDhAlWrVmXRokV4eHiY27z88stMnTrVfOv3zz777LZTPf+rU6dO7Nq1i2bNmvHoo4/y4YcfcuDAAWbMmMEjjzxCjx49iI6OJiAggAoVKvD++++za9cu81TC4OBgrl69yuuvv05KSgpeXl7Mnz9fF65KvjIYDPhVy/1ZaBERESlYBpMpm2/IlDyLiIiga9eu7N+/3+p3eMu4biwjaISGhvL777+zfPlyc5vQ0FDi4uKYMmWKVWrMb/7+/vTs2ZNOnTpZZPvJycnY2dkBN++UWLduXRYsWHDb70HLrYSEBKKiovDy8iqQi10Luj+xPo154aLxLlw03oWLxtt6cnPsrTqNUCzLZDLx/PPPM336dPN3VYWHh2e64cO+fftYsmQJQUFBVqz0wREeHk7z5s05fvw4KSkpzJs3DxcXlxxNURQRERGRwsWq0wgtYf/+/XTu3Dnb9RUrVmTLli0FWJH1GAwGpk+fzvjx46lfvz4uLi4899xzvPXWW8DN7wE7cuQIw4YNo2bNmubnLVy4kBkzZmS73YCAAMaNG2fp8u9Lbdu2JTo6mq5du3L9+nWqV6/OrFmzzLd/FxERERHJoGmEIvcRTSMUS9OYFy4a78JF4124aLytR9MIRURERERErExhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsQGFLRERERETEAhS2RERERERELEBhS0RERERExAIUtkRERERERCxAYUtERERERMQCFLZEREREREQsoIi1CxARkdszmUz8fOwcp68mULG4E02rlsVgMFi7LBEREckhndmSLNasWUPjxo0BiIiIwNPTk6SkJCtXJVK4rI2MxXPitzSf/R2dl+6k+ezv8Jz4LWsjY61dmoiIiOSQzmw9pI4dO8asWbP47bffuHHjBqVLl8bf35++fftSokQJa5f3wPH39yc+Ph4bm5t/nyhTpgwNGjSgR48eVK9ePdt2GcqXL8/WrVsLtGZ5cK2NjKXj4h2km0yZlkdfvEbHxTsI6+ZHO293K1UnIiIiOaWw9RCKioqic+fOdOrUiXXr1lGyZEn+/vtvJkyYQKdOnVi7di0ODg7WLvOBM3LkSDp16kRKSgqxsbGsXr2al19+mblz59KoUaMs7R4UV7DjrDENB1OKtUsRbk4dHLxud5aglSHdZGL4hj0E1nbTlEIREZH7nMLWQ2js2LE0adKEIUOGmJd5eXkxZ84cxo8fz7lz57C1tSUkJIS9e/eSnp5O8+bNGT16NM7OznnuNyIiguDgYKZPn86ECRM4e/Ys9erV45NPPsHV1RWTycS0adNYv349V69excPDgxEjRvDUU08B8MYbb9CoUSMOHTrEzp07qVSpEqGhoSxfvpzw8HCKFStGSEgIfn5+ABw+fJiJEydy8OBBihQpQuvWrRk2bBhFixa9a63Dhw/H0dGRtLQ01q9fT6lSpZgyZQp//vknX3zxBQBDhgyhffv2WZ5btGhRqlWrxrBhw7C1tWXkyJF899132Nra5vnY/ZfRaMy3bd2tn102FdgVmwRoquj94Hj8JU5cun7HNv9cuMa2qDgae5TJ9fYzXlsF9RoT69J4Fy4a78JF4209uTnmClsPmYsXL7Jnzx6WLFmSZZ2zszMTJ07EZDIRGBiIr68v06dPJyEhgUGDBjF58mRCQkLuqX+j0cjGjRtZuXIlRqORDh06EBYWRs+ePfn2228JDw9n9erVPPLII8yZM4f+/fuzc+dOc1AJCwsjNDSUSZMm0blzZ7p3707//v0ZOnQoISEhTJkyBT8/P4xGIz169OCNN95g/vz5xMfHExwczMKFC+ndu3eOat20aROTJk3igw8+oG/fvgwaNIhXXnmFn376iQULFjBhwgQCAwOzTAm81Ztvvsn8+fM5ePAgderUuadjd6uYmJh829Zd2VQuuL7krq4ZE3PUbvfhfyhlPJ/nfgr0NSZWp/EuXDTehYvG+/6msPWQiYuLA6BKlSrZtomMjOTo0aOsWLECR0dHHB0d6devH0FBQYwdO/ae+k9LS6NHjx64urri6upKvXr1OHbsGABt2rShRYsWuLi4APDSSy8xc+ZMTp8+jZubGwC+vr7m0FK/fn1++OEH89mlZs2aER4eDsCPP/6IyWSiV69eALi5uREUFMS8efNyHLY8PDxo3rw5AI0bNyYiIoKePXtiZ2dH8+bN+fTTT7l48SKPPPJIttsoU6YMxYsX5+TJk/katjw8PHB0dMy37WXHaDRSP+Y0FSpUwN7e3uL9yd154MLKnXdvV69mdbzyeGYrJiamwF5jYl0a78JF4124aLytJ+PY54TC1kMm4xqO9PT0bNvExcWRlpZGgwYNMi1PS0vj8uXL91xDpUqVzL87OjqSmHjzL/VGo5EJEyawY8cOrly5Ym6TnJxs/r18+fLm3+3t7SlXrpz5sZ2dnbltXFwcFy9exNvb27zeZDJhZ2eX4zr/21epUqXMz8/4b07uwpiamprp7Ne4ceOYMGFCpjYNGzZk/vz5Oa7N0dERJyenHLe/F64kU7mEU4H1J3dWpZQzozf/RfTFa9m2qV7GhZZe93bNVkG+xsT6NN6Fi8a7cNF4398Uth4y7u4371B29OjRTEHlVvb29jg5ObF3716L1JDdtLuPPvqII0eOsGzZMipXrkxcXBytWrW643Oz25a9vT2PPfYY69evz7c67zRdMDsnTpwgISGBqlWrmpc9aDfIkPuLwWBgchvf296NEMDGYGBSa1/dHENEROQBoO/ZesiULFmS+vXrm2/ycCuj0Uj79u0pXrw4CQkJ5imHANevX8+Xs1p3sn//ftq2bYuHhwcGg4GDBw/meVvu7u7ExcVx48YN87LLly9z/fqdbyyQ32bOnEmNGjWoUaNGgfYrD7d23u6EdfOjehmXTMurl3HRbd9FREQeIApbD6EPPviAffv2MWjQIM6ePUt6ejpRUVH06NEDBwcHfHx88PHxYfz48Vy6dImrV68yZswYhg4datG6KlWqRGRkJMnJyezbt4+NGzcCcO7cuVxvq0mTJpQqVYrJkydz/fp1zp8/z4ABA5g6dWp+l31b8fHxTJw4ke+//57x48cXSJ9SuLTzdufw8AB+CH6W5V2a8uM7z3J4eICCloiIyANEYeshVLNmTcLCwkhPT6ddu3b4+Pjw7rvv0rBhQxYtWkTRokWZNm0aJpOJFi1a0KpVK9LS0pg0aZJF6xo8eDDR0dHUr1+f6dOnM2rUKFq1akVwcHCuz3IVLVqU2bNnc+zYMRo3bkxgYCAeHh4MGzbMQtXfvBbL29ub2rVr07ZtW+Lj41m1alW+3hhD5FYGgwG/auV41ceDplXLaeqgiIjIA8ZgMmXzzZkiUuASEhKIiorCy8urQC52Lej+xPo05oWLxrtw0XgXLhpv68nNsdeZLREREREREQvQ3QglR/bv30/nzp2zXV+xYkW2bNlSgBVlLyQkhLCwsGzX9+nTh+Dg4AKsSEREREQKI4UtyZE6deoQGRlp7TJyZNSoUYwaNcraZYiIiIhIIadphCIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFlDE2gWIiOSFyWTi52PnOH01gYrFnWhatSwGg8HaZYmIiIiY6cxWIbdmzRoaN24MQEREBJ6eniQlJVm5KpE7WxsZi+fEb2k++zs6L91J89nf4TnxW9ZGxlq7NBEREREzha2HwLFjxxg8eDBPP/00TzzxBP7+/owbN45///3X2qXdF2bOnEnNmjXx9vbG29ubOnXq8NxzzzF37lzS0tIytd2wYQOvvfYaPj4+1KtXjw4dOvDNN99kapOSksLkyZOpWbMmO3bsyLQuMTGR8ePH4+fnx5NPPslbb73F33//bfF9LEzWRsbScfEOoi9ey7Q8+uI1Oi7eocAlIiIi9w1NI3zARUVF0blzZzp16sS6desoWbIkf//9NxMmTKBTp06sXbsWBwcHa5dpdXXq1CEsLAyA9PR0Dhw4QN++fbGxseHtt98GYMaMGYSFhTF27Fj8/PxITU3lxx9/ZPTo0Zw6dYr+/fuTkJBAt27dqF69OiaTKUs/U6ZMYe/evXz99deUKFGC8ePH07dvX7777rsC3d/cuIIdZ41pOJhSrF3KXZlMJgav2036bY49QLrJxPANewis7aYphSIiImJ1ClsPuLFjx9KkSROGDBliXubl5cWcOXMYP348586dw9bWlpCQEPbu3Ut6ejrNmzdn9OjRODs757nfiIgIgoODmT59OhMmTODs2bPUq1ePTz75BFdXV0wmE9OmTWP9+vVcvXoVDw8PRowYwVNPPQXAG2+8QaNGjTh06BA7d+6kUqVKhIaGsnz5csLDwylWrBghISH4+fkBcPjwYSZOnMjBgwcpUqQIrVu3ZtiwYRQtWjTXtdvY2FCnTh06derEd999x9tvv83x48eZO3cuc+fO5ZlnngHAzs6OF198EQcHByIjIwFISEjg5Zdf5rXXXmPNmjVZtu3s7MzQoUOpWLEiAN26dWP16tXEx8dTrly5HNdoNBpzvV95YTQa2WVTgV2xScD9P330ePwlTly6fsc2/1y4xraoOBp7lCmgqh4sGa+tgnqNiXVpvAsXjXfhovG2ntwcc4WtB9jFixfZs2cPS5YsybLO2dmZiRMnYjKZCAwMxNfXl+nTp5OQkMCgQYOYPHkyISEh99S/0Whk48aNrFy5EqPRSIcOHQgLC6Nnz558++23hIeHs3r1ah555BHmzJlD//792blzJ7a2tgCEhYURGhrKpEmT6Ny5M927d6d///4MHTqUkJAQpkyZgp+fH0ajkR49evDGG28wf/584uPjCQ4OZuHChfTu3TvP9aelpZlr2bZtG48++qg5aN3K398ff39/AMqUKcNrr72W7TYHDhyY6fGZM2ewt7enRIkSuaotJiYmV+3viU3lguvrHl0zJuao3e7D/1DKeN7C1TzYCvQ1Jlan8S5cNN6Fi8b7/qaw9QCLi4sDoEqVKtm2iYyM5OjRo6xYsQJHR0ccHR3p168fQUFBjB079p76T0tLo0ePHri6uuLq6kq9evU4duwYAG3atKFFixa4uLgA8NJLLzFz5kxOnz6Nm5sbAL6+vtSpUweA+vXr88MPP9C+fXsAmjVrRnh4OAA//vgjJpOJXr16AeDm5kZQUBDz5s3LU9hKS0vjwIEDrFy5kqCgIODmsfTw8MjzsbidK1euMH78eLp37469vX2unuvh4YGjo2O+1nM7RqOR+jGnqVChQq5rtAYPXFi58+7t6tWsjpfObN2W0WgkJiamwF5jYl0a78JF4124aLytJ+PY54TC1gMs45qU9PT0bNvExcWRlpZGgwYNMi1PS0vj8uXL91xDpUqVzL87OjqSmHjzzIPRaGTChAns2LGDK1eumNskJyebfy9fvrz5d3t7+0zT7Ozs7Mxt4+LiuHjxIt7e3ub1JpMJOzu7HNe5f/9+8/NtbGx49NFHeeutt+jatStw81je6Tjm1rlz5+jRowdeXl7069cv1893dHTEyckp3+q5E1eSqVzCqcD6uxdVSjkzevNfWW6OcavqZVxo6aVrtu6mIF9jYn0a78JF4124aLzvbwpbDzB3d3cAjh49mu31QPb29jg5ObF3716L1GBjc/sbWn700UccOXKEZcuWUblyZeLi4mjVqtUdn5vdtuzt7XnsscdYv359nuu89QYZt+Ph4cEPP/yAyWS65w/psbGxvPnmmzRr1oyRI0eapyrKvTMYDExu40vHxTtue5MMG4OBSa19FbRERETkvqBbvz/ASpYsSf369fniiy+yrDMajbRv357ixYuTkJBgnnIIcP369Xw5q3Un+/fvp23btnh4eGAwGDh48GCet+Xu7k5cXBw3btwwL7t8+TLXr9/5Rgm50bJlSy5cuMCGDRuyrNu5cycdO3YkNTX1rtu5dOkS3bt3p3379owZM0ZBywLaebsT1s2P6mVcMi2vXsaFsG5+tPN2t1JlIiIiIpkpbD3gPvjgA/bt28egQYM4e/Ys6enpREVF0aNHDxwcHPDx8cHHx4fx48dz6dIlrl69ypgxYxg6dKhF66pUqRKRkZEkJyezb98+Nm7cCNycXpdbTZo0oVSpUkyePJnr169z/vx5BgwYwNSpU/OtXjc3N4KDgxk1ahSrVq0iMTERo9HIhg0bGDhwIAEBARQpcvcTwZ988glPPPEEffv2zbfaJKt23u4cHh7AD8HPsrxLU35851kODw9Q0BIREZH7iqYRPuBq1qxJWFgYM2fOpF27diQkJFC+fHlat25Nz549KVq0KNOmTWPs2LG0aNECOzs7GjVqxKRJkyxa1+DBgxk6dCj169fniSee4OOPPwYgODiYpUuX5mpbRYsWZfbs2YwbN47GjRvj7OxMixYtGDZsWL7W3LdvX9zc3Fi6dCkTJkygaNGi1KhRgylTppjvUhgeHs6oUaPMzwkODsZgMBAQEMC4ceP45ptvsLW1zfK9WiEhIQQGBuZrvYWdwWDAr1rOb6cvIiIiUtAMptt9M6uIWEVCQgJRUVF4eXkVyMWuBd2fWJ/GvHDReBcuGu/CReNtPbk59ppGKCIiIiIiYgGaRihZ7N+/n86dO2e7vmLFimzZsqUAK8peSEjIHe8y2KdPH4KDgwuwIhERERGRmxS2JIs6deoQGRlp7TJyZNSoUZmuoRIRERERuV9oGqGIiIiIiIgFKGyJiIiIiIhYgMKWiIiIiIiIBShsiYiIiIiIWIDCloiIiIiIiAUobImIiIiIiFiAbv0uch9JT08HwGg0Fkh/Gf0UVH9ifRrzwkXjXbhovAsXjbf1ZBzzjM9td2IwmUwmSxckIjlz8eJFYmJirF2GiIiIiNyFh4cHpUuXvmMbhS2R+0hqaipXrlzB3t4eGxvN8hURERG536Snp5OUlISrqytFitx5oqDCloiIiIiIiAXoT+ciIiIiIiIWoLAlIiIiIiJiAQpbIiIiIiIiFqCwJSIiIiIiYgEKWyIiIiIiIhagsCUiIiIiImIBClsiIiIiIiIWoLAlIiIiIiJiAQpbIg+ZU6dO8fbbb9OgQQOaN2/OlClTSE9Pv23br776iueeew5fX186derEgQMHzOuSkpIYPXo0fn5+NGjQgP79+3P58uWC2g3Jhfwa8zfeeIPHH38cb29v80/btm0Lajckh3Iz3jdu3OC9997D09OT6OjoTOv+/fdf3n33XZ5++mmaNGnCBx98QGJiYkHsguRCfo23v78/tWvXzvT/d+/evQtiFyQXcjPeK1as4LnnnsPHx4eAgAC2bdtmXpeens706dNp0aIFTz31FEFBQcTFxRXUbsgtFLZEHjL9+vWjXLlybNu2jS+++IJt27axePHiLO22b9/OzJkz+fjjj/n1119p3rw5vXv3JiEhAYDp06dz8OBBVq5cyZYtWzCZTLz//vsFvTuSA/k15gAhISFERkaaf9atW1eQuyI5kNPxjo+Pp3379tja2t52O6NGjcJoNLJhwwa++eYboqOjmTp1qqXLl1zKr/EGWLhwYab/v+fOnWvJ0iUPcjreW7ZsYdq0aUyYMIFdu3bRpUsX3n33XXOgWrZsGevXr+fzzz/nhx9+wMPDg3feeQeTyVTQuyQmEXlo7N+/3+Tl5WX6999/zcuWL19ueu6557K0ffvtt00TJkwwP05LSzM1btzYtGHDBlNKSoqpXr16pm3btpnX//PPPyZPT0/T2bNnLbsTkiv5NeYmk8nUpUsX0zfffGP5oiXPcjPeUVFRpq1bt5ri4uJMNWrUMP3zzz/mdefPnzfVrFnTFBUVZV72008/merWrWtKTk627E5IjuXXeJtMJlPz5s1Nv//+u8VrlrzLzXiHh4ebli1blmlZ/fr1TevWrTOZTCbTSy+9ZFq8eLF53bVr10y1atUy7d271zLFS7Z0ZkvkIXLw4EEeffRRXF1dzcsef/xxjh8/zvXr17O0rVWrlvmxjY0NXl5eREZGEhsby7Vr13j88cfN66tVq4aDgwMHDx60/I5IjuXXmGfYtGkTL774Ij4+Prz55pvExsZafickx3Iz3jVr1qRly5a33U5UVBS2trZ4enpm2k5CQgLHjh2zTPGSa/k13hm++uorWrb8f+3deXBN9//H8WcisgghF6MtISqEVpK5DMq1JHatkqotC6OllthNTbXVCF8traWWJlo19q2LCGlVY6ozqUEEtbVjX9KSREfcENtN5P7+8HPHFUvCvQ36evzlnvO57/P+nPfcG++czzlpj9FoZNSoUVy4cMEpecujKUm9u3fvTmRkpO31pUuXuHLlCtWqVeP69escP37c7vu+fPny1KpVy+77Xv4darZEniFmsxkfHx+7bbe/tO++38psNtt9od8ee/HiRcxmM0CRWD4+Prpv6wnjqJrDrYa6bt26rF69ml9++QWDwcCgQYOwWCxOnIGUREnq/bA45cuXx8XF5bHiiHM5qt4ADRo0IDg4mA0bNrBp0ybMZjOjR492WK7y+B613larlYkTJxISEkLTpk3Jzc3FarU+8Pte/j1upZ2AiDiWtQTrsR82tiSxpPQ4quZxcXF2r6dMmUKzZs3Ys2cPzZs3f9T0xMEc9bnU5/vp4Kg6xcfH2/7t7e3NpEmTePXVV8nIyKBmzZoOOYY8vpLWOz8/nwkTJnD8+HGWL1/+WLHEOXRlS+QZYjAYbFelbjObzbi4uGAwGOy2+/r63nOswWCwjb17f25uLpUrV3Z02vIYHFXzeylfvjwVK1YkOzvbkSnLYyhJvR8WJy8vj5s3b9rFAfQZf4I4qt73Ur16dQDOnz//WHHEcUpa7+vXrzNkyBDOnTvHqlWrqFKlCgCVKlXC1dX1nrH0+f73qdkSeYY0bNiQzMxMcnJybNsOHjxIQEAA3t7eRcbeef/VzZs3+fPPPwkJCcHPz4+KFSva7T969CgWi4WGDRs6fyJSbI6qeV5eHnFxcXaNVU5ODjk5Ofj5+Tl/IlIsJan3gzRo0ACr1crhw4ft4vj4+FC7dm2H5iyPzlH1Pnv2LJMmTbJbEnz70fD6fD85SlJvq9XK2LFjcXNzY+nSpfj6+tr2eXh4ULduXbvv+0uXLpGRkUFwcLDzJyJ21GyJPENeeuklgoKCmDVrFnl5eZw4cYIlS5YQEREBQOfOndm9ezcAERERJCUlsW/fPq5du8aCBQtwd3cnNDSUMmXK0Lt3b7788ksyMzO5ePEis2fPpkOHDrbfnMmTwVE1L1++PPv372fq1KmYzWZyc3OZPHkygYGBGI3G0pyi3KEk9X4Qg8FAp06dmDNnDjk5OWRlZREfH0/Pnj1xc9MdBk8KR9W7cuXKbN26lenTp3P16lWys7OZNm0aYWFhVKtWzdnTkGIqSb2Tk5M5fvw4c+fOxcPDo0isiIgIli9fzokTJ8jLy2PmzJk0aNCAoKCgf3VOomZL5Jkzb948zp8/j8lkon///oSHh9ueWHTq1Cnb31Rq3bo148aNY8yYMTRt2pTt27ezcOFCPD09ARg1ahQhISF0796ddu3a4e3tzccff1xq85L7c1TN4+PjsVqtdOrUidDQUPLz81m4cCGurvpR8SQpbr0TEhIICgqic+fOwK2nlwUFBZGQkADcuievQoUKtGvXjm7duhEcHMzYsWNLZ1JyX46ot6enJ4sWLeLUqVO0bt2a1157DT8/Pz777LNSm5fcW3HrvW7dOs6ePUvTpk3t/lD1xIkTAejbty9vvPEG/fr1w2QykZWVxRdffFFq8/ovc7Hq7jkRERERERGH068rRUREREREnEDNloiIiIiIiBOo2RIREREREXECNVsiIiIiIiJOoGZLRERERETECdRsiYiIiIiIOIGaLRERERERESdQsyUiIiIiIuIEarZERESeMklJSQQFBWGxWIo1fv78+ZhMpgeOCQwMZM2aNY5IT0RE/p+aLREREScYOHAgERER990fGxtLWFgYN2/eLHHs8PBwDh48iLu7++Ok6FDFaehKy+7du9m+fXtppyEi/0FqtkRERJwgOjqavXv3cvjw4SL78vLySE5OJiIigjJlypRCdv8ty5YtU7MlIqVCzZaIiIgTtGnThpo1a7J69eoi+zZs2EBhYSG9e/fm9OnTDB06lMaNG2M0GunRowfbtm2zjZ0/fz7du3dn/vz5NGrUiM2bN5OYmEhgYCA3btwAeGiM23766Sc6duyI0Wikb9++HDly5L75f/PNN3Tr1g2j0YjJZGLKlClcu3at2POfMGECw4YNY/HixZhMJoxGI1OnTiUrK4u33noLo9FI586dSU9Pt70nMDCQZcuWERMTg9FopEmTJsyaNYvCwkLbmC1bttCjRw8aNWpEs2bNePfdd8nJyQHg77//JjAwkG+//Za2bdsSExNDr169SElJYfHixball1evXiUuLo7mzZsTHBxM+/btWbp0qe0YaWlpBAYGcuDAASIjIzEajbRt25akpCTbmIKCAubOnUtoaChGo5E+ffqQlpZm25+ZmcmoUaNo2bIlISEh9OzZUw2fyH+Qmi0REREncHV1JSoqiuTkZPLy8uz2rV27lq5du1KpUiVGjhxJ2bJlSU1NJS0tjZYtWzJy5EguXrxoG5+VlUVubi7bt2+nU6dORY5VnBiXLl0iJSWFtWvXkpqaSuXKlXnnnXcoKCgoEm/dunXMmDGD999/nz179rBixQrS09OJjY0t0TnYu3cvhYWF/Prrr0yaNIkVK1YwZswYPvjgA9LS0vDz82PatGl27/n666+JiooiPT2d2bNns3TpUtatWwfArl27GDlyJP3792fnzp2sW7eOkydPMmbMmCL5L1++nPj4eL777juqV6/O22+/bVt6OWvWLLZt28b69evZv38/EydOZNq0afz22292cebMmcMnn3xCeno6HTp04KOPPsJsNgO3muCNGzeyaNEi0tPT6dixI0OGDOHs2bNYLBYGDBiAh4cHycnJ7Nq1i65duzJ48GBOnDhRonMoIk83NVsiIiJO8uabbwLYXRFJT0/n6NGj9OvXD7jVeH366ad4e3vj7u5OeHg4V69e5ejRo7b35ObmMnz4cDw9PXFxcSlynOLEsFgsjB8/HoPBQIUKFYiJiSE7O5v9+/cXibdixQp69uxJ8+bNcXV15cUXX2T48OFs2rSp2A/lAHBzc2PgwIG4u7vbmsQWLVpQt25d3N3dCQ0N5fjx43bvCQsLw2Qy4ebmRqtWrTCZTPz8888ArFy5kubNmxMeHo67uzs1atQgJiaGtLQ0zp07Z4vRpUsXatSocc9zBfDee++RmJjIc889h4uLC6GhoVStWpV9+/bZjYuKisLf3x83Nze6du2KxWLhzJkzWK1W1q5dS3R0NAEBAbi5uTFgwAD+97//UaZMGVJTU8nIyCA2NhZfX188PDwYMGAA/v7+/PDDD8U+fyLy9HMr7QRERESeVRUqVCA8PNz2H3OANWvW0KRJE+rXrw/AgQMHiI+P58iRI3bL9G4vEQTw8fHB19f3vscpbowXXnjB9rpWrVrAreVudzt58iTHjh1j1apVdtutViuZmZm29z7M888/b2t4vLy8AOxy8PLysssRICAgwO51jRo12LlzJwBnzpzhlVdeuef4jIwMatSoAYCfn98D88rOzmbGjBns3r2by5cvA7ea0btzuXOe5cqVA+D69etcvHgRs9lsd5wyZcrw+uuvA7Bx40YKCwtp0aKFXTyr1crZs2cfmJuIPFvUbImIiDhRdHQ0q1evZteuXdSpU4eUlBRmzZoF3GoeBg8eTJ8+fZg3bx4Gg4GMjAw6dOhgF6Ns2bL3jV/cGK6u917M4uHhUWSbp6cngwcPZtCgQSWd7kOPeb88brvX0xlvN2x3N0OA7X6uO69iPeh8FRYWMmjQIKpUqcKaNWuoWbMmLi4utGnT5r7Hvdvth5rceS/ZnTw9PSlXrhy///77ffMQkf8GLSMUERFxojp16mAymUhMTGTjxo1UrVqV9u3bA3Do0CEsFgvDhg3DYDAAFFnK9jDFjWE2m/nnn39sr0+ePAncuvp0t9q1a/PHH3/YbcvNzSU3N7dEuT2K06dP273OyMiwXQ3z9/cv8lCPY8eO2fYVx4ULFzh9+jRRUVHUqlULFxcXMjMzyc7OLnaOFStWxNfXt8j9V8uWLePo0aPUrl2bq1evFtn/119/YbVai30cEXn6qdkSERFxsujoaLZs2UJiYqLd495r1qwJ3Hrwg8ViITU1lc2bNwP3Xt53L8WN4eHhwcyZM8nNzeXSpUvEx8fj7+/Pyy+/XCTmgAEDSElJYcOGDVgsFrKyshg9ejTjxo179JNQTFu3bmXHjh3k5+eTmprKjh076NKlCwARERHs3LmTpKQk8vPzOXPmDPHx8YSFhVGtWrX7xvTy8iIjI4PLly9TsWJFKlSowN69eykoKODIkSNMnjwZPz+/Yp9zgMjISFatWsWhQ4coKChgzZo1zJ49Gy8vL0wmE/Xq1SMuLo5z585RUFDAjz/+SJcuXdi7d+9jnyMReXpoGaGIiIiThYaGYjAYOHPmDL169bJtDwoKYsSIEUyePJmJEyfSokULpk6dipeXF1OnTi1W7OLGqFq1Kq1ataJHjx7k5ORQv359EhIS7rlUrkuXLuTk5JCQkMCHH36It7c37du3Z/z48Y9/Mh4iKiqKlStXEhMTQ9myZRk0aBDdu3cHbj1Of9q0aSxZsoTJkyfj6+tLu3btijyN8G6RkZHMnDmTsLAw1q9fz/Tp05k+fTrff/899erVIzY2lv379zNjxgzGjx9Pz549H5rniBEjcHFxYejQoVy5coWAgAC++uor231cCxYsYPr06XTr1o0bN25Qp04dPv/8cxo3bvzY50hEnh4uVl3PFhERkSdAYGAgcXFxRERElHYqIiIOoWWEIiIiIiIiTqBmS0RERERExAm0jFBERERERMQJdGVLRERERETECdRsiYiIiIiIOIGaLRERERERESdQsyUiIiIiIuIEarZEREREREScQM2WiIiIiIiIE6jZEhERERERcQI1WyIiIiIiIk7wf+YYS62dO1bmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_models_load['gbc']._final_estimator, 'feature')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}