06-regression-quantum-dots-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c772a64f-823c-4973-bf41-3c9a239c601a",
   "metadata": {},
   "source": [
    "# Group-based Regression 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 KFold\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.regressor import ResidualsPlot\n",
    "from yellowbrick.regressor import PredictionError\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.regression 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": "61725c0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "285b6541",
   "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','Surface_area_m2_g','Dose_microg_mL','Duration_h']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b175d88",
   "metadata": {},
   "source": [
    "# Quantum dots regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14e2baa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_group_name = \"quantum_dot\"\n",
    "group_name = \"quantum_dots\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6d7f7e",
   "metadata": {},
   "source": [
    "### Load the data and filter metal oxide instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da49b7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_df = pd.read_csv(\"output/datasets/universal/data_regression_train_df.csv\", encoding=\"utf-8\")\n",
    "data_test_df = pd.read_csv(\"output/datasets/universal/data_regression_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": "ce3fb75c",
   "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": "6edee9de",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_df = data_df[data_df['nanomaterial_group'] == group_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1e8494b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 20)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_group_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e5fc4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>QD</td>\n",
       "      <td>DHLA</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-26.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HaCat</td>\n",
       "      <td>Human</td>\n",
       "      <td>Skin</td>\n",
       "      <td>Keratinocyte</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>quantum_dots</td>\n",
       "      <td>86.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>QD</td>\n",
       "      <td>CyA</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HaCat</td>\n",
       "      <td>Human</td>\n",
       "      <td>Skin</td>\n",
       "      <td>Keratinocyte</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>quantum_dots</td>\n",
       "      <td>71.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>QD</td>\n",
       "      <td>PEI</td>\n",
       "      <td>26.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HaCat</td>\n",
       "      <td>Human</td>\n",
       "      <td>Skin</td>\n",
       "      <td>Keratinocyte</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>quantum_dots</td>\n",
       "      <td>58.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>QD</td>\n",
       "      <td>GSH</td>\n",
       "      <td>20.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>HaCat</td>\n",
       "      <td>Human</td>\n",
       "      <td>Skin</td>\n",
       "      <td>Keratinocyte</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>quantum_dots</td>\n",
       "      <td>101.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NP_type Coating  core_size_nm  hydro_size_nm  Surf_charge_mV  \\\n",
       "36        QD    DHLA           6.2            NaN           -26.2   \n",
       "637       QD     CyA           6.2            NaN            35.3   \n",
       "1906      QD     PEI          26.7            NaN            29.8   \n",
       "72        QD     GSH          20.9            NaN           -23.8   \n",
       "\n",
       "      Surface_area_m2_g   shape  Dose_microg_mL  Duration_h Cell_name  \\\n",
       "36                  NaN  Sphere             NaN        24.0     HaCat   \n",
       "637                 NaN  Sphere             NaN        24.0     HaCat   \n",
       "1906                NaN  Sphere             NaN        24.0     HaCat   \n",
       "72                  NaN  Sphere             NaN        24.0     HaCat   \n",
       "\n",
       "     Cell_species cell_Organ Cell_morphology Cell_age cell_type   sex Assay  \\\n",
       "36          Human       Skin    Keratinocyte    Adult    Normal  male   MTT   \n",
       "637         Human       Skin    Keratinocyte    Adult    Normal  male   MTT   \n",
       "1906        Human       Skin    Keratinocyte    Adult    Normal  male   MTT   \n",
       "72          Human       Skin    Keratinocyte    Adult    Normal  male   MTT   \n",
       "\n",
       "       Test_indicator nanomaterial_group  Viability  \n",
       "36    TetrazoliumSalt       quantum_dots      86.09  \n",
       "637   TetrazoliumSalt       quantum_dots      71.09  \n",
       "1906  TetrazoliumSalt       quantum_dots      58.59  \n",
       "72    TetrazoliumSalt       quantum_dots     101.72  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_group_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8e0454",
   "metadata": {},
   "source": [
    "### Create training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0b7eb48e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_df = f.quantile_discretize(data_group_df, 'Viability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94bc2dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 21)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_group_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d3b9c5b7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "IntProgress(value=0, description='Processing: ', max=3)"
      ]
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     "output_type": "display_data"
    },
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     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>17:44:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Preprocessing Data</td>\n",
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       "                                                                  \n",
       "                                                                  \n",
       "Initiated  . . . . . . . . . . . . . . . . . .            17:44:46\n",
       "Status     . . . . . . . . . . . . . . . . . .  Preprocessing Data"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [17]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m splitter \u001b[38;5;241m=\u001b[39m \u001b[43msetup\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdata_group_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mViability_discrete\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mtrain_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.8\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mdata_split_stratify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                 \u001b[49m\u001b[38;5;66;43;03m# don't change the following\u001b[39;49;00m\n\u001b[1;32m      6\u001b[0m \u001b[43m                 \u001b[49m\u001b[43msession_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m123\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m                 \u001b[49m\u001b[43muse_gpu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mlog_experiment\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m                 \u001b[49m\u001b[43msilent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mexperiment_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msplitter\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/pycaret/regression.py:593\u001b[0m, in \u001b[0;36msetup\u001b[0;34m(data, target, train_size, test_data, preprocess, imputation_type, iterative_imputation_iters, categorical_features, categorical_imputation, categorical_iterative_imputer, ordinal_features, high_cardinality_features, high_cardinality_method, numeric_features, numeric_imputation, numeric_iterative_imputer, date_features, ignore_features, normalize, normalize_method, transformation, transformation_method, handle_unknown_categorical, unknown_categorical_method, pca, pca_method, pca_components, ignore_low_variance, combine_rare_levels, rare_level_threshold, bin_numeric_features, remove_outliers, outliers_threshold, remove_multicollinearity, multicollinearity_threshold, remove_perfect_collinearity, create_clusters, cluster_iter, polynomial_features, polynomial_degree, trigonometry_features, polynomial_threshold, group_features, group_names, feature_selection, feature_selection_threshold, feature_selection_method, feature_interaction, feature_ratio, interaction_threshold, transform_target, transform_target_method, data_split_shuffle, data_split_stratify, fold_strategy, fold, fold_shuffle, fold_groups, n_jobs, use_gpu, custom_pipeline, html, session_id, log_experiment, experiment_name, experiment_custom_tags, log_plots, log_profile, log_data, silent, verbose, profile, profile_kwargs)\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m log_plots \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    591\u001b[0m     log_plots \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresiduals\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124merror\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 593\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpycaret\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtabular\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    594\u001b[0m \u001b[43m    \u001b[49m\u001b[43mml_usecase\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mregression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m    \u001b[49m\u001b[43mavailable_plots\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mavailable_plots\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    598\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    599\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtest_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    600\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    601\u001b[0m \u001b[43m    \u001b[49m\u001b[43mimputation_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimputation_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    602\u001b[0m \u001b[43m    \u001b[49m\u001b[43miterative_imputation_iters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miterative_imputation_iters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    603\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcategorical_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcategorical_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    604\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcategorical_imputation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcategorical_imputation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    605\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcategorical_iterative_imputer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcategorical_iterative_imputer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    606\u001b[0m \u001b[43m    \u001b[49m\u001b[43mordinal_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mordinal_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    607\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhigh_cardinality_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhigh_cardinality_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    608\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhigh_cardinality_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhigh_cardinality_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    609\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnumeric_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    610\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnumeric_imputation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_imputation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    611\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnumeric_iterative_imputer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_iterative_imputer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    612\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    613\u001b[0m \u001b[43m    \u001b[49m\u001b[43mignore_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    614\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    615\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnormalize_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    616\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtransformation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransformation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    617\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtransformation_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransformation_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    618\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhandle_unknown_categorical\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhandle_unknown_categorical\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    619\u001b[0m \u001b[43m    \u001b[49m\u001b[43munknown_categorical_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munknown_categorical_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    620\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpca\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpca\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    621\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpca_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpca_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpca_components\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpca_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[43m    \u001b[49m\u001b[43mignore_low_variance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_low_variance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    624\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcombine_rare_levels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcombine_rare_levels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    625\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrare_level_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrare_level_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    626\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbin_numeric_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbin_numeric_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    627\u001b[0m \u001b[43m    \u001b[49m\u001b[43mremove_outliers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremove_outliers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    628\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutliers_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutliers_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    629\u001b[0m \u001b[43m    \u001b[49m\u001b[43mremove_multicollinearity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremove_multicollinearity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    630\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmulticollinearity_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmulticollinearity_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    631\u001b[0m \u001b[43m    \u001b[49m\u001b[43mremove_perfect_collinearity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremove_perfect_collinearity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    632\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_clusters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_clusters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    633\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcluster_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcluster_iter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpolynomial_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpolynomial_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpolynomial_degree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpolynomial_degree\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    636\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrigonometry_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrigonometry_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpolynomial_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpolynomial_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    638\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    639\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    640\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeature_selection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_selection\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    641\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeature_selection_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_selection_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    642\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeature_selection_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_selection_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    643\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeature_interaction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_interaction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    644\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeature_ratio\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_ratio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    645\u001b[0m \u001b[43m    \u001b[49m\u001b[43minteraction_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minteraction_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    646\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtransform_target\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransform_target\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtransform_target_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransform_target_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    648\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_split_shuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_split_shuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    649\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_split_stratify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_split_stratify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    650\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfold_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfold_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    651\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    652\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfold_shuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfold_shuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    653\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfold_groups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfold_groups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    654\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    655\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_gpu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_gpu\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    656\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcustom_pipeline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_pipeline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    657\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhtml\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhtml\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    658\u001b[0m \u001b[43m    \u001b[49m\u001b[43msession_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    659\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlog_experiment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_experiment\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    660\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexperiment_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexperiment_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    661\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexperiment_custom_tags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexperiment_custom_tags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    662\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlog_plots\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_plots\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    663\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlog_profile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_profile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    664\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlog_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    665\u001b[0m \u001b[43m    \u001b[49m\u001b[43msilent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msilent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    666\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    667\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprofile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprofile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprofile_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprofile_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/pycaret/internal/tabular.py:1397\u001b[0m, in \u001b[0;36msetup\u001b[0;34m(data, target, ml_usecase, available_plots, train_size, test_data, preprocess, imputation_type, iterative_imputation_iters, categorical_features, categorical_imputation, categorical_iterative_imputer, ordinal_features, high_cardinality_features, high_cardinality_method, numeric_features, numeric_imputation, numeric_iterative_imputer, date_features, ignore_features, normalize, normalize_method, transformation, transformation_method, handle_unknown_categorical, unknown_categorical_method, pca, pca_method, pca_components, ignore_low_variance, combine_rare_levels, rare_level_threshold, bin_numeric_features, remove_outliers, outliers_threshold, remove_multicollinearity, multicollinearity_threshold, remove_perfect_collinearity, create_clusters, cluster_iter, polynomial_features, polynomial_degree, trigonometry_features, polynomial_threshold, group_features, group_names, feature_selection, feature_selection_threshold, feature_selection_method, feature_interaction, feature_ratio, interaction_threshold, fix_imbalance, fix_imbalance_method, transform_target, transform_target_method, data_split_shuffle, data_split_stratify, fold_strategy, fold, fold_shuffle, fold_groups, n_jobs, use_gpu, custom_pipeline, html, session_id, log_experiment, experiment_name, experiment_custom_tags, log_plots, log_profile, log_data, silent, verbose, profile, profile_kwargs, display)\u001b[0m\n\u001b[1;32m   1393\u001b[0m _stratify_columns \u001b[38;5;241m=\u001b[39m _get_columns_to_stratify_by(\n\u001b[1;32m   1394\u001b[0m     X_before_preprocess, y_before_preprocess, stratify_param, target\n\u001b[1;32m   1395\u001b[0m )\n\u001b[1;32m   1396\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m test_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1397\u001b[0m     X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_test_split\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1398\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX_before_preprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1399\u001b[0m \u001b[43m        \u001b[49m\u001b[43my_before_preprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1400\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtrain_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1401\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstratify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_stratify_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1402\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1403\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_split_shuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1404\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1405\u001b[0m     train_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([X_train, y_train], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m   1406\u001b[0m     test_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([X_test, y_test], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/sklearn/model_selection/_split.py:2152\u001b[0m, in \u001b[0;36mtrain_test_split\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m   2146\u001b[0m         CVClass \u001b[38;5;241m=\u001b[39m ShuffleSplit\n\u001b[1;32m   2148\u001b[0m     cv \u001b[38;5;241m=\u001b[39m CVClass(test_size\u001b[38;5;241m=\u001b[39mn_test,\n\u001b[1;32m   2149\u001b[0m                  train_size\u001b[38;5;241m=\u001b[39mn_train,\n\u001b[1;32m   2150\u001b[0m                  random_state\u001b[38;5;241m=\u001b[39mrandom_state)\n\u001b[0;32m-> 2152\u001b[0m     train, test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43marrays\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstratify\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2154\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(chain\u001b[38;5;241m.\u001b[39mfrom_iterable((_safe_indexing(a, train),\n\u001b[1;32m   2155\u001b[0m                                  _safe_indexing(a, test)) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m arrays))\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/sklearn/model_selection/_split.py:1341\u001b[0m, in \u001b[0;36mBaseShuffleSplit.split\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m   1311\u001b[0m \u001b[38;5;124;03m\"\"\"Generate indices to split data into training and test set.\u001b[39;00m\n\u001b[1;32m   1312\u001b[0m \n\u001b[1;32m   1313\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1338\u001b[0m \u001b[38;5;124;03mto an integer.\u001b[39;00m\n\u001b[1;32m   1339\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1340\u001b[0m X, y, groups \u001b[38;5;241m=\u001b[39m indexable(X, y, groups)\n\u001b[0;32m-> 1341\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train, test \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_indices(X, y, groups):\n\u001b[1;32m   1342\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m train, test\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/sklearn/model_selection/_split.py:1668\u001b[0m, in \u001b[0;36mStratifiedShuffleSplit._iter_indices\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m   1666\u001b[0m class_counts \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mbincount(y_indices)\n\u001b[1;32m   1667\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmin(class_counts) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m-> 1668\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe least populated class in y has only 1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1669\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m member, which is too few. The minimum\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1670\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m number of groups for any class cannot\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1671\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m be less than 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1673\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_train \u001b[38;5;241m<\u001b[39m n_classes:\n\u001b[1;32m   1674\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe train_size = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m should be greater or \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1675\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mequal to the number of classes = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1676\u001b[0m                      (n_train, n_classes))\n",
      "\u001b[0;31mValueError\u001b[0m: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2."
     ]
    }
   ],
   "source": [
    "splitter = setup(data = data_group_df, target = 'Viability_discrete',\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": null,
   "id": "22c4611b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_train_df = get_config(variable=\"X_train\").copy()\n",
    "#data_group_train_df['Viability'] = get_config(variable=\"y_train\").copy()\n",
    "\n",
    "data_group_test_df = get_config(variable=\"X_test\").copy()\n",
    "#data_group_test_df['Viability'] = get_config(variable=\"y_test\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fecd13a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data_group_train_df.shape)\n",
    "print(data_group_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e38fadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "f.plot_distribution_train_test(data_group_train_df, data_group_test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4881a22b",
   "metadata": {},
   "source": [
    "### Save cleaned datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e2f7ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_train_df.to_csv('output/datasets/'+model_group_name+'/data_regression_train_df.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab995e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_test_df.to_csv('output/datasets/'+model_group_name+'/data_regression_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": null,
   "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": null,
   "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": null,
   "id": "cb2a02f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "stup = setup(data = data_group_train_df, target = 'Viability', \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",
    "        transform_target = False, \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",
    "        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": null,
   "id": "ebe27ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(get_config(variable=\"X_train\").shape)\n",
    "print(get_config(variable=\"y_train\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34f3d922",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(get_config(variable=\"X\").shape)\n",
    "print(get_config(variable=\"y\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "id": "1b417175",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(get_config(variable=\"X_test\").shape)\n",
    "print(get_config(variable=\"y_test\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4387c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load_config('output/models/universal/regression_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "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": null,
   "id": "43f293b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_transformed_df.to_csv('output/datasets/'+model_group_name+'/transformed/data_regression_train_transformed_df.csv', encoding='utf-8', index=False)\n",
    "data_test_transformed_df.to_csv('output/datasets/'+model_group_name+'/transformed/data_regression_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 8 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8922c9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = ['lr', 'lasso', 'ridge', 'svm', 'rf', 'et', 'gbr', 'lightgbm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "id": "c2a73417",
   "metadata": {},
   "outputs": [],
   "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+'/' + 'regression_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+'/' + 'regression_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": null,
   "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": null,
   "id": "3c21760e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a9c93d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f052eebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_outer = KFold(n_splits=10, shuffle=True, random_state=cv_outer_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "564a8934",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "    scores = cross_val_score(tuned_models[model_name], X, y, cv=cv_outer)\n",
    "    scores = np.round(scores, 2);\n",
    "    outer_results[model_name] = scores.copy()\n",
    "    \n",
    "    with open('output/models_results/'+model_group_name+'/' + 'regression_model_'+model_name+'_tuned_outer_cross_validation_results.tsv', 'w', newline='') as file:\n",
    "        for score in scores:\n",
    "            file.write(str(score) + \"\\n\")\n",
    "        \n",
    "    print(model_name + \" scores: \" + ', '.join(scores.astype('str')))\n",
    "    print(model_name + \" mean/std: \" + str(np.round(scores.mean(),2)) + \" / \" + str(np.round(scores.std(), 2)))\n",
    "\n",
    "    #with open(the_filename, 'r') as f:\n",
    "    #    my_list = [line.rstrip('\\n') for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c1604eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "    scores = cross_validate(tuned_models[model_name], X, y, cv=cv_outer, scoring=('r2','neg_root_mean_squared_error','neg_mean_absolute_error'))\n",
    "    scores_r2 = np.round(scores['test_r2'], 2);\n",
    "    scores_rmse = np.round(scores['test_neg_root_mean_squared_error'], 2);\n",
    "    scores_mae = np.round(scores['test_neg_mean_absolute_error'], 2);\n",
    "    outer_results[model_name] = scores.copy()\n",
    "    \n",
    "    with open('output/models_results/'+model_group_name+'/' + 'regression_model_'+model_name+'_tuned_outer_cross_validation_results.tsv', 'w', newline='') as file:\n",
    "        file.write('R2\\tRMSE\\tMAE' + \"\\n\")\n",
    "        for idx, x in enumerate(scores_r2):\n",
    "            file.write(str(scores_r2[idx]) + '\\t' + str(scores_rmse[idx]) + '\\t' + str(scores_mae[idx]) + \"\\n\")\n",
    "\n",
    "    print(model_name + \" scores: \" + ', '.join(scores_r2.astype('str')))\n",
    "    print(model_name + \" mean/std: \" + str(np.round(scores_r2.mean(),2)) + \" / \" + str(np.round(scores_r2.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": null,
   "id": "bfae263b",
   "metadata": {},
   "outputs": [],
   "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+'/' + 'regression_model_'+model_name+'_hyperparams.tsv', \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")\n",
    "    \n",
    "    model_predict = predict_model(final_model, data=data_group_test_df, 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+'/' + 'regression_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": null,
   "id": "f7dbf7e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "    save_config('output/models/'+model_group_name+'/regression_config.cfg')\n",
    "    save_model(tuned_models[model_name], 'output/models/'+model_group_name+'/regression_model_'+model_name+'_tuned.mdl')\n",
    "    save_model(final_models[model_name], 'output/models/'+model_group_name+'/regression_model_'+model_name+'_final.mdl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8dee9d",
   "metadata": {},
   "source": [
    "### Create Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "id": "44efd5ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in ['rf', 'et', 'gbr', 'lr', 'lasso', 'ridge']:\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+'/' + 'regression_model_'+model_name+'_feature_importance_plot.png',dpi = 300)\n",
    "\n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4489866",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer2 = ResidualsPlot(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+'/' + 'regression_model_'+model_name+'_residuals_plot.png',dpi = 300)\n",
    "    \n",
    "    plt.clf() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a279e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer3 = PredictionError(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+'/' + 'regression_model_'+model_name+'_error_plot.png',dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2eafd5",
   "metadata": {},
   "source": [
    "### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2406f629",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = ['lr', 'lasso', 'ridge', 'svm', 'rf', 'et', 'gbr', 'lightgbm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f5acd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_config = load_config('output/models/'+model_group_name+'/regression_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7503a083",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_models_load = dict()\n",
    "final_models_load = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af29b475",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "    tuned_models_load[model_name] = load_model('output/models/'+model_group_name+'/regression_model_'+model_name+'_tuned.mdl')\n",
    "    final_models_load[model_name] = load_model('output/models/'+model_group_name+'/regression_model_'+model_name+'_final.mdl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acba0082",
   "metadata": {},
   "outputs": [],
   "source": [
    "vars(final_models_load['lightgbm']._final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41265456",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(tuned_models_load['lightgbm']._final_estimator, 'feature')"
   ]
  }
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