08-classification-metal-oxide-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": "eeb43bcc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6014a35",
   "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": "ea5e030a",
   "metadata": {},
   "source": [
    "# Metal Oxide classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a92e9968",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_group_name = \"oxide\"\n",
    "group_name = \"meta_oxide\""
   ]
  },
  {
   "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": [
       "(2544, 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>1</th>\n",
       "      <td>TiO2</td>\n",
       "      <td>None</td>\n",
       "      <td>30.6</td>\n",
       "      <td>266.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>2.50</td>\n",
       "      <td>24.0</td>\n",
       "      <td>WISH</td>\n",
       "      <td>Human</td>\n",
       "      <td>Amniotic-membrane</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>fetus</td>\n",
       "      <td>cancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>meta_oxide</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IronOxide</td>\n",
       "      <td>PEG_based</td>\n",
       "      <td>35.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>6.25</td>\n",
       "      <td>48.0</td>\n",
       "      <td>SKOV­3</td>\n",
       "      <td>Human</td>\n",
       "      <td>Ovary</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Young</td>\n",
       "      <td>cancer</td>\n",
       "      <td>female</td>\n",
       "      <td>MTS</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>meta_oxide</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ZnO</td>\n",
       "      <td>None</td>\n",
       "      <td>141.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>10.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>SIRC</td>\n",
       "      <td>Rabbit</td>\n",
       "      <td>Cornea</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>meta_oxide</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TiO2</td>\n",
       "      <td>None</td>\n",
       "      <td>40.0</td>\n",
       "      <td>322.3</td>\n",
       "      <td>-25.27</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>L-02</td>\n",
       "      <td>Human</td>\n",
       "      <td>Liver</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>female</td>\n",
       "      <td>ATPLite</td>\n",
       "      <td>luciferase</td>\n",
       "      <td>meta_oxide</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IronOxide</td>\n",
       "      <td>None</td>\n",
       "      <td>42.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>0.10</td>\n",
       "      <td>12.0</td>\n",
       "      <td>HCMEC</td>\n",
       "      <td>Human</td>\n",
       "      <td>Heart</td>\n",
       "      <td>Endothelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>meta_oxide</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",
       "1       TiO2       None          30.6          266.0             NaN   \n",
       "2  IronOxide  PEG_based          35.8            NaN           -0.52   \n",
       "3        ZnO       None         141.0          404.0             NaN   \n",
       "4       TiO2       None          40.0          322.3          -25.27   \n",
       "6  IronOxide       None          42.5            NaN           -8.20   \n",
       "\n",
       "   Surface_area_m2_g   shape  Dose_microg_mL  Duration_h Cell_name  \\\n",
       "1                NaN  Sphere            2.50        24.0      WISH   \n",
       "2                NaN  Sphere            6.25        48.0    SKOV­3   \n",
       "3                NaN  Sphere           10.00         9.0      SIRC   \n",
       "4               50.0     NaN            0.00        12.0      L-02   \n",
       "6                NaN  Sphere            0.10        12.0     HCMEC   \n",
       "\n",
       "  Cell_species         cell_Organ Cell_morphology Cell_age cell_type     sex  \\\n",
       "1        Human  Amniotic-membrane      Epithelial    fetus    cancer     NaN   \n",
       "2        Human              Ovary      Epithelial    Young    cancer  female   \n",
       "3       Rabbit             Cornea      Fibroblast    Adult    Normal     NaN   \n",
       "4        Human              Liver      Epithelial    Adult    cancer  female   \n",
       "6        Human              Heart     Endothelial    Adult    Normal     NaN   \n",
       "\n",
       "     Assay   Test_indicator nanomaterial_group viability_class  \n",
       "1      MTT  TetrazoliumSalt         meta_oxide        NonToxic  \n",
       "2      MTS  TetrazoliumSalt         meta_oxide        NonToxic  \n",
       "3      MTT  TetrazoliumSalt         meta_oxide        NonToxic  \n",
       "4  ATPLite       luciferase         meta_oxide        NonToxic  \n",
       "6      MTT  TetrazoliumSalt         meta_oxide        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_f4326_row5_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f4326\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f4326_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_f4326_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f4326_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_f4326_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f4326_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_f4326_row1_col1\" class=\"data row1 col1\" >viability_class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f4326_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "      <td id=\"T_f4326_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f4326_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "      <td id=\"T_f4326_row3_col1\" class=\"data row3 col1\" >NonToxic: 0, Toxic: 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f4326_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "      <td id=\"T_f4326_row4_col1\" class=\"data row4 col1\" >(2544, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_f4326_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "      <td id=\"T_f4326_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_f4326_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_f4326_row6_col1\" class=\"data row6 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_f4326_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_f4326_row7_col1\" class=\"data row7 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_f4326_row8_col0\" class=\"data row8 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_f4326_row8_col1\" class=\"data row8 col1\" >(2035, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_f4326_row9_col0\" class=\"data row9 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_f4326_row9_col1\" class=\"data row9 col1\" >(509, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_f4326_row10_col0\" class=\"data row10 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_f4326_row10_col1\" class=\"data row10 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_f4326_row11_col0\" class=\"data row11 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_f4326_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_f4326_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_f4326_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_f4326_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
       "      <td id=\"T_f4326_row13_col1\" class=\"data row13 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_f4326_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_f4326_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_f4326_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
       "      <td id=\"T_f4326_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_f4326_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_f4326_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_f4326_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_f4326_row17_col1\" class=\"data row17 col1\" >splitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_f4326_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
       "      <td id=\"T_f4326_row18_col1\" class=\"data row18 col1\" >8ecb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_f4326_row19_col0\" class=\"data row19 col0\" >Fix Imbalance</td>\n",
       "      <td id=\"T_f4326_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4326_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_f4326_row20_col0\" class=\"data row20 col0\" >Fix Imbalance Method</td>\n",
       "      <td id=\"T_f4326_row20_col1\" class=\"data row20 col1\" >SMOTE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff50d8f3d00>"
      ]
     },
     "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": [
      "(2035, 20)\n",
      "(509, 20)\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_6b21f_row5_col1, #T_6b21f_row29_col1, #T_6b21f_row36_col1, #T_6b21f_row44_col1, #T_6b21f_row58_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6b21f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6b21f_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_6b21f_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_6b21f_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_6b21f_row0_col1\" class=\"data row0 col1\" >123456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_6b21f_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_6b21f_row1_col1\" class=\"data row1 col1\" >viability_class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_6b21f_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "      <td id=\"T_6b21f_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_6b21f_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "      <td id=\"T_6b21f_row3_col1\" class=\"data row3 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_6b21f_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "      <td id=\"T_6b21f_row4_col1\" class=\"data row4 col1\" >(2035, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_6b21f_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "      <td id=\"T_6b21f_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_6b21f_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_6b21f_row6_col1\" class=\"data row6 col1\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_6b21f_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_6b21f_row7_col1\" class=\"data row7 col1\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_6b21f_row8_col0\" class=\"data row8 col0\" >Ordinal Features</td>\n",
       "      <td id=\"T_6b21f_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_6b21f_row9_col0\" class=\"data row9 col0\" >High Cardinality Features</td>\n",
       "      <td id=\"T_6b21f_row9_col1\" class=\"data row9 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_6b21f_row10_col0\" class=\"data row10 col0\" >High Cardinality Method</td>\n",
       "      <td id=\"T_6b21f_row10_col1\" class=\"data row10 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_6b21f_row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_6b21f_row11_col1\" class=\"data row11 col1\" >(2035, 145)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_6b21f_row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_6b21f_row12_col1\" class=\"data row12 col1\" >(509, 145)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_6b21f_row13_col0\" class=\"data row13 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_6b21f_row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_6b21f_row14_col0\" class=\"data row14 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_6b21f_row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_6b21f_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_6b21f_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_6b21f_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
       "      <td id=\"T_6b21f_row16_col1\" class=\"data row16 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_6b21f_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_6b21f_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_6b21f_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
       "      <td id=\"T_6b21f_row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_6b21f_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_6b21f_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_6b21f_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_6b21f_row20_col1\" class=\"data row20 col1\" >group_based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_6b21f_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
       "      <td id=\"T_6b21f_row21_col1\" class=\"data row21 col1\" >1b35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_6b21f_row22_col0\" class=\"data row22 col0\" >Imputation Type</td>\n",
       "      <td id=\"T_6b21f_row22_col1\" class=\"data row22 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_6b21f_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Iteration</td>\n",
       "      <td id=\"T_6b21f_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_6b21f_row24_col0\" class=\"data row24 col0\" >Numeric Imputer</td>\n",
       "      <td id=\"T_6b21f_row24_col1\" class=\"data row24 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_6b21f_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Numeric Model</td>\n",
       "      <td id=\"T_6b21f_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_6b21f_row26_col0\" class=\"data row26 col0\" >Categorical Imputer</td>\n",
       "      <td id=\"T_6b21f_row26_col1\" class=\"data row26 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_6b21f_row27_col0\" class=\"data row27 col0\" >Iterative Imputation Categorical Model</td>\n",
       "      <td id=\"T_6b21f_row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_6b21f_row28_col0\" class=\"data row28 col0\" >Unknown Categoricals Handling</td>\n",
       "      <td id=\"T_6b21f_row28_col1\" class=\"data row28 col1\" >least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_6b21f_row29_col0\" class=\"data row29 col0\" >Normalize</td>\n",
       "      <td id=\"T_6b21f_row29_col1\" class=\"data row29 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_6b21f_row30_col0\" class=\"data row30 col0\" >Normalize Method</td>\n",
       "      <td id=\"T_6b21f_row30_col1\" class=\"data row30 col1\" >zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_6b21f_row31_col0\" class=\"data row31 col0\" >Transformation</td>\n",
       "      <td id=\"T_6b21f_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_6b21f_row32_col0\" class=\"data row32 col0\" >Transformation Method</td>\n",
       "      <td id=\"T_6b21f_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_6b21f_row33_col0\" class=\"data row33 col0\" >PCA</td>\n",
       "      <td id=\"T_6b21f_row33_col1\" class=\"data row33 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_6b21f_row34_col0\" class=\"data row34 col0\" >PCA Method</td>\n",
       "      <td id=\"T_6b21f_row34_col1\" class=\"data row34 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_6b21f_row35_col0\" class=\"data row35 col0\" >PCA Components</td>\n",
       "      <td id=\"T_6b21f_row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_6b21f_row36_col0\" class=\"data row36 col0\" >Ignore Low Variance</td>\n",
       "      <td id=\"T_6b21f_row36_col1\" class=\"data row36 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_6b21f_row37_col0\" class=\"data row37 col0\" >Combine Rare Levels</td>\n",
       "      <td id=\"T_6b21f_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_6b21f_row38_col0\" class=\"data row38 col0\" >Rare Level Threshold</td>\n",
       "      <td id=\"T_6b21f_row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_6b21f_row39_col0\" class=\"data row39 col0\" >Numeric Binning</td>\n",
       "      <td id=\"T_6b21f_row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_6b21f_row40_col0\" class=\"data row40 col0\" >Remove Outliers</td>\n",
       "      <td id=\"T_6b21f_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_6b21f_row41_col0\" class=\"data row41 col0\" >Outliers Threshold</td>\n",
       "      <td id=\"T_6b21f_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_6b21f_row42_col0\" class=\"data row42 col0\" >Remove Multicollinearity</td>\n",
       "      <td id=\"T_6b21f_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_6b21f_row43_col0\" class=\"data row43 col0\" >Multicollinearity Threshold</td>\n",
       "      <td id=\"T_6b21f_row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_6b21f_row44_col0\" class=\"data row44 col0\" >Remove Perfect Collinearity</td>\n",
       "      <td id=\"T_6b21f_row44_col1\" class=\"data row44 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_6b21f_row45_col0\" class=\"data row45 col0\" >Clustering</td>\n",
       "      <td id=\"T_6b21f_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_6b21f_row46_col0\" class=\"data row46 col0\" >Clustering Iteration</td>\n",
       "      <td id=\"T_6b21f_row46_col1\" class=\"data row46 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_6b21f_row47_col0\" class=\"data row47 col0\" >Polynomial Features</td>\n",
       "      <td id=\"T_6b21f_row47_col1\" class=\"data row47 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_6b21f_row48_col0\" class=\"data row48 col0\" >Polynomial Degree</td>\n",
       "      <td id=\"T_6b21f_row48_col1\" class=\"data row48 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_6b21f_row49_col0\" class=\"data row49 col0\" >Trignometry Features</td>\n",
       "      <td id=\"T_6b21f_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_6b21f_row50_col0\" class=\"data row50 col0\" >Polynomial Threshold</td>\n",
       "      <td id=\"T_6b21f_row50_col1\" class=\"data row50 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_6b21f_row51_col0\" class=\"data row51 col0\" >Group Features</td>\n",
       "      <td id=\"T_6b21f_row51_col1\" class=\"data row51 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_6b21f_row52_col0\" class=\"data row52 col0\" >Feature Selection</td>\n",
       "      <td id=\"T_6b21f_row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_6b21f_row53_col0\" class=\"data row53 col0\" >Feature Selection Method</td>\n",
       "      <td id=\"T_6b21f_row53_col1\" class=\"data row53 col1\" >classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_6b21f_row54_col0\" class=\"data row54 col0\" >Features Selection Threshold</td>\n",
       "      <td id=\"T_6b21f_row54_col1\" class=\"data row54 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_6b21f_row55_col0\" class=\"data row55 col0\" >Feature Interaction</td>\n",
       "      <td id=\"T_6b21f_row55_col1\" class=\"data row55 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_6b21f_row56_col0\" class=\"data row56 col0\" >Feature Ratio</td>\n",
       "      <td id=\"T_6b21f_row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_6b21f_row57_col0\" class=\"data row57 col0\" >Interaction Threshold</td>\n",
       "      <td id=\"T_6b21f_row57_col1\" class=\"data row57 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "      <td id=\"T_6b21f_row58_col0\" class=\"data row58 col0\" >Fix Imbalance</td>\n",
       "      <td id=\"T_6b21f_row58_col1\" class=\"data row58 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b21f_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
       "      <td id=\"T_6b21f_row59_col0\" class=\"data row59 col0\" >Fix Imbalance Method</td>\n",
       "      <td id=\"T_6b21f_row59_col1\" class=\"data row59 col1\" >SMOTE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff4ec421a60>"
      ]
     },
     "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": [
      "(2035, 145)\n",
      "(2035,)\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": [
      "(2035, 145)\n",
      "(2035,)\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": [
      "(509, 145)\n",
      "(509,)\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": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6784a_row5_col0, #T_6784a_row5_col1, #T_6784a_row5_col2, #T_6784a_row5_col3, #T_6784a_row5_col4, #T_6784a_row5_col5, #T_6784a_row5_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6784a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6784a_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_6784a_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_6784a_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_6784a_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_6784a_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_6784a_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_6784a_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_6784a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_6784a_row0_col0\" class=\"data row0 col0\" >0.96</td>\n",
       "      <td id=\"T_6784a_row0_col1\" class=\"data row0 col1\" >0.98</td>\n",
       "      <td id=\"T_6784a_row0_col2\" class=\"data row0 col2\" >0.82</td>\n",
       "      <td id=\"T_6784a_row0_col3\" class=\"data row0 col3\" >0.90</td>\n",
       "      <td id=\"T_6784a_row0_col4\" class=\"data row0 col4\" >0.86</td>\n",
       "      <td id=\"T_6784a_row0_col5\" class=\"data row0 col5\" >0.83</td>\n",
       "      <td id=\"T_6784a_row0_col6\" class=\"data row0 col6\" >0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6784a_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_6784a_row1_col0\" class=\"data row1 col0\" >0.93</td>\n",
       "      <td id=\"T_6784a_row1_col1\" class=\"data row1 col1\" >0.96</td>\n",
       "      <td id=\"T_6784a_row1_col2\" class=\"data row1 col2\" >0.76</td>\n",
       "      <td id=\"T_6784a_row1_col3\" class=\"data row1 col3\" >0.78</td>\n",
       "      <td id=\"T_6784a_row1_col4\" class=\"data row1 col4\" >0.77</td>\n",
       "      <td id=\"T_6784a_row1_col5\" class=\"data row1 col5\" >0.73</td>\n",
       "      <td id=\"T_6784a_row1_col6\" class=\"data row1 col6\" >0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6784a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_6784a_row2_col0\" class=\"data row2 col0\" >0.95</td>\n",
       "      <td id=\"T_6784a_row2_col1\" class=\"data row2 col1\" >0.98</td>\n",
       "      <td id=\"T_6784a_row2_col2\" class=\"data row2 col2\" >0.84</td>\n",
       "      <td id=\"T_6784a_row2_col3\" class=\"data row2 col3\" >0.84</td>\n",
       "      <td id=\"T_6784a_row2_col4\" class=\"data row2 col4\" >0.84</td>\n",
       "      <td id=\"T_6784a_row2_col5\" class=\"data row2 col5\" >0.80</td>\n",
       "      <td id=\"T_6784a_row2_col6\" class=\"data row2 col6\" >0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6784a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_6784a_row3_col0\" class=\"data row3 col0\" >0.93</td>\n",
       "      <td id=\"T_6784a_row3_col1\" class=\"data row3 col1\" >0.97</td>\n",
       "      <td id=\"T_6784a_row3_col2\" class=\"data row3 col2\" >0.81</td>\n",
       "      <td id=\"T_6784a_row3_col3\" class=\"data row3 col3\" >0.76</td>\n",
       "      <td id=\"T_6784a_row3_col4\" class=\"data row3 col4\" >0.78</td>\n",
       "      <td id=\"T_6784a_row3_col5\" class=\"data row3 col5\" >0.74</td>\n",
       "      <td id=\"T_6784a_row3_col6\" class=\"data row3 col6\" >0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6784a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_6784a_row4_col0\" class=\"data row4 col0\" >0.94</td>\n",
       "      <td id=\"T_6784a_row4_col1\" class=\"data row4 col1\" >0.98</td>\n",
       "      <td id=\"T_6784a_row4_col2\" class=\"data row4 col2\" >0.82</td>\n",
       "      <td id=\"T_6784a_row4_col3\" class=\"data row4 col3\" >0.80</td>\n",
       "      <td id=\"T_6784a_row4_col4\" class=\"data row4 col4\" >0.81</td>\n",
       "      <td id=\"T_6784a_row4_col5\" class=\"data row4 col5\" >0.77</td>\n",
       "      <td id=\"T_6784a_row4_col6\" class=\"data row4 col6\" >0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6784a_level0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "      <td id=\"T_6784a_row5_col0\" class=\"data row5 col0\" >0.94</td>\n",
       "      <td id=\"T_6784a_row5_col1\" class=\"data row5 col1\" >0.97</td>\n",
       "      <td id=\"T_6784a_row5_col2\" class=\"data row5 col2\" >0.81</td>\n",
       "      <td id=\"T_6784a_row5_col3\" class=\"data row5 col3\" >0.82</td>\n",
       "      <td id=\"T_6784a_row5_col4\" class=\"data row5 col4\" >0.81</td>\n",
       "      <td id=\"T_6784a_row5_col5\" class=\"data row5 col5\" >0.77</td>\n",
       "      <td id=\"T_6784a_row5_col6\" class=\"data row5 col6\" >0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6784a_level0_row6\" class=\"row_heading level0 row6\" >Std</th>\n",
       "      <td id=\"T_6784a_row6_col0\" class=\"data row6 col0\" >0.01</td>\n",
       "      <td id=\"T_6784a_row6_col1\" class=\"data row6 col1\" >0.01</td>\n",
       "      <td id=\"T_6784a_row6_col2\" class=\"data row6 col2\" >0.03</td>\n",
       "      <td id=\"T_6784a_row6_col3\" class=\"data row6 col3\" >0.05</td>\n",
       "      <td id=\"T_6784a_row6_col4\" class=\"data row6 col4\" >0.03</td>\n",
       "      <td id=\"T_6784a_row6_col5\" class=\"data row6 col5\" >0.04</td>\n",
       "      <td id=\"T_6784a_row6_col6\" class=\"data row6 col6\" >0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff4e4deb5e0>"
      ]
     },
     "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": [
       "(2035, 145)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5a9c93d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2035,)"
      ]
     },
     "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: 0.65, 0.55, 0.58, 0.57, 0.59, 0.56, 0.67, 0.56, 0.63, 0.51\n",
      "lr mean/std: 0.59 / 0.05\n",
      "knn scores: 0.43, 0.38, 0.61, 0.51, 0.56, 0.49, 0.49, 0.51, 0.57, 0.5\n",
      "knn mean/std: 0.5 / 0.06\n",
      "svm scores: 0.52, 0.57, 0.58, 0.5, 0.52, 0.29, 0.51, 0.33, 0.59, 0.47\n",
      "svm mean/std: 0.49 / 0.1\n",
      "rf scores: 0.64, 0.83, 0.85, 0.72, 0.79, 0.76, 0.68, 0.79, 0.76, 0.72\n",
      "rf mean/std: 0.75 / 0.06\n",
      "et scores: 0.63, 0.74, 0.81, 0.63, 0.75, 0.83, 0.6, 0.82, 0.74, 0.75\n",
      "et mean/std: 0.73 / 0.08\n",
      "gbc scores: 0.75, 0.88, 0.91, 0.79, 0.78, 0.87, 0.72, 0.85, 0.93, 0.81\n",
      "gbc mean/std: 0.83 / 0.07\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "lightgbm scores: 0.77, 0.82, 0.87, 0.81, 0.73, 0.85, 0.7, 0.84, 0.88, 0.8\n",
      "lightgbm mean/std: 0.81 / 0.06\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": [
    {
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       "      <th id=\"T_ef904_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_ef904_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
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       "      <td id=\"T_ef904_row0_col1\" class=\"data row0 col1\" >0.81</td>\n",
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       "      <th id=\"T_db8e6_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
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       "      <td id=\"T_9cbb8_row0_col1\" class=\"data row0 col1\" >0.82</td>\n",
       "      <td id=\"T_9cbb8_row0_col2\" class=\"data row0 col2\" >0.81</td>\n",
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       "      <td id=\"T_e2037_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_e2037_row0_col1\" class=\"data row0 col1\" >0.93</td>\n",
       "      <td id=\"T_e2037_row0_col2\" class=\"data row0 col2\" >0.95</td>\n",
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       "      <td id=\"T_e2037_row0_col7\" class=\"data row0 col7\" >0.73</td>\n",
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       "      <td id=\"T_66e51_row0_col1\" class=\"data row0 col1\" >0.92</td>\n",
       "      <td id=\"T_66e51_row0_col2\" class=\"data row0 col2\" >0.95</td>\n",
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       "      <td id=\"T_f3530_row0_col1\" class=\"data row0 col1\" >0.96</td>\n",
       "      <td id=\"T_f3530_row0_col2\" class=\"data row0 col2\" >0.98</td>\n",
       "      <td id=\"T_f3530_row0_col3\" class=\"data row0 col3\" >0.88</td>\n",
       "      <td id=\"T_f3530_row0_col4\" class=\"data row0 col4\" >0.87</td>\n",
       "      <td id=\"T_f3530_row0_col5\" class=\"data row0 col5\" >0.88</td>\n",
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       "      <td id=\"T_f3530_row0_col7\" class=\"data row0 col7\" >0.85</td>\n",
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       "      <td id=\"T_ff6a6_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_ff6a6_row0_col1\" class=\"data row0 col1\" >0.95</td>\n",
       "      <td id=\"T_ff6a6_row0_col2\" class=\"data row0 col2\" >0.98</td>\n",
       "      <td id=\"T_ff6a6_row0_col3\" class=\"data row0 col3\" >0.83</td>\n",
       "      <td id=\"T_ff6a6_row0_col4\" class=\"data row0 col4\" >0.84</td>\n",
       "      <td id=\"T_ff6a6_row0_col5\" class=\"data row0 col5\" >0.84</td>\n",
       "      <td id=\"T_ff6a6_row0_col6\" class=\"data row0 col6\" >0.81</td>\n",
       "      <td id=\"T_ff6a6_row0_col7\" class=\"data row0 col7\" >0.81</td>\n",
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   ],
   "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": null,
   "id": "ef7e1f19",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "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': 119,\n",
       " 'max_depth': -1,\n",
       " 'learning_rate': 0.04144506302705949,\n",
       " 'n_estimators': 268,\n",
       " 'subsample_for_bin': 200000,\n",
       " 'min_split_gain': 0.03235571512573611,\n",
       " 'min_child_weight': 0.001,\n",
       " 'min_child_samples': 13,\n",
       " 'subsample': 1.0,\n",
       " 'subsample_freq': 0,\n",
       " 'colsample_bytree': 1.0,\n",
       " 'reg_alpha': 1.0325340954850423e-09,\n",
       " 'reg_lambda': 0.00012470468815033338,\n",
       " 'random_state': 123456,\n",
       " 'n_jobs': -1,\n",
       " 'silent': 'warn',\n",
       " 'importance_type': 'split',\n",
       " '_Booster': <lightgbm.basic.Booster at 0x7ff4ece0e490>,\n",
       " '_evals_result': None,\n",
       " '_best_score': defaultdict(collections.OrderedDict, {}),\n",
       " '_best_iteration': None,\n",
       " '_other_params': {'feature_fraction': 0.9965941043203894,\n",
       "  'bagging_fraction': 0.6897099664559665,\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': 145,\n",
       " '_n_features_in': 145,\n",
       " '_classes': array([0, 1]),\n",
       " '_n_classes': 2,\n",
       " 'feature_fraction': 0.9965941043203894,\n",
       " 'bagging_fraction': 0.6897099664559665,\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": 55,
   "id": "a462e8b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] bagging_fraction is set=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\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=0.6897099664559665, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6897099664559665\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9965941043203894, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9965941043203894\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n"
     ]
    }
   ],
   "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
}