04-regression-carbon-based-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": "6725f3a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f67f90b",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = ['NP_type', 'Coating','shape','Cell_name','Cell_species','cell_Organ','Cell_morphology',\n",
    "                       'Cell_age','cell_type','sex','Assay','Test_indicator']\n",
    "numerical_columns = ['core_size_nm','hydro_size_nm','Surf_charge_mV','Surface_area_m2_g','Dose_microg_mL','Duration_h',\n",
    "                    'Viability']\n",
    "\n",
    "numerical_columns_no_target = ['core_size_nm','hydro_size_nm','Surf_charge_mV','Dose_microg_mL','Duration_h']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b175d88",
   "metadata": {},
   "source": [
    "# Carbon-based NMs regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14e2baa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_group_name = \"carbon_based\"\n",
    "group_name = \"carbon\""
   ]
  },
  {
   "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": [
       "(116, 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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       "    }\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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>C70</td>\n",
       "      <td>Dextran</td>\n",
       "      <td>15.00</td>\n",
       "      <td>109.1</td>\n",
       "      <td>-20.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>L929</td>\n",
       "      <td>Mouse</td>\n",
       "      <td>Connective-tissue</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>neonate</td>\n",
       "      <td>Normal</td>\n",
       "      <td>male</td>\n",
       "      <td>NR</td>\n",
       "      <td>toluylene red</td>\n",
       "      <td>carbon</td>\n",
       "      <td>94.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>C60</td>\n",
       "      <td>None</td>\n",
       "      <td>34.64</td>\n",
       "      <td>142.0</td>\n",
       "      <td>-37.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>hPDLF</td>\n",
       "      <td>Human</td>\n",
       "      <td>Teeth</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>carbon</td>\n",
       "      <td>77.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>C60</td>\n",
       "      <td>None</td>\n",
       "      <td>34.64</td>\n",
       "      <td>142.0</td>\n",
       "      <td>-37.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>0.1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>mDF</td>\n",
       "      <td>Mouse</td>\n",
       "      <td>Skin</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>carbon</td>\n",
       "      <td>74.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>C60</td>\n",
       "      <td>None</td>\n",
       "      <td>34.64</td>\n",
       "      <td>142.0</td>\n",
       "      <td>-37.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>0.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mDF</td>\n",
       "      <td>Mouse</td>\n",
       "      <td>Skin</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>carbon</td>\n",
       "      <td>88.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>C60</td>\n",
       "      <td>Dextran</td>\n",
       "      <td>30.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>50.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C6</td>\n",
       "      <td>Rat</td>\n",
       "      <td>Brain</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>toluylene red</td>\n",
       "      <td>carbon</td>\n",
       "      <td>90.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    NP_type  Coating  core_size_nm  hydro_size_nm  Surf_charge_mV  \\\n",
       "66      C70  Dextran         15.00          109.1           -20.7   \n",
       "84      C60     None         34.64          142.0           -37.4   \n",
       "92      C60     None         34.64          142.0           -37.4   \n",
       "120     C60     None         34.64          142.0           -37.4   \n",
       "147     C60  Dextran         30.00            NaN           -16.9   \n",
       "\n",
       "     Surface_area_m2_g   shape  Dose_microg_mL  Duration_h Cell_name  \\\n",
       "66                 NaN  Sphere            10.0        24.0      L929   \n",
       "84                 NaN  Sphere            10.0         6.0     hPDLF   \n",
       "92                 NaN  Sphere             0.1        48.0       mDF   \n",
       "120                NaN  Sphere             0.1         6.0       mDF   \n",
       "147                NaN  Sphere            50.0        24.0        C6   \n",
       "\n",
       "    Cell_species         cell_Organ Cell_morphology Cell_age cell_type   sex  \\\n",
       "66         Mouse  Connective-tissue      Fibroblast  neonate    Normal  male   \n",
       "84         Human              Teeth      Fibroblast    Adult    cancer   NaN   \n",
       "92         Mouse               Skin      Fibroblast    Adult    Normal   NaN   \n",
       "120        Mouse               Skin      Fibroblast    Adult    Normal   NaN   \n",
       "147          Rat              Brain      Fibroblast    Adult    cancer   NaN   \n",
       "\n",
       "    Assay   Test_indicator nanomaterial_group  Viability  \n",
       "66     NR    toluylene red             carbon      94.62  \n",
       "84    MTT  TetrazoliumSalt             carbon      77.07  \n",
       "92    MTT  TetrazoliumSalt             carbon      74.35  \n",
       "120   MTT  TetrazoliumSalt             carbon      88.92  \n",
       "147    NR    toluylene red             carbon      90.66  "
      ]
     },
     "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": "d3b9c5b7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_488a3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_488a3_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_488a3_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_488a3_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_488a3_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_488a3_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_488a3_row1_col1\" class=\"data row1 col1\" >Viability_discrete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_488a3_row2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "      <td id=\"T_488a3_row2_col1\" class=\"data row2 col1\" >(116, 21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_488a3_row3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "      <td id=\"T_488a3_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_488a3_row4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_488a3_row4_col1\" class=\"data row4 col1\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_488a3_row5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_488a3_row5_col1\" class=\"data row5 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_488a3_row6_col0\" class=\"data row6 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_488a3_row6_col1\" class=\"data row6 col1\" >(92, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_488a3_row7_col0\" class=\"data row7 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_488a3_row7_col1\" class=\"data row7 col1\" >(24, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_488a3_row8_col0\" class=\"data row8 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_488a3_row8_col1\" class=\"data row8 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_488a3_row9_col0\" class=\"data row9 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_488a3_row9_col1\" class=\"data row9 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_488a3_row10_col0\" class=\"data row10 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_488a3_row10_col1\" class=\"data row10 col1\" >KFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_488a3_row11_col0\" class=\"data row11 col0\" >Fold Number</td>\n",
       "      <td id=\"T_488a3_row11_col1\" class=\"data row11 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_488a3_row12_col0\" class=\"data row12 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_488a3_row12_col1\" class=\"data row12 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_488a3_row13_col0\" class=\"data row13 col0\" >Use GPU</td>\n",
       "      <td id=\"T_488a3_row13_col1\" class=\"data row13 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_488a3_row14_col0\" class=\"data row14 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_488a3_row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_488a3_row15_col0\" class=\"data row15 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_488a3_row15_col1\" class=\"data row15 col1\" >splitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_488a3_row16_col0\" class=\"data row16 col0\" >USI</td>\n",
       "      <td id=\"T_488a3_row16_col1\" class=\"data row16 col1\" >b646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_488a3_row17_col0\" class=\"data row17 col0\" >Transform Target</td>\n",
       "      <td id=\"T_488a3_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_488a3_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_488a3_row18_col0\" class=\"data row18 col0\" >Transform Target Method</td>\n",
       "      <td id=\"T_488a3_row18_col1\" class=\"data row18 col1\" >box-cox</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f23884256d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 17,
   "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": 18,
   "id": "6fecd13a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 19)\n",
      "(24, 19)\n"
     ]
    }
   ],
   "source": [
    "print(data_group_train_df.shape)\n",
    "print(data_group_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e38fadb",
   "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": "4881a22b",
   "metadata": {},
   "source": [
    "### Save cleaned datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "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": "code",
   "execution_count": null,
   "id": "f3a52072",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d5fe80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_df = pd.read_csv('output/datasets/'+model_group_name+'/data_regression_train_df.csv', encoding=\"utf-8\")\n",
    "data_test_df = pd.read_csv('output/datasets/'+model_group_name+'/data_regression_test_df.csv', encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffe023c-bf03-4ba8-a278-8b1261da6572",
   "metadata": {},
   "source": [
    "### Use the best scenario identified in step 01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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": 24,
   "id": "cb2a02f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e0c28_row3_col1, #T_e0c28_row27_col1, #T_e0c28_row34_col1, #T_e0c28_row42_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e0c28\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e0c28_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_e0c28_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_e0c28_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_e0c28_row0_col1\" class=\"data row0 col1\" >123456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_e0c28_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_e0c28_row1_col1\" class=\"data row1 col1\" >Viability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_e0c28_row2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "      <td id=\"T_e0c28_row2_col1\" class=\"data row2 col1\" >(92, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_e0c28_row3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "      <td id=\"T_e0c28_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_e0c28_row4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_e0c28_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_e0c28_row5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_e0c28_row5_col1\" class=\"data row5 col1\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_e0c28_row6_col0\" class=\"data row6 col0\" >Ordinal Features</td>\n",
       "      <td id=\"T_e0c28_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_e0c28_row7_col0\" class=\"data row7 col0\" >High Cardinality Features</td>\n",
       "      <td id=\"T_e0c28_row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_e0c28_row8_col0\" class=\"data row8 col0\" >High Cardinality Method</td>\n",
       "      <td id=\"T_e0c28_row8_col1\" class=\"data row8 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_e0c28_row9_col0\" class=\"data row9 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_e0c28_row9_col1\" class=\"data row9 col1\" >(92, 31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_e0c28_row10_col0\" class=\"data row10 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_e0c28_row10_col1\" class=\"data row10 col1\" >(24, 31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_e0c28_row11_col0\" class=\"data row11 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_e0c28_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_e0c28_row12_col0\" class=\"data row12 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_e0c28_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_e0c28_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_e0c28_row13_col1\" class=\"data row13 col1\" >KFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_e0c28_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "      <td id=\"T_e0c28_row14_col1\" class=\"data row14 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_e0c28_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_e0c28_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_e0c28_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "      <td id=\"T_e0c28_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_e0c28_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_e0c28_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_e0c28_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_e0c28_row18_col1\" class=\"data row18 col1\" >group_based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_e0c28_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "      <td id=\"T_e0c28_row19_col1\" class=\"data row19 col1\" >04db</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_e0c28_row20_col0\" class=\"data row20 col0\" >Imputation Type</td>\n",
       "      <td id=\"T_e0c28_row20_col1\" class=\"data row20 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_e0c28_row21_col0\" class=\"data row21 col0\" >Iterative Imputation Iteration</td>\n",
       "      <td id=\"T_e0c28_row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_e0c28_row22_col0\" class=\"data row22 col0\" >Numeric Imputer</td>\n",
       "      <td id=\"T_e0c28_row22_col1\" class=\"data row22 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_e0c28_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Numeric Model</td>\n",
       "      <td id=\"T_e0c28_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_e0c28_row24_col0\" class=\"data row24 col0\" >Categorical Imputer</td>\n",
       "      <td id=\"T_e0c28_row24_col1\" class=\"data row24 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_e0c28_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Categorical Model</td>\n",
       "      <td id=\"T_e0c28_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_e0c28_row26_col0\" class=\"data row26 col0\" >Unknown Categoricals Handling</td>\n",
       "      <td id=\"T_e0c28_row26_col1\" class=\"data row26 col1\" >least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_e0c28_row27_col0\" class=\"data row27 col0\" >Normalize</td>\n",
       "      <td id=\"T_e0c28_row27_col1\" class=\"data row27 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_e0c28_row28_col0\" class=\"data row28 col0\" >Normalize Method</td>\n",
       "      <td id=\"T_e0c28_row28_col1\" class=\"data row28 col1\" >zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_e0c28_row29_col0\" class=\"data row29 col0\" >Transformation</td>\n",
       "      <td id=\"T_e0c28_row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_e0c28_row30_col0\" class=\"data row30 col0\" >Transformation Method</td>\n",
       "      <td id=\"T_e0c28_row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_e0c28_row31_col0\" class=\"data row31 col0\" >PCA</td>\n",
       "      <td id=\"T_e0c28_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_e0c28_row32_col0\" class=\"data row32 col0\" >PCA Method</td>\n",
       "      <td id=\"T_e0c28_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_e0c28_row33_col0\" class=\"data row33 col0\" >PCA Components</td>\n",
       "      <td id=\"T_e0c28_row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_e0c28_row34_col0\" class=\"data row34 col0\" >Ignore Low Variance</td>\n",
       "      <td id=\"T_e0c28_row34_col1\" class=\"data row34 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_e0c28_row35_col0\" class=\"data row35 col0\" >Combine Rare Levels</td>\n",
       "      <td id=\"T_e0c28_row35_col1\" class=\"data row35 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_e0c28_row36_col0\" class=\"data row36 col0\" >Rare Level Threshold</td>\n",
       "      <td id=\"T_e0c28_row36_col1\" class=\"data row36 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_e0c28_row37_col0\" class=\"data row37 col0\" >Numeric Binning</td>\n",
       "      <td id=\"T_e0c28_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_e0c28_row38_col0\" class=\"data row38 col0\" >Remove Outliers</td>\n",
       "      <td id=\"T_e0c28_row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_e0c28_row39_col0\" class=\"data row39 col0\" >Outliers Threshold</td>\n",
       "      <td id=\"T_e0c28_row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_e0c28_row40_col0\" class=\"data row40 col0\" >Remove Multicollinearity</td>\n",
       "      <td id=\"T_e0c28_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_e0c28_row41_col0\" class=\"data row41 col0\" >Multicollinearity Threshold</td>\n",
       "      <td id=\"T_e0c28_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_e0c28_row42_col0\" class=\"data row42 col0\" >Remove Perfect Collinearity</td>\n",
       "      <td id=\"T_e0c28_row42_col1\" class=\"data row42 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_e0c28_row43_col0\" class=\"data row43 col0\" >Clustering</td>\n",
       "      <td id=\"T_e0c28_row43_col1\" class=\"data row43 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_e0c28_row44_col0\" class=\"data row44 col0\" >Clustering Iteration</td>\n",
       "      <td id=\"T_e0c28_row44_col1\" class=\"data row44 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_e0c28_row45_col0\" class=\"data row45 col0\" >Polynomial Features</td>\n",
       "      <td id=\"T_e0c28_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_e0c28_row46_col0\" class=\"data row46 col0\" >Polynomial Degree</td>\n",
       "      <td id=\"T_e0c28_row46_col1\" class=\"data row46 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_e0c28_row47_col0\" class=\"data row47 col0\" >Trignometry Features</td>\n",
       "      <td id=\"T_e0c28_row47_col1\" class=\"data row47 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_e0c28_row48_col0\" class=\"data row48 col0\" >Polynomial Threshold</td>\n",
       "      <td id=\"T_e0c28_row48_col1\" class=\"data row48 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_e0c28_row49_col0\" class=\"data row49 col0\" >Group Features</td>\n",
       "      <td id=\"T_e0c28_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_e0c28_row50_col0\" class=\"data row50 col0\" >Feature Selection</td>\n",
       "      <td id=\"T_e0c28_row50_col1\" class=\"data row50 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_e0c28_row51_col0\" class=\"data row51 col0\" >Feature Selection Method</td>\n",
       "      <td id=\"T_e0c28_row51_col1\" class=\"data row51 col1\" >classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_e0c28_row52_col0\" class=\"data row52 col0\" >Features Selection Threshold</td>\n",
       "      <td id=\"T_e0c28_row52_col1\" class=\"data row52 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_e0c28_row53_col0\" class=\"data row53 col0\" >Feature Interaction</td>\n",
       "      <td id=\"T_e0c28_row53_col1\" class=\"data row53 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_e0c28_row54_col0\" class=\"data row54 col0\" >Feature Ratio</td>\n",
       "      <td id=\"T_e0c28_row54_col1\" class=\"data row54 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_e0c28_row55_col0\" class=\"data row55 col0\" >Interaction Threshold</td>\n",
       "      <td id=\"T_e0c28_row55_col1\" class=\"data row55 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_e0c28_row56_col0\" class=\"data row56 col0\" >Transform Target</td>\n",
       "      <td id=\"T_e0c28_row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e0c28_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_e0c28_row57_col0\" class=\"data row57 col0\" >Transform Target Method</td>\n",
       "      <td id=\"T_e0c28_row57_col1\" class=\"data row57 col1\" >box-cox</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f2368218ee0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 25,
   "id": "ebe27ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 31)\n",
      "(92,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X_train\").shape)\n",
    "print(get_config(variable=\"y_train\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "34f3d922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 31)\n",
      "(92,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X\").shape)\n",
    "print(get_config(variable=\"y\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "id": "1b417175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 31)\n",
      "(24,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X_test\").shape)\n",
    "print(get_config(variable=\"y_test\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e4387c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load_config('output/models/universal/regression_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "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": 32,
   "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": 33,
   "id": "8922c9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = ['lr', 'lasso', 'ridge', 'svm', 'rf', 'et', 'gbr', 'lightgbm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "id": "c2a73417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9666e_row5_col0, #T_9666e_row5_col1, #T_9666e_row5_col2, #T_9666e_row5_col3, #T_9666e_row5_col4, #T_9666e_row5_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9666e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9666e_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_9666e_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_9666e_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_9666e_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_9666e_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_9666e_level0_col5\" class=\"col_heading level0 col5\" >MAPE</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9666e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_9666e_row0_col0\" class=\"data row0 col0\" >4.85</td>\n",
       "      <td id=\"T_9666e_row0_col1\" class=\"data row0 col1\" >40.98</td>\n",
       "      <td id=\"T_9666e_row0_col2\" class=\"data row0 col2\" >6.40</td>\n",
       "      <td id=\"T_9666e_row0_col3\" class=\"data row0 col3\" >0.80</td>\n",
       "      <td id=\"T_9666e_row0_col4\" class=\"data row0 col4\" >0.08</td>\n",
       "      <td id=\"T_9666e_row0_col5\" class=\"data row0 col5\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9666e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_9666e_row1_col0\" class=\"data row1 col0\" >4.43</td>\n",
       "      <td id=\"T_9666e_row1_col1\" class=\"data row1 col1\" >33.78</td>\n",
       "      <td id=\"T_9666e_row1_col2\" class=\"data row1 col2\" >5.81</td>\n",
       "      <td id=\"T_9666e_row1_col3\" class=\"data row1 col3\" >0.87</td>\n",
       "      <td id=\"T_9666e_row1_col4\" class=\"data row1 col4\" >0.08</td>\n",
       "      <td id=\"T_9666e_row1_col5\" class=\"data row1 col5\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9666e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_9666e_row2_col0\" class=\"data row2 col0\" >8.03</td>\n",
       "      <td id=\"T_9666e_row2_col1\" class=\"data row2 col1\" >100.49</td>\n",
       "      <td id=\"T_9666e_row2_col2\" class=\"data row2 col2\" >10.02</td>\n",
       "      <td id=\"T_9666e_row2_col3\" class=\"data row2 col3\" >0.69</td>\n",
       "      <td id=\"T_9666e_row2_col4\" class=\"data row2 col4\" >0.14</td>\n",
       "      <td id=\"T_9666e_row2_col5\" class=\"data row2 col5\" >0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9666e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_9666e_row3_col0\" class=\"data row3 col0\" >7.75</td>\n",
       "      <td id=\"T_9666e_row3_col1\" class=\"data row3 col1\" >98.61</td>\n",
       "      <td id=\"T_9666e_row3_col2\" class=\"data row3 col2\" >9.93</td>\n",
       "      <td id=\"T_9666e_row3_col3\" class=\"data row3 col3\" >0.77</td>\n",
       "      <td id=\"T_9666e_row3_col4\" class=\"data row3 col4\" >0.14</td>\n",
       "      <td id=\"T_9666e_row3_col5\" class=\"data row3 col5\" >0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9666e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_9666e_row4_col0\" class=\"data row4 col0\" >3.52</td>\n",
       "      <td id=\"T_9666e_row4_col1\" class=\"data row4 col1\" >19.00</td>\n",
       "      <td id=\"T_9666e_row4_col2\" class=\"data row4 col2\" >4.36</td>\n",
       "      <td id=\"T_9666e_row4_col3\" class=\"data row4 col3\" >0.87</td>\n",
       "      <td id=\"T_9666e_row4_col4\" class=\"data row4 col4\" >0.06</td>\n",
       "      <td id=\"T_9666e_row4_col5\" class=\"data row4 col5\" >0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9666e_level0_row5\" class=\"row_heading level0 row5\" >Mean</th>\n",
       "      <td id=\"T_9666e_row5_col0\" class=\"data row5 col0\" >5.72</td>\n",
       "      <td id=\"T_9666e_row5_col1\" class=\"data row5 col1\" >58.57</td>\n",
       "      <td id=\"T_9666e_row5_col2\" class=\"data row5 col2\" >7.31</td>\n",
       "      <td id=\"T_9666e_row5_col3\" class=\"data row5 col3\" >0.80</td>\n",
       "      <td id=\"T_9666e_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
       "      <td id=\"T_9666e_row5_col5\" class=\"data row5 col5\" >0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9666e_level0_row6\" class=\"row_heading level0 row6\" >Std</th>\n",
       "      <td id=\"T_9666e_row6_col0\" class=\"data row6 col0\" >1.83</td>\n",
       "      <td id=\"T_9666e_row6_col1\" class=\"data row6 col1\" >34.20</td>\n",
       "      <td id=\"T_9666e_row6_col2\" class=\"data row6 col2\" >2.28</td>\n",
       "      <td id=\"T_9666e_row6_col3\" class=\"data row6 col3\" >0.07</td>\n",
       "      <td id=\"T_9666e_row6_col4\" class=\"data row6 col4\" >0.04</td>\n",
       "      <td id=\"T_9666e_row6_col5\" class=\"data row6 col5\" >0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f2367c5bb20>"
      ]
     },
     "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+'/' + '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": 36,
   "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": 37,
   "id": "3c21760e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92, 31)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5a9c93d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f052eebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_outer = KFold(n_splits=10, shuffle=True, random_state=cv_outer_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "564a8934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor model_name in model_names:\\n    scores = cross_val(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]\\n'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "for model_name in model_names:\n",
    "    scores = cross_val(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]\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b328b0ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr scores: 0.13, 0.65, 0.57, 0.87, 0.52, 0.86, 0.57, 0.53, 0.52, 0.59\n",
      "lr mean/std: 0.58 / 0.19\n",
      "lasso scores: 0.15, 0.67, 0.64, 0.86, 0.58, 0.85, 0.62, 0.73, 0.63, 0.59\n",
      "lasso mean/std: 0.63 / 0.19\n",
      "ridge scores: 0.09, 0.66, 0.65, 0.87, 0.56, 0.84, 0.63, 0.71, 0.62, 0.59\n",
      "ridge mean/std: 0.62 / 0.2\n",
      "svm scores: 0.19, 0.77, 0.64, 0.63, 0.47, 0.78, 0.21, 0.47, 0.67, 0.55\n",
      "svm mean/std: 0.54 / 0.2\n",
      "rf scores: 0.17, 0.95, 0.88, 0.91, 0.67, 0.96, 0.62, 0.86, 0.73, 0.7\n",
      "rf mean/std: 0.74 / 0.22\n",
      "et scores: 0.25, 0.94, 0.76, 0.91, 0.72, 0.96, 0.74, 0.76, 0.7, 0.71\n",
      "et mean/std: 0.74 / 0.19\n",
      "gbr scores: 0.31, 0.95, 0.91, 0.94, 0.72, 0.97, 0.7, 0.84, 0.73, 0.77\n",
      "gbr mean/std: 0.78 / 0.19\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.7925725958360194, subsample=1.0 will be ignored. Current value: bagging_fraction=0.7925725958360194\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8333333626748276, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8333333626748276\n",
      "[LightGBM] [Warning] bagging_freq is set=3, subsample_freq=0 will be ignored. Current value: bagging_freq=3\n",
      "lightgbm scores: 0.35, 0.94, 0.89, 0.9, 0.75, 0.95, 0.73, 0.78, 0.7, 0.68\n",
      "lightgbm mean/std: 0.77 / 0.17\n"
     ]
    }
   ],
   "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": 42,
   "id": "bfae263b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_1e8c4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1e8c4_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_1e8c4_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_1e8c4_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_1e8c4_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_1e8c4_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_1e8c4_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_1e8c4_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1e8c4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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       "      <td id=\"T_1e8c4_row0_col1\" class=\"data row0 col1\" >8.02</td>\n",
       "      <td id=\"T_1e8c4_row0_col2\" class=\"data row0 col2\" >120.09</td>\n",
       "      <td id=\"T_1e8c4_row0_col3\" class=\"data row0 col3\" >10.96</td>\n",
       "      <td id=\"T_1e8c4_row0_col4\" class=\"data row0 col4\" >0.58</td>\n",
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       "      <th id=\"T_88a83_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_88a83_row0_col0\" class=\"data row0 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_88a83_row0_col1\" class=\"data row0 col1\" >7.73</td>\n",
       "      <td id=\"T_88a83_row0_col2\" class=\"data row0 col2\" >116.32</td>\n",
       "      <td id=\"T_88a83_row0_col3\" class=\"data row0 col3\" >10.78</td>\n",
       "      <td id=\"T_88a83_row0_col4\" class=\"data row0 col4\" >0.59</td>\n",
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       "      <td id=\"T_fe6bb_row0_col0\" class=\"data row0 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_fe6bb_row0_col1\" class=\"data row0 col1\" >7.82</td>\n",
       "      <td id=\"T_fe6bb_row0_col2\" class=\"data row0 col2\" >116.61</td>\n",
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       "      <th id=\"T_84b75_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
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       "      <th id=\"T_84b75_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
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       "      <th id=\"T_84b75_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_84b75_row0_col0\" class=\"data row0 col0\" >Support Vector Regression</td>\n",
       "      <td id=\"T_84b75_row0_col1\" class=\"data row0 col1\" >7.89</td>\n",
       "      <td id=\"T_84b75_row0_col2\" class=\"data row0 col2\" >126.18</td>\n",
       "      <td id=\"T_84b75_row0_col3\" class=\"data row0 col3\" >11.23</td>\n",
       "      <td id=\"T_84b75_row0_col4\" class=\"data row0 col4\" >0.56</td>\n",
       "      <td id=\"T_84b75_row0_col5\" class=\"data row0 col5\" >0.17</td>\n",
       "      <td id=\"T_84b75_row0_col6\" class=\"data row0 col6\" >0.12</td>\n",
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       "      <th id=\"T_0dfa2_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_0dfa2_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_0dfa2_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_0dfa2_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_0dfa2_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_0dfa2_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_0dfa2_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
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       "      <th id=\"T_0dfa2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_0dfa2_row0_col0\" class=\"data row0 col0\" >Random Forest Regressor</td>\n",
       "      <td id=\"T_0dfa2_row0_col1\" class=\"data row0 col1\" >6.89</td>\n",
       "      <td id=\"T_0dfa2_row0_col2\" class=\"data row0 col2\" >103.52</td>\n",
       "      <td id=\"T_0dfa2_row0_col3\" class=\"data row0 col3\" >10.17</td>\n",
       "      <td id=\"T_0dfa2_row0_col4\" class=\"data row0 col4\" >0.64</td>\n",
       "      <td id=\"T_0dfa2_row0_col5\" class=\"data row0 col5\" >0.15</td>\n",
       "      <td id=\"T_0dfa2_row0_col6\" class=\"data row0 col6\" >0.11</td>\n",
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       "</table>\n"
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       "<style type=\"text/css\">\n",
       "</style>\n",
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8069e_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_8069e_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_8069e_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_8069e_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_8069e_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_8069e_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_8069e_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
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       "      <th id=\"T_8069e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8069e_row0_col0\" class=\"data row0 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_8069e_row0_col1\" class=\"data row0 col1\" >6.71</td>\n",
       "      <td id=\"T_8069e_row0_col2\" class=\"data row0 col2\" >93.95</td>\n",
       "      <td id=\"T_8069e_row0_col3\" class=\"data row0 col3\" >9.69</td>\n",
       "      <td id=\"T_8069e_row0_col4\" class=\"data row0 col4\" >0.67</td>\n",
       "      <td id=\"T_8069e_row0_col5\" class=\"data row0 col5\" >0.14</td>\n",
       "      <td id=\"T_8069e_row0_col6\" class=\"data row0 col6\" >0.10</td>\n",
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       "  </tbody>\n",
       "</table>\n"
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_3928b_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_3928b_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_3928b_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_3928b_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_3928b_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_3928b_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_3928b_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
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       "      <th id=\"T_3928b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_3928b_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_3928b_row0_col1\" class=\"data row0 col1\" >6.35</td>\n",
       "      <td id=\"T_3928b_row0_col2\" class=\"data row0 col2\" >83.17</td>\n",
       "      <td id=\"T_3928b_row0_col3\" class=\"data row0 col3\" >9.12</td>\n",
       "      <td id=\"T_3928b_row0_col4\" class=\"data row0 col4\" >0.71</td>\n",
       "      <td id=\"T_3928b_row0_col5\" class=\"data row0 col5\" >0.14</td>\n",
       "      <td id=\"T_3928b_row0_col6\" class=\"data row0 col6\" >0.10</td>\n",
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       "<style type=\"text/css\">\n",
       "</style>\n",
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_08af9_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_08af9_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_08af9_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_08af9_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_08af9_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_08af9_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_08af9_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_08af9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_08af9_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_08af9_row0_col1\" class=\"data row0 col1\" >7.42</td>\n",
       "      <td id=\"T_08af9_row0_col2\" class=\"data row0 col2\" >98.77</td>\n",
       "      <td id=\"T_08af9_row0_col3\" class=\"data row0 col3\" >9.94</td>\n",
       "      <td id=\"T_08af9_row0_col4\" class=\"data row0 col4\" >0.66</td>\n",
       "      <td id=\"T_08af9_row0_col5\" class=\"data row0 col5\" >0.14</td>\n",
       "      <td id=\"T_08af9_row0_col6\" class=\"data row0 col6\" >0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x7f2367c44c10>"
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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+'/' + '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": 43,
   "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",
      "Transformation Pipeline and Model Successfully Saved\n",
      "Transformation Pipeline and Model Successfully Saved\n"
     ]
    }
   ],
   "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": 44,
   "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": 45,
   "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', '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": 46,
   "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 = 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": 47,
   "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 = 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": 12,
   "id": "2406f629",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = ['lr', 'lasso', 'ridge', 'svm', 'rf', 'et', 'gbr', 'lightgbm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f5acd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_config = load_config('output/models/'+model_group_name+'/regression_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7503a083",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_models_load = dict()\n",
    "final_models_load = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
      "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+'/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": 16,
   "id": "acba0082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boosting_type': 'gbdt',\n",
       " 'objective': None,\n",
       " 'num_leaves': 211,\n",
       " 'max_depth': -1,\n",
       " 'learning_rate': 0.10445703504256473,\n",
       " 'n_estimators': 178,\n",
       " 'subsample_for_bin': 200000,\n",
       " 'min_split_gain': 0.8391075826292399,\n",
       " 'min_child_weight': 0.001,\n",
       " 'min_child_samples': 1,\n",
       " 'subsample': 1.0,\n",
       " 'subsample_freq': 0,\n",
       " 'colsample_bytree': 1.0,\n",
       " 'reg_alpha': 0.4602979838078623,\n",
       " 'reg_lambda': 0.0006107835356586477,\n",
       " 'random_state': 123456,\n",
       " 'n_jobs': -1,\n",
       " 'silent': 'warn',\n",
       " 'importance_type': 'split',\n",
       " '_Booster': <lightgbm.basic.Booster at 0x7f28e9186e50>,\n",
       " '_evals_result': None,\n",
       " '_best_score': defaultdict(collections.OrderedDict, {}),\n",
       " '_best_iteration': None,\n",
       " '_other_params': {'feature_fraction': 0.8333333626748276,\n",
       "  'bagging_fraction': 0.7925725958360194,\n",
       "  'bagging_freq': 3},\n",
       " '_objective': 'regression',\n",
       " 'class_weight': None,\n",
       " '_class_weight': None,\n",
       " '_class_map': None,\n",
       " '_n_features': 31,\n",
       " '_n_features_in': 31,\n",
       " '_classes': None,\n",
       " '_n_classes': None,\n",
       " 'feature_fraction': 0.8333333626748276,\n",
       " 'bagging_fraction': 0.7925725958360194,\n",
       " 'bagging_freq': 3,\n",
       " '_fobj': None,\n",
       " 'fitted_': True}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(final_models_load['lightgbm']._final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "99a5ebd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_models_load['gbr']._final_estimator, 'feature')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd90d9c",
   "metadata": {},
   "source": [
    "## Applicability Domain - Williams Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20aec360",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_train = predict_model(final_models_load['gbr'], data=data_train_df, round=2)\n",
    "res_test = predict_model(final_models_load['gbr'], data=data_test_df, round=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "db1b2fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f28e33d8a90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_plot = f.calculate_leverage(res_train)\n",
    "train_plot = f.calculate_residuals(train_plot)\n",
    "train_plot = f.calculate_standard_residuals(train_plot)\n",
    "\n",
    "test_plot = f.calculate_leverage(res_test)\n",
    "test_plot = f.calculate_residuals(test_plot)\n",
    "test_plot = f.calculate_standard_residuals(test_plot)\n",
    "\n",
    "hat = data_train_df.shape[1] * 3 / data_train_df.shape[0]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(train_plot['Leverage'], train_plot['Standard Residual'], marker='o', c='green', label = 'Train')\n",
    "ax.scatter(test_plot['Leverage'], test_plot['Standard Residual'], marker='o', c='orange', label = 'Test')\n",
    "ax.set_xlabel('Leverage')\n",
    "ax.set_ylabel('Standardized Residuals')\n",
    "ax.axhline(y=3.0, xmin=0, xmax=3.0, color='k')\n",
    "ax.axhline(y=-3.0, xmin=0.0, xmax=3.0, color='k')\n",
    "ax.axvline(x=hat, ymin=-10.0, ymax=10.0, color='k')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2a91743c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid    1.0\n",
      "Name: Within Domain, dtype: float64\n",
      "Valid    24\n",
      "Name: Within Domain, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(test_plot['Within Domain'].value_counts(normalize=True))\n",
    "print(test_plot['Within Domain'].value_counts(normalize=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f292949f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid    1.0\n",
      "Name: Within SD, dtype: float64\n",
      "Valid    24\n",
      "Name: Within SD, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(test_plot['Within SD'].value_counts(normalize=True))\n",
    "print(test_plot['Within SD'].value_counts(normalize=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b3956f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid    1.0\n",
      "Name: Within Domain and SD, dtype: float64\n",
      "Valid    24\n",
      "Name: Within Domain and SD, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(test_plot['Within Domain and SD'].value_counts(normalize=True))\n",
    "print(test_plot['Within Domain and SD'].value_counts(normalize=False))"
   ]
  }
 ],
 "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
}