{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.regression import *\n",
    "import pandas as pd\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>steps</th>\n",
       "      <th>distance</th>\n",
       "      <th>runDistance</th>\n",
       "      <th>calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1901</td>\n",
       "      <td>1279</td>\n",
       "      <td>179</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>995</td>\n",
       "      <td>669</td>\n",
       "      <td>122</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7950</td>\n",
       "      <td>5749</td>\n",
       "      <td>263</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1419</td>\n",
       "      <td>955</td>\n",
       "      <td>172</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1367</td>\n",
       "      <td>920</td>\n",
       "      <td>109</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   steps  distance  runDistance  calories\n",
       "0   1901      1279          179        44\n",
       "1    995       669          122        24\n",
       "2   7950      5749          263       182\n",
       "3   1419       955          172        36\n",
       "4   1367       920          109        33"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/MiFit/Export data/ACTIVITY/ACTIVITY_1704203167901.csv')\n",
    "df1 = pd.read_csv('../../data/MiFit/Export data 170420/ACTIVITY/ACTIVITY_1704202151453.csv')\n",
    "df = pd.concat([df, df1]) \n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "# drop date\n",
    "df = df.drop(columns=['date'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                           "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                    Model      MAE         MSE      RMSE  \\\n",
      "lr                      Linear Regression   4.7829     45.0952    6.6513   \n",
      "ridge                    Ridge Regression   4.7829     45.0952    6.6513   \n",
      "llar         Lasso Least Angle Regression   4.7837     45.0909    6.6511   \n",
      "br                         Bayesian Ridge   4.7844     45.0890    6.6511   \n",
      "lasso                    Lasso Regression   4.9302     47.7252    6.8417   \n",
      "en                            Elastic Net   4.9296     47.7112    6.8408   \n",
      "huber                     Huber Regressor   4.7640     48.5392    6.8969   \n",
      "et                  Extra Trees Regressor   5.5507     77.8235    8.6873   \n",
      "catboost               CatBoost Regressor   5.6985     85.5234    8.9096   \n",
      "gbr           Gradient Boosting Regressor   5.7667     81.8716    8.8728   \n",
      "rf                Random Forest Regressor   5.7330     82.1203    8.9179   \n",
      "knn                 K Neighbors Regressor   5.9197     95.4778    9.4639   \n",
      "xgboost         Extreme Gradient Boosting   6.1679    107.6687   10.0229   \n",
      "omp           Orthogonal Matching Pursuit   6.7567    110.3968   10.3185   \n",
      "dt                Decision Tree Regressor   6.3781    114.0464   10.4010   \n",
      "ada                    AdaBoost Regressor   9.1984    142.5733   11.7862   \n",
      "lightgbm  Light Gradient Boosting Machine   6.9481    187.2160   13.1084   \n",
      "lar                Least Angle Regression   9.7533    225.9983   14.1739   \n",
      "par          Passive Aggressive Regressor  10.0749    269.2730   14.9989   \n",
      "dummy                     Dummy Regressor  86.8189  10798.3589  102.9788   \n",
      "\n",
      "              R2   RMSLE    MAPE  TT (Sec)  \n",
      "lr        0.9956  0.5003  0.1049     0.004  \n",
      "ridge     0.9956  0.5003  0.1049     0.003  \n",
      "llar      0.9956  0.5000  0.1049     0.004  \n",
      "br        0.9956  0.4994  0.1047     0.003  \n",
      "lasso     0.9954  0.4383  0.0899     0.005  \n",
      "en        0.9954  0.4384  0.0899     0.003  \n",
      "huber     0.9953  0.4554  0.0939     0.004  \n",
      "et        0.9926  0.0725  0.0620     0.022  \n",
      "catboost  0.9923  0.0909  0.0615     0.145  \n",
      "gbr       0.9923  0.1229  0.0612     0.010  \n",
      "rf        0.9922  0.0760  0.0642     0.028  \n",
      "knn       0.9912  0.0781  0.0640     0.005  \n",
      "xgboost   0.9901  0.0937  0.0722     0.010  \n",
      "omp       0.9895  0.2696  0.0793     0.003  \n",
      "dt        0.9894  0.0928  0.0757     0.003  \n",
      "ada       0.9861  0.9246  0.3523     0.009  \n",
      "lightgbm  0.9830  0.0815  0.0693     0.212  \n",
      "lar       0.9797  0.7802  0.2691     0.003  \n",
      "par       0.9750  0.1154  0.0963     0.003  \n",
      "dummy    -0.0389  1.8488  3.6105     0.003  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "setup(df, target=\"calories\", verbose=False, session_id=42, html=False)\n",
    "setup_df = pull()\n",
    "best_model = compare_models()\n",
    "compare_df = pull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(best_model, plot='feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_09958_row10_col0, #T_09958_row10_col1, #T_09958_row10_col2, #T_09958_row10_col3, #T_09958_row10_col4, #T_09958_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_09958\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_09958_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_09958_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_09958_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_09958_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_09958_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_09958_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_09958_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_09958_row0_col0\" class=\"data row0 col0\" >6.3405</td>\n",
       "      <td id=\"T_09958_row0_col1\" class=\"data row0 col1\" >100.1166</td>\n",
       "      <td id=\"T_09958_row0_col2\" class=\"data row0 col2\" >10.0058</td>\n",
       "      <td id=\"T_09958_row0_col3\" class=\"data row0 col3\" >0.9907</td>\n",
       "      <td id=\"T_09958_row0_col4\" class=\"data row0 col4\" >0.0975</td>\n",
       "      <td id=\"T_09958_row0_col5\" class=\"data row0 col5\" >0.0732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_09958_row1_col0\" class=\"data row1 col0\" >8.6717</td>\n",
       "      <td id=\"T_09958_row1_col1\" class=\"data row1 col1\" >241.8569</td>\n",
       "      <td id=\"T_09958_row1_col2\" class=\"data row1 col2\" >15.5518</td>\n",
       "      <td id=\"T_09958_row1_col3\" class=\"data row1 col3\" >0.9857</td>\n",
       "      <td id=\"T_09958_row1_col4\" class=\"data row1 col4\" >0.1115</td>\n",
       "      <td id=\"T_09958_row1_col5\" class=\"data row1 col5\" >0.0898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_09958_row2_col0\" class=\"data row2 col0\" >4.0451</td>\n",
       "      <td id=\"T_09958_row2_col1\" class=\"data row2 col1\" >34.8048</td>\n",
       "      <td id=\"T_09958_row2_col2\" class=\"data row2 col2\" >5.8996</td>\n",
       "      <td id=\"T_09958_row2_col3\" class=\"data row2 col3\" >0.9955</td>\n",
       "      <td id=\"T_09958_row2_col4\" class=\"data row2 col4\" >0.1060</td>\n",
       "      <td id=\"T_09958_row2_col5\" class=\"data row2 col5\" >0.0688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_09958_row3_col0\" class=\"data row3 col0\" >5.2283</td>\n",
       "      <td id=\"T_09958_row3_col1\" class=\"data row3 col1\" >55.1768</td>\n",
       "      <td id=\"T_09958_row3_col2\" class=\"data row3 col2\" >7.4281</td>\n",
       "      <td id=\"T_09958_row3_col3\" class=\"data row3 col3\" >0.9924</td>\n",
       "      <td id=\"T_09958_row3_col4\" class=\"data row3 col4\" >0.1264</td>\n",
       "      <td id=\"T_09958_row3_col5\" class=\"data row3 col5\" >0.0867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_09958_row4_col0\" class=\"data row4 col0\" >6.9310</td>\n",
       "      <td id=\"T_09958_row4_col1\" class=\"data row4 col1\" >87.6129</td>\n",
       "      <td id=\"T_09958_row4_col2\" class=\"data row4 col2\" >9.3602</td>\n",
       "      <td id=\"T_09958_row4_col3\" class=\"data row4 col3\" >0.9923</td>\n",
       "      <td id=\"T_09958_row4_col4\" class=\"data row4 col4\" >0.0775</td>\n",
       "      <td id=\"T_09958_row4_col5\" class=\"data row4 col5\" >0.0644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_09958_row5_col0\" class=\"data row5 col0\" >5.8177</td>\n",
       "      <td id=\"T_09958_row5_col1\" class=\"data row5 col1\" >91.7191</td>\n",
       "      <td id=\"T_09958_row5_col2\" class=\"data row5 col2\" >9.5770</td>\n",
       "      <td id=\"T_09958_row5_col3\" class=\"data row5 col3\" >0.9913</td>\n",
       "      <td id=\"T_09958_row5_col4\" class=\"data row5 col4\" >0.0750</td>\n",
       "      <td id=\"T_09958_row5_col5\" class=\"data row5 col5\" >0.0598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_09958_row6_col0\" class=\"data row6 col0\" >5.3243</td>\n",
       "      <td id=\"T_09958_row6_col1\" class=\"data row6 col1\" >61.6801</td>\n",
       "      <td id=\"T_09958_row6_col2\" class=\"data row6 col2\" >7.8537</td>\n",
       "      <td id=\"T_09958_row6_col3\" class=\"data row6 col3\" >0.9919</td>\n",
       "      <td id=\"T_09958_row6_col4\" class=\"data row6 col4\" >0.0847</td>\n",
       "      <td id=\"T_09958_row6_col5\" class=\"data row6 col5\" >0.0742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_09958_row7_col0\" class=\"data row7 col0\" >7.1095</td>\n",
       "      <td id=\"T_09958_row7_col1\" class=\"data row7 col1\" >173.9765</td>\n",
       "      <td id=\"T_09958_row7_col2\" class=\"data row7 col2\" >13.1900</td>\n",
       "      <td id=\"T_09958_row7_col3\" class=\"data row7 col3\" >0.9844</td>\n",
       "      <td id=\"T_09958_row7_col4\" class=\"data row7 col4\" >0.0865</td>\n",
       "      <td id=\"T_09958_row7_col5\" class=\"data row7 col5\" >0.0697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_09958_row8_col0\" class=\"data row8 col0\" >5.7038</td>\n",
       "      <td id=\"T_09958_row8_col1\" class=\"data row8 col1\" >96.0615</td>\n",
       "      <td id=\"T_09958_row8_col2\" class=\"data row8 col2\" >9.8011</td>\n",
       "      <td id=\"T_09958_row8_col3\" class=\"data row8 col3\" >0.9880</td>\n",
       "      <td id=\"T_09958_row8_col4\" class=\"data row8 col4\" >0.1116</td>\n",
       "      <td id=\"T_09958_row8_col5\" class=\"data row8 col5\" >0.0880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_09958_row9_col0\" class=\"data row9 col0\" >6.5070</td>\n",
       "      <td id=\"T_09958_row9_col1\" class=\"data row9 col1\" >133.6823</td>\n",
       "      <td id=\"T_09958_row9_col2\" class=\"data row9 col2\" >11.5621</td>\n",
       "      <td id=\"T_09958_row9_col3\" class=\"data row9 col3\" >0.9891</td>\n",
       "      <td id=\"T_09958_row9_col4\" class=\"data row9 col4\" >0.0604</td>\n",
       "      <td id=\"T_09958_row9_col5\" class=\"data row9 col5\" >0.0469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_09958_row10_col0\" class=\"data row10 col0\" >6.1679</td>\n",
       "      <td id=\"T_09958_row10_col1\" class=\"data row10 col1\" >107.6687</td>\n",
       "      <td id=\"T_09958_row10_col2\" class=\"data row10 col2\" >10.0229</td>\n",
       "      <td id=\"T_09958_row10_col3\" class=\"data row10 col3\" >0.9901</td>\n",
       "      <td id=\"T_09958_row10_col4\" class=\"data row10 col4\" >0.0937</td>\n",
       "      <td id=\"T_09958_row10_col5\" class=\"data row10 col5\" >0.0722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_09958_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_09958_row11_col0\" class=\"data row11 col0\" >1.1967</td>\n",
       "      <td id=\"T_09958_row11_col1\" class=\"data row11 col1\" >58.4019</td>\n",
       "      <td id=\"T_09958_row11_col2\" class=\"data row11 col2\" >2.6851</td>\n",
       "      <td id=\"T_09958_row11_col3\" class=\"data row11 col3\" >0.0032</td>\n",
       "      <td id=\"T_09958_row11_col4\" class=\"data row11 col4\" >0.0193</td>\n",
       "      <td id=\"T_09958_row11_col5\" class=\"data row11 col5\" >0.0128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17e3ecf40>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('xgboost')\n",
    "params = lr.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.classification import *\n",
    "from pycaret.datasets import get_data\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ConnectionError",
     "evalue": "HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /pycaret/datasets/main/data/common/diabetes.csv (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x29db80fa0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known'))",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/connection.py:174\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 174\u001b[0m     conn \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dns_host\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mextra_kw\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout:\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/util/connection.py:72\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m six\u001b[38;5;241m.\u001b[39mraise_from(\n\u001b[1;32m     69\u001b[0m         LocationParseError(\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, label empty or too long\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m host), \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     70\u001b[0m     )\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43msocket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfamily\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msocket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSOCK_STREAM\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m     73\u001b[0m     af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/socket.py:918\u001b[0m, in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m    917\u001b[0m addrlist \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 918\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43m_socket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfamily\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproto\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    919\u001b[0m     af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n",
      "\u001b[0;31mgaierror\u001b[0m: [Errno 8] nodename nor servname provided, or not known",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/connectionpool.py:715\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    714\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 715\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    719\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    720\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    721\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    722\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    723\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    725\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m    726\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m    727\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m    728\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/connectionpool.py:404\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 404\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    406\u001b[0m     \u001b[38;5;66;03m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/connectionpool.py:1058\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(conn, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msock\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):  \u001b[38;5;66;03m# AppEngine might not have  `.sock`\u001b[39;00m\n\u001b[0;32m-> 1058\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1060\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_verified:\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/connection.py:363\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    361\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    362\u001b[0m     \u001b[38;5;66;03m# Add certificate verification\u001b[39;00m\n\u001b[0;32m--> 363\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_new_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    364\u001b[0m     hostname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/connection.py:186\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 186\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NewConnectionError(\n\u001b[1;32m    187\u001b[0m         \u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to establish a new connection: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m e\n\u001b[1;32m    188\u001b[0m     )\n\u001b[1;32m    190\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m conn\n",
      "\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPSConnection object at 0x29db80fa0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/connectionpool.py:799\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    797\u001b[0m     e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, e)\n\u001b[0;32m--> 799\u001b[0m retries \u001b[38;5;241m=\u001b[39m \u001b[43mretries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    802\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/urllib3/util/retry.py:592\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_retry\u001b[38;5;241m.\u001b[39mis_exhausted():\n\u001b[0;32m--> 592\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause))\n\u001b[1;32m    594\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
      "\u001b[0;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /pycaret/datasets/main/data/common/diabetes.csv (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x29db80fa0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdiabetes\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m df\u001b[38;5;241m.\u001b[39mdescribe()\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/pycaret/datasets.py:116\u001b[0m, in \u001b[0;36mget_data\u001b[0;34m(dataset, folder, save_copy, profile, verbose, address)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(filename):\n\u001b[1;32m    115\u001b[0m     data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(filename)\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcomplete_address\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m200\u001b[39m:\n\u001b[1;32m    117\u001b[0m     data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(complete_address)\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m sktime_datasets:\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/requests/api.py:73\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(url, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     63\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \n\u001b[1;32m     65\u001b[0m \u001b[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;124;03m    :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mget\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/requests/adapters.py:519\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    515\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, _SSLError):\n\u001b[1;32m    516\u001b[0m         \u001b[38;5;66;03m# This branch is for urllib3 v1.22 and later.\u001b[39;00m\n\u001b[1;32m    517\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m SSLError(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m--> 519\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m    521\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ClosedPoolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    522\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(e, request\u001b[38;5;241m=\u001b[39mrequest)\n",
      "\u001b[0;31mConnectionError\u001b[0m: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /pycaret/datasets/main/data/common/diabetes.csv (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x29db80fa0>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known'))"
     ]
    }
   ],
   "source": [
    "df = get_data('diabetes')\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6264 entries, 0 to 6263\n",
      "Data columns (total 18 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   age                   6264 non-null   int64  \n",
      " 1   gender                6264 non-null   int64  \n",
      " 2   height                6264 non-null   float64\n",
      " 3   weight                6264 non-null   float64\n",
      " 4   steps                 6264 non-null   float64\n",
      " 5   hear_rate             6264 non-null   float64\n",
      " 6   calories              6264 non-null   float64\n",
      " 7   distance              6264 non-null   float64\n",
      " 8   entropy_heart         6264 non-null   float64\n",
      " 9   entropy_setps         6264 non-null   float64\n",
      " 10  resting_heart         6264 non-null   float64\n",
      " 11  corr_heart_steps      6264 non-null   float64\n",
      " 12  norm_heart            6264 non-null   float64\n",
      " 13  intensity_karvonen    6264 non-null   float64\n",
      " 14  sd_norm_heart         6264 non-null   float64\n",
      " 15  steps_times_distance  6264 non-null   float64\n",
      " 16  device                6264 non-null   object \n",
      " 17  activity              6264 non-null   object \n",
      "dtypes: float64(14), int64(2), object(2)\n",
      "memory usage: 881.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/aw_fb/aw_fb_data.csv')\n",
    "df = df.drop(['Unnamed: 0', 'X1'], axis=1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Number of times pregnant</th>\n",
       "      <th>Plasma glucose concentration a 2 hours in an oral glucose tolerance test</th>\n",
       "      <th>Diastolic blood pressure (mm Hg)</th>\n",
       "      <th>Triceps skin fold thickness (mm)</th>\n",
       "      <th>2-Hour serum insulin (mu U/ml)</th>\n",
       "      <th>Body mass index (weight in kg/(height in m)^2)</th>\n",
       "      <th>Diabetes pedigree function</th>\n",
       "      <th>Age (years)</th>\n",
       "      <th>Class variable</th>\n",
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       "      <td>35</td>\n",
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       "      <td>2.288</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of times pregnant  \\\n",
       "0                         6   \n",
       "1                         1   \n",
       "2                         8   \n",
       "3                         1   \n",
       "4                         0   \n",
       "\n",
       "   Plasma glucose concentration a 2 hours in an oral glucose tolerance test  \\\n",
       "0                                                148                          \n",
       "1                                                 85                          \n",
       "2                                                183                          \n",
       "3                                                 89                          \n",
       "4                                                137                          \n",
       "\n",
       "   Diastolic blood pressure (mm Hg)  Triceps skin fold thickness (mm)  \\\n",
       "0                                72                                35   \n",
       "1                                66                                29   \n",
       "2                                64                                 0   \n",
       "3                                66                                23   \n",
       "4                                40                                35   \n",
       "\n",
       "   2-Hour serum insulin (mu U/ml)  \\\n",
       "0                               0   \n",
       "1                               0   \n",
       "2                               0   \n",
       "3                              94   \n",
       "4                             168   \n",
       "\n",
       "   Body mass index (weight in kg/(height in m)^2)  Diabetes pedigree function  \\\n",
       "0                                            33.6                       0.627   \n",
       "1                                            26.6                       0.351   \n",
       "2                                            23.3                       0.672   \n",
       "3                                            28.1                       0.167   \n",
       "4                                            43.1                       2.288   \n",
       "\n",
       "   Age (years)  Class variable  \n",
       "0           50               1  \n",
       "1           31               0  \n",
       "2           32               1  \n",
       "3           21               0  \n",
       "4           33               1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of times pregnant</th>\n",
       "      <th>Plasma glucose concentration a 2 hours in an oral glucose tolerance test</th>\n",
       "      <th>Diastolic blood pressure (mm Hg)</th>\n",
       "      <th>Triceps skin fold thickness (mm)</th>\n",
       "      <th>2-Hour serum insulin (mu U/ml)</th>\n",
       "      <th>Body mass index (weight in kg/(height in m)^2)</th>\n",
       "      <th>Diabetes pedigree function</th>\n",
       "      <th>Age (years)</th>\n",
       "      <th>Class variable</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>148</td>\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>32</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>94</td>\n",
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       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of times pregnant  \\\n",
       "0                         6   \n",
       "1                         1   \n",
       "2                         8   \n",
       "3                         1   \n",
       "\n",
       "   Plasma glucose concentration a 2 hours in an oral glucose tolerance test  \\\n",
       "0                                                148                          \n",
       "1                                                 85                          \n",
       "2                                                183                          \n",
       "3                                                 89                          \n",
       "\n",
       "   Diastolic blood pressure (mm Hg)  Triceps skin fold thickness (mm)  \\\n",
       "0                                72                                35   \n",
       "1                                66                                29   \n",
       "2                                64                                 0   \n",
       "3                                66                                23   \n",
       "\n",
       "   2-Hour serum insulin (mu U/ml)  \\\n",
       "0                               0   \n",
       "1                               0   \n",
       "2                               0   \n",
       "3                              94   \n",
       "\n",
       "   Body mass index (weight in kg/(height in m)^2)  Diabetes pedigree function  \\\n",
       "0                                            33.6                       0.627   \n",
       "1                                            26.6                       0.351   \n",
       "2                                            23.3                       0.672   \n",
       "3                                            28.1                       0.167   \n",
       "\n",
       "   Age (years)  Class variable  \n",
       "0           50               1  \n",
       "1           31               0  \n",
       "2           32               1  \n",
       "3           21               0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = get_data('diabetes')÷\n",
    "# group by last column\n",
    "train_df = df.groupby(df.columns[-1]).head(2)\n",
    "# drop train_df from df\n",
    "# df = df.drop(train_df.index)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df_aw['activity'] = le.fit_transform(df_aw['activity'])\n",
    "df_fb['activity'] = le.fit_transform(df_fb['activity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>steps</th>\n",
       "      <th>hear_rate</th>\n",
       "      <th>calories</th>\n",
       "      <th>distance</th>\n",
       "      <th>entropy_heart</th>\n",
       "      <th>entropy_setps</th>\n",
       "      <th>resting_heart</th>\n",
       "      <th>corr_heart_steps</th>\n",
       "      <th>norm_heart</th>\n",
       "      <th>intensity_karvonen</th>\n",
       "      <th>sd_norm_heart</th>\n",
       "      <th>steps_times_distance</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>19.540825</td>\n",
       "      <td>0.138587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115287</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>12.883117</td>\n",
       "      <td>78.628260</td>\n",
       "      <td>10.154556</td>\n",
       "      <td>0.010035</td>\n",
       "      <td>6.221612</td>\n",
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       "      <td>59.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.628260</td>\n",
       "      <td>0.139208</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129286</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>13.587013</td>\n",
       "      <td>78.715695</td>\n",
       "      <td>10.825111</td>\n",
       "      <td>0.010605</td>\n",
       "      <td>6.221612</td>\n",
       "      <td>6.116349</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>0.982816</td>\n",
       "      <td>19.715695</td>\n",
       "      <td>0.139828</td>\n",
       "      <td>0.241567</td>\n",
       "      <td>0.144088</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>71.4</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>157.250000</td>\n",
       "      <td>0.701500</td>\n",
       "      <td>0.075200</td>\n",
       "      <td>6.162427</td>\n",
       "      <td>5.655310</td>\n",
       "      <td>79.421795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.828205</td>\n",
       "      <td>0.822898</td>\n",
       "      <td>7.270204</td>\n",
       "      <td>12.257600</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>71.4</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>157.307692</td>\n",
       "      <td>0.701500</td>\n",
       "      <td>0.075475</td>\n",
       "      <td>6.162427</td>\n",
       "      <td>5.655310</td>\n",
       "      <td>79.421795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.885897</td>\n",
       "      <td>0.823508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.503167</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>71.4</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>156.250000</td>\n",
       "      <td>0.732000</td>\n",
       "      <td>0.075695</td>\n",
       "      <td>6.162427</td>\n",
       "      <td>5.655310</td>\n",
       "      <td>79.421795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.828205</td>\n",
       "      <td>0.812325</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.510941</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>71.4</td>\n",
       "      <td>6.791667</td>\n",
       "      <td>158.090909</td>\n",
       "      <td>0.612500</td>\n",
       "      <td>0.077270</td>\n",
       "      <td>6.162427</td>\n",
       "      <td>5.655310</td>\n",
       "      <td>79.421795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>78.669114</td>\n",
       "      <td>0.831789</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.524792</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3655</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>71.4</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>157.230769</td>\n",
       "      <td>0.671000</td>\n",
       "      <td>0.075965</td>\n",
       "      <td>6.162427</td>\n",
       "      <td>5.655310</td>\n",
       "      <td>79.421795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.808974</td>\n",
       "      <td>0.822695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.512764</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3656 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  height  weight       steps   hear_rate   calories  \\\n",
       "0      20       1   168.0    65.4   10.771429   78.531302   0.344533   \n",
       "1      20       1   168.0    65.4   11.475325   78.453390   3.287625   \n",
       "2      20       1   168.0    65.4   12.179221   78.540825   9.484000   \n",
       "3      20       1   168.0    65.4   12.883117   78.628260  10.154556   \n",
       "4      20       1   168.0    65.4   13.587013   78.715695  10.825111   \n",
       "...   ...     ...     ...     ...         ...         ...        ...   \n",
       "3651   46       0   157.5    71.4  163.000000  157.250000   0.701500   \n",
       "3652   46       0   157.5    71.4    6.666667  157.307692   0.701500   \n",
       "3653   46       0   157.5    71.4    6.750000  156.250000   0.732000   \n",
       "3654   46       0   157.5    71.4    6.791667  158.090909   0.612500   \n",
       "3655   46       0   157.5    71.4    6.750000  157.230769   0.671000   \n",
       "\n",
       "      distance  entropy_heart  entropy_setps  resting_heart  corr_heart_steps  \\\n",
       "0     0.008327       6.221612       6.116349      59.000000          1.000000   \n",
       "1     0.008896       6.221612       6.116349      59.000000          1.000000   \n",
       "2     0.009466       6.221612       6.116349      59.000000          1.000000   \n",
       "3     0.010035       6.221612       6.116349      59.000000          1.000000   \n",
       "4     0.010605       6.221612       6.116349      59.000000          0.982816   \n",
       "...        ...            ...            ...            ...               ...   \n",
       "3651  0.075200       6.162427       5.655310      79.421795          1.000000   \n",
       "3652  0.075475       6.162427       5.655310      79.421795          1.000000   \n",
       "3653  0.075695       6.162427       5.655310      79.421795          1.000000   \n",
       "3654  0.077270       6.162427       5.655310      79.421795          1.000000   \n",
       "3655  0.075965       6.162427       5.655310      79.421795          1.000000   \n",
       "\n",
       "      norm_heart  intensity_karvonen  sd_norm_heart  steps_times_distance  \\\n",
       "0      19.531302            0.138520       1.000000              0.089692   \n",
       "1      19.453390            0.137967       1.000000              0.102088   \n",
       "2      19.540825            0.138587       1.000000              0.115287   \n",
       "3      19.628260            0.139208       1.000000              0.129286   \n",
       "4      19.715695            0.139828       0.241567              0.144088   \n",
       "...          ...                 ...            ...                   ...   \n",
       "3651   77.828205            0.822898       7.270204             12.257600   \n",
       "3652   77.885897            0.823508       1.000000              0.503167   \n",
       "3653   76.828205            0.812325       1.000000              0.510941   \n",
       "3654   78.669114            0.831789       1.000000              0.524792   \n",
       "3655   77.808974            0.822695       1.000000              0.512764   \n",
       "\n",
       "      activity  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "...        ...  \n",
       "3651         3  \n",
       "3652         3  \n",
       "3653         3  \n",
       "3654         3  \n",
       "3655         3  \n",
       "\n",
       "[3656 rows x 17 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>steps</th>\n",
       "      <th>hear_rate</th>\n",
       "      <th>calories</th>\n",
       "      <th>distance</th>\n",
       "      <th>entropy_heart</th>\n",
       "      <th>entropy_setps</th>\n",
       "      <th>resting_heart</th>\n",
       "      <th>corr_heart_steps</th>\n",
       "      <th>norm_heart</th>\n",
       "      <th>intensity_karvonen</th>\n",
       "      <th>sd_norm_heart</th>\n",
       "      <th>steps_times_distance</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>10.771429</td>\n",
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       "      <td>59.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.531302</td>\n",
       "      <td>0.138520</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089692</td>\n",
       "      <td>Lying</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>11.475325</td>\n",
       "      <td>78.453390</td>\n",
       "      <td>3.287625</td>\n",
       "      <td>0.008896</td>\n",
       "      <td>6.221612</td>\n",
       "      <td>6.116349</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.453390</td>\n",
       "      <td>0.137967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102088</td>\n",
       "      <td>Lying</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>12.179221</td>\n",
       "      <td>78.540825</td>\n",
       "      <td>9.484000</td>\n",
       "      <td>0.009466</td>\n",
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       "      <td>59.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.540825</td>\n",
       "      <td>0.138587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115287</td>\n",
       "      <td>Lying</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>12.883117</td>\n",
       "      <td>78.628260</td>\n",
       "      <td>10.154556</td>\n",
       "      <td>0.010035</td>\n",
       "      <td>6.221612</td>\n",
       "      <td>6.116349</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.628260</td>\n",
       "      <td>0.139208</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129286</td>\n",
       "      <td>Lying</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>13.587013</td>\n",
       "      <td>78.715695</td>\n",
       "      <td>10.825111</td>\n",
       "      <td>0.010605</td>\n",
       "      <td>6.221612</td>\n",
       "      <td>6.116349</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>0.982816</td>\n",
       "      <td>19.715695</td>\n",
       "      <td>0.139828</td>\n",
       "      <td>0.241567</td>\n",
       "      <td>0.144088</td>\n",
       "      <td>Lying</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.822898</td>\n",
       "      <td>7.270204</td>\n",
       "      <td>12.257600</td>\n",
       "      <td>Running 7 METs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.823508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.503167</td>\n",
       "      <td>Running 7 METs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>46</td>\n",
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       "      <td>71.4</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>156.250000</td>\n",
       "      <td>0.732000</td>\n",
       "      <td>0.075695</td>\n",
       "      <td>6.162427</td>\n",
       "      <td>5.655310</td>\n",
       "      <td>79.421795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.828205</td>\n",
       "      <td>0.812325</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.510941</td>\n",
       "      <td>Running 7 METs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>71.4</td>\n",
       "      <td>6.791667</td>\n",
       "      <td>158.090909</td>\n",
       "      <td>0.612500</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.075965</td>\n",
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       "      <td>79.421795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.808974</td>\n",
       "      <td>0.822695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.512764</td>\n",
       "      <td>Running 7 METs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3656 rows × 17 columns</p>\n",
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      ],
      "text/plain": [
       "      age  gender  height  weight       steps   hear_rate   calories  \\\n",
       "0      20       1   168.0    65.4   10.771429   78.531302   0.344533   \n",
       "1      20       1   168.0    65.4   11.475325   78.453390   3.287625   \n",
       "2      20       1   168.0    65.4   12.179221   78.540825   9.484000   \n",
       "3      20       1   168.0    65.4   12.883117   78.628260  10.154556   \n",
       "4      20       1   168.0    65.4   13.587013   78.715695  10.825111   \n",
       "...   ...     ...     ...     ...         ...         ...        ...   \n",
       "3651   46       0   157.5    71.4  163.000000  157.250000   0.701500   \n",
       "3652   46       0   157.5    71.4    6.666667  157.307692   0.701500   \n",
       "3653   46       0   157.5    71.4    6.750000  156.250000   0.732000   \n",
       "3654   46       0   157.5    71.4    6.791667  158.090909   0.612500   \n",
       "3655   46       0   157.5    71.4    6.750000  157.230769   0.671000   \n",
       "\n",
       "      distance  entropy_heart  entropy_setps  resting_heart  corr_heart_steps  \\\n",
       "0     0.008327       6.221612       6.116349      59.000000          1.000000   \n",
       "1     0.008896       6.221612       6.116349      59.000000          1.000000   \n",
       "2     0.009466       6.221612       6.116349      59.000000          1.000000   \n",
       "3     0.010035       6.221612       6.116349      59.000000          1.000000   \n",
       "4     0.010605       6.221612       6.116349      59.000000          0.982816   \n",
       "...        ...            ...            ...            ...               ...   \n",
       "3651  0.075200       6.162427       5.655310      79.421795          1.000000   \n",
       "3652  0.075475       6.162427       5.655310      79.421795          1.000000   \n",
       "3653  0.075695       6.162427       5.655310      79.421795          1.000000   \n",
       "3654  0.077270       6.162427       5.655310      79.421795          1.000000   \n",
       "3655  0.075965       6.162427       5.655310      79.421795          1.000000   \n",
       "\n",
       "      norm_heart  intensity_karvonen  sd_norm_heart  steps_times_distance  \\\n",
       "0      19.531302            0.138520       1.000000              0.089692   \n",
       "1      19.453390            0.137967       1.000000              0.102088   \n",
       "2      19.540825            0.138587       1.000000              0.115287   \n",
       "3      19.628260            0.139208       1.000000              0.129286   \n",
       "4      19.715695            0.139828       0.241567              0.144088   \n",
       "...          ...                 ...            ...                   ...   \n",
       "3651   77.828205            0.822898       7.270204             12.257600   \n",
       "3652   77.885897            0.823508       1.000000              0.503167   \n",
       "3653   76.828205            0.812325       1.000000              0.510941   \n",
       "3654   78.669114            0.831789       1.000000              0.524792   \n",
       "3655   77.808974            0.822695       1.000000              0.512764   \n",
       "\n",
       "            activity  \n",
       "0              Lying  \n",
       "1              Lying  \n",
       "2              Lying  \n",
       "3              Lying  \n",
       "4              Lying  \n",
       "...              ...  \n",
       "3651  Running 7 METs  \n",
       "3652  Running 7 METs  \n",
       "3653  Running 7 METs  \n",
       "3654  Running 7 METs  \n",
       "3655  Running 7 METs  \n",
       "\n",
       "[3656 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aw = df[df['device'] == 'apple watch']\n",
    "df_fb = df[df['device'] == 'fitbit']\n",
    "df_aw = df_aw.drop('device', axis=1)\n",
    "df_fb = df_fb.drop('device',axis=1)\n",
    "df_aw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Lying', 'Running 3 METs', 'Running 5 METs', 'Running 7 METs',\n",
       "       'Self Pace walk', 'Sitting'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aw['activity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stratified_partition_with_all_values(df, column, n_partitions, partition_id):\n",
    "    # Group the data by the column of interest\n",
    "    grouped = df.groupby(column)\n",
    "\n",
    "    # Initialize empty list to store partitions\n",
    "    partitions = [[] for _ in range(n_partitions)]\n",
    "\n",
    "    # Iterate over groups\n",
    "    for name, group in grouped:\n",
    "        # Randomly shuffle the data within the group\n",
    "        group = group.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "        # Calculate the number of samples in each partition for this group\n",
    "        samples_per_partition = len(group) // n_partitions\n",
    "\n",
    "        # Distribute the data evenly among partitions, ensuring each partition has all values\n",
    "        for i in range(n_partitions):\n",
    "            start_idx = i * samples_per_partition\n",
    "            end_idx = (i + 1) * samples_per_partition\n",
    "            if i == n_partitions - 1:\n",
    "                end_idx = None  # Include remaining samples in the last partition\n",
    "            partition_data = group.iloc[start_idx:end_idx]\n",
    "            partitions[i].append(partition_data)\n",
    "\n",
    "    # Concatenate data frames in each partition\n",
    "    partitions = [pd.concat(partition) for partition in partitions]\n",
    "\n",
    "    return partitions[partition_id].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = stratified_partition_with_all_values(df_aw, 'activity', 3, 2)\n",
    "df['activity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_c3b0e_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_c3b0e_row0_col1\" class=\"data row0 col1\" >42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_c3b0e_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_c3b0e_row1_col1\" class=\"data row1 col1\" >activity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_c3b0e_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_c3b0e_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_c3b0e_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_c3b0e_row3_col1\" class=\"data row3 col1\" >(1222, 17)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_c3b0e_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_c3b0e_row4_col1\" class=\"data row4 col1\" >(1222, 17)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_c3b0e_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_c3b0e_row5_col1\" class=\"data row5 col1\" >(855, 17)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_c3b0e_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_c3b0e_row6_col1\" class=\"data row6 col1\" >(367, 17)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_c3b0e_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
       "      <td id=\"T_c3b0e_row7_col1\" class=\"data row7 col1\" >16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_c3b0e_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
       "      <td id=\"T_c3b0e_row8_col1\" class=\"data row8 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_c3b0e_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
       "      <td id=\"T_c3b0e_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_c3b0e_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_c3b0e_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_c3b0e_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_c3b0e_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_c3b0e_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_c3b0e_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_c3b0e_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
       "      <td id=\"T_c3b0e_row13_col1\" class=\"data row13 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_c3b0e_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_c3b0e_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_c3b0e_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
       "      <td id=\"T_c3b0e_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_c3b0e_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_c3b0e_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_c3b0e_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_c3b0e_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3b0e_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_c3b0e_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
       "      <td id=\"T_c3b0e_row18_col1\" class=\"data row18 col1\" >16c3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x28c525550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Name                                                          Log Loss\n",
       "Display Name                                                  Log Loss\n",
       "Score Function       <pycaret.internal.metrics.EncodedDecodedLabels...\n",
       "Scorer               make_scorer(log_loss, greater_is_better=False,...\n",
       "Target                                                      pred_proba\n",
       "Args                                                                {}\n",
       "Greater is Better                                                False\n",
       "Multiclass                                                        True\n",
       "Custom                                                            True\n",
       "Name: logloss, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import log_loss\n",
    "import numpy as np\n",
    "exp = ClassificationExperiment()\n",
    "N_CLIENTS = 3\n",
    "# df = np.array_split(df, N_CLIENTS)[\n",
    "# \tN_CLIENTS-1].reset_index(drop=True)\n",
    "df = stratified_partition_with_all_values(df_aw, 'activity', 3, 2)\n",
    "exp.setup(data=df, session_id=42)\n",
    "exp.add_metric('logloss', 'Log Loss', log_loss,\n",
    "               greater_is_better=False, target=\"pred_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Turbo</th>\n",
       "      <th>model_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>sklearn.linear_model._logistic.LogisticRegression</td>\n",
       "      <td>True</td>\n",
       "      <td>linear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>sklearn.neighbors._classification.KNeighborsCl...</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>sklearn.naive_bayes.GaussianNB</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>sklearn.tree._classes.DecisionTreeClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>SVM - Linear Kernel</td>\n",
       "      <td>sklearn.linear_model._stochastic_gradient.SGDC...</td>\n",
       "      <td>True</td>\n",
       "      <td>linear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rbfsvm</th>\n",
       "      <td>SVM - Radial Kernel</td>\n",
       "      <td>sklearn.svm._classes.SVC</td>\n",
       "      <td>False</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gpc</th>\n",
       "      <td>Gaussian Process Classifier</td>\n",
       "      <td>sklearn.gaussian_process._gpc.GaussianProcessC...</td>\n",
       "      <td>False</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>MLP Classifier</td>\n",
       "      <td>sklearn.neural_network._multilayer_perceptron....</td>\n",
       "      <td>False</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>Ridge Classifier</td>\n",
       "      <td>sklearn.linear_model._ridge.RidgeClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>linear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>sklearn.ensemble._forest.RandomForestClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qda</th>\n",
       "      <td>Quadratic Discriminant Analysis</td>\n",
       "      <td>sklearn.discriminant_analysis.QuadraticDiscrim...</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>Ada Boost Classifier</td>\n",
       "      <td>sklearn.ensemble._weight_boosting.AdaBoostClas...</td>\n",
       "      <td>True</td>\n",
       "      <td>ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gbc</th>\n",
       "      <td>Gradient Boosting Classifier</td>\n",
       "      <td>sklearn.ensemble._gb.GradientBoostingClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda</th>\n",
       "      <td>Linear Discriminant Analysis</td>\n",
       "      <td>sklearn.discriminant_analysis.LinearDiscrimina...</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>et</th>\n",
       "      <td>Extra Trees Classifier</td>\n",
       "      <td>sklearn.ensemble._forest.ExtraTreesClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>xgboost.sklearn.XGBClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>lightgbm.sklearn.LGBMClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Classifier</td>\n",
       "      <td>catboost.core.CatBoostClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>Dummy Classifier</td>\n",
       "      <td>sklearn.dummy.DummyClassifier</td>\n",
       "      <td>True</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Name  \\\n",
       "ID                                          \n",
       "lr                    Logistic Regression   \n",
       "knn                K Neighbors Classifier   \n",
       "nb                            Naive Bayes   \n",
       "dt               Decision Tree Classifier   \n",
       "svm                   SVM - Linear Kernel   \n",
       "rbfsvm                SVM - Radial Kernel   \n",
       "gpc           Gaussian Process Classifier   \n",
       "mlp                        MLP Classifier   \n",
       "ridge                    Ridge Classifier   \n",
       "rf               Random Forest Classifier   \n",
       "qda       Quadratic Discriminant Analysis   \n",
       "ada                  Ada Boost Classifier   \n",
       "gbc          Gradient Boosting Classifier   \n",
       "lda          Linear Discriminant Analysis   \n",
       "et                 Extra Trees Classifier   \n",
       "xgboost         Extreme Gradient Boosting   \n",
       "lightgbm  Light Gradient Boosting Machine   \n",
       "catboost              CatBoost Classifier   \n",
       "dummy                    Dummy Classifier   \n",
       "\n",
       "                                                  Reference  Turbo model_type  \n",
       "ID                                                                             \n",
       "lr        sklearn.linear_model._logistic.LogisticRegression   True     linear  \n",
       "knn       sklearn.neighbors._classification.KNeighborsCl...   True      other  \n",
       "nb                           sklearn.naive_bayes.GaussianNB   True      other  \n",
       "dt             sklearn.tree._classes.DecisionTreeClassifier   True       tree  \n",
       "svm       sklearn.linear_model._stochastic_gradient.SGDC...   True     linear  \n",
       "rbfsvm                             sklearn.svm._classes.SVC  False      other  \n",
       "gpc       sklearn.gaussian_process._gpc.GaussianProcessC...  False      other  \n",
       "mlp       sklearn.neural_network._multilayer_perceptron....  False      other  \n",
       "ridge           sklearn.linear_model._ridge.RidgeClassifier   True     linear  \n",
       "rf          sklearn.ensemble._forest.RandomForestClassifier   True   ensemble  \n",
       "qda       sklearn.discriminant_analysis.QuadraticDiscrim...   True      other  \n",
       "ada       sklearn.ensemble._weight_boosting.AdaBoostClas...   True   ensemble  \n",
       "gbc         sklearn.ensemble._gb.GradientBoostingClassifier   True   ensemble  \n",
       "lda       sklearn.discriminant_analysis.LinearDiscrimina...   True      other  \n",
       "et            sklearn.ensemble._forest.ExtraTreesClassifier   True   ensemble  \n",
       "xgboost                       xgboost.sklearn.XGBClassifier   True   ensemble  \n",
       "lightgbm                    lightgbm.sklearn.LGBMClassifier   True   ensemble  \n",
       "catboost                   catboost.core.CatBoostClassifier   True   ensemble  \n",
       "dummy                         sklearn.dummy.DummyClassifier   True      other  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_type = {\n",
    "    \"linear\": [\n",
    "        \"lr\",\n",
    "        \"ridge\",\n",
    "        \"svm\",\n",
    "        \"lasso\",\n",
    "        \"en\",\n",
    "        \"lar\",\n",
    "        \"llar\",\n",
    "        \"omp\",\n",
    "        \"br\",\n",
    "        \"ard\",\n",
    "        \"par\",\n",
    "        \"ransac\",\n",
    "        \"tr\",\n",
    "        \"huber\",\n",
    "        \"kr\",\n",
    "    ],\n",
    "    \"tree\": [\"dt\"],\n",
    "    \"ensemble\": [\n",
    "        \"rf\",\n",
    "        \"et\",\n",
    "        \"gbc\",\n",
    "        \"gbr\",\n",
    "        \"xgboost\",\n",
    "        \"lightgbm\",\n",
    "        \"catboost\",\n",
    "        \"ada\",\n",
    "    ],\n",
    "}\n",
    "\n",
    "models = exp.models()\n",
    "\n",
    "def fil(x):\n",
    "\treturn False\n",
    "\n",
    "# add model type to models dataframe based on index\n",
    "models['model_type'] = models.index.map(lambda x: 'linear' if x in model_type['linear'] else 'tree' if x in model_type['tree'] else 'ensemble' if x in model_type['ensemble'] else 'other')\n",
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model_weights(model):\n",
    "    \"\"\"\n",
    "    Returns model weights (coefficients) if the model supports them.\n",
    "    For ensemble models, it returns feature importances.\n",
    "    \"\"\"\n",
    "    model_type = exp._get_model_id(model)\n",
    "    print(model_type)\n",
    "    if model_type in model_type['linear']:\n",
    "        return model.coef_\n",
    "    elif model_type in model_type['tree']:\n",
    "        return model.feature_importances_\n",
    "    else:\n",
    "        raise ValueError(\n",
    "            f\"Model type {model_type} does not support weight extraction.\")\n",
    "\n",
    "\n",
    "def set_model_weights(model, weights):\n",
    "    \"\"\"\n",
    "    Sets model weights (coefficients) if the model allows it.\n",
    "    For ensemble models, setting weights is not allowed as it doesn't make sense.\n",
    "    \"\"\"\n",
    "    model_type = type(model)\n",
    "    if model_type in [LogisticRegression, DecisionTreeClassifier]:\n",
    "        model.coef_ = weights\n",
    "    elif model_type is RandomForestClassifier:\n",
    "        raise ValueError(\n",
    "            \"Cannot set weights for ensemble models like RandomForest.\")\n",
    "    else:\n",
    "        raise ValueError(\n",
    "            f\"Model type {model_type} does not support setting weights.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_bf702_row10_col0, #T_bf702_row10_col1, #T_bf702_row10_col2, #T_bf702_row10_col3, #T_bf702_row10_col4, #T_bf702_row10_col5, #T_bf702_row10_col6, #T_bf702_row10_col7 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_bf702\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bf702_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_bf702_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_bf702_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_bf702_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_bf702_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_bf702_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_bf702_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "      <th id=\"T_bf702_level0_col7\" class=\"col_heading level0 col7\" >Log Loss</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",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_bf702_row0_col0\" class=\"data row0 col0\" >0.7209</td>\n",
       "      <td id=\"T_bf702_row0_col1\" class=\"data row0 col1\" >0.9184</td>\n",
       "      <td id=\"T_bf702_row0_col2\" class=\"data row0 col2\" >0.7209</td>\n",
       "      <td id=\"T_bf702_row0_col3\" class=\"data row0 col3\" >0.7202</td>\n",
       "      <td id=\"T_bf702_row0_col4\" class=\"data row0 col4\" >0.7174</td>\n",
       "      <td id=\"T_bf702_row0_col5\" class=\"data row0 col5\" >0.6644</td>\n",
       "      <td id=\"T_bf702_row0_col6\" class=\"data row0 col6\" >0.6659</td>\n",
       "      <td id=\"T_bf702_row0_col7\" class=\"data row0 col7\" >1.0252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_bf702_row1_col0\" class=\"data row1 col0\" >0.6512</td>\n",
       "      <td id=\"T_bf702_row1_col1\" class=\"data row1 col1\" >0.8652</td>\n",
       "      <td id=\"T_bf702_row1_col2\" class=\"data row1 col2\" >0.6512</td>\n",
       "      <td id=\"T_bf702_row1_col3\" class=\"data row1 col3\" >0.6649</td>\n",
       "      <td id=\"T_bf702_row1_col4\" class=\"data row1 col4\" >0.6514</td>\n",
       "      <td id=\"T_bf702_row1_col5\" class=\"data row1 col5\" >0.5786</td>\n",
       "      <td id=\"T_bf702_row1_col6\" class=\"data row1 col6\" >0.5808</td>\n",
       "      <td id=\"T_bf702_row1_col7\" class=\"data row1 col7\" >1.6663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_bf702_row2_col0\" class=\"data row2 col0\" >0.6744</td>\n",
       "      <td id=\"T_bf702_row2_col1\" class=\"data row2 col1\" >0.9224</td>\n",
       "      <td id=\"T_bf702_row2_col2\" class=\"data row2 col2\" >0.6744</td>\n",
       "      <td id=\"T_bf702_row2_col3\" class=\"data row2 col3\" >0.6974</td>\n",
       "      <td id=\"T_bf702_row2_col4\" class=\"data row2 col4\" >0.6748</td>\n",
       "      <td id=\"T_bf702_row2_col5\" class=\"data row2 col5\" >0.6067</td>\n",
       "      <td id=\"T_bf702_row2_col6\" class=\"data row2 col6\" >0.6111</td>\n",
       "      <td id=\"T_bf702_row2_col7\" class=\"data row2 col7\" >1.0495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_bf702_row3_col0\" class=\"data row3 col0\" >0.6395</td>\n",
       "      <td id=\"T_bf702_row3_col1\" class=\"data row3 col1\" >0.9045</td>\n",
       "      <td id=\"T_bf702_row3_col2\" class=\"data row3 col2\" >0.6395</td>\n",
       "      <td id=\"T_bf702_row3_col3\" class=\"data row3 col3\" >0.6445</td>\n",
       "      <td id=\"T_bf702_row3_col4\" class=\"data row3 col4\" >0.6385</td>\n",
       "      <td id=\"T_bf702_row3_col5\" class=\"data row3 col5\" >0.5654</td>\n",
       "      <td id=\"T_bf702_row3_col6\" class=\"data row3 col6\" >0.5666</td>\n",
       "      <td id=\"T_bf702_row3_col7\" class=\"data row3 col7\" >1.1714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_bf702_row4_col0\" class=\"data row4 col0\" >0.6628</td>\n",
       "      <td id=\"T_bf702_row4_col1\" class=\"data row4 col1\" >0.8817</td>\n",
       "      <td id=\"T_bf702_row4_col2\" class=\"data row4 col2\" >0.6628</td>\n",
       "      <td id=\"T_bf702_row4_col3\" class=\"data row4 col3\" >0.6739</td>\n",
       "      <td id=\"T_bf702_row4_col4\" class=\"data row4 col4\" >0.6609</td>\n",
       "      <td id=\"T_bf702_row4_col5\" class=\"data row4 col5\" >0.5945</td>\n",
       "      <td id=\"T_bf702_row4_col6\" class=\"data row4 col6\" >0.5968</td>\n",
       "      <td id=\"T_bf702_row4_col7\" class=\"data row4 col7\" >1.3829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_bf702_row5_col0\" class=\"data row5 col0\" >0.6118</td>\n",
       "      <td id=\"T_bf702_row5_col1\" class=\"data row5 col1\" >0.8743</td>\n",
       "      <td id=\"T_bf702_row5_col2\" class=\"data row5 col2\" >0.6118</td>\n",
       "      <td id=\"T_bf702_row5_col3\" class=\"data row5 col3\" >0.6038</td>\n",
       "      <td id=\"T_bf702_row5_col4\" class=\"data row5 col4\" >0.6018</td>\n",
       "      <td id=\"T_bf702_row5_col5\" class=\"data row5 col5\" >0.5309</td>\n",
       "      <td id=\"T_bf702_row5_col6\" class=\"data row5 col6\" >0.5330</td>\n",
       "      <td id=\"T_bf702_row5_col7\" class=\"data row5 col7\" >1.5517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_bf702_row6_col0\" class=\"data row6 col0\" >0.7059</td>\n",
       "      <td id=\"T_bf702_row6_col1\" class=\"data row6 col1\" >0.9091</td>\n",
       "      <td id=\"T_bf702_row6_col2\" class=\"data row6 col2\" >0.7059</td>\n",
       "      <td id=\"T_bf702_row6_col3\" class=\"data row6 col3\" >0.7086</td>\n",
       "      <td id=\"T_bf702_row6_col4\" class=\"data row6 col4\" >0.7036</td>\n",
       "      <td id=\"T_bf702_row6_col5\" class=\"data row6 col5\" >0.6449</td>\n",
       "      <td id=\"T_bf702_row6_col6\" class=\"data row6 col6\" >0.6466</td>\n",
       "      <td id=\"T_bf702_row6_col7\" class=\"data row6 col7\" >1.1767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_bf702_row7_col0\" class=\"data row7 col0\" >0.7059</td>\n",
       "      <td id=\"T_bf702_row7_col1\" class=\"data row7 col1\" >0.9154</td>\n",
       "      <td id=\"T_bf702_row7_col2\" class=\"data row7 col2\" >0.7059</td>\n",
       "      <td id=\"T_bf702_row7_col3\" class=\"data row7 col3\" >0.7191</td>\n",
       "      <td id=\"T_bf702_row7_col4\" class=\"data row7 col4\" >0.7033</td>\n",
       "      <td id=\"T_bf702_row7_col5\" class=\"data row7 col5\" >0.6457</td>\n",
       "      <td id=\"T_bf702_row7_col6\" class=\"data row7 col6\" >0.6488</td>\n",
       "      <td id=\"T_bf702_row7_col7\" class=\"data row7 col7\" >1.1926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_bf702_row8_col0\" class=\"data row8 col0\" >0.6588</td>\n",
       "      <td id=\"T_bf702_row8_col1\" class=\"data row8 col1\" >0.8973</td>\n",
       "      <td id=\"T_bf702_row8_col2\" class=\"data row8 col2\" >0.6588</td>\n",
       "      <td id=\"T_bf702_row8_col3\" class=\"data row8 col3\" >0.6632</td>\n",
       "      <td id=\"T_bf702_row8_col4\" class=\"data row8 col4\" >0.6595</td>\n",
       "      <td id=\"T_bf702_row8_col5\" class=\"data row8 col5\" >0.5892</td>\n",
       "      <td id=\"T_bf702_row8_col6\" class=\"data row8 col6\" >0.5898</td>\n",
       "      <td id=\"T_bf702_row8_col7\" class=\"data row8 col7\" >1.2476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_bf702_row9_col0\" class=\"data row9 col0\" >0.7647</td>\n",
       "      <td id=\"T_bf702_row9_col1\" class=\"data row9 col1\" >0.9261</td>\n",
       "      <td id=\"T_bf702_row9_col2\" class=\"data row9 col2\" >0.7647</td>\n",
       "      <td id=\"T_bf702_row9_col3\" class=\"data row9 col3\" >0.7713</td>\n",
       "      <td id=\"T_bf702_row9_col4\" class=\"data row9 col4\" >0.7641</td>\n",
       "      <td id=\"T_bf702_row9_col5\" class=\"data row9 col5\" >0.7158</td>\n",
       "      <td id=\"T_bf702_row9_col6\" class=\"data row9 col6\" >0.7170</td>\n",
       "      <td id=\"T_bf702_row9_col7\" class=\"data row9 col7\" >1.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_bf702_row10_col0\" class=\"data row10 col0\" >0.6796</td>\n",
       "      <td id=\"T_bf702_row10_col1\" class=\"data row10 col1\" >0.9014</td>\n",
       "      <td id=\"T_bf702_row10_col2\" class=\"data row10 col2\" >0.6796</td>\n",
       "      <td id=\"T_bf702_row10_col3\" class=\"data row10 col3\" >0.6867</td>\n",
       "      <td id=\"T_bf702_row10_col4\" class=\"data row10 col4\" >0.6775</td>\n",
       "      <td id=\"T_bf702_row10_col5\" class=\"data row10 col5\" >0.6136</td>\n",
       "      <td id=\"T_bf702_row10_col6\" class=\"data row10 col6\" >0.6156</td>\n",
       "      <td id=\"T_bf702_row10_col7\" class=\"data row10 col7\" >1.2472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf702_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_bf702_row11_col0\" class=\"data row11 col0\" >0.0425</td>\n",
       "      <td id=\"T_bf702_row11_col1\" class=\"data row11 col1\" >0.0201</td>\n",
       "      <td id=\"T_bf702_row11_col2\" class=\"data row11 col2\" >0.0425</td>\n",
       "      <td id=\"T_bf702_row11_col3\" class=\"data row11 col3\" >0.0445</td>\n",
       "      <td id=\"T_bf702_row11_col4\" class=\"data row11 col4\" >0.0436</td>\n",
       "      <td id=\"T_bf702_row11_col5\" class=\"data row11 col5\" >0.0514</td>\n",
       "      <td id=\"T_bf702_row11_col6\" class=\"data row11 col6\" >0.0513</td>\n",
       "      <td id=\"T_bf702_row11_col7\" class=\"data row11 col7\" >0.2112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2a21482b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# best = exp.compare_models(cross_validation=False)\n",
    "model = exp.create_model('lightgbm', train_model=True)\n",
    "# metrics = exp.pull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 504,\n",
       " 'gender': 77,\n",
       " 'height': 462,\n",
       " 'weight': 541,\n",
       " 'steps': 1607,\n",
       " 'hear_rate': 1823,\n",
       " 'calories': 2296,\n",
       " 'distance': 1470,\n",
       " 'entropy_heart': 644,\n",
       " 'entropy_setps': 714,\n",
       " 'resting_heart': 478,\n",
       " 'corr_heart_steps': 1686,\n",
       " 'norm_heart': 1722,\n",
       " 'intensity_karvonen': 1260,\n",
       " 'sd_norm_heart': 1888,\n",
       " 'steps_times_distance': 802}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.booster_.dump_model()['feature_importances']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'ExtraTreesClassifier' object has no attribute 'named_parameters'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[75], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnamed_parameters\u001b[49m()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'ExtraTreesClassifier' object has no attribute 'named_parameters'"
     ]
    }
   ],
   "source": [
    "best.named_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boosting_type': 'gbdt',\n",
       " 'objective': None,\n",
       " 'num_leaves': 31,\n",
       " 'max_depth': -1,\n",
       " 'learning_rate': 0.1,\n",
       " 'n_estimators': 100,\n",
       " 'subsample_for_bin': 200000,\n",
       " 'min_split_gain': 0.0,\n",
       " 'min_child_weight': 0.001,\n",
       " 'min_child_samples': 20,\n",
       " 'subsample': 1.0,\n",
       " 'subsample_freq': 0,\n",
       " 'colsample_bytree': 1.0,\n",
       " 'reg_alpha': 0.0,\n",
       " 'reg_lambda': 0.0,\n",
       " 'random_state': 42,\n",
       " 'n_jobs': -1,\n",
       " 'importance_type': 'split',\n",
       " '_Booster': <lightgbm.basic.Booster at 0x2a066f160>,\n",
       " '_evals_result': {},\n",
       " '_best_score': defaultdict(collections.OrderedDict, {}),\n",
       " '_best_iteration': 0,\n",
       " '_other_params': {},\n",
       " '_objective': 'multiclass',\n",
       " 'class_weight': None,\n",
       " '_class_weight': None,\n",
       " '_class_map': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5},\n",
       " '_n_features': 16,\n",
       " '_n_features_in': 16,\n",
       " '_classes': array([0, 1, 2, 3, 4, 5], dtype=int8),\n",
       " '_n_classes': 6,\n",
       " '_le': LabelEncoder(),\n",
       " 'fitted_': True}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'class_weight': None,\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'l1_ratio': None,\n",
       " 'max_iter': 1000,\n",
       " 'multi_class': 'auto',\n",
       " 'n_jobs': None,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': 42,\n",
       " 'solver': 'lbfgs',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_88505\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_88505_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_88505_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_88505_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_88505_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_88505_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_88505_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_88505_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_88505_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_88505_level0_col8\" class=\"col_heading level0 col8\" >Log Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_88505_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_88505_row0_col0\" class=\"data row0 col0\" >CatBoost Classifier</td>\n",
       "      <td id=\"T_88505_row0_col1\" class=\"data row0 col1\" >0.7057</td>\n",
       "      <td id=\"T_88505_row0_col2\" class=\"data row0 col2\" >0.9274</td>\n",
       "      <td id=\"T_88505_row0_col3\" class=\"data row0 col3\" >0.7057</td>\n",
       "      <td id=\"T_88505_row0_col4\" class=\"data row0 col4\" >0.7090</td>\n",
       "      <td id=\"T_88505_row0_col5\" class=\"data row0 col5\" >0.7061</td>\n",
       "      <td id=\"T_88505_row0_col6\" class=\"data row0 col6\" >0.6447</td>\n",
       "      <td id=\"T_88505_row0_col7\" class=\"data row0 col7\" >0.6452</td>\n",
       "      <td id=\"T_88505_row0_col8\" class=\"data row0 col8\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x105fd89d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.predict_model(best)\n",
    "df = exp.pull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Model': 'CatBoost Classifier',\n",
       " 'Accuracy': 0.7057,\n",
       " 'AUC': 0.9274,\n",
       " 'Recall': 0.7057,\n",
       " 'Prec.': 0.709,\n",
       " 'F1': 0.7061,\n",
       " 'Kappa': 0.6447,\n",
       " 'MCC': 0.6452,\n",
       " 'Log Loss': 0}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0].to_dict()\n",
    "# remove model key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'model'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'model'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/pandas/core/generic.py:4243\u001b[0m, in \u001b[0;36mNDFrame.__delitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4238\u001b[0m             deleted \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   4239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m deleted:\n\u001b[1;32m   4240\u001b[0m     \u001b[38;5;66;03m# If the above loop ran and didn't delete anything because\u001b[39;00m\n\u001b[1;32m   4241\u001b[0m     \u001b[38;5;66;03m# there was no match, this call should raise the appropriate\u001b[39;00m\n\u001b[1;32m   4242\u001b[0m     \u001b[38;5;66;03m# exception:\u001b[39;00m\n\u001b[0;32m-> 4243\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4244\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39midelete(loc)\n\u001b[1;32m   4246\u001b[0m \u001b[38;5;66;03m# delete from the caches\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/3.10env/lib/python3.8/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'model'"
     ]
    }
   ],
   "source": [
    ".pop('key', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None,\n",
       " ('objective', 'multi:softprob'),\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " ('verbosity', 0),\n",
       " ('booster', 'gbtree'),\n",
       " ('tree_method', 'auto'),\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " ('missing', nan),\n",
       " None,\n",
       " ('random_state', 42),\n",
       " ('n_jobs', -1),\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " ('device', 'cpu'),\n",
       " None,\n",
       " ('enable_categorical', False),\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " ('kwargs', {'train': True}),\n",
       " ('n_classes_', 6),\n",
       " ('_Booster', <xgboost.core.Booster at 0x2af3e2e20>)]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(f,getattr(best, f)) if getattr(best,f) is not None else None for f in vars(best)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xgboost.sklearn.XGBClassifier"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'multi:softprob',\n",
       " 'base_score': None,\n",
       " 'booster': 'gbtree',\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': None,\n",
       " 'colsample_bynode': None,\n",
       " 'colsample_bytree': None,\n",
       " 'device': 'cpu',\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'feature_types': None,\n",
       " 'gamma': None,\n",
       " 'grow_policy': None,\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': None,\n",
       " 'learning_rate': None,\n",
       " 'max_bin': None,\n",
       " 'max_cat_threshold': None,\n",
       " 'max_cat_to_onehot': None,\n",
       " 'max_delta_step': None,\n",
       " 'max_depth': None,\n",
       " 'max_leaves': None,\n",
       " 'min_child_weight': None,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': None,\n",
       " 'multi_strategy': None,\n",
       " 'n_estimators': None,\n",
       " 'n_jobs': -1,\n",
       " 'num_parallel_tree': None,\n",
       " 'random_state': 42,\n",
       " 'reg_alpha': None,\n",
       " 'reg_lambda': None,\n",
       " 'sampling_method': None,\n",
       " 'scale_pos_weight': None,\n",
       " 'subsample': None,\n",
       " 'tree_method': 'auto',\n",
       " 'validate_parameters': None,\n",
       " 'verbosity': 0}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Running 3 METs', 'Lying', 'Sitting', 'Self Pace walk', 'Running 5 METs', 'Running 7 METs']\n",
       "Categories (6, object): ['Lying', 'Running 3 METs', 'Running 5 METs', 'Running 7 METs', 'Self Pace walk', 'Sitting']"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = exp.y_test\n",
    "y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_70c3c_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_70c3c_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_70c3c_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_70c3c_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_70c3c_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_70c3c_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_70c3c_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_70c3c_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_70c3c_level0_col8\" class=\"col_heading level0 col8\" >Log Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_70c3c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_70c3c_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_70c3c_row0_col1\" class=\"data row0 col1\" >0.6730</td>\n",
       "      <td id=\"T_70c3c_row0_col2\" class=\"data row0 col2\" >0.9080</td>\n",
       "      <td id=\"T_70c3c_row0_col3\" class=\"data row0 col3\" >0.6730</td>\n",
       "      <td id=\"T_70c3c_row0_col4\" class=\"data row0 col4\" >0.6815</td>\n",
       "      <td id=\"T_70c3c_row0_col5\" class=\"data row0 col5\" >0.6750</td>\n",
       "      <td id=\"T_70c3c_row0_col6\" class=\"data row0 col6\" >0.6060</td>\n",
       "      <td id=\"T_70c3c_row0_col7\" class=\"data row0 col7\" >0.6067</td>\n",
       "      <td id=\"T_70c3c_row0_col8\" class=\"data row0 col8\" >0</td>\n",
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       "  </tbody>\n",
       "</table>\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>steps</th>\n",
       "      <th>hear_rate</th>\n",
       "      <th>calories</th>\n",
       "      <th>distance</th>\n",
       "      <th>entropy_heart</th>\n",
       "      <th>entropy_setps</th>\n",
       "      <th>resting_heart</th>\n",
       "      <th>corr_heart_steps</th>\n",
       "      <th>norm_heart</th>\n",
       "      <th>intensity_karvonen</th>\n",
       "      <th>sd_norm_heart</th>\n",
       "      <th>steps_times_distance</th>\n",
       "      <th>activity</th>\n",
       "      <th>prediction_label</th>\n",
       "      <th>prediction_score</th>\n",
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       "  </thead>\n",
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       "      <td>25</td>\n",
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       "      <td>4.627800</td>\n",
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       "      <td>6.175485</td>\n",
       "      <td>5.841891</td>\n",
       "      <td>77.039764</td>\n",
       "      <td>0.175478</td>\n",
       "      <td>41.960236</td>\n",
       "      <td>0.355715</td>\n",
       "      <td>5.314812</td>\n",
       "      <td>8.143980</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>0.69</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>70.000000</td>\n",
       "      <td>65.290680</td>\n",
       "      <td>17.276400</td>\n",
       "      <td>0.052720</td>\n",
       "      <td>6.259761</td>\n",
       "      <td>6.259761</td>\n",
       "      <td>56.333332</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>8.957345</td>\n",
       "      <td>0.063678</td>\n",
       "      <td>0.905729</td>\n",
       "      <td>3.690367</td>\n",
       "      <td>Lying</td>\n",
       "      <td>Running 5 METs</td>\n",
       "      <td>0.43</td>\n",
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       "    <tr>\n",
       "      <th>374</th>\n",
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       "      <td>1</td>\n",
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       "      <td>4.923810</td>\n",
       "      <td>65.304779</td>\n",
       "      <td>16.844400</td>\n",
       "      <td>0.003682</td>\n",
       "      <td>5.943893</td>\n",
       "      <td>6.209454</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.304782</td>\n",
       "      <td>0.072569</td>\n",
       "      <td>1.474422</td>\n",
       "      <td>0.018131</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>Lying</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>57.700001</td>\n",
       "      <td>342.532471</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>19.954000</td>\n",
       "      <td>0.188781</td>\n",
       "      <td>6.142147</td>\n",
       "      <td>6.020552</td>\n",
       "      <td>78.531303</td>\n",
       "      <td>0.741713</td>\n",
       "      <td>14.468698</td>\n",
       "      <td>0.124228</td>\n",
       "      <td>3.095529</td>\n",
       "      <td>64.663727</td>\n",
       "      <td>Sitting</td>\n",
       "      <td>Sitting</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>112.059303</td>\n",
       "      <td>0.735000</td>\n",
       "      <td>0.044840</td>\n",
       "      <td>6.209454</td>\n",
       "      <td>5.972835</td>\n",
       "      <td>80.621513</td>\n",
       "      <td>-0.171754</td>\n",
       "      <td>31.437786</td>\n",
       "      <td>0.284818</td>\n",
       "      <td>3.282160</td>\n",
       "      <td>5.335960</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>71.400002</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>114.284355</td>\n",
       "      <td>0.758000</td>\n",
       "      <td>0.034200</td>\n",
       "      <td>6.162427</td>\n",
       "      <td>5.655310</td>\n",
       "      <td>79.421791</td>\n",
       "      <td>0.882916</td>\n",
       "      <td>34.862564</td>\n",
       "      <td>0.368611</td>\n",
       "      <td>2.931990</td>\n",
       "      <td>2.188800</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>59.099998</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>0.252000</td>\n",
       "      <td>0.002497</td>\n",
       "      <td>6.195296</td>\n",
       "      <td>6.001153</td>\n",
       "      <td>84.199997</td>\n",
       "      <td>0.258683</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.074427</td>\n",
       "      <td>2.623964</td>\n",
       "      <td>0.008205</td>\n",
       "      <td>Self Pace walk</td>\n",
       "      <td>Lying</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>183.0</td>\n",
       "      <td>65.699997</td>\n",
       "      <td>29.333334</td>\n",
       "      <td>90.920631</td>\n",
       "      <td>0.323600</td>\n",
       "      <td>0.021813</td>\n",
       "      <td>6.303781</td>\n",
       "      <td>6.278464</td>\n",
       "      <td>34.153847</td>\n",
       "      <td>0.205901</td>\n",
       "      <td>56.766788</td>\n",
       "      <td>0.340234</td>\n",
       "      <td>2.041293</td>\n",
       "      <td>0.639858</td>\n",
       "      <td>Sitting</td>\n",
       "      <td>Sitting</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>566.599976</td>\n",
       "      <td>60.333332</td>\n",
       "      <td>17.238728</td>\n",
       "      <td>0.388998</td>\n",
       "      <td>6.075165</td>\n",
       "      <td>6.153087</td>\n",
       "      <td>56.200001</td>\n",
       "      <td>-0.967908</td>\n",
       "      <td>4.133333</td>\n",
       "      <td>0.029149</td>\n",
       "      <td>1.009217</td>\n",
       "      <td>220.406265</td>\n",
       "      <td>Self Pace walk</td>\n",
       "      <td>Self Pace walk</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>178.0</td>\n",
       "      <td>77.300003</td>\n",
       "      <td>3.670748</td>\n",
       "      <td>62.382870</td>\n",
       "      <td>18.213600</td>\n",
       "      <td>0.002687</td>\n",
       "      <td>5.943893</td>\n",
       "      <td>6.209454</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.382869</td>\n",
       "      <td>0.051992</td>\n",
       "      <td>1.474422</td>\n",
       "      <td>0.009863</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>Running 3 METs</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  height     weight       steps   hear_rate   calories  \\\n",
       "299    25       0   166.0  68.000000  106.000000  119.000000   4.627800   \n",
       "251    23       1   181.0  95.199997   70.000000   65.290680  17.276400   \n",
       "374    23       1   178.0  77.300003    4.923810   65.304779  16.844400   \n",
       "1167   25       0   160.0  57.700001  342.532471   93.000000  19.954000   \n",
       "420    29       0   159.0  55.000000  119.000000  112.059303   0.735000   \n",
       "...   ...     ...     ...        ...         ...         ...        ...   \n",
       "423    46       0   157.5  71.400002   64.000000  114.284355   0.758000   \n",
       "979    31       0   158.0  59.099998    3.285714   92.000000   0.252000   \n",
       "1064   19       1   183.0  65.699997   29.333334   90.920631   0.323600   \n",
       "943    22       0   168.0  62.000000  566.599976   60.333332  17.238728   \n",
       "284    23       1   178.0  77.300003    3.670748   62.382870  18.213600   \n",
       "\n",
       "      distance  entropy_heart  entropy_setps  resting_heart  corr_heart_steps  \\\n",
       "299   0.076830       6.175485       5.841891      77.039764          0.175478   \n",
       "251   0.052720       6.259761       6.259761      56.333332         -1.000000   \n",
       "374   0.003682       5.943893       6.209454      55.000000          1.000000   \n",
       "1167  0.188781       6.142147       6.020552      78.531303          0.741713   \n",
       "420   0.044840       6.209454       5.972835      80.621513         -0.171754   \n",
       "...        ...            ...            ...            ...               ...   \n",
       "423   0.034200       6.162427       5.655310      79.421791          0.882916   \n",
       "979   0.002497       6.195296       6.001153      84.199997          0.258683   \n",
       "1064  0.021813       6.303781       6.278464      34.153847          0.205901   \n",
       "943   0.388998       6.075165       6.153087      56.200001         -0.967908   \n",
       "284   0.002687       5.943893       6.209454      55.000000          1.000000   \n",
       "\n",
       "      norm_heart  intensity_karvonen  sd_norm_heart  steps_times_distance  \\\n",
       "299    41.960236            0.355715       5.314812              8.143980   \n",
       "251     8.957345            0.063678       0.905729              3.690367   \n",
       "374    10.304782            0.072569       1.474422              0.018131   \n",
       "1167   14.468698            0.124228       3.095529             64.663727   \n",
       "420    31.437786            0.284818       3.282160              5.335960   \n",
       "...          ...                 ...            ...                   ...   \n",
       "423    34.862564            0.368611       2.931990              2.188800   \n",
       "979     7.800000            0.074427       2.623964              0.008205   \n",
       "1064   56.766788            0.340234       2.041293              0.639858   \n",
       "943     4.133333            0.029149       1.009217            220.406265   \n",
       "284     7.382869            0.051992       1.474422              0.009863   \n",
       "\n",
       "            activity prediction_label  prediction_score  \n",
       "299   Running 3 METs   Running 3 METs              0.69  \n",
       "251            Lying   Running 5 METs              0.43  \n",
       "374   Running 3 METs            Lying              0.86  \n",
       "1167         Sitting          Sitting              0.56  \n",
       "420   Running 3 METs   Running 3 METs              0.88  \n",
       "...              ...              ...               ...  \n",
       "423   Running 3 METs   Running 3 METs              0.62  \n",
       "979   Self Pace walk            Lying              0.61  \n",
       "1064         Sitting          Sitting              0.54  \n",
       "943   Self Pace walk   Self Pace walk              0.52  \n",
       "284   Running 3 METs   Running 3 METs              0.53  \n",
       "\n",
       "[367 rows x 19 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.predict_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>15:55:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Selecting Estimator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimator</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                         \n",
       "                                                                         \n",
       "Initiated  . . . . . . . . . . . . . . . . . .                   15:55:35\n",
       "Status     . . . . . . . . . . . . . . . . . .        Selecting Estimator\n",
       "Estimator  . . . . . . . . . . . . . . . . . .  Extreme Gradient Boosting"
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   ],
   "source": [
    "model = exp.create_model('xgboost', train_model=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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    {
     "data": {
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       "<style type=\"text/css\">\n",
       "#T_95b4b_row10_col0, #T_95b4b_row10_col1, #T_95b4b_row10_col2, #T_95b4b_row10_col3, #T_95b4b_row10_col4, #T_95b4b_row10_col5, #T_95b4b_row10_col6, #T_95b4b_row10_col7 {\n",
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       "</style>\n",
       "<table id=\"T_95b4b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_95b4b_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_95b4b_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_95b4b_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_95b4b_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_95b4b_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_95b4b_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_95b4b_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "      <th id=\"T_95b4b_level0_col7\" class=\"col_heading level0 col7\" >Log Loss</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",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_95b4b_row0_col0\" class=\"data row0 col0\" >0.7209</td>\n",
       "      <td id=\"T_95b4b_row0_col1\" class=\"data row0 col1\" >0.9341</td>\n",
       "      <td id=\"T_95b4b_row0_col2\" class=\"data row0 col2\" >0.7209</td>\n",
       "      <td id=\"T_95b4b_row0_col3\" class=\"data row0 col3\" >0.7212</td>\n",
       "      <td id=\"T_95b4b_row0_col4\" class=\"data row0 col4\" >0.7207</td>\n",
       "      <td id=\"T_95b4b_row0_col5\" class=\"data row0 col5\" >0.6631</td>\n",
       "      <td id=\"T_95b4b_row0_col6\" class=\"data row0 col6\" >0.6632</td>\n",
       "      <td id=\"T_95b4b_row0_col7\" class=\"data row0 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_95b4b_row1_col0\" class=\"data row1 col0\" >0.6047</td>\n",
       "      <td id=\"T_95b4b_row1_col1\" class=\"data row1 col1\" >0.8691</td>\n",
       "      <td id=\"T_95b4b_row1_col2\" class=\"data row1 col2\" >0.6047</td>\n",
       "      <td id=\"T_95b4b_row1_col3\" class=\"data row1 col3\" >0.6133</td>\n",
       "      <td id=\"T_95b4b_row1_col4\" class=\"data row1 col4\" >0.6058</td>\n",
       "      <td id=\"T_95b4b_row1_col5\" class=\"data row1 col5\" >0.5231</td>\n",
       "      <td id=\"T_95b4b_row1_col6\" class=\"data row1 col6\" >0.5244</td>\n",
       "      <td id=\"T_95b4b_row1_col7\" class=\"data row1 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_95b4b_row2_col0\" class=\"data row2 col0\" >0.6860</td>\n",
       "      <td id=\"T_95b4b_row2_col1\" class=\"data row2 col1\" >0.9137</td>\n",
       "      <td id=\"T_95b4b_row2_col2\" class=\"data row2 col2\" >0.6860</td>\n",
       "      <td id=\"T_95b4b_row2_col3\" class=\"data row2 col3\" >0.7059</td>\n",
       "      <td id=\"T_95b4b_row2_col4\" class=\"data row2 col4\" >0.6876</td>\n",
       "      <td id=\"T_95b4b_row2_col5\" class=\"data row2 col5\" >0.6216</td>\n",
       "      <td id=\"T_95b4b_row2_col6\" class=\"data row2 col6\" >0.6246</td>\n",
       "      <td id=\"T_95b4b_row2_col7\" class=\"data row2 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_95b4b_row3_col0\" class=\"data row3 col0\" >0.6395</td>\n",
       "      <td id=\"T_95b4b_row3_col1\" class=\"data row3 col1\" >0.9022</td>\n",
       "      <td id=\"T_95b4b_row3_col2\" class=\"data row3 col2\" >0.6395</td>\n",
       "      <td id=\"T_95b4b_row3_col3\" class=\"data row3 col3\" >0.6478</td>\n",
       "      <td id=\"T_95b4b_row3_col4\" class=\"data row3 col4\" >0.6411</td>\n",
       "      <td id=\"T_95b4b_row3_col5\" class=\"data row3 col5\" >0.5666</td>\n",
       "      <td id=\"T_95b4b_row3_col6\" class=\"data row3 col6\" >0.5675</td>\n",
       "      <td id=\"T_95b4b_row3_col7\" class=\"data row3 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_95b4b_row4_col0\" class=\"data row4 col0\" >0.6163</td>\n",
       "      <td id=\"T_95b4b_row4_col1\" class=\"data row4 col1\" >0.8744</td>\n",
       "      <td id=\"T_95b4b_row4_col2\" class=\"data row4 col2\" >0.6163</td>\n",
       "      <td id=\"T_95b4b_row4_col3\" class=\"data row4 col3\" >0.6211</td>\n",
       "      <td id=\"T_95b4b_row4_col4\" class=\"data row4 col4\" >0.6136</td>\n",
       "      <td id=\"T_95b4b_row4_col5\" class=\"data row4 col5\" >0.5370</td>\n",
       "      <td id=\"T_95b4b_row4_col6\" class=\"data row4 col6\" >0.5388</td>\n",
       "      <td id=\"T_95b4b_row4_col7\" class=\"data row4 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_95b4b_row5_col0\" class=\"data row5 col0\" >0.6353</td>\n",
       "      <td id=\"T_95b4b_row5_col1\" class=\"data row5 col1\" >0.8683</td>\n",
       "      <td id=\"T_95b4b_row5_col2\" class=\"data row5 col2\" >0.6353</td>\n",
       "      <td id=\"T_95b4b_row5_col3\" class=\"data row5 col3\" >0.6386</td>\n",
       "      <td id=\"T_95b4b_row5_col4\" class=\"data row5 col4\" >0.6340</td>\n",
       "      <td id=\"T_95b4b_row5_col5\" class=\"data row5 col5\" >0.5599</td>\n",
       "      <td id=\"T_95b4b_row5_col6\" class=\"data row5 col6\" >0.5609</td>\n",
       "      <td id=\"T_95b4b_row5_col7\" class=\"data row5 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_95b4b_row6_col0\" class=\"data row6 col0\" >0.6941</td>\n",
       "      <td id=\"T_95b4b_row6_col1\" class=\"data row6 col1\" >0.9151</td>\n",
       "      <td id=\"T_95b4b_row6_col2\" class=\"data row6 col2\" >0.6941</td>\n",
       "      <td id=\"T_95b4b_row6_col3\" class=\"data row6 col3\" >0.7027</td>\n",
       "      <td id=\"T_95b4b_row6_col4\" class=\"data row6 col4\" >0.6925</td>\n",
       "      <td id=\"T_95b4b_row6_col5\" class=\"data row6 col5\" >0.6312</td>\n",
       "      <td id=\"T_95b4b_row6_col6\" class=\"data row6 col6\" >0.6334</td>\n",
       "      <td id=\"T_95b4b_row6_col7\" class=\"data row6 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_95b4b_row7_col0\" class=\"data row7 col0\" >0.6588</td>\n",
       "      <td id=\"T_95b4b_row7_col1\" class=\"data row7 col1\" >0.9064</td>\n",
       "      <td id=\"T_95b4b_row7_col2\" class=\"data row7 col2\" >0.6588</td>\n",
       "      <td id=\"T_95b4b_row7_col3\" class=\"data row7 col3\" >0.6642</td>\n",
       "      <td id=\"T_95b4b_row7_col4\" class=\"data row7 col4\" >0.6573</td>\n",
       "      <td id=\"T_95b4b_row7_col5\" class=\"data row7 col5\" >0.5888</td>\n",
       "      <td id=\"T_95b4b_row7_col6\" class=\"data row7 col6\" >0.5904</td>\n",
       "      <td id=\"T_95b4b_row7_col7\" class=\"data row7 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_95b4b_row8_col0\" class=\"data row8 col0\" >0.6235</td>\n",
       "      <td id=\"T_95b4b_row8_col1\" class=\"data row8 col1\" >0.8861</td>\n",
       "      <td id=\"T_95b4b_row8_col2\" class=\"data row8 col2\" >0.6235</td>\n",
       "      <td id=\"T_95b4b_row8_col3\" class=\"data row8 col3\" >0.6374</td>\n",
       "      <td id=\"T_95b4b_row8_col4\" class=\"data row8 col4\" >0.6255</td>\n",
       "      <td id=\"T_95b4b_row8_col5\" class=\"data row8 col5\" >0.5461</td>\n",
       "      <td id=\"T_95b4b_row8_col6\" class=\"data row8 col6\" >0.5474</td>\n",
       "      <td id=\"T_95b4b_row8_col7\" class=\"data row8 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_95b4b_row9_col0\" class=\"data row9 col0\" >0.7529</td>\n",
       "      <td id=\"T_95b4b_row9_col1\" class=\"data row9 col1\" >0.9215</td>\n",
       "      <td id=\"T_95b4b_row9_col2\" class=\"data row9 col2\" >0.7529</td>\n",
       "      <td id=\"T_95b4b_row9_col3\" class=\"data row9 col3\" >0.7611</td>\n",
       "      <td id=\"T_95b4b_row9_col4\" class=\"data row9 col4\" >0.7536</td>\n",
       "      <td id=\"T_95b4b_row9_col5\" class=\"data row9 col5\" >0.7025</td>\n",
       "      <td id=\"T_95b4b_row9_col6\" class=\"data row9 col6\" >0.7037</td>\n",
       "      <td id=\"T_95b4b_row9_col7\" class=\"data row9 col7\" >-0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_95b4b_row10_col0\" class=\"data row10 col0\" >0.6632</td>\n",
       "      <td id=\"T_95b4b_row10_col1\" class=\"data row10 col1\" >0.8991</td>\n",
       "      <td id=\"T_95b4b_row10_col2\" class=\"data row10 col2\" >0.6632</td>\n",
       "      <td id=\"T_95b4b_row10_col3\" class=\"data row10 col3\" >0.6713</td>\n",
       "      <td id=\"T_95b4b_row10_col4\" class=\"data row10 col4\" >0.6632</td>\n",
       "      <td id=\"T_95b4b_row10_col5\" class=\"data row10 col5\" >0.5940</td>\n",
       "      <td id=\"T_95b4b_row10_col6\" class=\"data row10 col6\" >0.5954</td>\n",
       "      <td id=\"T_95b4b_row10_col7\" class=\"data row10 col7\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_95b4b_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_95b4b_row11_col0\" class=\"data row11 col0\" >0.0463</td>\n",
       "      <td id=\"T_95b4b_row11_col1\" class=\"data row11 col1\" >0.0221</td>\n",
       "      <td id=\"T_95b4b_row11_col2\" class=\"data row11 col2\" >0.0463</td>\n",
       "      <td id=\"T_95b4b_row11_col3\" class=\"data row11 col3\" >0.0463</td>\n",
       "      <td id=\"T_95b4b_row11_col4\" class=\"data row11 col4\" >0.0464</td>\n",
       "      <td id=\"T_95b4b_row11_col5\" class=\"data row11 col5\" >0.0559</td>\n",
       "      <td id=\"T_95b4b_row11_col6\" class=\"data row11 col6\" >0.0558</td>\n",
       "      <td id=\"T_95b4b_row11_col7\" class=\"data row11 col7\" >0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29dace820>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n",
      "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
     ]
    }
   ],
   "source": [
    "tuned = exp.tune_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('_Booster', <xgboost.core.Booster at 0x161d03eb0>), ('n_classes_', 6)]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find diff between attributes in model and tuned\n",
    "[(i, getattr(tuned, i)) for i in set(vars(tuned).keys()) - set(vars(model).keys())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['feature_names_in_',\n",
       " 'n_features_in_',\n",
       " 'n_outputs_',\n",
       " 'classes_',\n",
       " 'n_classes_',\n",
       " 'estimator_',\n",
       " 'estimators_',\n",
       " array(['age', 'gender', 'height', 'weight', 'steps', 'hear_rate',\n",
       "        'calories', 'distance', 'entropy_heart', 'entropy_setps',\n",
       "        'resting_heart', 'corr_heart_steps', 'norm_heart',\n",
       "        'intensity_karvonen', 'sd_norm_heart', 'steps_times_distance'],\n",
       "       dtype=object),\n",
       " 16,\n",
       " 1,\n",
       " array([0, 1, 2, 3, 4, 5], dtype=int8),\n",
       " 6,\n",
       " DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                        max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                        min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                        min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                        random_state=None, splitter='best'),\n",
       " [DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1608637542, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1273642419, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1935803228, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=787846414, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=996406378, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1201263687, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=423734972, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=415968276, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=670094950, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1914837113, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=669991378, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=429389014, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=249467210, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1972458954, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1572714583, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1433267572, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=434285667, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=613608295, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=893664919, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=648061058, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=88409749, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=242285876, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2018247425, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=953477463, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1427830251, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1883569565, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=911989541, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=3344769, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=780932287, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2114032571, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=787716372, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=504579232, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1306710475, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=479546681, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=106328085, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=30349564, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1855189739, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=99052376, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1250819632, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=106406362, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=480404538, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1717389822, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=599121577, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=200427519, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1254751707, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2034764475, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1573512143, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=999745294, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1958805693, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=389151677, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1224821422, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=508464061, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=857592370, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1642661739, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=61136438, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2075460851, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=396917567, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2004731384, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=199502978, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1545932260, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=461901618, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=774414982, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=732395540, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1934879560, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=279394470, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=56972561, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1927948675, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1899242072, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1999874363, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=271820813, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1324556529, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1655351289, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1308306184, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=68574553, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=419498548, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=991681409, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=791274835, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1035196507, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1890440558, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=787110843, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=524150214, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=472432043, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2126768636, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1431061255, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=147697582, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=744595490, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1758017741, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1679592528, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1111451555, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=782698033, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=698027879, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1096768899, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1338788865, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1826030589, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=86191493, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=893102645, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=200619113, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=290770691, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=793943861, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=134489564, splitter='best')]]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from flwr.common import NDArrays\n",
    "\n",
    "def get_model_parameters(model) -> NDArrays:\n",
    "    \"\"\"Returns the parameters of a sklearn LogisticRegression model.\"\"\"\n",
    "    attrs = [v for v in vars(model)\n",
    "             if v.endswith(\"_\") and not v.startswith(\"__\")]\n",
    "    params = attrs\n",
    "    params += [getattr(model, v) for v in vars(model)\n",
    "               if v.endswith(\"_\") and not v.startswith(\"__\")]\n",
    "\n",
    "    return params\n",
    "\n",
    "params = get_model_parameters(model)\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': -1,\n",
       " 'oob_score': False,\n",
       " 'random_state': 42,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'estimator': DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                        max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                        min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                        min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                        random_state=None, splitter='best'),\n",
       " 'n_estimators': 100,\n",
       " 'estimator_params': ('criterion',\n",
       "  'max_depth',\n",
       "  'min_samples_split',\n",
       "  'min_samples_leaf',\n",
       "  'min_weight_fraction_leaf',\n",
       "  'max_features',\n",
       "  'max_leaf_nodes',\n",
       "  'min_impurity_decrease',\n",
       "  'random_state',\n",
       "  'ccp_alpha'),\n",
       " 'base_estimator': 'deprecated',\n",
       " 'bootstrap': True,\n",
       " 'oob_score': False,\n",
       " 'n_jobs': -1,\n",
       " 'random_state': 42,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False,\n",
       " 'class_weight': None,\n",
       " 'max_samples': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'feature_names_in_': array(['age', 'gender', 'height', 'weight', 'steps', 'hear_rate',\n",
       "        'calories', 'distance', 'entropy_heart', 'entropy_setps',\n",
       "        'resting_heart', 'corr_heart_steps', 'norm_heart',\n",
       "        'intensity_karvonen', 'sd_norm_heart', 'steps_times_distance'],\n",
       "       dtype=object),\n",
       " 'n_features_in_': 16,\n",
       " 'n_outputs_': 1,\n",
       " 'classes_': array([0, 1, 2, 3, 4, 5]),\n",
       " 'n_classes_': 6,\n",
       " 'estimator_': DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                        max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                        min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                        min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                        random_state=None, splitter='best'),\n",
       " 'estimators_': [DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1608637542, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1273642419, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1935803228, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=787846414, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=996406378, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1201263687, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=423734972, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=415968276, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=670094950, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1914837113, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=669991378, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=429389014, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=249467210, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1972458954, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1572714583, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1433267572, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=434285667, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=613608295, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=893664919, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=648061058, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=88409749, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=242285876, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2018247425, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=953477463, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1427830251, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1883569565, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=911989541, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=3344769, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=780932287, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2114032571, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=787716372, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=504579232, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1306710475, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=479546681, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=106328085, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=30349564, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1855189739, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=99052376, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1250819632, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=106406362, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=480404538, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1717389822, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=599121577, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=200427519, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1254751707, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2034764475, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1573512143, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=999745294, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1958805693, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=389151677, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1224821422, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=508464061, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=857592370, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1642661739, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=61136438, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2075460851, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=396917567, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2004731384, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=199502978, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1545932260, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=461901618, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=774414982, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=732395540, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1934879560, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=279394470, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=56972561, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1927948675, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1899242072, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1999874363, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=271820813, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1324556529, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1655351289, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1308306184, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=68574553, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=419498548, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=991681409, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=791274835, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1035196507, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1890440558, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=787110843, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=524150214, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=472432043, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=2126768636, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1431061255, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=147697582, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=744595490, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1758017741, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1679592528, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1111451555, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=782698033, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=698027879, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1096768899, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1338788865, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=1826030589, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=86191493, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=893102645, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=200619113, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=290770691, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=793943861, splitter='best'),\n",
       "  DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                         max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                         min_impurity_decrease=0.0, min_samples_leaf=1,\n",
       "                         min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                         random_state=134489564, splitter='best')]}"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def set_model_params(model, params: NDArrays):\n",
    "    \"\"\"Sets the parameters of a sklean model.\"\"\"\n",
    "    for i in range(0, len(params) // 2):\n",
    "        k, v = params[i], params[i+len(params) // 2]\n",
    "        setattr(model, k, v)\n",
    "    return model\n",
    "\n",
    "vars(set_model_params(model, params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100, None, 2, 1]"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random classifier attribut\n",
    "attrs = [\"n_estimators\", \"max_depth\",\n",
    "             \"min_samples_split\", \"min_samples_leaf\"]\n",
    "[getattr(tuned, v) for v in attrs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>09:36:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Selecting Estimator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estimator</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Random Forest Classifier</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        \n",
       "                                                                        \n",
       "Initiated  . . . . . . . . . . . . . . . . . .                  09:36:24\n",
       "Status     . . . . . . . . . . . . . . . . . .       Selecting Estimator\n",
       "Estimator  . . . . . . . . . . . . . . . . . .  Random Forest Classifier"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unfitted = exp.create_model('rf', train_model=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[110, 95, 101, 115, 116, 105, 109, 97, 116, 111, 114, 115],\n",
       " 100,\n",
       " [109,\n",
       "  105,\n",
       "  110,\n",
       "  95,\n",
       "  115,\n",
       "  97,\n",
       "  109,\n",
       "  112,\n",
       "  108,\n",
       "  101,\n",
       "  115,\n",
       "  95,\n",
       "  115,\n",
       "  112,\n",
       "  108,\n",
       "  105,\n",
       "  116],\n",
       " 2,\n",
       " [109, 105, 110, 95, 115, 97, 109, 112, 108, 101, 115, 95, 108, 101, 97, 102],\n",
       " 1]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import utils\n",
    "params = utils.get_model_parameters(best)\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2936e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2936e_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_2936e_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_2936e_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_2936e_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_2936e_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_2936e_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_2936e_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_2936e_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_2936e_level0_col8\" class=\"col_heading level0 col8\" >Log Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2936e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_2936e_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_2936e_row0_col1\" class=\"data row0 col1\" >0.8306</td>\n",
       "      <td id=\"T_2936e_row0_col2\" class=\"data row0 col2\" >0.9688</td>\n",
       "      <td id=\"T_2936e_row0_col3\" class=\"data row0 col3\" >0.8306</td>\n",
       "      <td id=\"T_2936e_row0_col4\" class=\"data row0 col4\" >0.8307</td>\n",
       "      <td id=\"T_2936e_row0_col5\" class=\"data row0 col5\" >0.8302</td>\n",
       "      <td id=\"T_2936e_row0_col6\" class=\"data row0 col6\" >0.7961</td>\n",
       "      <td id=\"T_2936e_row0_col7\" class=\"data row0 col7\" >0.7962</td>\n",
       "      <td id=\"T_2936e_row0_col8\" class=\"data row0 col8\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x291919430>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setattr(model, '_label_binarizer', getattr(tuned, '_label_binarizer'))\n",
    "df = exp.predict_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils.validation import check_is_fitted\n",
    "check_is_fitted(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    9.362\n",
       " Name: Log Loss, dtype: float64,\n",
       " 0    0.7403\n",
       " Name: Accuracy, dtype: float64)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = exp.pull()\n",
    "df['Log Loss'], df['Accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8.54252966])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'class_weight': None,\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'l1_ratio': None,\n",
       " 'max_iter': 1000,\n",
       " 'multi_class': 'auto',\n",
       " 'n_jobs': None,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': 42,\n",
       " 'solver': 'lbfgs',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "    black  lstat  medv  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_03938_row8_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_03938\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_03938_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_03938_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_03938_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_03938_row0_col1\" class=\"data row0 col1\" >2305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_03938_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_03938_row1_col1\" class=\"data row1 col1\" >medv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_03938_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_03938_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_03938_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_03938_row3_col1\" class=\"data row3 col1\" >(506, 14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_03938_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_03938_row4_col1\" class=\"data row4 col1\" >(506, 14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_03938_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_03938_row5_col1\" class=\"data row5 col1\" >(354, 14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_03938_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_03938_row6_col1\" class=\"data row6 col1\" >(152, 14)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_03938_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
       "      <td id=\"T_03938_row7_col1\" class=\"data row7 col1\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_03938_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
       "      <td id=\"T_03938_row8_col1\" class=\"data row8 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_03938_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
       "      <td id=\"T_03938_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_03938_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_03938_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_03938_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_03938_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_03938_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_03938_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_03938_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
       "      <td id=\"T_03938_row13_col1\" class=\"data row13 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_03938_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_03938_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_03938_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
       "      <td id=\"T_03938_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_03938_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_03938_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_03938_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_03938_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_03938_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_03938_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
       "      <td id=\"T_03938_row18_col1\" class=\"data row18 col1\" >5063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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       "<style type=\"text/css\">\n",
       "#T_a64c2 th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_a64c2_row0_col0, #T_a64c2_row0_col5, #T_a64c2_row0_col6, #T_a64c2_row1_col0, #T_a64c2_row1_col1, #T_a64c2_row1_col2, #T_a64c2_row1_col3, #T_a64c2_row1_col4, #T_a64c2_row2_col0, #T_a64c2_row2_col1, #T_a64c2_row2_col2, #T_a64c2_row2_col3, #T_a64c2_row2_col4, #T_a64c2_row2_col5, #T_a64c2_row2_col6, #T_a64c2_row3_col0, #T_a64c2_row3_col1, #T_a64c2_row3_col2, #T_a64c2_row3_col3, #T_a64c2_row3_col4, #T_a64c2_row3_col5, #T_a64c2_row3_col6, #T_a64c2_row4_col0, #T_a64c2_row4_col1, #T_a64c2_row4_col2, #T_a64c2_row4_col3, #T_a64c2_row4_col4, #T_a64c2_row4_col5, #T_a64c2_row4_col6, #T_a64c2_row5_col0, #T_a64c2_row5_col1, #T_a64c2_row5_col2, #T_a64c2_row5_col3, #T_a64c2_row5_col4, #T_a64c2_row5_col5, #T_a64c2_row5_col6, #T_a64c2_row6_col0, #T_a64c2_row6_col1, #T_a64c2_row6_col2, #T_a64c2_row6_col3, #T_a64c2_row6_col4, #T_a64c2_row6_col5, #T_a64c2_row6_col6, #T_a64c2_row7_col0, #T_a64c2_row7_col1, #T_a64c2_row7_col2, #T_a64c2_row7_col3, #T_a64c2_row7_col4, #T_a64c2_row7_col5, #T_a64c2_row7_col6, #T_a64c2_row8_col0, #T_a64c2_row8_col1, #T_a64c2_row8_col2, #T_a64c2_row8_col3, #T_a64c2_row8_col4, #T_a64c2_row8_col5, #T_a64c2_row8_col6, #T_a64c2_row9_col0, #T_a64c2_row9_col1, #T_a64c2_row9_col2, #T_a64c2_row9_col3, #T_a64c2_row9_col4, #T_a64c2_row9_col5, #T_a64c2_row9_col6, #T_a64c2_row10_col0, #T_a64c2_row10_col1, #T_a64c2_row10_col2, #T_a64c2_row10_col3, #T_a64c2_row10_col4, #T_a64c2_row10_col5, #T_a64c2_row10_col6, #T_a64c2_row11_col0, #T_a64c2_row11_col1, #T_a64c2_row11_col2, #T_a64c2_row11_col3, #T_a64c2_row11_col4, #T_a64c2_row11_col5, #T_a64c2_row11_col6, #T_a64c2_row12_col0, #T_a64c2_row12_col1, #T_a64c2_row12_col2, #T_a64c2_row12_col3, #T_a64c2_row12_col4, #T_a64c2_row12_col5, #T_a64c2_row12_col6, #T_a64c2_row13_col0, #T_a64c2_row13_col1, #T_a64c2_row13_col2, #T_a64c2_row13_col3, #T_a64c2_row13_col4, #T_a64c2_row13_col5, #T_a64c2_row13_col6, #T_a64c2_row14_col0, #T_a64c2_row14_col1, #T_a64c2_row14_col2, #T_a64c2_row14_col3, #T_a64c2_row14_col4, #T_a64c2_row14_col5, #T_a64c2_row14_col6, #T_a64c2_row15_col0, #T_a64c2_row15_col1, #T_a64c2_row15_col2, #T_a64c2_row15_col3, #T_a64c2_row15_col4, #T_a64c2_row15_col5, #T_a64c2_row15_col6, #T_a64c2_row16_col0, #T_a64c2_row16_col1, #T_a64c2_row16_col2, #T_a64c2_row16_col3, #T_a64c2_row16_col4, #T_a64c2_row16_col5, #T_a64c2_row16_col6, #T_a64c2_row17_col0, #T_a64c2_row17_col1, #T_a64c2_row17_col2, #T_a64c2_row17_col3, #T_a64c2_row17_col4, #T_a64c2_row17_col5, #T_a64c2_row17_col6, #T_a64c2_row18_col0, #T_a64c2_row18_col1, #T_a64c2_row18_col2, #T_a64c2_row18_col3, #T_a64c2_row18_col4, #T_a64c2_row18_col5, #T_a64c2_row18_col6, #T_a64c2_row19_col0, #T_a64c2_row19_col1, #T_a64c2_row19_col2, #T_a64c2_row19_col3, #T_a64c2_row19_col4, #T_a64c2_row19_col5, #T_a64c2_row19_col6 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_a64c2_row0_col1, #T_a64c2_row0_col2, #T_a64c2_row0_col3, #T_a64c2_row0_col4, #T_a64c2_row1_col5, #T_a64c2_row1_col6 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "}\n",
       "#T_a64c2_row0_col7, #T_a64c2_row1_col7, #T_a64c2_row2_col7, #T_a64c2_row3_col7, #T_a64c2_row4_col7, #T_a64c2_row5_col7, #T_a64c2_row6_col7, #T_a64c2_row7_col7, #T_a64c2_row8_col7, #T_a64c2_row9_col7, #T_a64c2_row10_col7, #T_a64c2_row11_col7, #T_a64c2_row12_col7, #T_a64c2_row15_col7, #T_a64c2_row17_col7, #T_a64c2_row19_col7 {\n",
       "  text-align: left;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "#T_a64c2_row13_col7, #T_a64c2_row14_col7, #T_a64c2_row16_col7, #T_a64c2_row18_col7 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a64c2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a64c2_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_a64c2_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_a64c2_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_a64c2_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_a64c2_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_a64c2_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_a64c2_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "      <th id=\"T_a64c2_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row0\" class=\"row_heading level0 row0\" >gbr</th>\n",
       "      <td id=\"T_a64c2_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_a64c2_row0_col1\" class=\"data row0 col1\" >2.3513</td>\n",
       "      <td id=\"T_a64c2_row0_col2\" class=\"data row0 col2\" >10.5775</td>\n",
       "      <td id=\"T_a64c2_row0_col3\" class=\"data row0 col3\" >3.2523</td>\n",
       "      <td id=\"T_a64c2_row0_col4\" class=\"data row0 col4\" >0.8816</td>\n",
       "      <td id=\"T_a64c2_row0_col5\" class=\"data row0 col5\" >0.1644</td>\n",
       "      <td id=\"T_a64c2_row0_col6\" class=\"data row0 col6\" >0.1277</td>\n",
       "      <td id=\"T_a64c2_row0_col7\" class=\"data row0 col7\" >0.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row1\" class=\"row_heading level0 row1\" >catboost</th>\n",
       "      <td id=\"T_a64c2_row1_col0\" class=\"data row1 col0\" >CatBoost Regressor</td>\n",
       "      <td id=\"T_a64c2_row1_col1\" class=\"data row1 col1\" >2.3582</td>\n",
       "      <td id=\"T_a64c2_row1_col2\" class=\"data row1 col2\" >12.1789</td>\n",
       "      <td id=\"T_a64c2_row1_col3\" class=\"data row1 col3\" >3.4898</td>\n",
       "      <td id=\"T_a64c2_row1_col4\" class=\"data row1 col4\" >0.8637</td>\n",
       "      <td id=\"T_a64c2_row1_col5\" class=\"data row1 col5\" >0.1627</td>\n",
       "      <td id=\"T_a64c2_row1_col6\" class=\"data row1 col6\" >0.1235</td>\n",
       "      <td id=\"T_a64c2_row1_col7\" class=\"data row1 col7\" >0.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row2\" class=\"row_heading level0 row2\" >xgboost</th>\n",
       "      <td id=\"T_a64c2_row2_col0\" class=\"data row2 col0\" >Extreme Gradient Boosting</td>\n",
       "      <td id=\"T_a64c2_row2_col1\" class=\"data row2 col1\" >2.4241</td>\n",
       "      <td id=\"T_a64c2_row2_col2\" class=\"data row2 col2\" >13.4620</td>\n",
       "      <td id=\"T_a64c2_row2_col3\" class=\"data row2 col3\" >3.6691</td>\n",
       "      <td id=\"T_a64c2_row2_col4\" class=\"data row2 col4\" >0.8494</td>\n",
       "      <td id=\"T_a64c2_row2_col5\" class=\"data row2 col5\" >0.1701</td>\n",
       "      <td id=\"T_a64c2_row2_col6\" class=\"data row2 col6\" >0.1275</td>\n",
       "      <td id=\"T_a64c2_row2_col7\" class=\"data row2 col7\" >0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
       "      <td id=\"T_a64c2_row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
       "      <td id=\"T_a64c2_row3_col1\" class=\"data row3 col1\" >2.5463</td>\n",
       "      <td id=\"T_a64c2_row3_col2\" class=\"data row3 col2\" >13.5374</td>\n",
       "      <td id=\"T_a64c2_row3_col3\" class=\"data row3 col3\" >3.6793</td>\n",
       "      <td id=\"T_a64c2_row3_col4\" class=\"data row3 col4\" >0.8485</td>\n",
       "      <td id=\"T_a64c2_row3_col5\" class=\"data row3 col5\" >0.1847</td>\n",
       "      <td id=\"T_a64c2_row3_col6\" class=\"data row3 col6\" >0.1429</td>\n",
       "      <td id=\"T_a64c2_row3_col7\" class=\"data row3 col7\" >0.0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row4\" class=\"row_heading level0 row4\" >et</th>\n",
       "      <td id=\"T_a64c2_row4_col0\" class=\"data row4 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_a64c2_row4_col1\" class=\"data row4 col1\" >2.3803</td>\n",
       "      <td id=\"T_a64c2_row4_col2\" class=\"data row4 col2\" >13.9484</td>\n",
       "      <td id=\"T_a64c2_row4_col3\" class=\"data row4 col3\" >3.7348</td>\n",
       "      <td id=\"T_a64c2_row4_col4\" class=\"data row4 col4\" >0.8439</td>\n",
       "      <td id=\"T_a64c2_row4_col5\" class=\"data row4 col5\" >0.1726</td>\n",
       "      <td id=\"T_a64c2_row4_col6\" class=\"data row4 col6\" >0.1259</td>\n",
       "      <td id=\"T_a64c2_row4_col7\" class=\"data row4 col7\" >0.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row5\" class=\"row_heading level0 row5\" >lightgbm</th>\n",
       "      <td id=\"T_a64c2_row5_col0\" class=\"data row5 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_a64c2_row5_col1\" class=\"data row5 col1\" >2.6446</td>\n",
       "      <td id=\"T_a64c2_row5_col2\" class=\"data row5 col2\" >15.8780</td>\n",
       "      <td id=\"T_a64c2_row5_col3\" class=\"data row5 col3\" >3.9847</td>\n",
       "      <td id=\"T_a64c2_row5_col4\" class=\"data row5 col4\" >0.8223</td>\n",
       "      <td id=\"T_a64c2_row5_col5\" class=\"data row5 col5\" >0.1942</td>\n",
       "      <td id=\"T_a64c2_row5_col6\" class=\"data row5 col6\" >0.1475</td>\n",
       "      <td id=\"T_a64c2_row5_col7\" class=\"data row5 col7\" >0.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
       "      <td id=\"T_a64c2_row6_col0\" class=\"data row6 col0\" >AdaBoost Regressor</td>\n",
       "      <td id=\"T_a64c2_row6_col1\" class=\"data row6 col1\" >3.0395</td>\n",
       "      <td id=\"T_a64c2_row6_col2\" class=\"data row6 col2\" >18.8047</td>\n",
       "      <td id=\"T_a64c2_row6_col3\" class=\"data row6 col3\" >4.3364</td>\n",
       "      <td id=\"T_a64c2_row6_col4\" class=\"data row6 col4\" >0.7896</td>\n",
       "      <td id=\"T_a64c2_row6_col5\" class=\"data row6 col5\" >0.2177</td>\n",
       "      <td id=\"T_a64c2_row6_col6\" class=\"data row6 col6\" >0.1770</td>\n",
       "      <td id=\"T_a64c2_row6_col7\" class=\"data row6 col7\" >0.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row7\" class=\"row_heading level0 row7\" >dt</th>\n",
       "      <td id=\"T_a64c2_row7_col0\" class=\"data row7 col0\" >Decision Tree Regressor</td>\n",
       "      <td id=\"T_a64c2_row7_col1\" class=\"data row7 col1\" >3.3605</td>\n",
       "      <td id=\"T_a64c2_row7_col2\" class=\"data row7 col2\" >23.3951</td>\n",
       "      <td id=\"T_a64c2_row7_col3\" class=\"data row7 col3\" >4.8369</td>\n",
       "      <td id=\"T_a64c2_row7_col4\" class=\"data row7 col4\" >0.7382</td>\n",
       "      <td id=\"T_a64c2_row7_col5\" class=\"data row7 col5\" >0.2098</td>\n",
       "      <td id=\"T_a64c2_row7_col6\" class=\"data row7 col6\" >0.1718</td>\n",
       "      <td id=\"T_a64c2_row7_col7\" class=\"data row7 col7\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row8\" class=\"row_heading level0 row8\" >lr</th>\n",
       "      <td id=\"T_a64c2_row8_col0\" class=\"data row8 col0\" >Linear Regression</td>\n",
       "      <td id=\"T_a64c2_row8_col1\" class=\"data row8 col1\" >3.7052</td>\n",
       "      <td id=\"T_a64c2_row8_col2\" class=\"data row8 col2\" >26.3696</td>\n",
       "      <td id=\"T_a64c2_row8_col3\" class=\"data row8 col3\" >5.1351</td>\n",
       "      <td id=\"T_a64c2_row8_col4\" class=\"data row8 col4\" >0.7049</td>\n",
       "      <td id=\"T_a64c2_row8_col5\" class=\"data row8 col5\" >0.2646</td>\n",
       "      <td id=\"T_a64c2_row8_col6\" class=\"data row8 col6\" >0.1875</td>\n",
       "      <td id=\"T_a64c2_row8_col7\" class=\"data row8 col7\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row9\" class=\"row_heading level0 row9\" >lar</th>\n",
       "      <td id=\"T_a64c2_row9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n",
       "      <td id=\"T_a64c2_row9_col1\" class=\"data row9 col1\" >3.7315</td>\n",
       "      <td id=\"T_a64c2_row9_col2\" class=\"data row9 col2\" >26.7222</td>\n",
       "      <td id=\"T_a64c2_row9_col3\" class=\"data row9 col3\" >5.1694</td>\n",
       "      <td id=\"T_a64c2_row9_col4\" class=\"data row9 col4\" >0.7010</td>\n",
       "      <td id=\"T_a64c2_row9_col5\" class=\"data row9 col5\" >0.2645</td>\n",
       "      <td id=\"T_a64c2_row9_col6\" class=\"data row9 col6\" >0.1884</td>\n",
       "      <td id=\"T_a64c2_row9_col7\" class=\"data row9 col7\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
       "      <td id=\"T_a64c2_row10_col0\" class=\"data row10 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_a64c2_row10_col1\" class=\"data row10 col1\" >3.7410</td>\n",
       "      <td id=\"T_a64c2_row10_col2\" class=\"data row10 col2\" >27.0079</td>\n",
       "      <td id=\"T_a64c2_row10_col3\" class=\"data row10 col3\" >5.1969</td>\n",
       "      <td id=\"T_a64c2_row10_col4\" class=\"data row10 col4\" >0.6978</td>\n",
       "      <td id=\"T_a64c2_row10_col5\" class=\"data row10 col5\" >0.2917</td>\n",
       "      <td id=\"T_a64c2_row10_col6\" class=\"data row10 col6\" >0.1914</td>\n",
       "      <td id=\"T_a64c2_row10_col7\" class=\"data row10 col7\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row11\" class=\"row_heading level0 row11\" >br</th>\n",
       "      <td id=\"T_a64c2_row11_col0\" class=\"data row11 col0\" >Bayesian Ridge</td>\n",
       "      <td id=\"T_a64c2_row11_col1\" class=\"data row11 col1\" >3.7858</td>\n",
       "      <td id=\"T_a64c2_row11_col2\" class=\"data row11 col2\" >27.5915</td>\n",
       "      <td id=\"T_a64c2_row11_col3\" class=\"data row11 col3\" >5.2528</td>\n",
       "      <td id=\"T_a64c2_row11_col4\" class=\"data row11 col4\" >0.6913</td>\n",
       "      <td id=\"T_a64c2_row11_col5\" class=\"data row11 col5\" >0.2893</td>\n",
       "      <td id=\"T_a64c2_row11_col6\" class=\"data row11 col6\" >0.1957</td>\n",
       "      <td id=\"T_a64c2_row11_col7\" class=\"data row11 col7\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row12\" class=\"row_heading level0 row12\" >en</th>\n",
       "      <td id=\"T_a64c2_row12_col0\" class=\"data row12 col0\" >Elastic Net</td>\n",
       "      <td id=\"T_a64c2_row12_col1\" class=\"data row12 col1\" >4.0508</td>\n",
       "      <td id=\"T_a64c2_row12_col2\" class=\"data row12 col2\" >31.5715</td>\n",
       "      <td id=\"T_a64c2_row12_col3\" class=\"data row12 col3\" >5.6189</td>\n",
       "      <td id=\"T_a64c2_row12_col4\" class=\"data row12 col4\" >0.6467</td>\n",
       "      <td id=\"T_a64c2_row12_col5\" class=\"data row12 col5\" >0.3107</td>\n",
       "      <td id=\"T_a64c2_row12_col6\" class=\"data row12 col6\" >0.2064</td>\n",
       "      <td id=\"T_a64c2_row12_col7\" class=\"data row12 col7\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row13\" class=\"row_heading level0 row13\" >lasso</th>\n",
       "      <td id=\"T_a64c2_row13_col0\" class=\"data row13 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_a64c2_row13_col1\" class=\"data row13 col1\" >4.1660</td>\n",
       "      <td id=\"T_a64c2_row13_col2\" class=\"data row13 col2\" >33.2969</td>\n",
       "      <td id=\"T_a64c2_row13_col3\" class=\"data row13 col3\" >5.7703</td>\n",
       "      <td id=\"T_a64c2_row13_col4\" class=\"data row13 col4\" >0.6274</td>\n",
       "      <td id=\"T_a64c2_row13_col5\" class=\"data row13 col5\" >0.3204</td>\n",
       "      <td id=\"T_a64c2_row13_col6\" class=\"data row13 col6\" >0.2122</td>\n",
       "      <td id=\"T_a64c2_row13_col7\" class=\"data row13 col7\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row14\" class=\"row_heading level0 row14\" >llar</th>\n",
       "      <td id=\"T_a64c2_row14_col0\" class=\"data row14 col0\" >Lasso Least Angle Regression</td>\n",
       "      <td id=\"T_a64c2_row14_col1\" class=\"data row14 col1\" >4.1663</td>\n",
       "      <td id=\"T_a64c2_row14_col2\" class=\"data row14 col2\" >33.2995</td>\n",
       "      <td id=\"T_a64c2_row14_col3\" class=\"data row14 col3\" >5.7706</td>\n",
       "      <td id=\"T_a64c2_row14_col4\" class=\"data row14 col4\" >0.6274</td>\n",
       "      <td id=\"T_a64c2_row14_col5\" class=\"data row14 col5\" >0.3206</td>\n",
       "      <td id=\"T_a64c2_row14_col6\" class=\"data row14 col6\" >0.2122</td>\n",
       "      <td id=\"T_a64c2_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row15\" class=\"row_heading level0 row15\" >huber</th>\n",
       "      <td id=\"T_a64c2_row15_col0\" class=\"data row15 col0\" >Huber Regressor</td>\n",
       "      <td id=\"T_a64c2_row15_col1\" class=\"data row15 col1\" >4.0945</td>\n",
       "      <td id=\"T_a64c2_row15_col2\" class=\"data row15 col2\" >34.1371</td>\n",
       "      <td id=\"T_a64c2_row15_col3\" class=\"data row15 col3\" >5.8427</td>\n",
       "      <td id=\"T_a64c2_row15_col4\" class=\"data row15 col4\" >0.6180</td>\n",
       "      <td id=\"T_a64c2_row15_col5\" class=\"data row15 col5\" >0.3113</td>\n",
       "      <td id=\"T_a64c2_row15_col6\" class=\"data row15 col6\" >0.2044</td>\n",
       "      <td id=\"T_a64c2_row15_col7\" class=\"data row15 col7\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row16\" class=\"row_heading level0 row16\" >knn</th>\n",
       "      <td id=\"T_a64c2_row16_col0\" class=\"data row16 col0\" >K Neighbors Regressor</td>\n",
       "      <td id=\"T_a64c2_row16_col1\" class=\"data row16 col1\" >4.8197</td>\n",
       "      <td id=\"T_a64c2_row16_col2\" class=\"data row16 col2\" >45.4761</td>\n",
       "      <td id=\"T_a64c2_row16_col3\" class=\"data row16 col3\" >6.7436</td>\n",
       "      <td id=\"T_a64c2_row16_col4\" class=\"data row16 col4\" >0.4912</td>\n",
       "      <td id=\"T_a64c2_row16_col5\" class=\"data row16 col5\" >0.2721</td>\n",
       "      <td id=\"T_a64c2_row16_col6\" class=\"data row16 col6\" >0.2307</td>\n",
       "      <td id=\"T_a64c2_row16_col7\" class=\"data row16 col7\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row17\" class=\"row_heading level0 row17\" >omp</th>\n",
       "      <td id=\"T_a64c2_row17_col0\" class=\"data row17 col0\" >Orthogonal Matching Pursuit</td>\n",
       "      <td id=\"T_a64c2_row17_col1\" class=\"data row17 col1\" >6.0011</td>\n",
       "      <td id=\"T_a64c2_row17_col2\" class=\"data row17 col2\" >69.8449</td>\n",
       "      <td id=\"T_a64c2_row17_col3\" class=\"data row17 col3\" >8.3573</td>\n",
       "      <td id=\"T_a64c2_row17_col4\" class=\"data row17 col4\" >0.2185</td>\n",
       "      <td id=\"T_a64c2_row17_col5\" class=\"data row17 col5\" >0.3426</td>\n",
       "      <td id=\"T_a64c2_row17_col6\" class=\"data row17 col6\" >0.3017</td>\n",
       "      <td id=\"T_a64c2_row17_col7\" class=\"data row17 col7\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row18\" class=\"row_heading level0 row18\" >dummy</th>\n",
       "      <td id=\"T_a64c2_row18_col0\" class=\"data row18 col0\" >Dummy Regressor</td>\n",
       "      <td id=\"T_a64c2_row18_col1\" class=\"data row18 col1\" >6.8948</td>\n",
       "      <td id=\"T_a64c2_row18_col2\" class=\"data row18 col2\" >89.9663</td>\n",
       "      <td id=\"T_a64c2_row18_col3\" class=\"data row18 col3\" >9.4851</td>\n",
       "      <td id=\"T_a64c2_row18_col4\" class=\"data row18 col4\" >-0.0067</td>\n",
       "      <td id=\"T_a64c2_row18_col5\" class=\"data row18 col5\" >0.4101</td>\n",
       "      <td id=\"T_a64c2_row18_col6\" class=\"data row18 col6\" >0.3809</td>\n",
       "      <td id=\"T_a64c2_row18_col7\" class=\"data row18 col7\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a64c2_level0_row19\" class=\"row_heading level0 row19\" >par</th>\n",
       "      <td id=\"T_a64c2_row19_col0\" class=\"data row19 col0\" >Passive Aggressive Regressor</td>\n",
       "      <td id=\"T_a64c2_row19_col1\" class=\"data row19 col1\" >8.6557</td>\n",
       "      <td id=\"T_a64c2_row19_col2\" class=\"data row19 col2\" >126.6179</td>\n",
       "      <td id=\"T_a64c2_row19_col3\" class=\"data row19 col3\" >11.2525</td>\n",
       "      <td id=\"T_a64c2_row19_col4\" class=\"data row19 col4\" >-0.4168</td>\n",
       "      <td id=\"T_a64c2_row19_col5\" class=\"data row19 col5\" >0.6046</td>\n",
       "      <td id=\"T_a64c2_row19_col6\" class=\"data row19 col6\" >0.3750</td>\n",
       "      <td id=\"T_a64c2_row19_col7\" class=\"data row19 col7\" >0.0100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x2977b0cd0>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "      <th>TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gbr</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>2.3513</td>\n",
       "      <td>10.5775</td>\n",
       "      <td>3.2523</td>\n",
       "      <td>0.8816</td>\n",
       "      <td>0.1644</td>\n",
       "      <td>0.1277</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>2.3582</td>\n",
       "      <td>12.1789</td>\n",
       "      <td>3.4898</td>\n",
       "      <td>0.8637</td>\n",
       "      <td>0.1627</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>2.4241</td>\n",
       "      <td>13.4620</td>\n",
       "      <td>3.6691</td>\n",
       "      <td>0.8494</td>\n",
       "      <td>0.1701</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>2.5463</td>\n",
       "      <td>13.5374</td>\n",
       "      <td>3.6793</td>\n",
       "      <td>0.8485</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>et</th>\n",
       "      <td>Extra Trees Regressor</td>\n",
       "      <td>2.3803</td>\n",
       "      <td>13.9484</td>\n",
       "      <td>3.7348</td>\n",
       "      <td>0.8439</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.1259</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>2.6446</td>\n",
       "      <td>15.8780</td>\n",
       "      <td>3.9847</td>\n",
       "      <td>0.8223</td>\n",
       "      <td>0.1942</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>3.0395</td>\n",
       "      <td>18.8047</td>\n",
       "      <td>4.3364</td>\n",
       "      <td>0.7896</td>\n",
       "      <td>0.2177</td>\n",
       "      <td>0.1770</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>Decision Tree Regressor</td>\n",
       "      <td>3.3605</td>\n",
       "      <td>23.3951</td>\n",
       "      <td>4.8369</td>\n",
       "      <td>0.7382</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.1718</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>3.7052</td>\n",
       "      <td>26.3696</td>\n",
       "      <td>5.1351</td>\n",
       "      <td>0.7049</td>\n",
       "      <td>0.2646</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lar</th>\n",
       "      <td>Least Angle Regression</td>\n",
       "      <td>3.7315</td>\n",
       "      <td>26.7222</td>\n",
       "      <td>5.1694</td>\n",
       "      <td>0.7010</td>\n",
       "      <td>0.2645</td>\n",
       "      <td>0.1884</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>3.7410</td>\n",
       "      <td>27.0079</td>\n",
       "      <td>5.1969</td>\n",
       "      <td>0.6978</td>\n",
       "      <td>0.2917</td>\n",
       "      <td>0.1914</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>br</th>\n",
       "      <td>Bayesian Ridge</td>\n",
       "      <td>3.7858</td>\n",
       "      <td>27.5915</td>\n",
       "      <td>5.2528</td>\n",
       "      <td>0.6913</td>\n",
       "      <td>0.2893</td>\n",
       "      <td>0.1957</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en</th>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>4.0508</td>\n",
       "      <td>31.5715</td>\n",
       "      <td>5.6189</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.3107</td>\n",
       "      <td>0.2064</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lasso</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>4.1660</td>\n",
       "      <td>33.2969</td>\n",
       "      <td>5.7703</td>\n",
       "      <td>0.6274</td>\n",
       "      <td>0.3204</td>\n",
       "      <td>0.2122</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llar</th>\n",
       "      <td>Lasso Least Angle Regression</td>\n",
       "      <td>4.1663</td>\n",
       "      <td>33.2995</td>\n",
       "      <td>5.7706</td>\n",
       "      <td>0.6274</td>\n",
       "      <td>0.3206</td>\n",
       "      <td>0.2122</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber</th>\n",
       "      <td>Huber Regressor</td>\n",
       "      <td>4.0945</td>\n",
       "      <td>34.1371</td>\n",
       "      <td>5.8427</td>\n",
       "      <td>0.6180</td>\n",
       "      <td>0.3113</td>\n",
       "      <td>0.2044</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K Neighbors Regressor</td>\n",
       "      <td>4.8197</td>\n",
       "      <td>45.4761</td>\n",
       "      <td>6.7436</td>\n",
       "      <td>0.4912</td>\n",
       "      <td>0.2721</td>\n",
       "      <td>0.2307</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omp</th>\n",
       "      <td>Orthogonal Matching Pursuit</td>\n",
       "      <td>6.0011</td>\n",
       "      <td>69.8449</td>\n",
       "      <td>8.3573</td>\n",
       "      <td>0.2185</td>\n",
       "      <td>0.3426</td>\n",
       "      <td>0.3017</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>Dummy Regressor</td>\n",
       "      <td>6.8948</td>\n",
       "      <td>89.9663</td>\n",
       "      <td>9.4851</td>\n",
       "      <td>-0.0067</td>\n",
       "      <td>0.4101</td>\n",
       "      <td>0.3809</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>par</th>\n",
       "      <td>Passive Aggressive Regressor</td>\n",
       "      <td>8.6557</td>\n",
       "      <td>126.6179</td>\n",
       "      <td>11.2525</td>\n",
       "      <td>-0.4168</td>\n",
       "      <td>0.6046</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Model     MAE       MSE     RMSE      R2  \\\n",
       "gbr           Gradient Boosting Regressor  2.3513   10.5775   3.2523  0.8816   \n",
       "catboost               CatBoost Regressor  2.3582   12.1789   3.4898  0.8637   \n",
       "xgboost         Extreme Gradient Boosting  2.4241   13.4620   3.6691  0.8494   \n",
       "rf                Random Forest Regressor  2.5463   13.5374   3.6793  0.8485   \n",
       "et                  Extra Trees Regressor  2.3803   13.9484   3.7348  0.8439   \n",
       "lightgbm  Light Gradient Boosting Machine  2.6446   15.8780   3.9847  0.8223   \n",
       "ada                    AdaBoost Regressor  3.0395   18.8047   4.3364  0.7896   \n",
       "dt                Decision Tree Regressor  3.3605   23.3951   4.8369  0.7382   \n",
       "lr                      Linear Regression  3.7052   26.3696   5.1351  0.7049   \n",
       "lar                Least Angle Regression  3.7315   26.7222   5.1694  0.7010   \n",
       "ridge                    Ridge Regression  3.7410   27.0079   5.1969  0.6978   \n",
       "br                         Bayesian Ridge  3.7858   27.5915   5.2528  0.6913   \n",
       "en                            Elastic Net  4.0508   31.5715   5.6189  0.6467   \n",
       "lasso                    Lasso Regression  4.1660   33.2969   5.7703  0.6274   \n",
       "llar         Lasso Least Angle Regression  4.1663   33.2995   5.7706  0.6274   \n",
       "huber                     Huber Regressor  4.0945   34.1371   5.8427  0.6180   \n",
       "knn                 K Neighbors Regressor  4.8197   45.4761   6.7436  0.4912   \n",
       "omp           Orthogonal Matching Pursuit  6.0011   69.8449   8.3573  0.2185   \n",
       "dummy                     Dummy Regressor  6.8948   89.9663   9.4851 -0.0067   \n",
       "par          Passive Aggressive Regressor  8.6557  126.6179  11.2525 -0.4168   \n",
       "\n",
       "           RMSLE    MAPE  TT (Sec)  \n",
       "gbr       0.1644  0.1277      0.07  \n",
       "catboost  0.1627  0.1235      0.62  \n",
       "xgboost   0.1701  0.1275      0.13  \n",
       "rf        0.1847  0.1429      0.08  \n",
       "et        0.1726  0.1259      0.07  \n",
       "lightgbm  0.1942  0.1475      0.13  \n",
       "ada       0.2177  0.1770      0.04  \n",
       "dt        0.2098  0.1718      0.01  \n",
       "lr        0.2646  0.1875      0.01  \n",
       "lar       0.2645  0.1884      0.01  \n",
       "ridge     0.2917  0.1914      0.01  \n",
       "br        0.2893  0.1957      0.01  \n",
       "en        0.3107  0.2064      0.01  \n",
       "lasso     0.3204  0.2122      0.00  \n",
       "llar      0.3206  0.2122      0.00  \n",
       "huber     0.3113  0.2044      0.02  \n",
       "knn       0.2721  0.2307      0.00  \n",
       "omp       0.3426  0.3017      0.01  \n",
       "dummy     0.4101  0.3809      0.00  \n",
       "par       0.6046  0.3750      0.01  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write example for regression\n",
    "from pycaret.regression import RegressionExperiment\n",
    "exp1 = RegressionExperiment()\n",
    "df = get_data('boston')\n",
    "exp1.setup(data=df, target='medv')\n",
    "# exp1.add_metric('mae', 'Mean Absolute Error', 'mean_absolute_error', greater_is_better=False)\n",
    "# exp1.add_metric('r2', 'R^2', 'r2', greater_is_better=True)\n",
    "# add loss function\n",
    "\n",
    "best = exp1.compare_models(cross_validation=False)\n",
    "df = exp1.pull()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
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       "      <th>TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Model, MAE, MSE, RMSE, R2, RMSLE, MAPE, TT (Sec)]\n",
       "Index: []"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ALLOWED_MODEL = ['lr', 'lf']\n",
    "# get only allowed indices from df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['Gradient Boosting Regressor', 'CatBoost Regressor', 'Extreme Gradient Boosting', 'Random Forest Regressor', 'Extra Trees Regressor', 'Light Gradient Boosting Machine', 'AdaBoost Regressor', 'Decision Tree Regressor', 'Linear Regression', 'Least Angle Regression', 'Ridge Regression', 'Bayesian Ridge', 'Elastic Net', 'Lasso Regression', 'Lasso Least Angle Regression', 'Huber Regressor', 'K Neighbors Regressor', 'Orthogonal Matching Pursuit', 'Dummy Regressor', 'Passive Aggressive Regressor'])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get key value pairs of df where key is df index and value is model name\n",
    "dfl = df.to_dict()['Model']\n",
    "dfl.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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