04_baseline_pycaret.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "64c7a494",
   "metadata": {},
   "source": [
    "# Time to do more serious stuff...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46b72469",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:09:56.960438Z",
     "start_time": "2022-08-29T11:09:55.972510Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pycaret.classification import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "249569e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:10:29.125490Z",
     "start_time": "2022-08-29T11:09:56.961776Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (102759, 1541)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"datasets/df_tsfresh_features.tar.gz\")\n",
    "print(\"df shape:\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6b92c974",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:11:59.178438Z",
     "start_time": "2022-08-29T11:11:59.172046Z"
    }
   },
   "outputs": [],
   "source": [
    "# In this tutorial, we are only doing sleep/wake binary classification\n",
    "\n",
    "# Type of sleep statging problems:\n",
    "# -------------------------------\n",
    "#\n",
    "#      5-class | 4-class | 3-class | 2-Class\n",
    "# 0 -> Wake    | Wake    | Wake    | Wake\n",
    "# 1 -> N1      | Light   | NREM    | Sleep\n",
    "# 2 -> N2      | Light   | NREM    | Sleep\n",
    "# 3 -> N3      | Deep    | NREM    | Sleep\n",
    "# 4 -> N4      | Deep    | NREM    | Sleep\n",
    "# 5 -> REM     | REM     | REM     | Sleep\n",
    "#\n",
    "#\n",
    "\n",
    "df[\"sleep\"] = df[\"sleep_phase\"] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32baeedf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:10:29.449523Z",
     "start_time": "2022-08-29T11:10:29.126785Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>act</th>\n",
       "      <th>sleep_phase</th>\n",
       "      <th>hr</th>\n",
       "      <th>pid</th>\n",
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       "      <th>act__has_duplicate</th>\n",
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       "      <th>hr__fourier_entropy__bins_3</th>\n",
       "      <th>hr__fourier_entropy__bins_5</th>\n",
       "      <th>hr__fourier_entropy__bins_10</th>\n",
       "      <th>hr__fourier_entropy__bins_100</th>\n",
       "      <th>hr__permutation_entropy__dimension_3__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_4__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_5__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_6__tau_1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.0</td>\n",
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       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.098612</td>\n",
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       "      <td>1.386294</td>\n",
       "      <td>1.386294</td>\n",
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       "      <td>-0.000000</td>\n",
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       "      <td>1.329661</td>\n",
       "      <td>1.560710</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.522955</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>622.0</td>\n",
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       "      <td>1.242453</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.149060</td>\n",
       "      <td>1.667462</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>1151</td>\n",
       "      <td>87.0</td>\n",
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       "      <td>945.0</td>\n",
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       "      <td>1.560710</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>0.848686</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>1.747868</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>False</td>\n",
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       "</table>\n",
       "<p>9536 rows × 1542 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      time    act  sleep_phase    hr  pid  \\\n",
       "0        1    2.0          0.0  71.0    0   \n",
       "1        2    0.0          0.0  76.0    0   \n",
       "2        3    1.0          0.0  78.0    0   \n",
       "3        4    2.0          0.0  73.0    0   \n",
       "4        5   87.0          0.0  80.0    0   \n",
       "...    ...    ...          ...   ...  ...   \n",
       "9531  1147  189.0          0.0  49.0    9   \n",
       "9532  1148  204.0          0.0  50.0    9   \n",
       "9533  1149   30.0          0.0  50.0    9   \n",
       "9534  1150   38.0          0.0  50.0    9   \n",
       "9535  1151   87.0          0.0  50.0    9   \n",
       "\n",
       "      act__variance_larger_than_standard_deviation  act__has_duplicate_max  \\\n",
       "0                                              0.0                     0.0   \n",
       "1                                              0.0                     0.0   \n",
       "2                                              0.0                     1.0   \n",
       "3                                              1.0                     0.0   \n",
       "4                                              1.0                     0.0   \n",
       "...                                            ...                     ...   \n",
       "9531                                           1.0                     0.0   \n",
       "9532                                           1.0                     0.0   \n",
       "9533                                           1.0                     0.0   \n",
       "9534                                           1.0                     0.0   \n",
       "9535                                           1.0                     0.0   \n",
       "\n",
       "      act__has_duplicate_min  act__has_duplicate  act__sum_values  ...  \\\n",
       "0                        0.0                 0.0              2.0  ...   \n",
       "1                        0.0                 0.0              3.0  ...   \n",
       "2                        0.0                 1.0              5.0  ...   \n",
       "3                        0.0                 1.0             92.0  ...   \n",
       "4                        1.0                 1.0             92.0  ...   \n",
       "...                      ...                 ...              ...  ...   \n",
       "9531                     1.0                 1.0            467.0  ...   \n",
       "9532                     1.0                 1.0            497.0  ...   \n",
       "9533                     1.0                 1.0            535.0  ...   \n",
       "9534                     1.0                 1.0            622.0  ...   \n",
       "9535                     1.0                 1.0            945.0  ...   \n",
       "\n",
       "      hr__fourier_entropy__bins_3  hr__fourier_entropy__bins_5  \\\n",
       "0                       -0.000000                    -0.000000   \n",
       "1                        0.693147                     0.693147   \n",
       "2                        0.636514                     0.636514   \n",
       "3                        0.636514                     0.636514   \n",
       "4                        0.693147                     0.693147   \n",
       "...                           ...                          ...   \n",
       "9531                     0.867563                     1.329661   \n",
       "9532                     1.098612                     1.098612   \n",
       "9533                     0.636514                     1.098612   \n",
       "9534                     0.867563                     0.867563   \n",
       "9535                     1.011404                     1.011404   \n",
       "\n",
       "      hr__fourier_entropy__bins_10  hr__fourier_entropy__bins_100  \\\n",
       "0                        -0.000000                      -0.000000   \n",
       "1                         0.693147                       0.693147   \n",
       "2                         0.636514                       0.636514   \n",
       "3                         0.636514                       1.098612   \n",
       "4                         0.693147                       1.386294   \n",
       "...                            ...                            ...   \n",
       "9531                      1.560710                       1.791759   \n",
       "9532                      1.098612                       1.791759   \n",
       "9533                      1.560710                       1.791759   \n",
       "9534                      1.242453                       1.791759   \n",
       "9535                      1.560710                       1.791759   \n",
       "\n",
       "      hr__permutation_entropy__dimension_3__tau_1  \\\n",
       "0                                             NaN   \n",
       "1                                       -0.000000   \n",
       "2                                        0.693147   \n",
       "3                                        1.098612   \n",
       "4                                        1.386294   \n",
       "...                                           ...   \n",
       "9531                                     1.522955   \n",
       "9532                                     1.273028   \n",
       "9533                                     1.273028   \n",
       "9534                                     1.149060   \n",
       "9535                                     0.848686   \n",
       "\n",
       "      hr__permutation_entropy__dimension_4__tau_1  \\\n",
       "0                                             NaN   \n",
       "1                                             NaN   \n",
       "2                                       -0.000000   \n",
       "3                                        0.693147   \n",
       "4                                        1.098612   \n",
       "...                                           ...   \n",
       "9531                                     2.079442   \n",
       "9532                                     2.079442   \n",
       "9533                                     1.906155   \n",
       "9534                                     1.667462   \n",
       "9535                                     1.386294   \n",
       "\n",
       "      hr__permutation_entropy__dimension_5__tau_1  \\\n",
       "0                                             NaN   \n",
       "1                                             NaN   \n",
       "2                                             NaN   \n",
       "3                                       -0.000000   \n",
       "4                                        0.693147   \n",
       "...                                           ...   \n",
       "9531                                     1.945910   \n",
       "9532                                     1.945910   \n",
       "9533                                     1.945910   \n",
       "9534                                     1.945910   \n",
       "9535                                     1.747868   \n",
       "\n",
       "      hr__permutation_entropy__dimension_6__tau_1  \\\n",
       "0                                             NaN   \n",
       "1                                             NaN   \n",
       "2                                             NaN   \n",
       "3                                             NaN   \n",
       "4                                       -0.000000   \n",
       "...                                           ...   \n",
       "9531                                     1.791759   \n",
       "9532                                     1.791759   \n",
       "9533                                     1.791759   \n",
       "9534                                     1.791759   \n",
       "9535                                     1.791759   \n",
       "\n",
       "      hr__permutation_entropy__dimension_7__tau_1  sleep  \n",
       "0                                             NaN  False  \n",
       "1                                             NaN  False  \n",
       "2                                             NaN  False  \n",
       "3                                             NaN  False  \n",
       "4                                             NaN  False  \n",
       "...                                           ...    ...  \n",
       "9531                                     1.609438  False  \n",
       "9532                                     1.609438  False  \n",
       "9533                                     1.609438  False  \n",
       "9534                                     1.609438  False  \n",
       "9535                                     1.609438  False  \n",
       "\n",
       "[9536 rows x 1542 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_small = df[df[\"pid\"].isin(range(10))]\n",
    "df_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "480d6a1e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:12:00.124650Z",
     "start_time": "2022-08-29T11:12:00.114909Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['act',\n",
       " 'act__variance_larger_than_standard_deviation',\n",
       " 'act__has_duplicate_max',\n",
       " 'act__has_duplicate_min',\n",
       " 'act__has_duplicate',\n",
       " 'act__sum_values',\n",
       " 'act__abs_energy',\n",
       " 'act__mean_abs_change',\n",
       " 'act__mean_change',\n",
       " 'act__mean_second_derivative_central',\n",
       " 'hr',\n",
       " 'hr__variance_larger_than_standard_deviation',\n",
       " 'hr__has_duplicate_max',\n",
       " 'hr__has_duplicate_min',\n",
       " 'hr__has_duplicate']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_keys = [k for k in df.keys() if k.startswith(\"hr\")]\n",
    "act_keys = [k for k in df.keys() if k.startswith(\"act\")]\n",
    "act_keys[:10] + hr_keys[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2651bce",
   "metadata": {},
   "source": [
    "### Run the experiments on a tiny part of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d484e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can do your own pre-cleaning....\n",
    "\n",
    "# Replace infinity by 0\n",
    "# df_clean = df_small.replace([np.inf, -np.inf], 0)\n",
    "# Replace NAs by 0\n",
    "# df_clean = df_clean.fillna(0.0)\n",
    "\n",
    "# Or let pycaret do it for you (hopefully it wont result in downstream classifier errors)\n",
    "df_clean = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e7a3f308",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes: df_train (3500, 1542), df_test (5895, 1542)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>act</th>\n",
       "      <th>sleep_phase</th>\n",
       "      <th>hr</th>\n",
       "      <th>pid</th>\n",
       "      <th>act__variance_larger_than_standard_deviation</th>\n",
       "      <th>act__has_duplicate_max</th>\n",
       "      <th>act__has_duplicate_min</th>\n",
       "      <th>act__has_duplicate</th>\n",
       "      <th>act__sum_values</th>\n",
       "      <th>...</th>\n",
       "      <th>hr__fourier_entropy__bins_3</th>\n",
       "      <th>hr__fourier_entropy__bins_5</th>\n",
       "      <th>hr__fourier_entropy__bins_10</th>\n",
       "      <th>hr__fourier_entropy__bins_100</th>\n",
       "      <th>hr__permutation_entropy__dimension_3__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_4__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_5__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_7__tau_1</th>\n",
       "      <th>sleep</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>1.560710</td>\n",
       "      <td>1.560710</td>\n",
       "      <td>1.676988</td>\n",
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       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4774</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.011404</td>\n",
       "      <td>1.560710</td>\n",
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       "      <td>1.609438</td>\n",
       "      <td>True</td>\n",
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       "      <td>1.906155</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>876</td>\n",
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       "      <td>50.0</td>\n",
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       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4777</th>\n",
       "      <td>877</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>1.735126</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 1542 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      time  act  sleep_phase    hr  pid  \\\n",
       "1278     1  1.0          0.0  66.0    1   \n",
       "1279     2  0.0          0.0  67.0    1   \n",
       "1280     3  0.0          1.0  66.0    1   \n",
       "1281     4  1.0          1.0  67.0    1   \n",
       "1282     5  1.0          1.0  64.0    1   \n",
       "...    ...  ...          ...   ...  ...   \n",
       "4773   873  1.0          5.0  49.0    4   \n",
       "4774   874  1.0          5.0  47.0    4   \n",
       "4775   875  1.0          5.0  54.0    4   \n",
       "4776   876  2.0          0.0  50.0    4   \n",
       "4777   877  1.0          5.0  56.0    4   \n",
       "\n",
       "      act__variance_larger_than_standard_deviation  act__has_duplicate_max  \\\n",
       "1278                                           0.0                     0.0   \n",
       "1279                                           0.0                     0.0   \n",
       "1280                                           0.0                     1.0   \n",
       "1281                                           0.0                     1.0   \n",
       "1282                                           0.0                     1.0   \n",
       "...                                            ...                     ...   \n",
       "4773                                           0.0                     1.0   \n",
       "4774                                           0.0                     1.0   \n",
       "4775                                           0.0                     0.0   \n",
       "4776                                           0.0                     0.0   \n",
       "4777                                           0.0                     0.0   \n",
       "\n",
       "      act__has_duplicate_min  act__has_duplicate  act__sum_values  ...  \\\n",
       "1278                     0.0                 0.0              1.0  ...   \n",
       "1279                     1.0                 1.0              1.0  ...   \n",
       "1280                     1.0                 1.0              2.0  ...   \n",
       "1281                     1.0                 1.0              3.0  ...   \n",
       "1282                     1.0                 1.0              3.0  ...   \n",
       "...                      ...                 ...              ...  ...   \n",
       "4773                     1.0                 1.0              4.0  ...   \n",
       "4774                     1.0                 1.0              5.0  ...   \n",
       "4775                     1.0                 1.0              7.0  ...   \n",
       "4776                     1.0                 1.0              8.0  ...   \n",
       "4777                     1.0                 1.0              8.0  ...   \n",
       "\n",
       "      hr__fourier_entropy__bins_3  hr__fourier_entropy__bins_5  \\\n",
       "1278                    -0.000000                    -0.000000   \n",
       "1279                     0.693147                     0.693147   \n",
       "1280                     1.098612                     1.098612   \n",
       "1281                     0.636514                     1.098612   \n",
       "1282                     1.039721                     1.039721   \n",
       "...                           ...                          ...   \n",
       "4773                     0.867563                     1.329661   \n",
       "4774                     1.011404                     1.560710   \n",
       "4775                     0.450561                     1.011404   \n",
       "4776                     0.867563                     0.867563   \n",
       "4777                     1.011404                     1.329661   \n",
       "\n",
       "      hr__fourier_entropy__bins_10  hr__fourier_entropy__bins_100  \\\n",
       "1278                     -0.000000                      -0.000000   \n",
       "1279                      0.693147                       0.693147   \n",
       "1280                      1.098612                       1.098612   \n",
       "1281                      1.098612                       1.098612   \n",
       "1282                      1.039721                       1.039721   \n",
       "...                            ...                            ...   \n",
       "4773                      1.560710                       1.560710   \n",
       "4774                      1.560710                       1.791759   \n",
       "4775                      1.011404                       1.560710   \n",
       "4776                      1.242453                       1.791759   \n",
       "4777                      1.560710                       1.791759   \n",
       "\n",
       "      hr__permutation_entropy__dimension_3__tau_1  \\\n",
       "1278                                          NaN   \n",
       "1279                                    -0.000000   \n",
       "1280                                     0.693147   \n",
       "1281                                     1.098612   \n",
       "1282                                     1.386294   \n",
       "...                                           ...   \n",
       "4773                                     1.676988   \n",
       "4774                                     1.676988   \n",
       "4775                                     1.735126   \n",
       "4776                                     1.735126   \n",
       "4777                                     1.735126   \n",
       "\n",
       "      hr__permutation_entropy__dimension_4__tau_1  \\\n",
       "1278                                          NaN   \n",
       "1279                                          NaN   \n",
       "1280                                    -0.000000   \n",
       "1281                                     0.693147   \n",
       "1282                                     1.098612   \n",
       "...                                           ...   \n",
       "4773                                     2.079442   \n",
       "4774                                     2.079442   \n",
       "4775                                     1.906155   \n",
       "4776                                     2.079442   \n",
       "4777                                     2.079442   \n",
       "\n",
       "      hr__permutation_entropy__dimension_5__tau_1  \\\n",
       "1278                                          NaN   \n",
       "1279                                          NaN   \n",
       "1280                                          NaN   \n",
       "1281                                    -0.000000   \n",
       "1282                                     0.693147   \n",
       "...                                           ...   \n",
       "4773                                     1.945910   \n",
       "4774                                     1.945910   \n",
       "4775                                     1.945910   \n",
       "4776                                     1.945910   \n",
       "4777                                     1.945910   \n",
       "\n",
       "      hr__permutation_entropy__dimension_6__tau_1  \\\n",
       "1278                                          NaN   \n",
       "1279                                          NaN   \n",
       "1280                                          NaN   \n",
       "1281                                          NaN   \n",
       "1282                                    -0.000000   \n",
       "...                                           ...   \n",
       "4773                                     1.791759   \n",
       "4774                                     1.791759   \n",
       "4775                                     1.791759   \n",
       "4776                                     1.791759   \n",
       "4777                                     1.791759   \n",
       "\n",
       "      hr__permutation_entropy__dimension_7__tau_1  sleep  \n",
       "1278                                          NaN  False  \n",
       "1279                                          NaN  False  \n",
       "1280                                          NaN   True  \n",
       "1281                                          NaN   True  \n",
       "1282                                          NaN   True  \n",
       "...                                           ...    ...  \n",
       "4773                                     1.609438   True  \n",
       "4774                                     1.609438   True  \n",
       "4775                                     1.609438   True  \n",
       "4776                                     1.609438  False  \n",
       "4777                                     1.609438   True  \n",
       "\n",
       "[3500 rows x 1542 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_clean[df_clean[\"pid\"].isin(range(1,5))]\n",
    "df_test  = df_clean[df_clean[\"pid\"].isin(range(5,11))]\n",
    "\n",
    "print(f\"Shapes: df_train {df_train.shape}, df_test {df_test.shape}\")\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fbc50b7",
   "metadata": {},
   "source": [
    "### Pycaret basic function: setup\n",
    "It is where we tell what our data is and how we would like to preprocess it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04ac152c",
   "metadata": {},
   "outputs": [],
   "source": [
    "setup?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "761dcfa9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:28:49.605368Z",
     "start_time": "2022-08-29T11:12:00.643100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_0644a_row10_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0644a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0644a_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_0644a_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_0644a_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_0644a_row0_col1\" class=\"data row0 col1\" >4821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_0644a_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_0644a_row1_col1\" class=\"data row1 col1\" >sleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_0644a_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_0644a_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_0644a_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_0644a_row3_col1\" class=\"data row3 col1\" >(9395, 1539)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_0644a_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_0644a_row4_col1\" class=\"data row4 col1\" >(9395, 611)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_0644a_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_0644a_row5_col1\" class=\"data row5 col1\" >(3500, 611)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_0644a_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_0644a_row6_col1\" class=\"data row6 col1\" >(5895, 611)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_0644a_row7_col0\" class=\"data row7 col0\" >Ignore features</td>\n",
       "      <td id=\"T_0644a_row7_col1\" class=\"data row7 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_0644a_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
       "      <td id=\"T_0644a_row8_col1\" class=\"data row8 col1\" >1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_0644a_row9_col0\" class=\"data row9 col0\" >Rows with missing values</td>\n",
       "      <td id=\"T_0644a_row9_col1\" class=\"data row9 col1\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_0644a_row10_col0\" class=\"data row10 col0\" >Preprocess</td>\n",
       "      <td id=\"T_0644a_row10_col1\" class=\"data row10 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_0644a_row11_col0\" class=\"data row11 col0\" >Imputation type</td>\n",
       "      <td id=\"T_0644a_row11_col1\" class=\"data row11 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_0644a_row12_col0\" class=\"data row12 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_0644a_row12_col1\" class=\"data row12 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_0644a_row13_col0\" class=\"data row13 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_0644a_row13_col1\" class=\"data row13 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_0644a_row14_col0\" class=\"data row14 col0\" >Low variance threshold</td>\n",
       "      <td id=\"T_0644a_row14_col1\" class=\"data row14 col1\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_0644a_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_0644a_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_0644a_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
       "      <td id=\"T_0644a_row16_col1\" class=\"data row16 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_0644a_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_0644a_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_0644a_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
       "      <td id=\"T_0644a_row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_0644a_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_0644a_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_0644a_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_0644a_row20_col1\" class=\"data row20 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0644a_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_0644a_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
       "      <td id=\"T_0644a_row21_col1\" class=\"data row21 col1\" >282d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d1032c40>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.49 s, sys: 438 ms, total: 2.93 s\n",
      "Wall time: 3.07 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "s = setup(data=df_train,\n",
    "      test_data=df_test,\n",
    "      target='sleep',\n",
    "      ignore_features=[\"pid\", \"sleep_phase\", \"time\"],\n",
    "      numeric_features=act_keys + hr_keys,\n",
    "      )\n",
    "\n",
    "# Important: \n",
    "#\n",
    "# -- Have a look at the shape of the original and transformed datasets\n",
    "# -- Have a look at the different preprocessing techniques that are applied by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c90ba047",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T13:23:48.051458Z",
     "start_time": "2022-08-29T12:16:45.129241Z"
    }
   },
   "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_8e54e th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_8e54e_row0_col0, #T_8e54e_row0_col3, #T_8e54e_row0_col5, #T_8e54e_row1_col0, #T_8e54e_row1_col1, #T_8e54e_row1_col2, #T_8e54e_row1_col3, #T_8e54e_row1_col4, #T_8e54e_row1_col5, #T_8e54e_row1_col6, #T_8e54e_row1_col7, #T_8e54e_row2_col0, #T_8e54e_row2_col1, #T_8e54e_row2_col2, #T_8e54e_row2_col3, #T_8e54e_row2_col4, #T_8e54e_row2_col5, #T_8e54e_row2_col6, #T_8e54e_row2_col7, #T_8e54e_row3_col0, #T_8e54e_row3_col1, #T_8e54e_row3_col2, #T_8e54e_row3_col3, #T_8e54e_row3_col4, #T_8e54e_row3_col5, #T_8e54e_row3_col6, #T_8e54e_row3_col7, #T_8e54e_row4_col0, #T_8e54e_row4_col1, #T_8e54e_row4_col2, #T_8e54e_row4_col3, #T_8e54e_row4_col4, #T_8e54e_row4_col5, #T_8e54e_row4_col6, #T_8e54e_row4_col7, #T_8e54e_row5_col0, #T_8e54e_row5_col1, #T_8e54e_row5_col2, #T_8e54e_row5_col3, #T_8e54e_row5_col4, #T_8e54e_row5_col5, #T_8e54e_row5_col6, #T_8e54e_row5_col7, #T_8e54e_row6_col0, #T_8e54e_row6_col1, #T_8e54e_row6_col2, #T_8e54e_row6_col4, #T_8e54e_row6_col6, #T_8e54e_row6_col7, #T_8e54e_row7_col0, #T_8e54e_row7_col1, #T_8e54e_row7_col2, #T_8e54e_row7_col3, #T_8e54e_row7_col4, #T_8e54e_row7_col5, #T_8e54e_row7_col6, #T_8e54e_row7_col7, #T_8e54e_row8_col0, #T_8e54e_row8_col1, #T_8e54e_row8_col2, #T_8e54e_row8_col3, #T_8e54e_row8_col4, #T_8e54e_row8_col5, #T_8e54e_row8_col6, #T_8e54e_row8_col7, #T_8e54e_row9_col0, #T_8e54e_row9_col1, #T_8e54e_row9_col2, #T_8e54e_row9_col3, #T_8e54e_row9_col4, #T_8e54e_row9_col5, #T_8e54e_row9_col6, #T_8e54e_row9_col7, #T_8e54e_row10_col0, #T_8e54e_row10_col1, #T_8e54e_row10_col2, #T_8e54e_row10_col3, #T_8e54e_row10_col5, #T_8e54e_row10_col6, #T_8e54e_row10_col7, #T_8e54e_row11_col0, #T_8e54e_row11_col1, #T_8e54e_row11_col2, #T_8e54e_row11_col3, #T_8e54e_row11_col4, #T_8e54e_row11_col5, #T_8e54e_row11_col6, #T_8e54e_row11_col7, #T_8e54e_row12_col0, #T_8e54e_row12_col1, #T_8e54e_row12_col2, #T_8e54e_row12_col3, #T_8e54e_row12_col4, #T_8e54e_row12_col5, #T_8e54e_row12_col6, #T_8e54e_row12_col7 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_8e54e_row0_col1, #T_8e54e_row0_col2, #T_8e54e_row0_col4, #T_8e54e_row0_col6, #T_8e54e_row0_col7, #T_8e54e_row6_col3, #T_8e54e_row6_col5, #T_8e54e_row10_col4 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "}\n",
       "#T_8e54e_row0_col8, #T_8e54e_row1_col8, #T_8e54e_row2_col8, #T_8e54e_row3_col8, #T_8e54e_row4_col8, #T_8e54e_row5_col8, #T_8e54e_row6_col8, #T_8e54e_row7_col8, #T_8e54e_row8_col8, #T_8e54e_row9_col8, #T_8e54e_row10_col8, #T_8e54e_row12_col8 {\n",
       "  text-align: left;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "#T_8e54e_row11_col8 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8e54e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8e54e_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_8e54e_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_8e54e_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_8e54e_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_8e54e_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_8e54e_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_8e54e_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_8e54e_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_8e54e_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row0\" class=\"row_heading level0 row0\" >gbc</th>\n",
       "      <td id=\"T_8e54e_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_8e54e_row0_col1\" class=\"data row0 col1\" >0.8166</td>\n",
       "      <td id=\"T_8e54e_row0_col2\" class=\"data row0 col2\" >0.8401</td>\n",
       "      <td id=\"T_8e54e_row0_col3\" class=\"data row0 col3\" >0.9035</td>\n",
       "      <td id=\"T_8e54e_row0_col4\" class=\"data row0 col4\" >0.8740</td>\n",
       "      <td id=\"T_8e54e_row0_col5\" class=\"data row0 col5\" >0.8841</td>\n",
       "      <td id=\"T_8e54e_row0_col6\" class=\"data row0 col6\" >0.4050</td>\n",
       "      <td id=\"T_8e54e_row0_col7\" class=\"data row0 col7\" >0.4385</td>\n",
       "      <td id=\"T_8e54e_row0_col8\" class=\"data row0 col8\" >2.2060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
       "      <td id=\"T_8e54e_row1_col0\" class=\"data row1 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_8e54e_row1_col1\" class=\"data row1 col1\" >0.8097</td>\n",
       "      <td id=\"T_8e54e_row1_col2\" class=\"data row1 col2\" >0.8211</td>\n",
       "      <td id=\"T_8e54e_row1_col3\" class=\"data row1 col3\" >0.9006</td>\n",
       "      <td id=\"T_8e54e_row1_col4\" class=\"data row1 col4\" >0.8694</td>\n",
       "      <td id=\"T_8e54e_row1_col5\" class=\"data row1 col5\" >0.8801</td>\n",
       "      <td id=\"T_8e54e_row1_col6\" class=\"data row1 col6\" >0.3766</td>\n",
       "      <td id=\"T_8e54e_row1_col7\" class=\"data row1 col7\" >0.4109</td>\n",
       "      <td id=\"T_8e54e_row1_col8\" class=\"data row1 col8\" >0.3040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row2\" class=\"row_heading level0 row2\" >ridge</th>\n",
       "      <td id=\"T_8e54e_row2_col0\" class=\"data row2 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_8e54e_row2_col1\" class=\"data row2 col1\" >0.8086</td>\n",
       "      <td id=\"T_8e54e_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row2_col3\" class=\"data row2 col3\" >0.9167</td>\n",
       "      <td id=\"T_8e54e_row2_col4\" class=\"data row2 col4\" >0.8550</td>\n",
       "      <td id=\"T_8e54e_row2_col5\" class=\"data row2 col5\" >0.8815</td>\n",
       "      <td id=\"T_8e54e_row2_col6\" class=\"data row2 col6\" >0.3426</td>\n",
       "      <td id=\"T_8e54e_row2_col7\" class=\"data row2 col7\" >0.3749</td>\n",
       "      <td id=\"T_8e54e_row2_col8\" class=\"data row2 col8\" >0.0510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row3\" class=\"row_heading level0 row3\" >lda</th>\n",
       "      <td id=\"T_8e54e_row3_col0\" class=\"data row3 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_8e54e_row3_col1\" class=\"data row3 col1\" >0.8023</td>\n",
       "      <td id=\"T_8e54e_row3_col2\" class=\"data row3 col2\" >0.7689</td>\n",
       "      <td id=\"T_8e54e_row3_col3\" class=\"data row3 col3\" >0.8995</td>\n",
       "      <td id=\"T_8e54e_row3_col4\" class=\"data row3 col4\" >0.8607</td>\n",
       "      <td id=\"T_8e54e_row3_col5\" class=\"data row3 col5\" >0.8748</td>\n",
       "      <td id=\"T_8e54e_row3_col6\" class=\"data row3 col6\" >0.3520</td>\n",
       "      <td id=\"T_8e54e_row3_col7\" class=\"data row3 col7\" >0.3824</td>\n",
       "      <td id=\"T_8e54e_row3_col8\" class=\"data row3 col8\" >0.1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row4\" class=\"row_heading level0 row4\" >et</th>\n",
       "      <td id=\"T_8e54e_row4_col0\" class=\"data row4 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_8e54e_row4_col1\" class=\"data row4 col1\" >0.7986</td>\n",
       "      <td id=\"T_8e54e_row4_col2\" class=\"data row4 col2\" >0.8005</td>\n",
       "      <td id=\"T_8e54e_row4_col3\" class=\"data row4 col3\" >0.8988</td>\n",
       "      <td id=\"T_8e54e_row4_col4\" class=\"data row4 col4\" >0.8586</td>\n",
       "      <td id=\"T_8e54e_row4_col5\" class=\"data row4 col5\" >0.8742</td>\n",
       "      <td id=\"T_8e54e_row4_col6\" class=\"data row4 col6\" >0.3297</td>\n",
       "      <td id=\"T_8e54e_row4_col7\" class=\"data row4 col7\" >0.3652</td>\n",
       "      <td id=\"T_8e54e_row4_col8\" class=\"data row4 col8\" >0.1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row5\" class=\"row_heading level0 row5\" >ada</th>\n",
       "      <td id=\"T_8e54e_row5_col0\" class=\"data row5 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_8e54e_row5_col1\" class=\"data row5 col1\" >0.7980</td>\n",
       "      <td id=\"T_8e54e_row5_col2\" class=\"data row5 col2\" >0.7790</td>\n",
       "      <td id=\"T_8e54e_row5_col3\" class=\"data row5 col3\" >0.8841</td>\n",
       "      <td id=\"T_8e54e_row5_col4\" class=\"data row5 col4\" >0.8702</td>\n",
       "      <td id=\"T_8e54e_row5_col5\" class=\"data row5 col5\" >0.8695</td>\n",
       "      <td id=\"T_8e54e_row5_col6\" class=\"data row5 col6\" >0.3574</td>\n",
       "      <td id=\"T_8e54e_row5_col7\" class=\"data row5 col7\" >0.4000</td>\n",
       "      <td id=\"T_8e54e_row5_col8\" class=\"data row5 col8\" >0.5030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row6\" class=\"row_heading level0 row6\" >dummy</th>\n",
       "      <td id=\"T_8e54e_row6_col0\" class=\"data row6 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_8e54e_row6_col1\" class=\"data row6 col1\" >0.7951</td>\n",
       "      <td id=\"T_8e54e_row6_col2\" class=\"data row6 col2\" >0.5000</td>\n",
       "      <td id=\"T_8e54e_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
       "      <td id=\"T_8e54e_row6_col4\" class=\"data row6 col4\" >0.7951</td>\n",
       "      <td id=\"T_8e54e_row6_col5\" class=\"data row6 col5\" >0.8859</td>\n",
       "      <td id=\"T_8e54e_row6_col6\" class=\"data row6 col6\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row6_col7\" class=\"data row6 col7\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row6_col8\" class=\"data row6 col8\" >0.0510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row7\" class=\"row_heading level0 row7\" >qda</th>\n",
       "      <td id=\"T_8e54e_row7_col0\" class=\"data row7 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_8e54e_row7_col1\" class=\"data row7 col1\" >0.7897</td>\n",
       "      <td id=\"T_8e54e_row7_col2\" class=\"data row7 col2\" >0.6718</td>\n",
       "      <td id=\"T_8e54e_row7_col3\" class=\"data row7 col3\" >0.8712</td>\n",
       "      <td id=\"T_8e54e_row7_col4\" class=\"data row7 col4\" >0.8728</td>\n",
       "      <td id=\"T_8e54e_row7_col5\" class=\"data row7 col5\" >0.8430</td>\n",
       "      <td id=\"T_8e54e_row7_col6\" class=\"data row7 col6\" >0.3764</td>\n",
       "      <td id=\"T_8e54e_row7_col7\" class=\"data row7 col7\" >0.4179</td>\n",
       "      <td id=\"T_8e54e_row7_col8\" class=\"data row7 col8\" >0.0940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row8\" class=\"row_heading level0 row8\" >nb</th>\n",
       "      <td id=\"T_8e54e_row8_col0\" class=\"data row8 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_8e54e_row8_col1\" class=\"data row8 col1\" >0.7866</td>\n",
       "      <td id=\"T_8e54e_row8_col2\" class=\"data row8 col2\" >0.5034</td>\n",
       "      <td id=\"T_8e54e_row8_col3\" class=\"data row8 col3\" >0.9874</td>\n",
       "      <td id=\"T_8e54e_row8_col4\" class=\"data row8 col4\" >0.7942</td>\n",
       "      <td id=\"T_8e54e_row8_col5\" class=\"data row8 col5\" >0.8803</td>\n",
       "      <td id=\"T_8e54e_row8_col6\" class=\"data row8 col6\" >-0.0080</td>\n",
       "      <td id=\"T_8e54e_row8_col7\" class=\"data row8 col7\" >-0.0074</td>\n",
       "      <td id=\"T_8e54e_row8_col8\" class=\"data row8 col8\" >0.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row9\" class=\"row_heading level0 row9\" >knn</th>\n",
       "      <td id=\"T_8e54e_row9_col0\" class=\"data row9 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_8e54e_row9_col1\" class=\"data row9 col1\" >0.7829</td>\n",
       "      <td id=\"T_8e54e_row9_col2\" class=\"data row9 col2\" >0.7060</td>\n",
       "      <td id=\"T_8e54e_row9_col3\" class=\"data row9 col3\" >0.8805</td>\n",
       "      <td id=\"T_8e54e_row9_col4\" class=\"data row9 col4\" >0.8551</td>\n",
       "      <td id=\"T_8e54e_row9_col5\" class=\"data row9 col5\" >0.8602</td>\n",
       "      <td id=\"T_8e54e_row9_col6\" class=\"data row9 col6\" >0.2985</td>\n",
       "      <td id=\"T_8e54e_row9_col7\" class=\"data row9 col7\" >0.3236</td>\n",
       "      <td id=\"T_8e54e_row9_col8\" class=\"data row9 col8\" >0.0560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row10\" class=\"row_heading level0 row10\" >dt</th>\n",
       "      <td id=\"T_8e54e_row10_col0\" class=\"data row10 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_8e54e_row10_col1\" class=\"data row10 col1\" >0.7686</td>\n",
       "      <td id=\"T_8e54e_row10_col2\" class=\"data row10 col2\" >0.6791</td>\n",
       "      <td id=\"T_8e54e_row10_col3\" class=\"data row10 col3\" >0.8309</td>\n",
       "      <td id=\"T_8e54e_row10_col4\" class=\"data row10 col4\" >0.8740</td>\n",
       "      <td id=\"T_8e54e_row10_col5\" class=\"data row10 col5\" >0.8471</td>\n",
       "      <td id=\"T_8e54e_row10_col6\" class=\"data row10 col6\" >0.3437</td>\n",
       "      <td id=\"T_8e54e_row10_col7\" class=\"data row10 col7\" >0.3580</td>\n",
       "      <td id=\"T_8e54e_row10_col8\" class=\"data row10 col8\" >0.2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row11\" class=\"row_heading level0 row11\" >svm</th>\n",
       "      <td id=\"T_8e54e_row11_col0\" class=\"data row11 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_8e54e_row11_col1\" class=\"data row11 col1\" >0.2849</td>\n",
       "      <td id=\"T_8e54e_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row11_col3\" class=\"data row11 col3\" >0.1431</td>\n",
       "      <td id=\"T_8e54e_row11_col4\" class=\"data row11 col4\" >0.7276</td>\n",
       "      <td id=\"T_8e54e_row11_col5\" class=\"data row11 col5\" >0.1772</td>\n",
       "      <td id=\"T_8e54e_row11_col6\" class=\"data row11 col6\" >0.0005</td>\n",
       "      <td id=\"T_8e54e_row11_col7\" class=\"data row11 col7\" >-0.0317</td>\n",
       "      <td id=\"T_8e54e_row11_col8\" class=\"data row11 col8\" >0.0460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8e54e_level0_row12\" class=\"row_heading level0 row12\" >lr</th>\n",
       "      <td id=\"T_8e54e_row12_col0\" class=\"data row12 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_8e54e_row12_col1\" class=\"data row12 col1\" >0.2049</td>\n",
       "      <td id=\"T_8e54e_row12_col2\" class=\"data row12 col2\" >0.5000</td>\n",
       "      <td id=\"T_8e54e_row12_col3\" class=\"data row12 col3\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row12_col4\" class=\"data row12 col4\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row12_col5\" class=\"data row12 col5\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row12_col6\" class=\"data row12 col6\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row12_col7\" class=\"data row12 col7\" >0.0000</td>\n",
       "      <td id=\"T_8e54e_row12_col8\" class=\"data row12 col8\" >0.1740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x297f087c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/61 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n",
      "[LightGBM] [Fatal] Do not support special JSON characters in feature name.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(ccp_alpha=0.0, criterion=&#x27;friedman_mse&#x27;, init=None,\n",
       "                           learning_rate=0.1, loss=&#x27;log_loss&#x27;, max_depth=3,\n",
       "                           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",
       "                           n_estimators=100, n_iter_no_change=None,\n",
       "                           random_state=4821, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(ccp_alpha=0.0, criterion=&#x27;friedman_mse&#x27;, init=None,\n",
       "                           learning_rate=0.1, loss=&#x27;log_loss&#x27;, max_depth=3,\n",
       "                           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",
       "                           n_estimators=100, n_iter_no_change=None,\n",
       "                           random_state=4821, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='log_loss', max_depth=3,\n",
       "                           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",
       "                           n_estimators=100, n_iter_no_change=None,\n",
       "                           random_state=4821, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %%time \n",
    "# Warn this cell takes ~1 minute and a half\n",
    "\n",
    "compare_models()\n",
    "# The numbers below refer to the cross validation on the training set!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c06d8b61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T15:30:25.157518Z",
     "start_time": "2022-08-29T15:28:41.207654Z"
    }
   },
   "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_05536_row10_col0, #T_05536_row10_col1, #T_05536_row10_col2, #T_05536_row10_col3, #T_05536_row10_col4, #T_05536_row10_col5, #T_05536_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_05536\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_05536_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_05536_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_05536_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_05536_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_05536_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_05536_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_05536_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_05536_row0_col0\" class=\"data row0 col0\" >0.8600</td>\n",
       "      <td id=\"T_05536_row0_col1\" class=\"data row0 col1\" >0.8326</td>\n",
       "      <td id=\"T_05536_row0_col2\" class=\"data row0 col2\" >0.9964</td>\n",
       "      <td id=\"T_05536_row0_col3\" class=\"data row0 col3\" >0.8523</td>\n",
       "      <td id=\"T_05536_row0_col4\" class=\"data row0 col4\" >0.9187</td>\n",
       "      <td id=\"T_05536_row0_col5\" class=\"data row0 col5\" >0.4349</td>\n",
       "      <td id=\"T_05536_row0_col6\" class=\"data row0 col6\" >0.5175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_05536_row1_col0\" class=\"data row1 col0\" >0.8657</td>\n",
       "      <td id=\"T_05536_row1_col1\" class=\"data row1 col1\" >0.9174</td>\n",
       "      <td id=\"T_05536_row1_col2\" class=\"data row1 col2\" >0.9784</td>\n",
       "      <td id=\"T_05536_row1_col3\" class=\"data row1 col3\" >0.8690</td>\n",
       "      <td id=\"T_05536_row1_col4\" class=\"data row1 col4\" >0.9205</td>\n",
       "      <td id=\"T_05536_row1_col5\" class=\"data row1 col5\" >0.4988</td>\n",
       "      <td id=\"T_05536_row1_col6\" class=\"data row1 col6\" >0.5377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_05536_row2_col0\" class=\"data row2 col0\" >0.8229</td>\n",
       "      <td id=\"T_05536_row2_col1\" class=\"data row2 col1\" >0.7755</td>\n",
       "      <td id=\"T_05536_row2_col2\" class=\"data row2 col2\" >0.9712</td>\n",
       "      <td id=\"T_05536_row2_col3\" class=\"data row2 col3\" >0.8333</td>\n",
       "      <td id=\"T_05536_row2_col4\" class=\"data row2 col4\" >0.8970</td>\n",
       "      <td id=\"T_05536_row2_col5\" class=\"data row2 col5\" >0.2898</td>\n",
       "      <td id=\"T_05536_row2_col6\" class=\"data row2 col6\" >0.3410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_05536_row3_col0\" class=\"data row3 col0\" >0.7829</td>\n",
       "      <td id=\"T_05536_row3_col1\" class=\"data row3 col1\" >0.7055</td>\n",
       "      <td id=\"T_05536_row3_col2\" class=\"data row3 col2\" >0.8849</td>\n",
       "      <td id=\"T_05536_row3_col3\" class=\"data row3 col3\" >0.8483</td>\n",
       "      <td id=\"T_05536_row3_col4\" class=\"data row3 col4\" >0.8662</td>\n",
       "      <td id=\"T_05536_row3_col5\" class=\"data row3 col5\" >0.2918</td>\n",
       "      <td id=\"T_05536_row3_col6\" class=\"data row3 col6\" >0.2936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_05536_row4_col0\" class=\"data row4 col0\" >0.8200</td>\n",
       "      <td id=\"T_05536_row4_col1\" class=\"data row4 col1\" >0.9107</td>\n",
       "      <td id=\"T_05536_row4_col2\" class=\"data row4 col2\" >0.9784</td>\n",
       "      <td id=\"T_05536_row4_col3\" class=\"data row4 col3\" >0.8267</td>\n",
       "      <td id=\"T_05536_row4_col4\" class=\"data row4 col4\" >0.8962</td>\n",
       "      <td id=\"T_05536_row4_col5\" class=\"data row4 col5\" >0.2532</td>\n",
       "      <td id=\"T_05536_row4_col6\" class=\"data row4 col6\" >0.3179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_05536_row5_col0\" class=\"data row5 col0\" >0.8857</td>\n",
       "      <td id=\"T_05536_row5_col1\" class=\"data row5 col1\" >0.9536</td>\n",
       "      <td id=\"T_05536_row5_col2\" class=\"data row5 col2\" >0.9388</td>\n",
       "      <td id=\"T_05536_row5_col3\" class=\"data row5 col3\" >0.9190</td>\n",
       "      <td id=\"T_05536_row5_col4\" class=\"data row5 col4\" >0.9288</td>\n",
       "      <td id=\"T_05536_row5_col5\" class=\"data row5 col5\" >0.6391</td>\n",
       "      <td id=\"T_05536_row5_col6\" class=\"data row5 col6\" >0.6401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_05536_row6_col0\" class=\"data row6 col0\" >0.9171</td>\n",
       "      <td id=\"T_05536_row6_col1\" class=\"data row6 col1\" >0.9874</td>\n",
       "      <td id=\"T_05536_row6_col2\" class=\"data row6 col2\" >0.9065</td>\n",
       "      <td id=\"T_05536_row6_col3\" class=\"data row6 col3\" >0.9882</td>\n",
       "      <td id=\"T_05536_row6_col4\" class=\"data row6 col4\" >0.9456</td>\n",
       "      <td id=\"T_05536_row6_col5\" class=\"data row6 col5\" >0.7733</td>\n",
       "      <td id=\"T_05536_row6_col6\" class=\"data row6 col6\" >0.7861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_05536_row7_col0\" class=\"data row7 col0\" >0.8400</td>\n",
       "      <td id=\"T_05536_row7_col1\" class=\"data row7 col1\" >0.8537</td>\n",
       "      <td id=\"T_05536_row7_col2\" class=\"data row7 col2\" >0.9713</td>\n",
       "      <td id=\"T_05536_row7_col3\" class=\"data row7 col3\" >0.8495</td>\n",
       "      <td id=\"T_05536_row7_col4\" class=\"data row7 col4\" >0.9064</td>\n",
       "      <td id=\"T_05536_row7_col5\" class=\"data row7 col5\" >0.3738</td>\n",
       "      <td id=\"T_05536_row7_col6\" class=\"data row7 col6\" >0.4179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_05536_row8_col0\" class=\"data row8 col0\" >0.7143</td>\n",
       "      <td id=\"T_05536_row8_col1\" class=\"data row8 col1\" >0.7689</td>\n",
       "      <td id=\"T_05536_row8_col2\" class=\"data row8 col2\" >0.7384</td>\n",
       "      <td id=\"T_05536_row8_col3\" class=\"data row8 col3\" >0.8841</td>\n",
       "      <td id=\"T_05536_row8_col4\" class=\"data row8 col4\" >0.8047</td>\n",
       "      <td id=\"T_05536_row8_col5\" class=\"data row8 col5\" >0.2884</td>\n",
       "      <td id=\"T_05536_row8_col6\" class=\"data row8 col6\" >0.3052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_05536_row9_col0\" class=\"data row9 col0\" >0.6571</td>\n",
       "      <td id=\"T_05536_row9_col1\" class=\"data row9 col1\" >0.6957</td>\n",
       "      <td id=\"T_05536_row9_col2\" class=\"data row9 col2\" >0.6703</td>\n",
       "      <td id=\"T_05536_row9_col3\" class=\"data row9 col3\" >0.8698</td>\n",
       "      <td id=\"T_05536_row9_col4\" class=\"data row9 col4\" >0.7571</td>\n",
       "      <td id=\"T_05536_row9_col5\" class=\"data row9 col5\" >0.2065</td>\n",
       "      <td id=\"T_05536_row9_col6\" class=\"data row9 col6\" >0.2279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_05536_row10_col0\" class=\"data row10 col0\" >0.8166</td>\n",
       "      <td id=\"T_05536_row10_col1\" class=\"data row10 col1\" >0.8401</td>\n",
       "      <td id=\"T_05536_row10_col2\" class=\"data row10 col2\" >0.9035</td>\n",
       "      <td id=\"T_05536_row10_col3\" class=\"data row10 col3\" >0.8740</td>\n",
       "      <td id=\"T_05536_row10_col4\" class=\"data row10 col4\" >0.8841</td>\n",
       "      <td id=\"T_05536_row10_col5\" class=\"data row10 col5\" >0.4050</td>\n",
       "      <td id=\"T_05536_row10_col6\" class=\"data row10 col6\" >0.4385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_05536_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_05536_row11_col0\" class=\"data row11 col0\" >0.0754</td>\n",
       "      <td id=\"T_05536_row11_col1\" class=\"data row11 col1\" >0.0969</td>\n",
       "      <td id=\"T_05536_row11_col2\" class=\"data row11 col2\" >0.1060</td>\n",
       "      <td id=\"T_05536_row11_col3\" class=\"data row11 col3\" >0.0456</td>\n",
       "      <td id=\"T_05536_row11_col4\" class=\"data row11 col4\" >0.0565</td>\n",
       "      <td id=\"T_05536_row11_col5\" class=\"data row11 col5\" >0.1741</td>\n",
       "      <td id=\"T_05536_row11_col6\" class=\"data row11 col6\" >0.1689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x297f3e850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = create_model(\"gbc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a7788a",
   "metadata": {},
   "source": [
    "Have a look at https://pycaret.gitbook.io/docs/get-started/tutorials to see other things you can get from this package. But a few interesting things are:\n",
    " - tune_model\n",
    " - plot_model\n",
    " - evaluate_model\n",
    " - blend_models\n",
    " - stack_models\n",
    " - ...\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5c62cef",
   "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_7b83e_row10_col0, #T_7b83e_row10_col1, #T_7b83e_row10_col2, #T_7b83e_row10_col3, #T_7b83e_row10_col4, #T_7b83e_row10_col5, #T_7b83e_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7b83e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7b83e_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_7b83e_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_7b83e_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_7b83e_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_7b83e_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_7b83e_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_7b83e_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7b83e_row0_col0\" class=\"data row0 col0\" >0.8429</td>\n",
       "      <td id=\"T_7b83e_row0_col1\" class=\"data row0 col1\" >0.8637</td>\n",
       "      <td id=\"T_7b83e_row0_col2\" class=\"data row0 col2\" >1.0000</td>\n",
       "      <td id=\"T_7b83e_row0_col3\" class=\"data row0 col3\" >0.8348</td>\n",
       "      <td id=\"T_7b83e_row0_col4\" class=\"data row0 col4\" >0.9100</td>\n",
       "      <td id=\"T_7b83e_row0_col5\" class=\"data row0 col5\" >0.3293</td>\n",
       "      <td id=\"T_7b83e_row0_col6\" class=\"data row0 col6\" >0.4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7b83e_row1_col0\" class=\"data row1 col0\" >0.8714</td>\n",
       "      <td id=\"T_7b83e_row1_col1\" class=\"data row1 col1\" >0.9371</td>\n",
       "      <td id=\"T_7b83e_row1_col2\" class=\"data row1 col2\" >0.9856</td>\n",
       "      <td id=\"T_7b83e_row1_col3\" class=\"data row1 col3\" >0.8698</td>\n",
       "      <td id=\"T_7b83e_row1_col4\" class=\"data row1 col4\" >0.9241</td>\n",
       "      <td id=\"T_7b83e_row1_col5\" class=\"data row1 col5\" >0.5140</td>\n",
       "      <td id=\"T_7b83e_row1_col6\" class=\"data row1 col6\" >0.5607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7b83e_row2_col0\" class=\"data row2 col0\" >0.8543</td>\n",
       "      <td id=\"T_7b83e_row2_col1\" class=\"data row2 col1\" >0.7352</td>\n",
       "      <td id=\"T_7b83e_row2_col2\" class=\"data row2 col2\" >0.9820</td>\n",
       "      <td id=\"T_7b83e_row2_col3\" class=\"data row2 col3\" >0.8558</td>\n",
       "      <td id=\"T_7b83e_row2_col4\" class=\"data row2 col4\" >0.9146</td>\n",
       "      <td id=\"T_7b83e_row2_col5\" class=\"data row2 col5\" >0.4349</td>\n",
       "      <td id=\"T_7b83e_row2_col6\" class=\"data row2 col6\" >0.4882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7b83e_row3_col0\" class=\"data row3 col0\" >0.7971</td>\n",
       "      <td id=\"T_7b83e_row3_col1\" class=\"data row3 col1\" >0.7239</td>\n",
       "      <td id=\"T_7b83e_row3_col2\" class=\"data row3 col2\" >0.9496</td>\n",
       "      <td id=\"T_7b83e_row3_col3\" class=\"data row3 col3\" >0.8224</td>\n",
       "      <td id=\"T_7b83e_row3_col4\" class=\"data row3 col4\" >0.8815</td>\n",
       "      <td id=\"T_7b83e_row3_col5\" class=\"data row3 col5\" >0.2029</td>\n",
       "      <td id=\"T_7b83e_row3_col6\" class=\"data row3 col6\" >0.2316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7b83e_row4_col0\" class=\"data row4 col0\" >0.8314</td>\n",
       "      <td id=\"T_7b83e_row4_col1\" class=\"data row4 col1\" >0.9156</td>\n",
       "      <td id=\"T_7b83e_row4_col2\" class=\"data row4 col2\" >0.9964</td>\n",
       "      <td id=\"T_7b83e_row4_col3\" class=\"data row4 col3\" >0.8269</td>\n",
       "      <td id=\"T_7b83e_row4_col4\" class=\"data row4 col4\" >0.9038</td>\n",
       "      <td id=\"T_7b83e_row4_col5\" class=\"data row4 col5\" >0.2701</td>\n",
       "      <td id=\"T_7b83e_row4_col6\" class=\"data row4 col6\" >0.3809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7b83e_row5_col0\" class=\"data row5 col0\" >0.9343</td>\n",
       "      <td id=\"T_7b83e_row5_col1\" class=\"data row5 col1\" >0.9837</td>\n",
       "      <td id=\"T_7b83e_row5_col2\" class=\"data row5 col2\" >0.9640</td>\n",
       "      <td id=\"T_7b83e_row5_col3\" class=\"data row5 col3\" >0.9537</td>\n",
       "      <td id=\"T_7b83e_row5_col4\" class=\"data row5 col4\" >0.9589</td>\n",
       "      <td id=\"T_7b83e_row5_col5\" class=\"data row5 col5\" >0.7958</td>\n",
       "      <td id=\"T_7b83e_row5_col6\" class=\"data row5 col6\" >0.7960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_7b83e_row6_col0\" class=\"data row6 col0\" >0.9286</td>\n",
       "      <td id=\"T_7b83e_row6_col1\" class=\"data row6 col1\" >0.9918</td>\n",
       "      <td id=\"T_7b83e_row6_col2\" class=\"data row6 col2\" >0.9209</td>\n",
       "      <td id=\"T_7b83e_row6_col3\" class=\"data row6 col3\" >0.9884</td>\n",
       "      <td id=\"T_7b83e_row6_col4\" class=\"data row6 col4\" >0.9534</td>\n",
       "      <td id=\"T_7b83e_row6_col5\" class=\"data row6 col5\" >0.8009</td>\n",
       "      <td id=\"T_7b83e_row6_col6\" class=\"data row6 col6\" >0.8102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_7b83e_row7_col0\" class=\"data row7 col0\" >0.8429</td>\n",
       "      <td id=\"T_7b83e_row7_col1\" class=\"data row7 col1\" >0.9033</td>\n",
       "      <td id=\"T_7b83e_row7_col2\" class=\"data row7 col2\" >0.9928</td>\n",
       "      <td id=\"T_7b83e_row7_col3\" class=\"data row7 col3\" >0.8394</td>\n",
       "      <td id=\"T_7b83e_row7_col4\" class=\"data row7 col4\" >0.9097</td>\n",
       "      <td id=\"T_7b83e_row7_col5\" class=\"data row7 col5\" >0.3364</td>\n",
       "      <td id=\"T_7b83e_row7_col6\" class=\"data row7 col6\" >0.4268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7b83e_row8_col0\" class=\"data row8 col0\" >0.7857</td>\n",
       "      <td id=\"T_7b83e_row8_col1\" class=\"data row8 col1\" >0.8130</td>\n",
       "      <td id=\"T_7b83e_row8_col2\" class=\"data row8 col2\" >0.8459</td>\n",
       "      <td id=\"T_7b83e_row8_col3\" class=\"data row8 col3\" >0.8806</td>\n",
       "      <td id=\"T_7b83e_row8_col4\" class=\"data row8 col4\" >0.8629</td>\n",
       "      <td id=\"T_7b83e_row8_col5\" class=\"data row8 col5\" >0.3736</td>\n",
       "      <td id=\"T_7b83e_row8_col6\" class=\"data row8 col6\" >0.3752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7b83e_row9_col0\" class=\"data row9 col0\" >0.7571</td>\n",
       "      <td id=\"T_7b83e_row9_col1\" class=\"data row9 col1\" >0.6807</td>\n",
       "      <td id=\"T_7b83e_row9_col2\" class=\"data row9 col2\" >0.8136</td>\n",
       "      <td id=\"T_7b83e_row9_col3\" class=\"data row9 col3\" >0.8731</td>\n",
       "      <td id=\"T_7b83e_row9_col4\" class=\"data row9 col4\" >0.8423</td>\n",
       "      <td id=\"T_7b83e_row9_col5\" class=\"data row9 col5\" >0.3172</td>\n",
       "      <td id=\"T_7b83e_row9_col6\" class=\"data row9 col6\" >0.3210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_7b83e_row10_col0\" class=\"data row10 col0\" >0.8446</td>\n",
       "      <td id=\"T_7b83e_row10_col1\" class=\"data row10 col1\" >0.8548</td>\n",
       "      <td id=\"T_7b83e_row10_col2\" class=\"data row10 col2\" >0.9451</td>\n",
       "      <td id=\"T_7b83e_row10_col3\" class=\"data row10 col3\" >0.8745</td>\n",
       "      <td id=\"T_7b83e_row10_col4\" class=\"data row10 col4\" >0.9061</td>\n",
       "      <td id=\"T_7b83e_row10_col5\" class=\"data row10 col5\" >0.4375</td>\n",
       "      <td id=\"T_7b83e_row10_col6\" class=\"data row10 col6\" >0.4835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b83e_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_7b83e_row11_col0\" class=\"data row11 col0\" >0.0543</td>\n",
       "      <td id=\"T_7b83e_row11_col1\" class=\"data row11 col1\" >0.1057</td>\n",
       "      <td id=\"T_7b83e_row11_col2\" class=\"data row11 col2\" >0.0625</td>\n",
       "      <td id=\"T_7b83e_row11_col3\" class=\"data row11 col3\" >0.0524</td>\n",
       "      <td id=\"T_7b83e_row11_col4\" class=\"data row11 col4\" >0.0346</td>\n",
       "      <td id=\"T_7b83e_row11_col5\" class=\"data row11 col5\" >0.1974</td>\n",
       "      <td id=\"T_7b83e_row11_col6\" class=\"data row11 col6\" >0.1809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x297f0e1c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 2 candidates, totalling 20 fits\n",
      "CPU times: user 23.8 s, sys: 307 ms, total: 24.1 s\n",
      "Wall time: 1min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# WARN: DO NOT RUN ME! It will take long to finish...\n",
    "tuned_model = tune_model(model, n_iter=2, optimize=\"Accuracy\", choose_better=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f7ded756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b33dd833458e491ea1af70664af34701",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc84dbf4",
   "metadata": {},
   "source": [
    "# Using PyCaret on the full dataset\n",
    "(if, and only if, we have time for that)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab31f309",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's NOT leave the whole cleaning process to pycaret preprocessing \n",
    "# One of its limitations is that it does not deal well with infinity...\n",
    "# So lets replace infinity by something else...\n",
    "# Note that this should be done with much more care than we are doing right now...\n",
    "\n",
    "# Replace infinity by 0\n",
    "df_clean = df.replace([np.inf, -np.inf], 0)\n",
    "# Replace NAs by 0\n",
    "# df_clean = df_clean.fillna(0.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c2d13fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:10:29.453400Z",
     "start_time": "2022-08-29T11:10:29.450489Z"
    }
   },
   "outputs": [],
   "source": [
    "def assign_grps(df, ngrps=5):\n",
    "    \n",
    "    pid_grp = {}\n",
    "    df_ = df.copy()\n",
    "\n",
    "    i = 0\n",
    "    for pid in df[\"pid\"].unique():\n",
    "        pid_grp[pid] = i\n",
    "        i = (i+1) % ngrps\n",
    "\n",
    "    df_[\"grp\"] = df_[\"pid\"].apply(lambda x: pid_grp[x])\n",
    "    return df_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54e16783",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_clean[df_clean[\"pid\"].isin(range(0,50))]\n",
    "df_test  = df_clean[df_clean[\"pid\"].isin(range(50,100))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0d0deca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:10:30.505233Z",
     "start_time": "2022-08-29T11:10:29.454658Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>1.609438</td>\n",
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       "    <tr>\n",
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       "      <td>958</td>\n",
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       "      <td>1.609438</td>\n",
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       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>51930</th>\n",
       "      <td>960</td>\n",
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       "      <td>49</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.329661</td>\n",
       "      <td>1.560710</td>\n",
       "      <td>1.560710</td>\n",
       "      <td>1.522955</td>\n",
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       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>51931</th>\n",
       "      <td>961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.609438</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51932 rows × 1543 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time   act  sleep_phase    hr  pid  \\\n",
       "0         1   2.0          0.0  71.0    0   \n",
       "1         2   0.0          0.0  76.0    0   \n",
       "2         3   1.0          0.0  78.0    0   \n",
       "3         4   2.0          0.0  73.0    0   \n",
       "4         5  87.0          0.0  80.0    0   \n",
       "...     ...   ...          ...   ...  ...   \n",
       "51927   957   0.0          2.0  54.0   49   \n",
       "51928   958   0.0          0.0  55.0   49   \n",
       "51929   959   0.0          2.0  52.0   49   \n",
       "51930   960   0.0          2.0  63.0   49   \n",
       "51931   961   0.0          0.0  55.0   49   \n",
       "\n",
       "       act__variance_larger_than_standard_deviation  act__has_duplicate_max  \\\n",
       "0                                               0.0                     0.0   \n",
       "1                                               0.0                     0.0   \n",
       "2                                               0.0                     1.0   \n",
       "3                                               1.0                     0.0   \n",
       "4                                               1.0                     0.0   \n",
       "...                                             ...                     ...   \n",
       "51927                                           0.0                     1.0   \n",
       "51928                                           0.0                     1.0   \n",
       "51929                                           0.0                     1.0   \n",
       "51930                                           0.0                     1.0   \n",
       "51931                                           0.0                     1.0   \n",
       "\n",
       "       act__has_duplicate_min  act__has_duplicate  act__sum_values  ...  \\\n",
       "0                         0.0                 0.0              2.0  ...   \n",
       "1                         0.0                 0.0              3.0  ...   \n",
       "2                         0.0                 1.0              5.0  ...   \n",
       "3                         0.0                 1.0             92.0  ...   \n",
       "4                         1.0                 1.0             92.0  ...   \n",
       "...                       ...                 ...              ...  ...   \n",
       "51927                     1.0                 1.0              5.0  ...   \n",
       "51928                     1.0                 1.0              5.0  ...   \n",
       "51929                     1.0                 1.0              5.0  ...   \n",
       "51930                     1.0                 1.0              4.0  ...   \n",
       "51931                     1.0                 1.0              5.0  ...   \n",
       "\n",
       "       hr__fourier_entropy__bins_5  hr__fourier_entropy__bins_10  \\\n",
       "0                        -0.000000                     -0.000000   \n",
       "1                         0.693147                      0.693147   \n",
       "2                         0.636514                      0.636514   \n",
       "3                         0.636514                      0.636514   \n",
       "4                         0.693147                      0.693147   \n",
       "...                            ...                           ...   \n",
       "51927                     1.242453                      1.242453   \n",
       "51928                     1.011404                      1.560710   \n",
       "51929                     1.011404                      1.329661   \n",
       "51930                     1.329661                      1.560710   \n",
       "51931                     1.560710                      1.791759   \n",
       "\n",
       "       hr__fourier_entropy__bins_100  \\\n",
       "0                          -0.000000   \n",
       "1                           0.693147   \n",
       "2                           0.636514   \n",
       "3                           1.098612   \n",
       "4                           1.386294   \n",
       "...                              ...   \n",
       "51927                       1.791759   \n",
       "51928                       1.560710   \n",
       "51929                       1.560710   \n",
       "51930                       1.560710   \n",
       "51931                       1.791759   \n",
       "\n",
       "       hr__permutation_entropy__dimension_3__tau_1  \\\n",
       "0                                              NaN   \n",
       "1                                        -0.000000   \n",
       "2                                         0.693147   \n",
       "3                                         1.098612   \n",
       "4                                         1.386294   \n",
       "...                                            ...   \n",
       "51927                                     1.310784   \n",
       "51928                                     1.273028   \n",
       "51929                                     1.368922   \n",
       "51930                                     1.522955   \n",
       "51931                                     1.676988   \n",
       "\n",
       "       hr__permutation_entropy__dimension_4__tau_1  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                        -0.000000   \n",
       "3                                         0.693147   \n",
       "4                                         1.098612   \n",
       "...                                            ...   \n",
       "51927                                     1.906155   \n",
       "51928                                     2.079442   \n",
       "51929                                     1.906155   \n",
       "51930                                     1.906155   \n",
       "51931                                     1.906155   \n",
       "\n",
       "       hr__permutation_entropy__dimension_5__tau_1  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                        -0.000000   \n",
       "4                                         0.693147   \n",
       "...                                            ...   \n",
       "51927                                     1.945910   \n",
       "51928                                     1.945910   \n",
       "51929                                     1.945910   \n",
       "51930                                     1.945910   \n",
       "51931                                     1.945910   \n",
       "\n",
       "       hr__permutation_entropy__dimension_6__tau_1  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                              NaN   \n",
       "4                                        -0.000000   \n",
       "...                                            ...   \n",
       "51927                                     1.791759   \n",
       "51928                                     1.791759   \n",
       "51929                                     1.791759   \n",
       "51930                                     1.791759   \n",
       "51931                                     1.791759   \n",
       "\n",
       "       hr__permutation_entropy__dimension_7__tau_1  sleep  grp  \n",
       "0                                              NaN  False    0  \n",
       "1                                              NaN  False    0  \n",
       "2                                              NaN  False    0  \n",
       "3                                              NaN  False    0  \n",
       "4                                              NaN  False    0  \n",
       "...                                            ...    ...  ...  \n",
       "51927                                     1.609438   True    4  \n",
       "51928                                     1.609438  False    4  \n",
       "51929                                     1.609438   True    4  \n",
       "51930                                     1.609438   True    4  \n",
       "51931                                     1.609438  False    4  \n",
       "\n",
       "[51932 rows x 1543 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = assign_grps(df_train, 5)\n",
    "df_test[\"grp\"] = 5\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "01a51de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_26c91_row10_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_26c91\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_26c91_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_26c91_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_26c91_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_26c91_row0_col1\" class=\"data row0 col1\" >3175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_26c91_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_26c91_row1_col1\" class=\"data row1 col1\" >sleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_26c91_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_26c91_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_26c91_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_26c91_row3_col1\" class=\"data row3 col1\" >(102759, 1539)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_26c91_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_26c91_row4_col1\" class=\"data row4 col1\" >(102759, 625)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_26c91_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_26c91_row5_col1\" class=\"data row5 col1\" >(51932, 625)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_26c91_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_26c91_row6_col1\" class=\"data row6 col1\" >(50827, 625)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_26c91_row7_col0\" class=\"data row7 col0\" >Ignore features</td>\n",
       "      <td id=\"T_26c91_row7_col1\" class=\"data row7 col1\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_26c91_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
       "      <td id=\"T_26c91_row8_col1\" class=\"data row8 col1\" >1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_26c91_row9_col0\" class=\"data row9 col0\" >Rows with missing values</td>\n",
       "      <td id=\"T_26c91_row9_col1\" class=\"data row9 col1\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_26c91_row10_col0\" class=\"data row10 col0\" >Preprocess</td>\n",
       "      <td id=\"T_26c91_row10_col1\" class=\"data row10 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_26c91_row11_col0\" class=\"data row11 col0\" >Imputation type</td>\n",
       "      <td id=\"T_26c91_row11_col1\" class=\"data row11 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_26c91_row12_col0\" class=\"data row12 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_26c91_row12_col1\" class=\"data row12 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_26c91_row13_col0\" class=\"data row13 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_26c91_row13_col1\" class=\"data row13 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_26c91_row14_col0\" class=\"data row14 col0\" >Low variance threshold</td>\n",
       "      <td id=\"T_26c91_row14_col1\" class=\"data row14 col1\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_26c91_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_26c91_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_26c91_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
       "      <td id=\"T_26c91_row16_col1\" class=\"data row16 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_26c91_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_26c91_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_26c91_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
       "      <td id=\"T_26c91_row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_26c91_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_26c91_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_26c91_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_26c91_row20_col1\" class=\"data row20 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26c91_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_26c91_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
       "      <td id=\"T_26c91_row21_col1\" class=\"data row21 col1\" >8416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x297ede190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.3 s, sys: 6.12 s, total: 25.4 s\n",
      "Wall time: 27.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# This is a bit slow now (>30 sec), as all the preprocessing steps need to be applied to the whole dataset\n",
    "# \n",
    "s = setup(data=df_train,\n",
    "      test_data=df_test,\n",
    "      target='sleep',\n",
    "      ignore_features=[\"pid\", \"sleep_phase\", \"time\", \"grp\"],\n",
    "      numeric_features=act_keys + hr_keys,\n",
    "      # fold_strategy=\"groupkfold\", fold=5, fold_groups=\"grp\", \n",
    "      )\n",
    "\n",
    "# Important (again): \n",
    "#\n",
    "# -- Have a look at the shape of the original and transformed datasets\n",
    "# -- Have a look at the different preprocessing techniques that are applied by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9718c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_models?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c695f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
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    {
     "data": {
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       "#T_b0208_row0_col0, #T_b0208_row0_col4, #T_b0208_row1_col0, #T_b0208_row1_col1, #T_b0208_row1_col2, #T_b0208_row1_col3, #T_b0208_row1_col5, #T_b0208_row1_col6, #T_b0208_row1_col7, #T_b0208_row2_col0, #T_b0208_row2_col1, #T_b0208_row2_col2, #T_b0208_row2_col3, #T_b0208_row2_col4, #T_b0208_row2_col5, #T_b0208_row2_col6, #T_b0208_row2_col7, #T_b0208_row3_col0, #T_b0208_row3_col1, #T_b0208_row3_col2, #T_b0208_row3_col3, #T_b0208_row3_col4, #T_b0208_row3_col5, #T_b0208_row3_col6, #T_b0208_row3_col7 {\n",
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       "}\n",
       "#T_b0208_row0_col1, #T_b0208_row0_col2, #T_b0208_row0_col3, #T_b0208_row0_col5, #T_b0208_row0_col6, #T_b0208_row0_col7, #T_b0208_row1_col4 {\n",
       "  text-align: left;\n",
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       "#T_b0208_row3_col8 {\n",
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       "</style>\n",
       "<table id=\"T_b0208\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b0208_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_b0208_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_b0208_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_b0208_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_b0208_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_b0208_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_b0208_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_b0208_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_b0208_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b0208_level0_row0\" class=\"row_heading level0 row0\" >gbc</th>\n",
       "      <td id=\"T_b0208_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_b0208_row0_col1\" class=\"data row0 col1\" >0.8160</td>\n",
       "      <td id=\"T_b0208_row0_col2\" class=\"data row0 col2\" >0.8455</td>\n",
       "      <td id=\"T_b0208_row0_col3\" class=\"data row0 col3\" >0.9154</td>\n",
       "      <td id=\"T_b0208_row0_col4\" class=\"data row0 col4\" >0.8337</td>\n",
       "      <td id=\"T_b0208_row0_col5\" class=\"data row0 col5\" >0.8719</td>\n",
       "      <td id=\"T_b0208_row0_col6\" class=\"data row0 col6\" >0.5464</td>\n",
       "      <td id=\"T_b0208_row0_col7\" class=\"data row0 col7\" >0.5582</td>\n",
       "      <td id=\"T_b0208_row0_col8\" class=\"data row0 col8\" >46.1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b0208_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
       "      <td id=\"T_b0208_row1_col0\" class=\"data row1 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_b0208_row1_col1\" class=\"data row1 col1\" >0.8102</td>\n",
       "      <td id=\"T_b0208_row1_col2\" class=\"data row1 col2\" >0.8332</td>\n",
       "      <td id=\"T_b0208_row1_col3\" class=\"data row1 col3\" >0.9015</td>\n",
       "      <td id=\"T_b0208_row1_col4\" class=\"data row1 col4\" >0.8357</td>\n",
       "      <td id=\"T_b0208_row1_col5\" class=\"data row1 col5\" >0.8666</td>\n",
       "      <td id=\"T_b0208_row1_col6\" class=\"data row1 col6\" >0.5382</td>\n",
       "      <td id=\"T_b0208_row1_col7\" class=\"data row1 col7\" >0.5469</td>\n",
       "      <td id=\"T_b0208_row1_col8\" class=\"data row1 col8\" >7.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b0208_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
       "      <td id=\"T_b0208_row2_col0\" class=\"data row2 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_b0208_row2_col1\" class=\"data row2 col1\" >0.8088</td>\n",
       "      <td id=\"T_b0208_row2_col2\" class=\"data row2 col2\" >0.8325</td>\n",
       "      <td id=\"T_b0208_row2_col3\" class=\"data row2 col3\" >0.9003</td>\n",
       "      <td id=\"T_b0208_row2_col4\" class=\"data row2 col4\" >0.8348</td>\n",
       "      <td id=\"T_b0208_row2_col5\" class=\"data row2 col5\" >0.8656</td>\n",
       "      <td id=\"T_b0208_row2_col6\" class=\"data row2 col6\" >0.5349</td>\n",
       "      <td id=\"T_b0208_row2_col7\" class=\"data row2 col7\" >0.5431</td>\n",
       "      <td id=\"T_b0208_row2_col8\" class=\"data row2 col8\" >2.7810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b0208_level0_row3\" class=\"row_heading level0 row3\" >lr</th>\n",
       "      <td id=\"T_b0208_row3_col0\" class=\"data row3 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_b0208_row3_col1\" class=\"data row3 col1\" >0.5372</td>\n",
       "      <td id=\"T_b0208_row3_col2\" class=\"data row3 col2\" >0.5049</td>\n",
       "      <td id=\"T_b0208_row3_col3\" class=\"data row3 col3\" >0.5807</td>\n",
       "      <td id=\"T_b0208_row3_col4\" class=\"data row3 col4\" >0.5214</td>\n",
       "      <td id=\"T_b0208_row3_col5\" class=\"data row3 col5\" >0.5030</td>\n",
       "      <td id=\"T_b0208_row3_col6\" class=\"data row3 col6\" >0.0371</td>\n",
       "      <td id=\"T_b0208_row3_col7\" class=\"data row3 col7\" >0.0437</td>\n",
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      "text/plain": [
       "Processing:   0%|          | 0/21 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6min 6s, sys: 4.81 s, total: 6min 11s\n",
      "Wall time: 15min 39s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# This will be VERY SLOW (> 15 minutes...). Lets NOT run it today.\n",
    "\n",
    "best_model = compare_models(include=[\"gbc\", \"rf\", \"et\", \"lr\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "02309b07",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T15:34:08.106619Z",
     "start_time": "2022-08-29T15:30:25.158950Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# In case you want to search for better parameters\n",
    "# Have a look at its many options with tun`_model?\n",
    "# In particular, `search_algorithm` and `n_iter` \n",
    "# best_model = tune_model(best_model, n_iter=10, choose_better=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d6740cac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T15:34:08.538939Z",
     "start_time": "2022-08-29T15:34:08.107687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a143b27b1eef43c2b3f888324d8db4c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c80ba814",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T15:34:10.603208Z",
     "start_time": "2022-08-29T15:34:08.540480Z"
    }
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      "text/plain": [
       "         act  act__variance_larger_than_standard_deviation  \\\n",
       "51932   33.0                                           1.0   \n",
       "51933   13.0                                           1.0   \n",
       "51934    7.0                                           1.0   \n",
       "51935   53.0                                           1.0   \n",
       "51936   79.0                                           1.0   \n",
       "...      ...                                           ...   \n",
       "102754   0.0                                           1.0   \n",
       "102755   9.0                                           1.0   \n",
       "102756  29.0                                           1.0   \n",
       "102757  53.0                                           1.0   \n",
       "102758  74.0                                           1.0   \n",
       "\n",
       "        act__has_duplicate_max  act__has_duplicate_min  act__has_duplicate  \\\n",
       "51932                      0.0                     0.0                 0.0   \n",
       "51933                      0.0                     0.0                 0.0   \n",
       "51934                      0.0                     0.0                 0.0   \n",
       "51935                      0.0                     0.0                 0.0   \n",
       "51936                      0.0                     0.0                 0.0   \n",
       "...                        ...                     ...                 ...   \n",
       "102754                     1.0                     1.0                 1.0   \n",
       "102755                     1.0                     1.0                 1.0   \n",
       "102756                     1.0                     1.0                 1.0   \n",
       "102757                     0.0                     1.0                 1.0   \n",
       "102758                     0.0                     1.0                 1.0   \n",
       "\n",
       "        act__sum_values  act__abs_energy  act__mean_abs_change  \\\n",
       "51932              46.0           1258.0             20.000000   \n",
       "51933              53.0           1307.0             13.000000   \n",
       "51934             106.0           4116.0             24.000000   \n",
       "51935             185.0          10357.0             24.500000   \n",
       "51936             279.0          19193.0             22.600000   \n",
       "...                 ...              ...                   ...   \n",
       "102754            131.0           7051.0             12.900000   \n",
       "102755            158.0           7888.0             14.700000   \n",
       "102756            211.0          10697.0             17.100000   \n",
       "102757            285.0          16173.0             19.200001   \n",
       "102758            326.0          17854.0             22.500000   \n",
       "\n",
       "        act__mean_change  act__mean_second_derivative_central  ...  \\\n",
       "51932         -20.000000                            -0.011797  ...   \n",
       "51933         -13.000000                             7.000000  ...   \n",
       "51934           6.666667                            16.500000  ...   \n",
       "51935          11.500000                             7.666667  ...   \n",
       "51936          12.200000                             4.375000  ...   \n",
       "...                  ...                                  ...  ...   \n",
       "102754          0.700000                             0.611111  ...   \n",
       "102755          2.900000                             1.111111  ...   \n",
       "102756          5.300000                             1.333333  ...   \n",
       "102757          7.400000                             1.166667  ...   \n",
       "102758          4.100000                            -1.944444  ...   \n",
       "\n",
       "        hr__fourier_entropy__bins_10  hr__fourier_entropy__bins_100  \\\n",
       "51932                      -0.000000                      -0.000000   \n",
       "51933                       0.693147                       0.693147   \n",
       "51934                       0.636514                       1.098612   \n",
       "51935                       1.098612                       1.098612   \n",
       "51936                       1.386294                       1.386294   \n",
       "...                              ...                            ...   \n",
       "102754                      1.560710                       1.791759   \n",
       "102755                      1.791759                       1.791759   \n",
       "102756                      1.560710                       1.791759   \n",
       "102757                      1.560710                       1.560710   \n",
       "102758                      1.560710                       1.791759   \n",
       "\n",
       "        hr__permutation_entropy__dimension_3__tau_1  \\\n",
       "51932                                      1.500956   \n",
       "51933                                     -0.000000   \n",
       "51934                                      0.693147   \n",
       "51935                                      1.098612   \n",
       "51936                                      1.386294   \n",
       "...                                             ...   \n",
       "102754                                     1.581094   \n",
       "102755                                     1.522955   \n",
       "102756                                     1.368922   \n",
       "102757                                     1.581094   \n",
       "102758                                     1.522955   \n",
       "\n",
       "        hr__permutation_entropy__dimension_4__tau_1  \\\n",
       "51932                                      1.905591   \n",
       "51933                                      1.905591   \n",
       "51934                                     -0.000000   \n",
       "51935                                      0.693147   \n",
       "51936                                      1.098612   \n",
       "...                                             ...   \n",
       "102754                                     1.906155   \n",
       "102755                                     1.732868   \n",
       "102756                                     1.732868   \n",
       "102757                                     1.732868   \n",
       "102758                                     1.906155   \n",
       "\n",
       "        hr__permutation_entropy__dimension_5__tau_1  \\\n",
       "51932                                      1.909721   \n",
       "51933                                      1.909721   \n",
       "51934                                      1.909721   \n",
       "51935                                     -0.000000   \n",
       "51936                                      0.693147   \n",
       "...                                             ...   \n",
       "102754                                     1.945910   \n",
       "102755                                     1.945910   \n",
       "102756                                     1.945910   \n",
       "102757                                     1.945910   \n",
       "102758                                     1.945910   \n",
       "\n",
       "        hr__permutation_entropy__dimension_6__tau_1  \\\n",
       "51932                                      1.781712   \n",
       "51933                                      1.781712   \n",
       "51934                                      1.781712   \n",
       "51935                                      1.781712   \n",
       "51936                                     -0.000000   \n",
       "...                                             ...   \n",
       "102754                                     1.791759   \n",
       "102755                                     1.791759   \n",
       "102756                                     1.791759   \n",
       "102757                                     1.791759   \n",
       "102758                                     1.791759   \n",
       "\n",
       "        hr__permutation_entropy__dimension_7__tau_1  sleep  Label   Score  \n",
       "51932                                      1.604948  False      0  0.8610  \n",
       "51933                                      1.604948  False      0  0.9359  \n",
       "51934                                      1.604948  False      0  0.8914  \n",
       "51935                                      1.604948  False      0  0.9401  \n",
       "51936                                      1.604948  False      0  0.9434  \n",
       "...                                             ...    ...    ...     ...  \n",
       "102754                                     1.609438  False      0  0.6238  \n",
       "102755                                     1.609438  False      0  0.7995  \n",
       "102756                                     1.609438  False      0  0.7407  \n",
       "102757                                     1.609438  False      0  0.8158  \n",
       "102758                                     1.609438  False      0  0.8860  \n",
       "\n",
       "[50827 rows x 627 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_model(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "202e4015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_56de5_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_56de5_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_56de5_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_56de5_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_56de5_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_56de5_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_56de5_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_56de5_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_56de5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_56de5_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_56de5_row0_col1\" class=\"data row0 col1\" >0.8451</td>\n",
       "      <td id=\"T_56de5_row0_col2\" class=\"data row0 col2\" >0.8721</td>\n",
       "      <td id=\"T_56de5_row0_col3\" class=\"data row0 col3\" >0.9265</td>\n",
       "      <td id=\"T_56de5_row0_col4\" class=\"data row0 col4\" >0.8641</td>\n",
       "      <td id=\"T_56de5_row0_col5\" class=\"data row0 col5\" >0.8942</td>\n",
       "      <td id=\"T_56de5_row0_col6\" class=\"data row0 col6\" >0.6062</td>\n",
       "      <td id=\"T_56de5_row0_col7\" class=\"data row0 col7\" >0.6114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x291556310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_result = predict_model(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "043f8cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test[\"yhat\"] = df_result[\"Label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5cb6d10",
   "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>Accuracy</th>\n",
       "      <th>MCC</th>\n",
       "      <th>F1_weighted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.846</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.075</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy    MCC  F1_weighted\n",
       "mean     0.846  0.615        0.840\n",
       "std      0.075  0.151        0.077"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "def classification_results(y, yhat):\n",
    "    acc = metrics.accuracy_score(y, yhat)\n",
    "    mcc = metrics.matthews_corrcoef(y, yhat)\n",
    "    f1_weighted = metrics.f1_score(y, yhat, average=\"weighted\")\n",
    "    return acc, mcc, f1_weighted\n",
    "\n",
    "final_results = df_test.groupby([\"pid\"])[[\"sleep\", \"yhat\"]].apply(lambda x: \n",
    "                                                                  classification_results(x[\"sleep\"].values, \n",
    "                                                                                         x[\"yhat\"].values))\n",
    "\n",
    "final_results = final_results.apply(pd.Series).rename(columns={0: \"Accuracy\", 1: \"MCC\", 2: \"F1_weighted\"})\n",
    "\n",
    "final_results.agg([\"mean\", \"std\"]).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a68f473",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(best_model, 'models/pycaret_best_model')"
   ]
  }
 ],
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