predictions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import pycaret.classification as pc\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Self-reported questionnaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_exposure = pd.read_excel('03_Self-Reported-Questionnaires/Pre-Exposure-Ratings.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_exposure = pd.read_excel('03_Self-Reported-Questionnaires/Post-Exposure-Ratings.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Trial_Num</th>\n",
       "      <th>Quad_Cat</th>\n",
       "      <th>Arousal_Cat</th>\n",
       "      <th>Valence_Cat</th>\n",
       "      <th>Str_Code</th>\n",
       "      <th>Num_Code</th>\n",
       "      <th>POST_Valence</th>\n",
       "      <th>POST_Arousal</th>\n",
       "      <th>POST_Joy</th>\n",
       "      <th>...</th>\n",
       "      <th>POST_Sadness</th>\n",
       "      <th>POST_Dizzy</th>\n",
       "      <th>POST_PQ1</th>\n",
       "      <th>POST_PQ2</th>\n",
       "      <th>POST_PQ3</th>\n",
       "      <th>POST_PQ4</th>\n",
       "      <th>POST_PQ5</th>\n",
       "      <th>POST_PQ6</th>\n",
       "      <th>POST_PQ7</th>\n",
       "      <th>POST_PQ8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PRS</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>RPW</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>BRZ</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>130</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>BRZ</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>131</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>BRZ</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>132</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DST</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>133</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DST</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>134</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>JNG</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  Trial_Num  Quad_Cat  Arousal_Cat  Valence_Cat Str_Code  Num_Code  \\\n",
       "0    101          1         2            0            0      BOT        11   \n",
       "1    102          1         2            0            0      BOT        11   \n",
       "2    103          1         2            0            0      PRS        12   \n",
       "3    104          1         0            1            1      RPW         1   \n",
       "4    105          1         0            1            1      BRZ         3   \n",
       "..   ...        ...       ...          ...          ...      ...       ...   \n",
       "403  130         12         0            1            1      BRZ         3   \n",
       "404  131         12         0            1            1      BRZ         3   \n",
       "405  132         12         0            1            1      DST         2   \n",
       "406  133         12         0            1            1      DST         2   \n",
       "407  134         12         1            0            1      JNG         5   \n",
       "\n",
       "     POST_Valence  POST_Arousal  POST_Joy  ...  POST_Sadness  POST_Dizzy  \\\n",
       "0             4.0           3.0       2.0  ...         100.0         2.0   \n",
       "1             3.0           3.0       0.0  ...          41.0         0.0   \n",
       "2             2.0           8.0       0.0  ...          84.0         0.0   \n",
       "3             7.0           3.0      64.0  ...           0.0         0.0   \n",
       "4             7.0           4.0      61.0  ...           1.0         0.0   \n",
       "..            ...           ...       ...  ...           ...         ...   \n",
       "403           NaN           NaN       NaN  ...           NaN         NaN   \n",
       "404           7.0           9.0      82.0  ...           0.0         0.0   \n",
       "405           6.0           4.0      30.0  ...           1.0        16.0   \n",
       "406           8.0           8.0      95.0  ...           0.0         2.0   \n",
       "407           9.0           1.0      83.0  ...           0.0         0.0   \n",
       "\n",
       "     POST_PQ1  POST_PQ2  POST_PQ3  POST_PQ4  POST_PQ5  POST_PQ6  POST_PQ7  \\\n",
       "0         6.0       1.0       3.0       3.0       1.0       3.0       4.0   \n",
       "1         2.0       2.0       2.0       1.0       2.0       4.0       4.0   \n",
       "2         7.0       5.0       3.0       1.0       7.0       1.0       6.0   \n",
       "3         2.0       4.0       2.0       6.0       1.0       5.0       3.0   \n",
       "4         5.0       3.0       1.0       3.0       1.0       7.0       5.0   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "403       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "404       3.0       1.0       3.0       4.0       1.0       5.0       4.0   \n",
       "405       6.0       2.0       2.0       4.0       3.0       5.0       5.0   \n",
       "406       4.0       2.0       1.0       2.0       1.0       6.0       2.0   \n",
       "407       6.0       2.0       1.0       1.0       6.0       2.0       7.0   \n",
       "\n",
       "     POST_PQ8  \n",
       "0         4.0  \n",
       "1         6.0  \n",
       "2         6.0  \n",
       "3         5.0  \n",
       "4         4.0  \n",
       "..        ...  \n",
       "403       NaN  \n",
       "404       4.0  \n",
       "405       3.0  \n",
       "406       7.0  \n",
       "407       7.0  \n",
       "\n",
       "[408 rows x 27 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post_exposure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eye tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "eye = pd.read_csv('04_Eye-Tracking-Data/extracted/EyeTracking_FeaturesExtracted.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 312 entries, 0 to 311\n",
      "Data columns (total 50 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Quad_Cat                 312 non-null    int64  \n",
      " 1   Num_of_Fixations         312 non-null    float64\n",
      " 2   Mean_Fixation_Duration   312 non-null    float64\n",
      " 3   SD_Fixation_Duration     312 non-null    float64\n",
      " 4   Skew_Fixation_Duration   312 non-null    float64\n",
      " 5   Max_Fixation_Duration    312 non-null    int64  \n",
      " 6   First_Fixation_Duration  312 non-null    int64  \n",
      " 7   Num_of_Saccade           312 non-null    float64\n",
      " 8   Mean_Saccade_Duration    312 non-null    float64\n",
      " 9   SD_Saccade_Duration      312 non-null    float64\n",
      " 10  Skew_Saccade_Duration    312 non-null    float64\n",
      " 11  Max_Saccade_Duration     312 non-null    int64  \n",
      " 12  Mean_Saccade_Amplitude   312 non-null    float64\n",
      " 13  SD_Saccade_Amplitude     312 non-null    float64\n",
      " 14  Skew_Saccade_Amplitude   312 non-null    float64\n",
      " 15  Max_Saccade_Amplitude    312 non-null    float64\n",
      " 16  Mean_Saccade_Direction   312 non-null    float64\n",
      " 17  SD_Saccade_Direction     312 non-null    float64\n",
      " 18  Skew_Saccade_Direction   312 non-null    float64\n",
      " 19  Max_Saccade_Direction    312 non-null    float64\n",
      " 20  Mean_Saccade_Length      312 non-null    float64\n",
      " 21  SD_Saccade_Length        312 non-null    float64\n",
      " 22  Skew_Saccade_Length      312 non-null    float64\n",
      " 23  Max_Saccade_Length       312 non-null    float64\n",
      " 24  Num_of_Blink             312 non-null    float64\n",
      " 25  Mean_Blink_Duration      301 non-null    float64\n",
      " 26  SD_Blink_Duration        275 non-null    float64\n",
      " 27  Skew_Blink_Duration      275 non-null    float64\n",
      " 28  Max_Blink_Duration       301 non-null    float64\n",
      " 29  Num_of_Microsac          312 non-null    float64\n",
      " 30  Mean_Microsac_Peak_Vel   312 non-null    float64\n",
      " 31  SD_Microsac_Peak_Vel     312 non-null    float64\n",
      " 32  Skew_Microsac_Peak_Vel   312 non-null    float64\n",
      " 33  Max_Microsac_Peak_Vel    312 non-null    float64\n",
      " 34  Mean_Microsac_Ampl       312 non-null    float64\n",
      " 35  SD_Microsac_Ampl         312 non-null    float64\n",
      " 36  Skew_Microsac_Ampl       312 non-null    float64\n",
      " 37  Max_Microsac_Ampl        312 non-null    float64\n",
      " 38  Mean_Microsac_Dir        312 non-null    float64\n",
      " 39  SD_Microsac_Dir          312 non-null    float64\n",
      " 40  Skew_Microsac_Dir        312 non-null    float64\n",
      " 41  Max_Microsac_Dir         312 non-null    float64\n",
      " 42  Mean_Microsac_H_Amp      312 non-null    float64\n",
      " 43  SD_Microsac_H_Amp        312 non-null    float64\n",
      " 44  Skew_Microsac_H_Amp      312 non-null    float64\n",
      " 45  Max_Microsac_H_Amp       312 non-null    float64\n",
      " 46  Mean_Microsac_V_Amp      312 non-null    float64\n",
      " 47  SD_Microsac_V_Amp        312 non-null    float64\n",
      " 48  Skew_Microsac_V_Amp      312 non-null    float64\n",
      " 49  Max_Microsac_V_Amp       312 non-null    float64\n",
      "dtypes: float64(46), int64(4)\n",
      "memory usage: 122.0 KB\n"
     ]
    }
   ],
   "source": [
    "eye.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "eye = eye.fillna(eye.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ECG and GSR data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecg = pd.read_csv('05_ECG-GSR-Data/extracted/ECG_FeaturesExtracted.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsr = pd.read_csv('05_ECG-GSR-Data/extracted/GSR_FeaturesExtracted.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 312 entries, 0 to 311\n",
      "Data columns (total 19 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Quad_Cat    312 non-null    int64  \n",
      " 1   Mean        312 non-null    float64\n",
      " 2   Min         312 non-null    float64\n",
      " 3   Max         312 non-null    float64\n",
      " 4   MeanRR      312 non-null    int64  \n",
      " 5   MedianRR    312 non-null    float64\n",
      " 6   MinRR       312 non-null    int64  \n",
      " 7   MaxRR       312 non-null    int64  \n",
      " 8   LF          312 non-null    float64\n",
      " 9   HF          312 non-null    float64\n",
      " 10  VLF         312 non-null    float64\n",
      " 11  Ibi         312 non-null    float64\n",
      " 12  Bpm         312 non-null    float64\n",
      " 13  Sdnn        312 non-null    float64\n",
      " 14  Sdsd        312 non-null    float64\n",
      " 15  Rmssd       312 non-null    float64\n",
      " 16  Pnn50       312 non-null    float64\n",
      " 17  pnn20       312 non-null    float64\n",
      " 18  Pnn50pnn20  312 non-null    float64\n",
      "dtypes: float64(15), int64(4)\n",
      "memory usage: 46.4 KB\n"
     ]
    }
   ],
   "source": [
    "ecg.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 312 entries, 0 to 311\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Quad_Cat           312 non-null    int64  \n",
      " 1   Mean               312 non-null    float64\n",
      " 2   SD                 312 non-null    float64\n",
      " 3   Variance           312 non-null    float64\n",
      " 4   Minimum            312 non-null    float64\n",
      " 5   Maximum            312 non-null    float64\n",
      " 6   Number of Peaks    312 non-null    float64\n",
      " 7   Number of Valleys  312 non-null    float64\n",
      " 8   Ratio              312 non-null    float64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 22.1 KB\n"
     ]
    }
   ],
   "source": [
    "gsr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importances exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eye tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01339031, 0.0042735 , 0.        , 0.01343393, 0.00683761,\n",
       "       0.01528102, 0.01117686, 0.02088294, 0.02795907, 0.01358025,\n",
       "       0.01583352, 0.        , 0.01065089, 0.0042735 , 0.03923858,\n",
       "       0.01399363, 0.05298248, 0.02924611, 0.03900696, 0.01111111,\n",
       "       0.        , 0.01595442, 0.02136752, 0.        , 0.04651161,\n",
       "       0.00569801, 0.04223027, 0.00797721, 0.06903641, 0.        ,\n",
       "       0.05443143, 0.00569801, 0.02045177, 0.00902778, 0.01575585,\n",
       "       0.03493707, 0.02148558, 0.0199905 , 0.06665306, 0.0042735 ,\n",
       "       0.07633991, 0.02401498, 0.        , 0.01111111, 0.        ,\n",
       "       0.04549273, 0.0042735 , 0.02142271, 0.01271278])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cols = [col for col in eye.columns.tolist() if col != 'Quad_Cat']\n",
    "X, y = eye[X_cols], eye['Quad_Cat']\n",
    "dt.fit(X,y)\n",
    "dt.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ECG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Mean', 0.06977475830761283),\n",
       " ('Min', 0.08699199825994086),\n",
       " ('Max', 0.05859367288675854),\n",
       " ('MeanRR', 0.09106599744498428),\n",
       " ('MedianRR', 0.06189148045610227),\n",
       " ('MinRR', 0.0499215212402834),\n",
       " ('MaxRR', 0.1095723983292755),\n",
       " ('LF', 0.013669663669663669),\n",
       " ('HF', 0.0),\n",
       " ('VLF', 0.0),\n",
       " ('Ibi', 0.06252843784895067),\n",
       " ('Bpm', 0.04084365926471189),\n",
       " ('Sdnn', 0.07418988246365656),\n",
       " ('Sdsd', 0.10275078045562287),\n",
       " ('Rmssd', 0.03199172831525772),\n",
       " ('Pnn50', 0.07886441336420126),\n",
       " ('pnn20', 0.03329826038159371),\n",
       " ('Pnn50pnn20', 0.03405134731138396)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cols = [col for col in ecg.columns.tolist() if col != 'Quad_Cat']\n",
    "X, y = ecg[X_cols], ecg['Quad_Cat']\n",
    "dt.fit(X,y)\n",
    "list(zip(X_cols, dt.feature_importances_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GSR data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Mean', 0.10390350390350392),\n",
       " ('SD', 0.1135824520241445),\n",
       " ('Variance', 0.10548931088404773),\n",
       " ('Minimum', 0.1465946302592119),\n",
       " ('Maximum', 0.0847453888033598),\n",
       " ('Number of Peaks', 0.15791531454018182),\n",
       " ('Number of Valleys', 0.2877693995855503),\n",
       " ('Ratio', 0.0)]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cols = [col for col in gsr.columns.tolist() if col != 'Quad_Cat']\n",
    "X, y = gsr[X_cols], gsr['Quad_Cat']\n",
    "dt.fit(X,y)\n",
    "list(zip(X_cols, dt.feature_importances_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge1 = pd.merge(eye, ecg, left_index=True, right_index=True)\n",
    "merged = pd.merge(merge1, gsr, left_index=True, right_index=True)\n",
    "merged = merged.drop(['Quad_Cat_x', 'Quad_Cat_y'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Num_of_Fixations', 0.008547008547008548),\n",
       " ('Mean_Fixation_Duration', 0.010256410256410258),\n",
       " ('SD_Fixation_Duration', 0.004273504273504274),\n",
       " ('Skew_Fixation_Duration', 0.02770691796332822),\n",
       " ('Max_Fixation_Duration', 0.012820512820512824),\n",
       " ('First_Fixation_Duration', 0.022390240338958298),\n",
       " ('Num_of_Saccade', 0.012210012210012212),\n",
       " ('Mean_Saccade_Duration', 0.01142191142191143),\n",
       " ('SD_Saccade_Duration', 0.009157509157509161),\n",
       " ('Skew_Saccade_Duration', 0.0),\n",
       " ('Max_Saccade_Duration', 0.004273504273504274),\n",
       " ('Mean_Saccade_Amplitude', 0.0),\n",
       " ('SD_Saccade_Amplitude', 0.0),\n",
       " ('Skew_Saccade_Amplitude', 0.021888929783666626),\n",
       " ('Max_Saccade_Amplitude', 0.0074000074000074),\n",
       " ('Mean_Saccade_Direction', 0.0),\n",
       " ('SD_Saccade_Direction', 0.0468590460914214),\n",
       " ('Skew_Saccade_Direction', 0.012820512820512825),\n",
       " ('Max_Saccade_Direction', 0.038193080403225325),\n",
       " ('Mean_Saccade_Length', 0.006410256410256412),\n",
       " ('SD_Saccade_Length', 0.04739880896758595),\n",
       " ('Skew_Saccade_Length', 0.004273504273504274),\n",
       " ('Max_Saccade_Length', 0.03487808487808488),\n",
       " ('Num_of_Blink', 0.0),\n",
       " ('Mean_Blink_Duration', 0.02524109877051057),\n",
       " ('SD_Blink_Duration', 0.01618425147836913),\n",
       " ('Skew_Blink_Duration', 0.021762310727477283),\n",
       " ('Max_Blink_Duration', 0.007326007326007329),\n",
       " ('Num_of_Microsac', 0.013295346628679965),\n",
       " ('Mean_Microsac_Peak_Vel', 0.0),\n",
       " ('SD_Microsac_Peak_Vel', 0.06735868732921052),\n",
       " ('Skew_Microsac_Peak_Vel', 0.00797720797720798),\n",
       " ('Max_Microsac_Peak_Vel', 0.0),\n",
       " ('Mean_Microsac_Ampl', 0.0),\n",
       " ('SD_Microsac_Ampl', 0.0),\n",
       " ('Skew_Microsac_Ampl', 0.006837606837606838),\n",
       " ('Max_Microsac_Ampl', 0.012820512820512824),\n",
       " ('Mean_Microsac_Dir', 0.014144461203284733),\n",
       " ('SD_Microsac_Dir', 0.040558329342284344),\n",
       " ('Skew_Microsac_Dir', 0.008179528469383543),\n",
       " ('Max_Microsac_Dir', 0.0),\n",
       " ('Mean_Microsac_H_Amp', 0.0),\n",
       " ('SD_Microsac_H_Amp', 0.0),\n",
       " ('Skew_Microsac_H_Amp', 0.0),\n",
       " ('Max_Microsac_H_Amp', 0.0),\n",
       " ('Mean_Microsac_V_Amp', 0.0),\n",
       " ('SD_Microsac_V_Amp', 0.0),\n",
       " ('Skew_Microsac_V_Amp', 0.017849985263778376),\n",
       " ('Max_Microsac_V_Amp', 0.03209626630679263),\n",
       " ('Mean_x', 0.004273504273504274),\n",
       " ('Min', 0.0),\n",
       " ('Max', 0.020383937050603717),\n",
       " ('MeanRR', 0.0),\n",
       " ('MedianRR', 0.0),\n",
       " ('MinRR', 0.024310730374803607),\n",
       " ('MaxRR', 0.0),\n",
       " ('LF', 0.012820512820512824),\n",
       " ('HF', 0.012820512820512824),\n",
       " ('VLF', 0.0),\n",
       " ('Ibi', 0.02663906242199062),\n",
       " ('Bpm', 0.031845496551378895),\n",
       " ('Sdnn', 0.0),\n",
       " ('Sdsd', 0.0),\n",
       " ('Rmssd', 0.005698005698005698),\n",
       " ('Pnn50', 0.018944411122773706),\n",
       " ('pnn20', 0.010897435897435897),\n",
       " ('Pnn50pnn20', 0.005698005698005698),\n",
       " ('Mean_y', 0.0),\n",
       " ('SD', 0.010122218093232596),\n",
       " ('Variance', 0.03624792893269516),\n",
       " ('Minimum', 0.0),\n",
       " ('Maximum', 0.0074997466301814164),\n",
       " ('Number of Peaks', 0.02468614007075546),\n",
       " ('Number of Valleys', 0.11230098877157708),\n",
       " ('Ratio', 0.0)]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cols = [col for col in merged.columns.tolist() if col != 'Quad_Cat']\n",
    "X, y = merged[X_cols], merged['Quad_Cat']\n",
    "dt.fit(X,y);\n",
    "list(zip(X_cols, dt.feature_importances_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there a statistically significant difference in eye gaze parameters between:\n",
    "(a) low and high valence responses;\n",
    "\n",
    "(b) low and high arousal responses;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 - HA HV, 1 - LA HV, 2 - LA LV, 3 - HA LV\n",
    "low_val= eye[(eye['Quad_Cat'] == 2) | (eye['Quad_Cat'] == 3)].drop(['Quad_Cat'], axis=1)\n",
    "high_val = eye[((eye['Quad_Cat'] == 0) | (eye['Quad_Cat'] == 1))].drop(['Quad_Cat'], axis=1)\n",
    "t, p = ttest_ind(low_val, high_val, equal_var=False)\n",
    "significant_val = [el for el in list(zip(X_cols,p)) if el[1] < 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 - HA HV, 1 - LA HV, 2 - LA LV, 3 - HA LV\n",
    "low_arousal= eye[(eye['Quad_Cat'] == 0) | (eye['Quad_Cat'] == 3)].drop(['Quad_Cat'], axis=1)\n",
    "high_arousal = eye[((eye['Quad_Cat'] == 2) | (eye['Quad_Cat'] == 1))].drop(['Quad_Cat'], axis=1)\n",
    "t, p = ttest_ind(low_arousal, high_arousal, equal_var=False)\n",
    "significant_aro = [el for el in list(zip(X_cols,p)) if el[1] < 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "significant_both = set(significant_val).intersection(set(significant_aro))\n",
    "significant_both"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there a statistically significant difference in ECG and GSR params between:\n",
    "(a) low and high valence responses;\n",
    "\n",
    "(b) low and high arousal responses;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ECG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 - HA HV, 1 - LA HV, 2 - LA LV, 3 - HA LV\n",
    "low_val= ecg[(ecg['Quad_Cat'] == 2) | (ecg['Quad_Cat'] == 3)].drop(['Quad_Cat'], axis=1)\n",
    "high_val = ecg[((eye['Quad_Cat'] == 0) | (ecg['Quad_Cat'] == 1))].drop(['Quad_Cat'], axis=1)\n",
    "t, p = ttest_ind(low_val, high_val, equal_var=False)\n",
    "significant_val = [el for el in list(zip(X_cols,p)) if el[1] < 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 - HA HV, 1 - LA HV, 2 - LA LV, 3 - HA LV\n",
    "low_arousal= ecg[(eye['Quad_Cat'] == 0) | (ecg['Quad_Cat'] == 3)].drop(['Quad_Cat'], axis=1)\n",
    "high_arousal = ecg[((ecg['Quad_Cat'] == 2) | (ecg['Quad_Cat'] == 1))].drop(['Quad_Cat'], axis=1)\n",
    "t, p = ttest_ind(low_arousal, high_arousal, equal_var=False)\n",
    "significant_aro = [el for el in list(zip(X_cols,p)) if el[1] < 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "significant_both = set(significant_val).intersection(set(significant_aro))\n",
    "significant_both"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GSR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 - HA HV, 1 - LA HV, 2 - LA LV, 3 - HA LV\n",
    "low_val= gsr[(gsr['Quad_Cat'] == 2) | (gsr['Quad_Cat'] == 3)].drop(['Quad_Cat'], axis=1)\n",
    "high_val = gsr[((gsr['Quad_Cat'] == 0) | (gsr['Quad_Cat'] == 1))].drop(['Quad_Cat'], axis=1)\n",
    "t, p = ttest_ind(low_val, high_val, equal_var=False)\n",
    "significant_val = [el for el in list(zip(X_cols,p)) if el[1] < 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 - HA HV, 1 - LA HV, 2 - LA LV, 3 - HA LV\n",
    "low_arousal= gsr[(eye['Quad_Cat'] == 0) | (gsr['Quad_Cat'] == 3)].drop(['Quad_Cat'], axis=1)\n",
    "high_arousal = gsr[((ecg['Quad_Cat'] == 2) | (gsr['Quad_Cat'] == 1))].drop(['Quad_Cat'], axis=1)\n",
    "t, p = ttest_ind(low_arousal, high_arousal, equal_var=False)\n",
    "significant_aro = [el for el in list(zip(X_cols,p)) if el[1] < 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "significant_both = set(significant_val).intersection(set(significant_aro))\n",
    "significant_both"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = X_train.join(y_train)\n",
    "test = X_test.join(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_002d1_row44_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_002d1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_002d1_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_002d1_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_002d1_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_002d1_row0_col1\" class=\"data row0 col1\" >6906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_002d1_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_002d1_row1_col1\" class=\"data row1 col1\" >Quad_Cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_002d1_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "      <td id=\"T_002d1_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_002d1_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "      <td id=\"T_002d1_row3_col1\" class=\"data row3 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_002d1_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "      <td id=\"T_002d1_row4_col1\" class=\"data row4 col1\" >(249, 76)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_002d1_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "      <td id=\"T_002d1_row5_col1\" class=\"data row5 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_002d1_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_002d1_row6_col1\" class=\"data row6 col1\" >75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_002d1_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_002d1_row7_col1\" class=\"data row7 col1\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_002d1_row8_col0\" class=\"data row8 col0\" >Ordinal Features</td>\n",
       "      <td id=\"T_002d1_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_002d1_row9_col0\" class=\"data row9 col0\" >High Cardinality Features</td>\n",
       "      <td id=\"T_002d1_row9_col1\" class=\"data row9 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_002d1_row10_col0\" class=\"data row10 col0\" >High Cardinality Method</td>\n",
       "      <td id=\"T_002d1_row10_col1\" class=\"data row10 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_002d1_row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_002d1_row11_col1\" class=\"data row11 col1\" >(246, 70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_002d1_row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_002d1_row12_col1\" class=\"data row12 col1\" >(3, 70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_002d1_row13_col0\" class=\"data row13 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_002d1_row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_002d1_row14_col0\" class=\"data row14 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_002d1_row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_002d1_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_002d1_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_002d1_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
       "      <td id=\"T_002d1_row16_col1\" class=\"data row16 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_002d1_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_002d1_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_002d1_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
       "      <td id=\"T_002d1_row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_002d1_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_002d1_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_002d1_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_002d1_row20_col1\" class=\"data row20 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_002d1_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
       "      <td id=\"T_002d1_row21_col1\" class=\"data row21 col1\" >944e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_002d1_row22_col0\" class=\"data row22 col0\" >Imputation Type</td>\n",
       "      <td id=\"T_002d1_row22_col1\" class=\"data row22 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_002d1_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Iteration</td>\n",
       "      <td id=\"T_002d1_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_002d1_row24_col0\" class=\"data row24 col0\" >Numeric Imputer</td>\n",
       "      <td id=\"T_002d1_row24_col1\" class=\"data row24 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_002d1_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Numeric Model</td>\n",
       "      <td id=\"T_002d1_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_002d1_row26_col0\" class=\"data row26 col0\" >Categorical Imputer</td>\n",
       "      <td id=\"T_002d1_row26_col1\" class=\"data row26 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_002d1_row27_col0\" class=\"data row27 col0\" >Iterative Imputation Categorical Model</td>\n",
       "      <td id=\"T_002d1_row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_002d1_row28_col0\" class=\"data row28 col0\" >Unknown Categoricals Handling</td>\n",
       "      <td id=\"T_002d1_row28_col1\" class=\"data row28 col1\" >least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_002d1_row29_col0\" class=\"data row29 col0\" >Normalize</td>\n",
       "      <td id=\"T_002d1_row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_002d1_row30_col0\" class=\"data row30 col0\" >Normalize Method</td>\n",
       "      <td id=\"T_002d1_row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_002d1_row31_col0\" class=\"data row31 col0\" >Transformation</td>\n",
       "      <td id=\"T_002d1_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_002d1_row32_col0\" class=\"data row32 col0\" >Transformation Method</td>\n",
       "      <td id=\"T_002d1_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_002d1_row33_col0\" class=\"data row33 col0\" >PCA</td>\n",
       "      <td id=\"T_002d1_row33_col1\" class=\"data row33 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_002d1_row34_col0\" class=\"data row34 col0\" >PCA Method</td>\n",
       "      <td id=\"T_002d1_row34_col1\" class=\"data row34 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_002d1_row35_col0\" class=\"data row35 col0\" >PCA Components</td>\n",
       "      <td id=\"T_002d1_row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_002d1_row36_col0\" class=\"data row36 col0\" >Ignore Low Variance</td>\n",
       "      <td id=\"T_002d1_row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_002d1_row37_col0\" class=\"data row37 col0\" >Combine Rare Levels</td>\n",
       "      <td id=\"T_002d1_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_002d1_row38_col0\" class=\"data row38 col0\" >Rare Level Threshold</td>\n",
       "      <td id=\"T_002d1_row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_002d1_row39_col0\" class=\"data row39 col0\" >Numeric Binning</td>\n",
       "      <td id=\"T_002d1_row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_002d1_row40_col0\" class=\"data row40 col0\" >Remove Outliers</td>\n",
       "      <td id=\"T_002d1_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_002d1_row41_col0\" class=\"data row41 col0\" >Outliers Threshold</td>\n",
       "      <td id=\"T_002d1_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_002d1_row42_col0\" class=\"data row42 col0\" >Remove Multicollinearity</td>\n",
       "      <td id=\"T_002d1_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_002d1_row43_col0\" class=\"data row43 col0\" >Multicollinearity Threshold</td>\n",
       "      <td id=\"T_002d1_row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_002d1_row44_col0\" class=\"data row44 col0\" >Remove Perfect Collinearity</td>\n",
       "      <td id=\"T_002d1_row44_col1\" class=\"data row44 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_002d1_row45_col0\" class=\"data row45 col0\" >Clustering</td>\n",
       "      <td id=\"T_002d1_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_002d1_row46_col0\" class=\"data row46 col0\" >Clustering Iteration</td>\n",
       "      <td id=\"T_002d1_row46_col1\" class=\"data row46 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_002d1_row47_col0\" class=\"data row47 col0\" >Polynomial Features</td>\n",
       "      <td id=\"T_002d1_row47_col1\" class=\"data row47 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_002d1_row48_col0\" class=\"data row48 col0\" >Polynomial Degree</td>\n",
       "      <td id=\"T_002d1_row48_col1\" class=\"data row48 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_002d1_row49_col0\" class=\"data row49 col0\" >Trignometry Features</td>\n",
       "      <td id=\"T_002d1_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_002d1_row50_col0\" class=\"data row50 col0\" >Polynomial Threshold</td>\n",
       "      <td id=\"T_002d1_row50_col1\" class=\"data row50 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_002d1_row51_col0\" class=\"data row51 col0\" >Group Features</td>\n",
       "      <td id=\"T_002d1_row51_col1\" class=\"data row51 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_002d1_row52_col0\" class=\"data row52 col0\" >Feature Selection</td>\n",
       "      <td id=\"T_002d1_row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_002d1_row53_col0\" class=\"data row53 col0\" >Feature Selection Method</td>\n",
       "      <td id=\"T_002d1_row53_col1\" class=\"data row53 col1\" >classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_002d1_row54_col0\" class=\"data row54 col0\" >Features Selection Threshold</td>\n",
       "      <td id=\"T_002d1_row54_col1\" class=\"data row54 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_002d1_row55_col0\" class=\"data row55 col0\" >Feature Interaction</td>\n",
       "      <td id=\"T_002d1_row55_col1\" class=\"data row55 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_002d1_row56_col0\" class=\"data row56 col0\" >Feature Ratio</td>\n",
       "      <td id=\"T_002d1_row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_002d1_row57_col0\" class=\"data row57 col0\" >Interaction Threshold</td>\n",
       "      <td id=\"T_002d1_row57_col1\" class=\"data row57 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "      <td id=\"T_002d1_row58_col0\" class=\"data row58 col0\" >Fix Imbalance</td>\n",
       "      <td id=\"T_002d1_row58_col1\" class=\"data row58 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002d1_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
       "      <td id=\"T_002d1_row59_col0\" class=\"data row59 col0\" >Fix Imbalance Method</td>\n",
       "      <td id=\"T_002d1_row59_col1\" class=\"data row59 col1\" >SMOTE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x137b2dca0>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(False,\n",
       " 6906,\n",
       " 72    1\n",
       " 50    0\n",
       " 27    3\n",
       " Name: Quad_Cat, dtype: int64,\n",
       " 5,\n",
       " [('Setup Config',\n",
       "                                  Description             Value\n",
       "   0                               session_id              6906\n",
       "   1                                   Target          Quad_Cat\n",
       "   2                              Target Type        Multiclass\n",
       "   3                            Label Encoded              None\n",
       "   4                            Original Data         (249, 76)\n",
       "   5                           Missing Values             False\n",
       "   6                         Numeric Features                75\n",
       "   7                     Categorical Features                 0\n",
       "   8                         Ordinal Features             False\n",
       "   9                High Cardinality Features             False\n",
       "   10                 High Cardinality Method              None\n",
       "   11                   Transformed Train Set         (246, 70)\n",
       "   12                    Transformed Test Set           (3, 70)\n",
       "   13                      Shuffle Train-Test              True\n",
       "   14                     Stratify Train-Test             False\n",
       "   15                          Fold Generator   StratifiedKFold\n",
       "   16                             Fold Number                10\n",
       "   17                                CPU Jobs                -1\n",
       "   18                                 Use GPU             False\n",
       "   19                          Log Experiment             False\n",
       "   20                         Experiment Name  clf-default-name\n",
       "   21                                     USI              944e\n",
       "   22                         Imputation Type            simple\n",
       "   23          Iterative Imputation Iteration              None\n",
       "   24                         Numeric Imputer              mean\n",
       "   25      Iterative Imputation Numeric Model              None\n",
       "   26                     Categorical Imputer          constant\n",
       "   27  Iterative Imputation Categorical Model              None\n",
       "   28           Unknown Categoricals Handling    least_frequent\n",
       "   29                               Normalize             False\n",
       "   30                        Normalize Method              None\n",
       "   31                          Transformation             False\n",
       "   32                   Transformation Method              None\n",
       "   33                                     PCA             False\n",
       "   34                              PCA Method              None\n",
       "   35                          PCA Components              None\n",
       "   36                     Ignore Low Variance             False\n",
       "   37                     Combine Rare Levels             False\n",
       "   38                    Rare Level Threshold              None\n",
       "   39                         Numeric Binning             False\n",
       "   40                         Remove Outliers             False\n",
       "   41                      Outliers Threshold              None\n",
       "   42                Remove Multicollinearity             False\n",
       "   43             Multicollinearity Threshold              None\n",
       "   44             Remove Perfect Collinearity              True\n",
       "   45                              Clustering             False\n",
       "   46                    Clustering Iteration              None\n",
       "   47                     Polynomial Features             False\n",
       "   48                       Polynomial Degree              None\n",
       "   49                    Trignometry Features             False\n",
       "   50                    Polynomial Threshold              None\n",
       "   51                          Group Features             False\n",
       "   52                       Feature Selection             False\n",
       "   53                Feature Selection Method           classic\n",
       "   54            Features Selection Threshold              None\n",
       "   55                     Feature Interaction             False\n",
       "   56                           Feature Ratio             False\n",
       "   57                   Interaction Threshold              None\n",
       "   58                           Fix Imbalance             False\n",
       "   59                    Fix Imbalance Method             SMOTE),\n",
       "  ('X_training Set',\n",
       "        Mean_Fixation_Duration  SD_Fixation_Duration  Skew_Fixation_Duration  \\\n",
       "   183             1839.037720           2671.840576                3.257574   \n",
       "   210             1625.297607           1486.344604                1.540023   \n",
       "   35              1989.906250           2174.858154                1.741497   \n",
       "   236             2605.865479           3009.629150                2.121084   \n",
       "   305             1865.644775           2137.663574                2.398181   \n",
       "   ..                      ...                   ...                     ...   \n",
       "   269             3193.560059           4014.913574                3.460284   \n",
       "   88              2124.968262           2462.066650                2.383313   \n",
       "   128             2190.338867           2160.196777                1.376639   \n",
       "   150             1098.763184            902.898682                2.109686   \n",
       "   272             3960.277832           4283.001465                1.477632   \n",
       "   \n",
       "        Max_Fixation_Duration  First_Fixation_Duration  Num_of_Saccade  \\\n",
       "   183                15011.0                   3779.0        0.429752   \n",
       "   210                 7300.0                   4683.0        0.482759   \n",
       "   35                  9411.0                    787.0        0.413333   \n",
       "   236                13529.0                   6626.0        0.344371   \n",
       "   305                11162.0                   1848.0        0.436047   \n",
       "   ..                     ...                      ...             ...   \n",
       "   269                25200.0                   2852.0        0.284884   \n",
       "   88                 11587.0                   1735.0        0.350000   \n",
       "   128                 8543.0                   6797.0        0.384106   \n",
       "   150                 5154.0                   2088.0        0.655172   \n",
       "   272                17412.0                   8302.0        0.231788   \n",
       "   \n",
       "        Mean_Saccade_Duration  SD_Saccade_Duration  Skew_Saccade_Duration  \\\n",
       "   183             453.826935           502.064392               2.139299   \n",
       "   210             443.071442           504.930725               1.577045   \n",
       "   35              375.161285           490.076385               1.747441   \n",
       "   236             298.365387           287.413879               1.256010   \n",
       "   305             400.399994           488.487732               2.511481   \n",
       "   ..                     ...                  ...                    ...   \n",
       "   269             251.122452           297.804230               1.666275   \n",
       "   88              729.111084           638.089722               0.962485   \n",
       "   128             374.913788           365.418152               1.923541   \n",
       "   150             425.017548           496.984772               2.148797   \n",
       "   272             242.885712           247.483536               1.132426   \n",
       "   \n",
       "        Max_Saccade_Duration  ...     pnn20  Pnn50pnn20     Mean_y        SD  \\\n",
       "   183                2741.0  ...  0.372727    0.719512   9.539515  0.453543   \n",
       "   210                2363.0  ...  0.480094    0.848781   8.722909  0.230131   \n",
       "   35                 1942.0  ...  0.437500    1.000000   2.142146  0.027386   \n",
       "   236                1164.0  ...  0.518797    0.961353   8.779233  0.151946   \n",
       "   305                2738.0  ...  0.584582    0.948718  13.194764  0.322736   \n",
       "   ..                    ...  ...       ...         ...        ...       ...   \n",
       "   269                1181.0  ...  0.331361    0.892857   6.889442  0.215162   \n",
       "   88                 2879.0  ...  0.383074    0.901163  13.975972  0.354104   \n",
       "   128                1970.0  ...  0.413203    0.952663   5.290108  0.429669   \n",
       "   150                2562.0  ...  0.458234    0.979167  10.692984  0.500868   \n",
       "   272                 841.0  ...  0.261719    0.731343   7.566581  0.257260   \n",
       "   \n",
       "        Variance    Minimum    Maximum  Number of Peaks  Number of Valleys  \\\n",
       "   183  0.205701   7.990161  10.227064         0.000069           0.000064   \n",
       "   210  0.052960   8.390570   9.157029         0.000026           0.000023   \n",
       "   35   0.000750   2.081282   2.198466         0.000029           0.000029   \n",
       "   236  0.023088   8.582669   9.273891         0.000027           0.000027   \n",
       "   305  0.104159  12.871466  14.329449         0.000035           0.000035   \n",
       "   ..        ...        ...        ...              ...                ...   \n",
       "   269  0.046294   6.561305   7.363595         0.000015           0.000015   \n",
       "   88   0.125390  13.300699  14.878328         0.000041           0.000041   \n",
       "   128  0.184615   4.658332   6.176735         0.000040           0.000040   \n",
       "   150  0.250869   9.921503  11.543913         0.000023           0.000023   \n",
       "   272  0.066183   7.155118   8.352950         0.000037           0.000034   \n",
       "   \n",
       "               Ratio  \n",
       "   183  3.190000e-10  \n",
       "   210  7.620000e-11  \n",
       "   35   2.030000e-10  \n",
       "   236  8.990000e-11  \n",
       "   305  1.000000e-10  \n",
       "   ..            ...  \n",
       "   269  4.190000e-11  \n",
       "   88   1.140000e-10  \n",
       "   128  1.350000e-10  \n",
       "   150  6.770000e-11  \n",
       "   272  1.240000e-10  \n",
       "   \n",
       "   [246 rows x 70 columns]),\n",
       "  ('y_training Set',\n",
       "   183    3\n",
       "   210    2\n",
       "   35     3\n",
       "   236    0\n",
       "   305    2\n",
       "         ..\n",
       "   269    2\n",
       "   88     1\n",
       "   128    0\n",
       "   150    2\n",
       "   272    0\n",
       "   Name: Quad_Cat, Length: 246, dtype: int64),\n",
       "  ('X_test Set',\n",
       "       Mean_Fixation_Duration  SD_Fixation_Duration  Skew_Fixation_Duration  \\\n",
       "   72             1374.061890           1262.074707                2.125073   \n",
       "   50             2819.083252           2372.221924                1.542180   \n",
       "   27             2348.553223           3202.058105                3.271473   \n",
       "   \n",
       "       Max_Fixation_Duration  First_Fixation_Duration  Num_of_Saccade  \\\n",
       "   72                 7141.0                    322.0        0.533333   \n",
       "   50                10828.0                   1338.0        0.306667   \n",
       "   27                17731.0                   1862.0        0.380165   \n",
       "   \n",
       "       Mean_Saccade_Duration  SD_Saccade_Duration  Skew_Saccade_Duration  \\\n",
       "   72             482.958344           564.042969               3.204663   \n",
       "   50             324.304352           300.714813               1.107017   \n",
       "   27             233.347824           236.311111               1.267317   \n",
       "   \n",
       "       Max_Saccade_Duration  ...     pnn20  Pnn50pnn20    Mean_y        SD  \\\n",
       "   72                4144.0  ...  0.500000      1.0000  8.988783  0.818031   \n",
       "   50                1043.0  ...  0.484848      0.9875  7.740371  0.251584   \n",
       "   27                 903.0  ...  0.485830      1.0000  2.407923  0.290574   \n",
       "   \n",
       "       Variance   Minimum    Maximum  Number of Peaks  Number of Valleys  \\\n",
       "   72  0.669175  8.079311  11.896380         0.000022           0.000022   \n",
       "   50  0.063295  7.265676   8.204041         0.000021           0.000021   \n",
       "   27  0.084433  2.081209   2.948496         0.000037           0.000037   \n",
       "   \n",
       "              Ratio  \n",
       "   72  6.050000e-11  \n",
       "   50  1.410000e-10  \n",
       "   27  1.700000e-10  \n",
       "   \n",
       "   [3 rows x 70 columns]),\n",
       "  ('y_test Set',\n",
       "   72    1\n",
       "   50    0\n",
       "   27    3\n",
       "   Name: Quad_Cat, dtype: int64),\n",
       "  ('Transformation Pipeline',\n",
       "   Pipeline(memory=None,\n",
       "            steps=[('dtypes',\n",
       "                    DataTypes_Auto_infer(categorical_features=[],\n",
       "                                         display_types=True, features_todrop=[],\n",
       "                                         id_columns=[],\n",
       "                                         ml_usecase='classification',\n",
       "                                         numerical_features=[], target='Quad_Cat',\n",
       "                                         time_features=[])),\n",
       "                   ('imputer',\n",
       "                    Simple_Imputer(categorical_strategy='not_available',\n",
       "                                   fill_value_categorical=None,\n",
       "                                   fill_value_numerical=None,\n",
       "                                   numeric_str...\n",
       "                   ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
       "                   ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
       "                   ('cluster_all', 'passthrough'),\n",
       "                   ('dummy', Dummify(target='Quad_Cat')),\n",
       "                   ('fix_perfect', Remove_100(target='Quad_Cat')),\n",
       "                   ('clean_names', Clean_Colum_Names()),\n",
       "                   ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
       "                   ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
       "            verbose=False))],\n",
       " 'lightgbm',\n",
       " 111    3\n",
       " 208    1\n",
       " 145    0\n",
       " 203    3\n",
       " 78     2\n",
       "       ..\n",
       " 188    0\n",
       " 71     3\n",
       " 106    3\n",
       " 270    2\n",
       " 102    2\n",
       " Name: Quad_Cat, Length: 249, dtype: int64,\n",
       " None,\n",
       " 183    3\n",
       " 210    2\n",
       " 35     3\n",
       " 236    0\n",
       " 305    2\n",
       "       ..\n",
       " 269    2\n",
       " 88     1\n",
       " 128    0\n",
       " 150    2\n",
       " 272    0\n",
       " Name: Quad_Cat, Length: 246, dtype: int64,\n",
       " Pipeline(memory=None,\n",
       "          steps=[('dtypes',\n",
       "                  DataTypes_Auto_infer(categorical_features=[],\n",
       "                                       display_types=True, features_todrop=[],\n",
       "                                       id_columns=[],\n",
       "                                       ml_usecase='classification',\n",
       "                                       numerical_features=[], target='Quad_Cat',\n",
       "                                       time_features=[])),\n",
       "                 ('imputer',\n",
       "                  Simple_Imputer(categorical_strategy='not_available',\n",
       "                                 fill_value_categorical=None,\n",
       "                                 fill_value_numerical=None,\n",
       "                                 numeric_str...\n",
       "                 ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
       "                 ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
       "                 ('cluster_all', 'passthrough'),\n",
       "                 ('dummy', Dummify(target='Quad_Cat')),\n",
       "                 ('fix_perfect', Remove_100(target='Quad_Cat')),\n",
       "                 ('clean_names', Clean_Colum_Names()),\n",
       "                 ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
       "                 ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
       "          verbose=False),\n",
       " [<pandas.io.formats.style.Styler at 0x137b2dca0>],\n",
       "     Mean_Fixation_Duration  SD_Fixation_Duration  Skew_Fixation_Duration  \\\n",
       " 72             1374.061890           1262.074707                2.125073   \n",
       " 50             2819.083252           2372.221924                1.542180   \n",
       " 27             2348.553223           3202.058105                3.271473   \n",
       " \n",
       "     Max_Fixation_Duration  First_Fixation_Duration  Num_of_Saccade  \\\n",
       " 72                 7141.0                    322.0        0.533333   \n",
       " 50                10828.0                   1338.0        0.306667   \n",
       " 27                17731.0                   1862.0        0.380165   \n",
       " \n",
       "     Mean_Saccade_Duration  SD_Saccade_Duration  Skew_Saccade_Duration  \\\n",
       " 72             482.958344           564.042969               3.204663   \n",
       " 50             324.304352           300.714813               1.107017   \n",
       " 27             233.347824           236.311111               1.267317   \n",
       " \n",
       "     Max_Saccade_Duration  ...     pnn20  Pnn50pnn20    Mean_y        SD  \\\n",
       " 72                4144.0  ...  0.500000      1.0000  8.988783  0.818031   \n",
       " 50                1043.0  ...  0.484848      0.9875  7.740371  0.251584   \n",
       " 27                 903.0  ...  0.485830      1.0000  2.407923  0.290574   \n",
       " \n",
       "     Variance   Minimum    Maximum  Number of Peaks  Number of Valleys  \\\n",
       " 72  0.669175  8.079311  11.896380         0.000022           0.000022   \n",
       " 50  0.063295  7.265676   8.204041         0.000021           0.000021   \n",
       " 27  0.084433  2.081209   2.948496         0.000037           0.000037   \n",
       " \n",
       "            Ratio  \n",
       " 72  6.050000e-11  \n",
       " 50  1.410000e-10  \n",
       " 27  1.700000e-10  \n",
       " \n",
       " [3 rows x 70 columns],\n",
       " False,\n",
       " {'USI',\n",
       "  'X',\n",
       "  'X_test',\n",
       "  'X_train',\n",
       "  '_all_metrics',\n",
       "  '_all_models',\n",
       "  '_all_models_internal',\n",
       "  '_available_plots',\n",
       "  '_gpu_n_jobs_param',\n",
       "  '_internal_pipeline',\n",
       "  '_ml_usecase',\n",
       "  'create_model_container',\n",
       "  'dashboard_logger',\n",
       "  'data_before_preprocess',\n",
       "  'display_container',\n",
       "  'exp_name_log',\n",
       "  'experiment__',\n",
       "  'fix_imbalance_method_param',\n",
       "  'fix_imbalance_param',\n",
       "  'fold_generator',\n",
       "  'fold_groups_param',\n",
       "  'fold_groups_param_full',\n",
       "  'fold_param',\n",
       "  'fold_shuffle_param',\n",
       "  'gpu_param',\n",
       "  'html_param',\n",
       "  'imputation_classifier',\n",
       "  'imputation_regressor',\n",
       "  'iterative_imputation_iters_param',\n",
       "  'log_plots_param',\n",
       "  'logging_param',\n",
       "  'master_model_container',\n",
       "  'n_jobs_param',\n",
       "  'prep_pipe',\n",
       "  'pycaret_globals',\n",
       "  'seed',\n",
       "  'stratify_param',\n",
       "  'target_param',\n",
       "  'transform_target_method_param',\n",
       "  'transform_target_param',\n",
       "  'y',\n",
       "  'y_test',\n",
       "  'y_train'},\n",
       " {'parameter': 'Hyperparameters',\n",
       "  'auc': 'AUC',\n",
       "  'confusion_matrix': 'Confusion Matrix',\n",
       "  'threshold': 'Threshold',\n",
       "  'pr': 'Precision Recall',\n",
       "  'error': 'Prediction Error',\n",
       "  'class_report': 'Class Report',\n",
       "  'rfe': 'Feature Selection',\n",
       "  'learning': 'Learning Curve',\n",
       "  'manifold': 'Manifold Learning',\n",
       "  'calibration': 'Calibration Curve',\n",
       "  'vc': 'Validation Curve',\n",
       "  'dimension': 'Dimensions',\n",
       "  'feature': 'Feature Importance',\n",
       "  'feature_all': 'Feature Importance (All)',\n",
       "  'boundary': 'Decision Boundary',\n",
       "  'lift': 'Lift Chart',\n",
       "  'gain': 'Gain Chart',\n",
       "  'tree': 'Decision Tree',\n",
       "  'ks': 'KS Statistic Plot'},\n",
       " {'lr': <pycaret.containers.models.classification.LogisticRegressionClassifierContainer at 0x13820e340>,\n",
       "  'knn': <pycaret.containers.models.classification.KNeighborsClassifierContainer at 0x13820e670>,\n",
       "  'nb': <pycaret.containers.models.classification.GaussianNBClassifierContainer at 0x13820e6d0>,\n",
       "  'dt': <pycaret.containers.models.classification.DecisionTreeClassifierContainer at 0x13820eb80>,\n",
       "  'svm': <pycaret.containers.models.classification.SGDClassifierContainer at 0x13820e040>,\n",
       "  'rbfsvm': <pycaret.containers.models.classification.SVCClassifierContainer at 0x1382038b0>,\n",
       "  'gpc': <pycaret.containers.models.classification.GaussianProcessClassifierContainer at 0x138203e20>,\n",
       "  'mlp': <pycaret.containers.models.classification.MLPClassifierContainer at 0x138203ee0>,\n",
       "  'ridge': <pycaret.containers.models.classification.RidgeClassifierContainer at 0x138203370>,\n",
       "  'rf': <pycaret.containers.models.classification.RandomForestClassifierContainer at 0x138203190>,\n",
       "  'qda': <pycaret.containers.models.classification.QuadraticDiscriminantAnalysisContainer at 0x138203c40>,\n",
       "  'ada': <pycaret.containers.models.classification.AdaBoostClassifierContainer at 0x137aff9a0>,\n",
       "  'gbc': <pycaret.containers.models.classification.GradientBoostingClassifierContainer at 0x137aff580>,\n",
       "  'lda': <pycaret.containers.models.classification.LinearDiscriminantAnalysisContainer at 0x137affdc0>,\n",
       "  'et': <pycaret.containers.models.classification.ExtraTreesClassifierContainer at 0x137affd30>,\n",
       "  'lightgbm': <pycaret.containers.models.classification.LGBMClassifierContainer at 0x137aff4f0>,\n",
       "  'dummy': <pycaret.containers.models.classification.DummyClassifierContainer at 0x1378d6d60>},\n",
       " [],\n",
       " 'box-cox',\n",
       " {'lr': <pycaret.containers.models.classification.LogisticRegressionClassifierContainer at 0x137a5eeb0>,\n",
       "  'knn': <pycaret.containers.models.classification.KNeighborsClassifierContainer at 0x137a5ea00>,\n",
       "  'nb': <pycaret.containers.models.classification.GaussianNBClassifierContainer at 0x137a5e280>,\n",
       "  'dt': <pycaret.containers.models.classification.DecisionTreeClassifierContainer at 0x137a5ed90>,\n",
       "  'svm': <pycaret.containers.models.classification.SGDClassifierContainer at 0x137a5e8b0>,\n",
       "  'rbfsvm': <pycaret.containers.models.classification.SVCClassifierContainer at 0x137b7e5b0>,\n",
       "  'gpc': <pycaret.containers.models.classification.GaussianProcessClassifierContainer at 0x137b7ea00>,\n",
       "  'mlp': <pycaret.containers.models.classification.MLPClassifierContainer at 0x137b7ea60>,\n",
       "  'ridge': <pycaret.containers.models.classification.RidgeClassifierContainer at 0x137b7ef10>,\n",
       "  'rf': <pycaret.containers.models.classification.RandomForestClassifierContainer at 0x137b7ec40>,\n",
       "  'qda': <pycaret.containers.models.classification.QuadraticDiscriminantAnalysisContainer at 0x137b7e700>,\n",
       "  'ada': <pycaret.containers.models.classification.AdaBoostClassifierContainer at 0x138259af0>,\n",
       "  'gbc': <pycaret.containers.models.classification.GradientBoostingClassifierContainer at 0x138259550>,\n",
       "  'lda': <pycaret.containers.models.classification.LinearDiscriminantAnalysisContainer at 0x1382598b0>,\n",
       "  'et': <pycaret.containers.models.classification.ExtraTreesClassifierContainer at 0x138259970>,\n",
       "  'lightgbm': <pycaret.containers.models.classification.LGBMClassifierContainer at 0x137affca0>,\n",
       "  'dummy': <pycaret.containers.models.classification.DummyClassifierContainer at 0x138259d30>,\n",
       "  'Bagging': <pycaret.containers.models.classification.BaggingClassifierContainer at 0x137a5eee0>,\n",
       "  'Stacking': <pycaret.containers.models.classification.StackingClassifierContainer at 0x138259e50>,\n",
       "  'Voting': <pycaret.containers.models.classification.VotingClassifierContainer at 0x1382493d0>,\n",
       "  'CalibratedCV': <pycaret.containers.models.classification.CalibratedClassifierCVContainer at 0x138249880>},\n",
       " False,\n",
       " StratifiedKFold(n_splits=10, random_state=None, shuffle=False),\n",
       " 'lightgbm',\n",
       " Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False),\n",
       " <MLUsecase.CLASSIFICATION: 1>,\n",
       "      Mean_Fixation_Duration  SD_Fixation_Duration  Skew_Fixation_Duration  \\\n",
       " 111             6104.947266          16404.093750                3.284097   \n",
       " 208             2810.736816           3688.940674                3.864777   \n",
       " 145             1867.930176           2415.526611                3.358285   \n",
       " 203             3389.149902           3877.064941                1.464668   \n",
       " 78              1349.731934           1624.854492                3.899621   \n",
       " ..                      ...                   ...                     ...   \n",
       " 188             2434.924561           3351.020264                2.493314   \n",
       " 71              3597.649902           4559.771973                2.063140   \n",
       " 106             1949.161255           5224.796875                4.754883   \n",
       " 270             3019.961426           3281.450195                2.756763   \n",
       " 102             1132.887695           1007.971802                2.660838   \n",
       " \n",
       "      Max_Fixation_Duration  First_Fixation_Duration  Num_of_Saccade  \\\n",
       " 111                71116.0                   1666.0        0.157025   \n",
       " 208                24384.0                   2948.0        0.311111   \n",
       " 145                14114.0                   1846.0        0.428571   \n",
       " 203                14887.0                   3617.0        0.253333   \n",
       " 78                 11600.0                   1675.0        0.557471   \n",
       " ..                     ...                      ...             ...   \n",
       " 188                16282.0                  10707.0        0.350993   \n",
       " 71                 19293.0                   1766.0        0.253333   \n",
       " 106                29571.0                    678.0        0.387500   \n",
       " 270                19380.0                  19380.0        0.293103   \n",
       " 102                 6501.0                   1472.0        0.563218   \n",
       " \n",
       "      Mean_Saccade_Duration  SD_Saccade_Duration  Skew_Saccade_Duration  \\\n",
       " 111             273.684204           531.042053               3.167967   \n",
       " 208             351.464294           399.242157               1.956607   \n",
       " 145             423.023804           547.865662               2.502783   \n",
       " 203             398.105255           766.409607               3.305010   \n",
       " 78              439.865967           479.895660               2.002260   \n",
       " ..                     ...                  ...                    ...   \n",
       " 188             413.905670           481.556274               1.667801   \n",
       " 71              179.631577           211.285538               2.252046   \n",
       " 106             632.258057           682.392334               1.793059   \n",
       " 270             332.078430           409.546265               1.738233   \n",
       " 102             639.918396           938.236938               2.735721   \n",
       " \n",
       "      Max_Saccade_Duration  ...     pnn20  Pnn50pnn20     Mean_y        SD  \\\n",
       " 111                2355.0  ...  0.466443    0.971223   7.566999  0.376687   \n",
       " 208                2094.0  ...  0.597536    0.903780   9.251482  0.201812   \n",
       " 145                2899.0  ...  0.483333    0.991379  12.351123  0.793334   \n",
       " 203                3456.0  ...  0.477987    0.973684  16.114616  0.273790   \n",
       " 78                 2600.0  ...  0.520788    0.915966   6.739538  0.782211   \n",
       " ..                    ...  ...       ...         ...        ...       ...   \n",
       " 188                2014.0  ...  0.463415    0.868421   7.088746  0.132030   \n",
       " 71                  913.0  ...  0.540816    0.971698  25.808836  1.049616   \n",
       " 106                3155.0  ...  0.452381    0.986842  28.074049  1.128630   \n",
       " 270                1841.0  ...  0.270030    0.824176   7.699881  0.484059   \n",
       " 102                4784.0  ...  0.411932    0.896552  21.704523  0.923396   \n",
       " \n",
       "      Variance    Minimum    Maximum  Number of Peaks  Number of Valleys  \\\n",
       " 111  0.141893   6.884995   8.317933         0.000041           0.000041   \n",
       " 208  0.040728   8.893362   9.742991         0.000033           0.000030   \n",
       " 145  0.629380  11.456935  15.096478         0.000031           0.000031   \n",
       " 203  0.074961  15.811350  16.946985         0.000021           0.000014   \n",
       " 78   0.611854   5.924464   9.682770         0.000009           0.000006   \n",
       " ..        ...        ...        ...              ...                ...   \n",
       " 188  0.017432   6.901923   7.655449         0.000047           0.000047   \n",
       " 71   1.101693  24.506308  29.432470         0.000050           0.000050   \n",
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       " 270  0.234313   6.989363   9.002745         0.000038           0.000038   \n",
       " 102  0.852659  20.327318  24.584368         0.000035           0.000032   \n",
       " \n",
       "             Ratio  \n",
       " 111  1.910000e-10  \n",
       " 208  9.110000e-11  \n",
       " 145  1.610000e-10  \n",
       " 203  1.520000e-10  \n",
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       " ..            ...  \n",
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       " 106  3.690000e-10  \n",
       " 270  1.100000e-10  \n",
       " 102  1.020000e-10  \n",
       " \n",
       " [249 rows x 70 columns],\n",
       " False,\n",
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       " '944e',\n",
       " 'Quad_Cat',\n",
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       " 'clf-default-name',\n",
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       " 10,\n",
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       " None,\n",
       "      Mean_Fixation_Duration  SD_Fixation_Duration  Skew_Fixation_Duration  \\\n",
       " 183             1839.037720           2671.840576                3.257574   \n",
       " 210             1625.297607           1486.344604                1.540023   \n",
       " 35              1989.906250           2174.858154                1.741497   \n",
       " 236             2605.865479           3009.629150                2.121084   \n",
       " 305             1865.644775           2137.663574                2.398181   \n",
       " ..                      ...                   ...                     ...   \n",
       " 269             3193.560059           4014.913574                3.460284   \n",
       " 88              2124.968262           2462.066650                2.383313   \n",
       " 128             2190.338867           2160.196777                1.376639   \n",
       " 150             1098.763184            902.898682                2.109686   \n",
       " 272             3960.277832           4283.001465                1.477632   \n",
       " \n",
       "      Max_Fixation_Duration  First_Fixation_Duration  Num_of_Saccade  \\\n",
       " 183                15011.0                   3779.0        0.429752   \n",
       " 210                 7300.0                   4683.0        0.482759   \n",
       " 35                  9411.0                    787.0        0.413333   \n",
       " 236                13529.0                   6626.0        0.344371   \n",
       " 305                11162.0                   1848.0        0.436047   \n",
       " ..                     ...                      ...             ...   \n",
       " 269                25200.0                   2852.0        0.284884   \n",
       " 88                 11587.0                   1735.0        0.350000   \n",
       " 128                 8543.0                   6797.0        0.384106   \n",
       " 150                 5154.0                   2088.0        0.655172   \n",
       " 272                17412.0                   8302.0        0.231788   \n",
       " \n",
       "      Mean_Saccade_Duration  SD_Saccade_Duration  Skew_Saccade_Duration  \\\n",
       " 183             453.826935           502.064392               2.139299   \n",
       " 210             443.071442           504.930725               1.577045   \n",
       " 35              375.161285           490.076385               1.747441   \n",
       " 236             298.365387           287.413879               1.256010   \n",
       " 305             400.399994           488.487732               2.511481   \n",
       " ..                     ...                  ...                    ...   \n",
       " 269             251.122452           297.804230               1.666275   \n",
       " 88              729.111084           638.089722               0.962485   \n",
       " 128             374.913788           365.418152               1.923541   \n",
       " 150             425.017548           496.984772               2.148797   \n",
       " 272             242.885712           247.483536               1.132426   \n",
       " \n",
       "      Max_Saccade_Duration  ...     pnn20  Pnn50pnn20     Mean_y        SD  \\\n",
       " 183                2741.0  ...  0.372727    0.719512   9.539515  0.453543   \n",
       " 210                2363.0  ...  0.480094    0.848781   8.722909  0.230131   \n",
       " 35                 1942.0  ...  0.437500    1.000000   2.142146  0.027386   \n",
       " 236                1164.0  ...  0.518797    0.961353   8.779233  0.151946   \n",
       " 305                2738.0  ...  0.584582    0.948718  13.194764  0.322736   \n",
       " ..                    ...  ...       ...         ...        ...       ...   \n",
       " 269                1181.0  ...  0.331361    0.892857   6.889442  0.215162   \n",
       " 88                 2879.0  ...  0.383074    0.901163  13.975972  0.354104   \n",
       " 128                1970.0  ...  0.413203    0.952663   5.290108  0.429669   \n",
       " 150                2562.0  ...  0.458234    0.979167  10.692984  0.500868   \n",
       " 272                 841.0  ...  0.261719    0.731343   7.566581  0.257260   \n",
       " \n",
       "      Variance    Minimum    Maximum  Number of Peaks  Number of Valleys  \\\n",
       " 183  0.205701   7.990161  10.227064         0.000069           0.000064   \n",
       " 210  0.052960   8.390570   9.157029         0.000026           0.000023   \n",
       " 35   0.000750   2.081282   2.198466         0.000029           0.000029   \n",
       " 236  0.023088   8.582669   9.273891         0.000027           0.000027   \n",
       " 305  0.104159  12.871466  14.329449         0.000035           0.000035   \n",
       " ..        ...        ...        ...              ...                ...   \n",
       " 269  0.046294   6.561305   7.363595         0.000015           0.000015   \n",
       " 88   0.125390  13.300699  14.878328         0.000041           0.000041   \n",
       " 128  0.184615   4.658332   6.176735         0.000040           0.000040   \n",
       " 150  0.250869   9.921503  11.543913         0.000023           0.000023   \n",
       " 272  0.066183   7.155118   8.352950         0.000037           0.000034   \n",
       " \n",
       "             Ratio  \n",
       " 183  3.190000e-10  \n",
       " 210  7.620000e-11  \n",
       " 35   2.030000e-10  \n",
       " 236  8.990000e-11  \n",
       " 305  1.000000e-10  \n",
       " ..            ...  \n",
       " 269  4.190000e-11  \n",
       " 88   1.140000e-10  \n",
       " 128  1.350000e-10  \n",
       " 150  6.770000e-11  \n",
       " 272  1.240000e-10  \n",
       " \n",
       " [246 rows x 70 columns],\n",
       "      Num_of_Fixations  Mean_Fixation_Duration  SD_Fixation_Duration  \\\n",
       " 111          0.157025             6104.947368          16404.093650   \n",
       " 208          0.316667             2810.736842           3688.940641   \n",
       " 145          0.438776             1867.930233           2415.526526   \n",
       " 203          0.266667             3389.150000           3877.065022   \n",
       " 78           0.557471             1349.731959           1624.854542   \n",
       " ..                ...                     ...                   ...   \n",
       " 188          0.350993             2434.924528           3351.020171   \n",
       " 71           0.266667             3597.650000           4559.772031   \n",
       " 106          0.387500             1949.161290           5224.796852   \n",
       " 270          0.298851             3019.961538           3281.450074   \n",
       " 102          0.563218             1132.887755           1007.971832   \n",
       " \n",
       "      Skew_Fixation_Duration  Max_Fixation_Duration  First_Fixation_Duration  \\\n",
       " 111                3.284097                  71116                     1666   \n",
       " 208                3.864777                  24384                     2948   \n",
       " 145                3.358285                  14114                     1846   \n",
       " 203                1.464668                  14887                     3617   \n",
       " 78                 3.899621                  11600                     1675   \n",
       " ..                      ...                    ...                      ...   \n",
       " 188                2.493314                  16282                    10707   \n",
       " 71                 2.063140                  19293                     1766   \n",
       " 106                4.754883                  29571                      678   \n",
       " 270                2.756763                  19380                    19380   \n",
       " 102                2.660838                   6501                     1472   \n",
       " \n",
       "      Num_of_Saccade  Mean_Saccade_Duration  SD_Saccade_Duration  \\\n",
       " 111        0.157025             273.684211           531.042063   \n",
       " 208        0.311111             351.464286           399.242144   \n",
       " 145        0.428571             423.023810           547.865636   \n",
       " 203        0.253333             398.105263           766.409587   \n",
       " 78         0.557471             439.865979           479.895662   \n",
       " ..              ...                    ...                  ...   \n",
       " 188        0.350993             413.905660           481.556262   \n",
       " 71         0.253333             179.631579           211.285539   \n",
       " 106        0.387500             632.258065           682.392359   \n",
       " 270        0.293103             332.078431           409.546278   \n",
       " 102        0.563218             639.918367           938.236926   \n",
       " \n",
       "      Skew_Saccade_Duration  ...  Pnn50pnn20     Mean_y        SD  Variance  \\\n",
       " 111               3.167967  ...    0.971223   7.566999  0.376687  0.141893   \n",
       " 208               1.956607  ...    0.903780   9.251482  0.201812  0.040728   \n",
       " 145               2.502783  ...    0.991379  12.351123  0.793334  0.629380   \n",
       " 203               3.305010  ...    0.973684  16.114616  0.273790  0.074961   \n",
       " 78                2.002260  ...    0.915966   6.739538  0.782211  0.611854   \n",
       " ..                     ...  ...         ...        ...       ...       ...   \n",
       " 188               1.667801  ...    0.868421   7.088746  0.132030  0.017432   \n",
       " 71                2.252046  ...    0.971698  25.808836  1.049615  1.101693   \n",
       " 106               1.793059  ...    0.986842  28.074050  1.128630  1.273807   \n",
       " 270               1.738233  ...    0.824176   7.699881  0.484059  0.234313   \n",
       " 102               2.735721  ...    0.896552  21.704524  0.923396  0.852659   \n",
       " \n",
       "        Minimum    Maximum  Number of Peaks  Number of Valleys         Ratio  \\\n",
       " 111   6.884995   8.317933         0.000041           0.000041  1.910000e-10   \n",
       " 208   8.893362   9.742991         0.000033           0.000030  9.110000e-11   \n",
       " 145  11.456935  15.096479         0.000031           0.000031  1.610000e-10   \n",
       " 203  15.811350  16.946985         0.000021           0.000014  1.520000e-10   \n",
       " 78    5.924464   9.682770         0.000009           0.000006  2.540000e-11   \n",
       " ..         ...        ...              ...                ...           ...   \n",
       " 188   6.901923   7.655449         0.000047           0.000047  1.570000e-10   \n",
       " 71   24.506307  29.432470         0.000050           0.000050  3.540000e-10   \n",
       " 106  25.832931  29.816504         0.000054           0.000048  3.690000e-10   \n",
       " 270   6.989363   9.002745         0.000038           0.000038  1.100000e-10   \n",
       " 102  20.327319  24.584369         0.000035           0.000032  1.020000e-10   \n",
       " \n",
       "      Quad_Cat  \n",
       " 111         3  \n",
       " 208         1  \n",
       " 145         0  \n",
       " 203         3  \n",
       " 78          2  \n",
       " ..        ...  \n",
       " 188         0  \n",
       " 71          3  \n",
       " 106         3  \n",
       " 270         2  \n",
       " 102         2  \n",
       " \n",
       " [249 rows x 76 columns])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc.setup(train, target = \"Quad_Cat\", train_size=0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
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       "}\n",
       "#T_70aaf_row1_col8 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_70aaf\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_70aaf_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_70aaf_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_70aaf_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_70aaf_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_70aaf_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_70aaf_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_70aaf_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_70aaf_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_70aaf_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row0\" class=\"row_heading level0 row0\" >lightgbm</th>\n",
       "      <td id=\"T_70aaf_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_70aaf_row0_col1\" class=\"data row0 col1\" >0.6870</td>\n",
       "      <td id=\"T_70aaf_row0_col2\" class=\"data row0 col2\" >0.8906</td>\n",
       "      <td id=\"T_70aaf_row0_col3\" class=\"data row0 col3\" >0.6831</td>\n",
       "      <td id=\"T_70aaf_row0_col4\" class=\"data row0 col4\" >0.7029</td>\n",
       "      <td id=\"T_70aaf_row0_col5\" class=\"data row0 col5\" >0.6790</td>\n",
       "      <td id=\"T_70aaf_row0_col6\" class=\"data row0 col6\" >0.5807</td>\n",
       "      <td id=\"T_70aaf_row0_col7\" class=\"data row0 col7\" >0.5882</td>\n",
       "      <td id=\"T_70aaf_row0_col8\" class=\"data row0 col8\" >0.1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
       "      <td id=\"T_70aaf_row1_col0\" class=\"data row1 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_70aaf_row1_col1\" class=\"data row1 col1\" >0.6420</td>\n",
       "      <td id=\"T_70aaf_row1_col2\" class=\"data row1 col2\" >0.0000</td>\n",
       "      <td id=\"T_70aaf_row1_col3\" class=\"data row1 col3\" >0.6381</td>\n",
       "      <td id=\"T_70aaf_row1_col4\" class=\"data row1 col4\" >0.6619</td>\n",
       "      <td id=\"T_70aaf_row1_col5\" class=\"data row1 col5\" >0.6379</td>\n",
       "      <td id=\"T_70aaf_row1_col6\" class=\"data row1 col6\" >0.5210</td>\n",
       "      <td id=\"T_70aaf_row1_col7\" class=\"data row1 col7\" >0.5288</td>\n",
       "      <td id=\"T_70aaf_row1_col8\" class=\"data row1 col8\" >0.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row2\" class=\"row_heading level0 row2\" >lda</th>\n",
       "      <td id=\"T_70aaf_row2_col0\" class=\"data row2 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_70aaf_row2_col1\" class=\"data row2 col1\" >0.6420</td>\n",
       "      <td id=\"T_70aaf_row2_col2\" class=\"data row2 col2\" >0.8753</td>\n",
       "      <td id=\"T_70aaf_row2_col3\" class=\"data row2 col3\" >0.6373</td>\n",
       "      <td id=\"T_70aaf_row2_col4\" class=\"data row2 col4\" >0.6702</td>\n",
       "      <td id=\"T_70aaf_row2_col5\" class=\"data row2 col5\" >0.6393</td>\n",
       "      <td id=\"T_70aaf_row2_col6\" class=\"data row2 col6\" >0.5226</td>\n",
       "      <td id=\"T_70aaf_row2_col7\" class=\"data row2 col7\" >0.5305</td>\n",
       "      <td id=\"T_70aaf_row2_col8\" class=\"data row2 col8\" >0.0150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
       "      <td id=\"T_70aaf_row3_col0\" class=\"data row3 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_70aaf_row3_col1\" class=\"data row3 col1\" >0.5683</td>\n",
       "      <td id=\"T_70aaf_row3_col2\" class=\"data row3 col2\" >0.8310</td>\n",
       "      <td id=\"T_70aaf_row3_col3\" class=\"data row3 col3\" >0.5656</td>\n",
       "      <td id=\"T_70aaf_row3_col4\" class=\"data row3 col4\" >0.6040</td>\n",
       "      <td id=\"T_70aaf_row3_col5\" class=\"data row3 col5\" >0.5639</td>\n",
       "      <td id=\"T_70aaf_row3_col6\" class=\"data row3 col6\" >0.4234</td>\n",
       "      <td id=\"T_70aaf_row3_col7\" class=\"data row3 col7\" >0.4337</td>\n",
       "      <td id=\"T_70aaf_row3_col8\" class=\"data row3 col8\" >0.1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row4\" class=\"row_heading level0 row4\" >gbc</th>\n",
       "      <td id=\"T_70aaf_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_70aaf_row4_col1\" class=\"data row4 col1\" >0.5650</td>\n",
       "      <td id=\"T_70aaf_row4_col2\" class=\"data row4 col2\" >0.8319</td>\n",
       "      <td id=\"T_70aaf_row4_col3\" class=\"data row4 col3\" >0.5586</td>\n",
       "      <td id=\"T_70aaf_row4_col4\" class=\"data row4 col4\" >0.5855</td>\n",
       "      <td id=\"T_70aaf_row4_col5\" class=\"data row4 col5\" >0.5513</td>\n",
       "      <td id=\"T_70aaf_row4_col6\" class=\"data row4 col6\" >0.4179</td>\n",
       "      <td id=\"T_70aaf_row4_col7\" class=\"data row4 col7\" >0.4287</td>\n",
       "      <td id=\"T_70aaf_row4_col8\" class=\"data row4 col8\" >0.5890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row5\" class=\"row_heading level0 row5\" >et</th>\n",
       "      <td id=\"T_70aaf_row5_col0\" class=\"data row5 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_70aaf_row5_col1\" class=\"data row5 col1\" >0.5650</td>\n",
       "      <td id=\"T_70aaf_row5_col2\" class=\"data row5 col2\" >0.8254</td>\n",
       "      <td id=\"T_70aaf_row5_col3\" class=\"data row5 col3\" >0.5625</td>\n",
       "      <td id=\"T_70aaf_row5_col4\" class=\"data row5 col4\" >0.5914</td>\n",
       "      <td id=\"T_70aaf_row5_col5\" class=\"data row5 col5\" >0.5625</td>\n",
       "      <td id=\"T_70aaf_row5_col6\" class=\"data row5 col6\" >0.4181</td>\n",
       "      <td id=\"T_70aaf_row5_col7\" class=\"data row5 col7\" >0.4248</td>\n",
       "      <td id=\"T_70aaf_row5_col8\" class=\"data row5 col8\" >0.1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
       "      <td id=\"T_70aaf_row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_70aaf_row6_col1\" class=\"data row6 col1\" >0.4433</td>\n",
       "      <td id=\"T_70aaf_row6_col2\" class=\"data row6 col2\" >0.7033</td>\n",
       "      <td id=\"T_70aaf_row6_col3\" class=\"data row6 col3\" >0.4381</td>\n",
       "      <td id=\"T_70aaf_row6_col4\" class=\"data row6 col4\" >0.4868</td>\n",
       "      <td id=\"T_70aaf_row6_col5\" class=\"data row6 col5\" >0.4410</td>\n",
       "      <td id=\"T_70aaf_row6_col6\" class=\"data row6 col6\" >0.2578</td>\n",
       "      <td id=\"T_70aaf_row6_col7\" class=\"data row6 col7\" >0.2635</td>\n",
       "      <td id=\"T_70aaf_row6_col8\" class=\"data row6 col8\" >0.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row7\" class=\"row_heading level0 row7\" >dt</th>\n",
       "      <td id=\"T_70aaf_row7_col0\" class=\"data row7 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_70aaf_row7_col1\" class=\"data row7 col1\" >0.4305</td>\n",
       "      <td id=\"T_70aaf_row7_col2\" class=\"data row7 col2\" >0.6209</td>\n",
       "      <td id=\"T_70aaf_row7_col3\" class=\"data row7 col3\" >0.4252</td>\n",
       "      <td id=\"T_70aaf_row7_col4\" class=\"data row7 col4\" >0.4447</td>\n",
       "      <td id=\"T_70aaf_row7_col5\" class=\"data row7 col5\" >0.4244</td>\n",
       "      <td id=\"T_70aaf_row7_col6\" class=\"data row7 col6\" >0.2395</td>\n",
       "      <td id=\"T_70aaf_row7_col7\" class=\"data row7 col7\" >0.2443</td>\n",
       "      <td id=\"T_70aaf_row7_col8\" class=\"data row7 col8\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row8\" class=\"row_heading level0 row8\" >lr</th>\n",
       "      <td id=\"T_70aaf_row8_col0\" class=\"data row8 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_70aaf_row8_col1\" class=\"data row8 col1\" >0.4187</td>\n",
       "      <td id=\"T_70aaf_row8_col2\" class=\"data row8 col2\" >0.6570</td>\n",
       "      <td id=\"T_70aaf_row8_col3\" class=\"data row8 col3\" >0.4188</td>\n",
       "      <td id=\"T_70aaf_row8_col4\" class=\"data row8 col4\" >0.4465</td>\n",
       "      <td id=\"T_70aaf_row8_col5\" class=\"data row8 col5\" >0.4071</td>\n",
       "      <td id=\"T_70aaf_row8_col6\" class=\"data row8 col6\" >0.2216</td>\n",
       "      <td id=\"T_70aaf_row8_col7\" class=\"data row8 col7\" >0.2271</td>\n",
       "      <td id=\"T_70aaf_row8_col8\" class=\"data row8 col8\" >0.6240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row9\" class=\"row_heading level0 row9\" >nb</th>\n",
       "      <td id=\"T_70aaf_row9_col0\" class=\"data row9 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_70aaf_row9_col1\" class=\"data row9 col1\" >0.3867</td>\n",
       "      <td id=\"T_70aaf_row9_col2\" class=\"data row9 col2\" >0.6271</td>\n",
       "      <td id=\"T_70aaf_row9_col3\" class=\"data row9 col3\" >0.3820</td>\n",
       "      <td id=\"T_70aaf_row9_col4\" class=\"data row9 col4\" >0.3410</td>\n",
       "      <td id=\"T_70aaf_row9_col5\" class=\"data row9 col5\" >0.3384</td>\n",
       "      <td id=\"T_70aaf_row9_col6\" class=\"data row9 col6\" >0.1773</td>\n",
       "      <td id=\"T_70aaf_row9_col7\" class=\"data row9 col7\" >0.1924</td>\n",
       "      <td id=\"T_70aaf_row9_col8\" class=\"data row9 col8\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row10\" class=\"row_heading level0 row10\" >qda</th>\n",
       "      <td id=\"T_70aaf_row10_col0\" class=\"data row10 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_70aaf_row10_col1\" class=\"data row10 col1\" >0.3087</td>\n",
       "      <td id=\"T_70aaf_row10_col2\" class=\"data row10 col2\" >0.5471</td>\n",
       "      <td id=\"T_70aaf_row10_col3\" class=\"data row10 col3\" >0.3158</td>\n",
       "      <td id=\"T_70aaf_row10_col4\" class=\"data row10 col4\" >0.3657</td>\n",
       "      <td id=\"T_70aaf_row10_col5\" class=\"data row10 col5\" >0.2886</td>\n",
       "      <td id=\"T_70aaf_row10_col6\" class=\"data row10 col6\" >0.0912</td>\n",
       "      <td id=\"T_70aaf_row10_col7\" class=\"data row10 col7\" >0.1027</td>\n",
       "      <td id=\"T_70aaf_row10_col8\" class=\"data row10 col8\" >0.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
       "      <td id=\"T_70aaf_row11_col0\" class=\"data row11 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_70aaf_row11_col1\" class=\"data row11 col1\" >0.2890</td>\n",
       "      <td id=\"T_70aaf_row11_col2\" class=\"data row11 col2\" >0.5404</td>\n",
       "      <td id=\"T_70aaf_row11_col3\" class=\"data row11 col3\" >0.2860</td>\n",
       "      <td id=\"T_70aaf_row11_col4\" class=\"data row11 col4\" >0.2906</td>\n",
       "      <td id=\"T_70aaf_row11_col5\" class=\"data row11 col5\" >0.2769</td>\n",
       "      <td id=\"T_70aaf_row11_col6\" class=\"data row11 col6\" >0.0545</td>\n",
       "      <td id=\"T_70aaf_row11_col7\" class=\"data row11 col7\" >0.0564</td>\n",
       "      <td id=\"T_70aaf_row11_col8\" class=\"data row11 col8\" >0.0120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row12\" class=\"row_heading level0 row12\" >dummy</th>\n",
       "      <td id=\"T_70aaf_row12_col0\" class=\"data row12 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_70aaf_row12_col1\" class=\"data row12 col1\" >0.2440</td>\n",
       "      <td id=\"T_70aaf_row12_col2\" class=\"data row12 col2\" >0.5000</td>\n",
       "      <td id=\"T_70aaf_row12_col3\" class=\"data row12 col3\" >0.2500</td>\n",
       "      <td id=\"T_70aaf_row12_col4\" class=\"data row12 col4\" >0.0596</td>\n",
       "      <td id=\"T_70aaf_row12_col5\" class=\"data row12 col5\" >0.0957</td>\n",
       "      <td id=\"T_70aaf_row12_col6\" class=\"data row12 col6\" >0.0000</td>\n",
       "      <td id=\"T_70aaf_row12_col7\" class=\"data row12 col7\" >0.0000</td>\n",
       "      <td id=\"T_70aaf_row12_col8\" class=\"data row12 col8\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_70aaf_level0_row13\" class=\"row_heading level0 row13\" >svm</th>\n",
       "      <td id=\"T_70aaf_row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_70aaf_row13_col1\" class=\"data row13 col1\" >0.2435</td>\n",
       "      <td id=\"T_70aaf_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
       "      <td id=\"T_70aaf_row13_col3\" class=\"data row13 col3\" >0.2533</td>\n",
       "      <td id=\"T_70aaf_row13_col4\" class=\"data row13 col4\" >0.1020</td>\n",
       "      <td id=\"T_70aaf_row13_col5\" class=\"data row13 col5\" >0.1231</td>\n",
       "      <td id=\"T_70aaf_row13_col6\" class=\"data row13 col6\" >0.0038</td>\n",
       "      <td id=\"T_70aaf_row13_col7\" class=\"data row13 col7\" >0.0050</td>\n",
       "      <td id=\"T_70aaf_row13_col8\" class=\"data row13 col8\" >0.0100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x137b6b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top5 = pc.compare_models(n_select=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_222bb_row10_col0, #T_222bb_row10_col1, #T_222bb_row10_col2, #T_222bb_row10_col3, #T_222bb_row10_col4, #T_222bb_row10_col5, #T_222bb_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_222bb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_222bb_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_222bb_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_222bb_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_222bb_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_222bb_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_222bb_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_222bb_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_222bb_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_222bb_row0_col0\" class=\"data row0 col0\" >0.5600</td>\n",
       "      <td id=\"T_222bb_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row0_col2\" class=\"data row0 col2\" >0.5750</td>\n",
       "      <td id=\"T_222bb_row0_col3\" class=\"data row0 col3\" >0.5623</td>\n",
       "      <td id=\"T_222bb_row0_col4\" class=\"data row0 col4\" >0.5533</td>\n",
       "      <td id=\"T_222bb_row0_col5\" class=\"data row0 col5\" >0.4149</td>\n",
       "      <td id=\"T_222bb_row0_col6\" class=\"data row0 col6\" >0.4185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_222bb_row1_col0\" class=\"data row1 col0\" >0.6800</td>\n",
       "      <td id=\"T_222bb_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row1_col2\" class=\"data row1 col2\" >0.6655</td>\n",
       "      <td id=\"T_222bb_row1_col3\" class=\"data row1 col3\" >0.7074</td>\n",
       "      <td id=\"T_222bb_row1_col4\" class=\"data row1 col4\" >0.6587</td>\n",
       "      <td id=\"T_222bb_row1_col5\" class=\"data row1 col5\" >0.5662</td>\n",
       "      <td id=\"T_222bb_row1_col6\" class=\"data row1 col6\" >0.5790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_222bb_row2_col0\" class=\"data row2 col0\" >0.6800</td>\n",
       "      <td id=\"T_222bb_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row2_col2\" class=\"data row2 col2\" >0.6845</td>\n",
       "      <td id=\"T_222bb_row2_col3\" class=\"data row2 col3\" >0.7271</td>\n",
       "      <td id=\"T_222bb_row2_col4\" class=\"data row2 col4\" >0.6828</td>\n",
       "      <td id=\"T_222bb_row2_col5\" class=\"data row2 col5\" >0.5754</td>\n",
       "      <td id=\"T_222bb_row2_col6\" class=\"data row2 col6\" >0.5841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_222bb_row3_col0\" class=\"data row3 col0\" >0.8400</td>\n",
       "      <td id=\"T_222bb_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row3_col2\" class=\"data row3 col2\" >0.8333</td>\n",
       "      <td id=\"T_222bb_row3_col3\" class=\"data row3 col3\" >0.8484</td>\n",
       "      <td id=\"T_222bb_row3_col4\" class=\"data row3 col4\" >0.8387</td>\n",
       "      <td id=\"T_222bb_row3_col5\" class=\"data row3 col5\" >0.7859</td>\n",
       "      <td id=\"T_222bb_row3_col6\" class=\"data row3 col6\" >0.7893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_222bb_row4_col0\" class=\"data row4 col0\" >0.6000</td>\n",
       "      <td id=\"T_222bb_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row4_col2\" class=\"data row4 col2\" >0.6012</td>\n",
       "      <td id=\"T_222bb_row4_col3\" class=\"data row4 col3\" >0.6251</td>\n",
       "      <td id=\"T_222bb_row4_col4\" class=\"data row4 col4\" >0.6024</td>\n",
       "      <td id=\"T_222bb_row4_col5\" class=\"data row4 col5\" >0.4681</td>\n",
       "      <td id=\"T_222bb_row4_col6\" class=\"data row4 col6\" >0.4731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_222bb_row5_col0\" class=\"data row5 col0\" >0.6400</td>\n",
       "      <td id=\"T_222bb_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row5_col2\" class=\"data row5 col2\" >0.6369</td>\n",
       "      <td id=\"T_222bb_row5_col3\" class=\"data row5 col3\" >0.6076</td>\n",
       "      <td id=\"T_222bb_row5_col4\" class=\"data row5 col4\" >0.6086</td>\n",
       "      <td id=\"T_222bb_row5_col5\" class=\"data row5 col5\" >0.5172</td>\n",
       "      <td id=\"T_222bb_row5_col6\" class=\"data row5 col6\" >0.5275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_222bb_row6_col0\" class=\"data row6 col0\" >0.6250</td>\n",
       "      <td id=\"T_222bb_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row6_col2\" class=\"data row6 col2\" >0.6119</td>\n",
       "      <td id=\"T_222bb_row6_col3\" class=\"data row6 col3\" >0.6329</td>\n",
       "      <td id=\"T_222bb_row6_col4\" class=\"data row6 col4\" >0.6202</td>\n",
       "      <td id=\"T_222bb_row6_col5\" class=\"data row6 col5\" >0.4953</td>\n",
       "      <td id=\"T_222bb_row6_col6\" class=\"data row6 col6\" >0.4989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_222bb_row7_col0\" class=\"data row7 col0\" >0.7083</td>\n",
       "      <td id=\"T_222bb_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row7_col2\" class=\"data row7 col2\" >0.6893</td>\n",
       "      <td id=\"T_222bb_row7_col3\" class=\"data row7 col3\" >0.7083</td>\n",
       "      <td id=\"T_222bb_row7_col4\" class=\"data row7 col4\" >0.6872</td>\n",
       "      <td id=\"T_222bb_row7_col5\" class=\"data row7 col5\" >0.6056</td>\n",
       "      <td id=\"T_222bb_row7_col6\" class=\"data row7 col6\" >0.6175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_222bb_row8_col0\" class=\"data row8 col0\" >0.4583</td>\n",
       "      <td id=\"T_222bb_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row8_col2\" class=\"data row8 col2\" >0.4548</td>\n",
       "      <td id=\"T_222bb_row8_col3\" class=\"data row8 col3\" >0.4550</td>\n",
       "      <td id=\"T_222bb_row8_col4\" class=\"data row8 col4\" >0.4524</td>\n",
       "      <td id=\"T_222bb_row8_col5\" class=\"data row8 col5\" >0.2794</td>\n",
       "      <td id=\"T_222bb_row8_col6\" class=\"data row8 col6\" >0.2814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_222bb_row9_col0\" class=\"data row9 col0\" >0.7083</td>\n",
       "      <td id=\"T_222bb_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row9_col2\" class=\"data row9 col2\" >0.7036</td>\n",
       "      <td id=\"T_222bb_row9_col3\" class=\"data row9 col3\" >0.7479</td>\n",
       "      <td id=\"T_222bb_row9_col4\" class=\"data row9 col4\" >0.7058</td>\n",
       "      <td id=\"T_222bb_row9_col5\" class=\"data row9 col5\" >0.6102</td>\n",
       "      <td id=\"T_222bb_row9_col6\" class=\"data row9 col6\" >0.6264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_222bb_row10_col0\" class=\"data row10 col0\" >0.6500</td>\n",
       "      <td id=\"T_222bb_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row10_col2\" class=\"data row10 col2\" >0.6456</td>\n",
       "      <td id=\"T_222bb_row10_col3\" class=\"data row10 col3\" >0.6622</td>\n",
       "      <td id=\"T_222bb_row10_col4\" class=\"data row10 col4\" >0.6410</td>\n",
       "      <td id=\"T_222bb_row10_col5\" class=\"data row10 col5\" >0.5318</td>\n",
       "      <td id=\"T_222bb_row10_col6\" class=\"data row10 col6\" >0.5396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_222bb_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_222bb_row11_col0\" class=\"data row11 col0\" >0.0963</td>\n",
       "      <td id=\"T_222bb_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
       "      <td id=\"T_222bb_row11_col2\" class=\"data row11 col2\" >0.0933</td>\n",
       "      <td id=\"T_222bb_row11_col3\" class=\"data row11 col3\" >0.1042</td>\n",
       "      <td id=\"T_222bb_row11_col4\" class=\"data row11 col4\" >0.0968</td>\n",
       "      <td id=\"T_222bb_row11_col5\" class=\"data row11 col5\" >0.1274</td>\n",
       "      <td id=\"T_222bb_row11_col6\" class=\"data row11 col6\" >0.1292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13497df70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blender = pc.blend_models(top5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_b8498\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b8498_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_b8498_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_b8498_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_b8498_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_b8498_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_b8498_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_b8498_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_b8498_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b8498_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_b8498_row0_col0\" class=\"data row0 col0\" >Voting Classifier</td>\n",
       "      <td id=\"T_b8498_row0_col1\" class=\"data row0 col1\" >0.7143</td>\n",
       "      <td id=\"T_b8498_row0_col2\" class=\"data row0 col2\" >0</td>\n",
       "      <td id=\"T_b8498_row0_col3\" class=\"data row0 col3\" >0.7053</td>\n",
       "      <td id=\"T_b8498_row0_col4\" class=\"data row0 col4\" >0.7357</td>\n",
       "      <td id=\"T_b8498_row0_col5\" class=\"data row0 col5\" >0.7213</td>\n",
       "      <td id=\"T_b8498_row0_col6\" class=\"data row0 col6\" >0.6086</td>\n",
       "      <td id=\"T_b8498_row0_col7\" class=\"data row0 col7\" >0.6107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13858e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = pc.predict_model(blender, data = test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, multilabel_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = preds['Quad_Cat']\n",
    "y_pred = preds['Label']\n",
    "conf = confusion_matrix(y_true, y_pred)\n",
    "multiconf = multilabel_confusion_matrix(y_true, y_pred)\n",
    "# HA HV, LA HV, LA LV, HA LV\n",
    "# e.g. True 6 were LA HV and predicted HA HV -> Didnt predict this arousal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  0,  4,  1],\n",
       "       [ 1, 18,  4,  0],\n",
       "       [ 1,  5,  7,  0],\n",
       "       [ 0,  1,  1, 10]])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eye tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = np.load(('04_Eye-Tracking-Data/preprocessed/101_EyeTracking_PreProcessed.dat'), allow_pickle=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = np.load(('05_ECG-GSR-Data/01 ECG-GSR Data (Pre-Processed)/101_ECG_GSR_PreProcessed.dat'), allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Labels': array([[1.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [3.],\n",
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       "        [2.],\n",
       "        [2.],\n",
       "        [0.],\n",
       "        [1.],\n",
       "        [3.],\n",
       "        [3.]]),\n",
       " 'Data': [array([[1.50411953e+12, 1.50411953e+12, 1.50411953e+12, ...,\n",
       "          1.50411971e+12, 1.50411971e+12, 1.50411971e+12],\n",
       "         [1.88055790e+02, 1.80763041e+02, 1.80786310e+02, ...,\n",
       "          2.91429446e+02, 2.91361128e+02, 2.91287595e+02],\n",
       "         [8.76797455e+01, 9.14254262e+01, 9.14155081e+01, ...,\n",
       "          7.67016716e+01, 7.68751151e+01, 7.70447519e+01],\n",
       "         ...,\n",
       "         [1.89871401e+02, 1.82091391e+02, 1.82118575e+02, ...,\n",
       "          2.91394500e+02, 2.91332822e+02, 2.91264055e+02],\n",
       "         [8.77176552e+01, 9.12346093e+01, 9.12243324e+01, ...,\n",
       "          7.61893631e+01, 7.63615894e+01, 7.65420271e+01],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411484e+12, 1.50411484e+12, 1.50411484e+12, ...,\n",
       "          1.50411494e+12, 1.50411494e+12, 1.50411494e+12],\n",
       "         [1.83641534e+02, 1.98650848e+02, 1.98494941e+02, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "         [8.85112126e+01, 9.65558716e+01, 9.65827373e+01, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "         ...,\n",
       "         [1.85586124e+02, 2.01632080e+02, 2.01653318e+02, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "         [8.85116295e+01, 9.67989938e+01, 9.67748499e+01, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]),\n",
       "  array([[1.50411537e+12, 1.50411537e+12, 1.50411537e+12, ...,\n",
       "          1.50411545e+12, 1.50411545e+12, 1.50411545e+12],\n",
       "         [1.84939843e+02, 2.11602978e+02, 2.11623823e+02, ...,\n",
       "          2.50781928e+02, 2.50697333e+02, 2.50690485e+02],\n",
       "         [8.85219713e+01, 9.83440417e+01, 9.85515671e+01, ...,\n",
       "          8.92901892e+01, 8.93066460e+01, 8.93249338e+01],\n",
       "         ...,\n",
       "         [1.86841714e+02, 2.12529551e+02, 2.12657144e+02, ...,\n",
       "          2.50058116e+02, 2.50050754e+02, 2.50061554e+02],\n",
       "         [8.85315615e+01, 9.87301478e+01, 9.89615272e+01, ...,\n",
       "          8.85260140e+01, 8.85664931e+01, 8.85846089e+01],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411718e+12, 1.50411718e+12, 1.50411718e+12, ...,\n",
       "          1.50411730e+12, 1.50411730e+12, 1.50411730e+12],\n",
       "         [1.79348566e+02, 1.68634680e+02, 1.71539753e+02, ...,\n",
       "          1.75849651e+02, 1.75882787e+02, 1.75859906e+02],\n",
       "         [9.29870973e+01, 6.84716145e+01, 6.86106255e+01, ...,\n",
       "          8.01469303e+01, 8.01279485e+01, 8.01230820e+01],\n",
       "         ...,\n",
       "         [1.81464607e+02, 1.71095128e+02, 1.73736463e+02, ...,\n",
       "          1.75817578e+02, 1.75868080e+02, 1.75806797e+02],\n",
       "         [9.29794792e+01, 6.83826910e+01, 6.84997442e+01, ...,\n",
       "          7.99617570e+01, 7.99744941e+01, 7.99457478e+01],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411864e+12, 1.50411864e+12, 1.50411864e+12, ...,\n",
       "          1.50411882e+12, 1.50411882e+12, 1.50411882e+12],\n",
       "         [1.85432694e+02, 1.66923324e+02, 1.66728110e+02, ...,\n",
       "          2.44252280e+02, 2.44248623e+02, 2.44228739e+02],\n",
       "         [8.85444679e+01, 9.76327050e+01, 9.75502536e+01, ...,\n",
       "          8.27962520e+01, 8.27771235e+01, 8.28084062e+01],\n",
       "         ...,\n",
       "         [1.87321061e+02, 1.67972930e+02, 1.67775079e+02, ...,\n",
       "          2.43931469e+02, 2.43933548e+02, 2.43983266e+02],\n",
       "         [8.85552545e+01, 9.79351295e+01, 9.78543578e+01, ...,\n",
       "          8.27263535e+01, 8.27204431e+01, 8.27740240e+01],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411407e+12, 1.50411407e+12, 1.50411407e+12, ...,\n",
       "          1.50411424e+12, 1.50411424e+12, 1.50411424e+12],\n",
       "         [1.85247777e+02, 1.70062495e+02, 1.67925434e+02, ...,\n",
       "          2.47529349e+02, 2.47514075e+02, 2.47532554e+02],\n",
       "         [8.85237243e+01, 9.63790301e+01, 9.66424484e+01, ...,\n",
       "          8.91980101e+01, 8.91590214e+01, 8.91301950e+01],\n",
       "         ...,\n",
       "         [1.87146470e+02, 1.70607696e+02, 1.68131299e+02, ...,\n",
       "          2.47917164e+02, 2.47897396e+02, 2.47911732e+02],\n",
       "         [8.85328219e+01, 9.68507924e+01, 9.74704699e+01, ...,\n",
       "          8.90954687e+01, 8.90712417e+01, 8.90382332e+01],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411245e+12, 1.50411245e+12, 1.50411245e+12, ...,\n",
       "          1.50411262e+12, 1.50411262e+12, 1.50411262e+12],\n",
       "         [0.00000000e+00, 1.98332694e+02, 1.95162379e+02, ...,\n",
       "          2.59651456e+02, 2.59698686e+02, 2.59663089e+02],\n",
       "         [0.00000000e+00, 9.51856722e+01, 9.60703620e+01, ...,\n",
       "          6.97045036e+01, 6.96746941e+01, 6.96868294e+01],\n",
       "         ...,\n",
       "         [0.00000000e+00, 1.94212699e+02, 1.96293592e+02, ...,\n",
       "          2.58562184e+02, 2.58666255e+02, 2.58806810e+02],\n",
       "         [0.00000000e+00, 9.67897836e+01, 9.59112242e+01, ...,\n",
       "          6.98442373e+01, 6.98183671e+01, 6.98055864e+01],\n",
       "         [1.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411347e+12, 1.50411347e+12, 1.50411347e+12, ...,\n",
       "          1.50411358e+12, 1.50411358e+12, 1.50411358e+12],\n",
       "         [1.83782885e+02, 1.81381674e+02, 1.81573099e+02, ...,\n",
       "          2.00452191e+02, 2.00498340e+02, 2.00515175e+02],\n",
       "         [8.86170127e+01, 8.89055391e+01, 8.88858630e+01, ...,\n",
       "          8.40489912e+01, 8.40615634e+01, 8.40604658e+01],\n",
       "         ...,\n",
       "         [1.85720701e+02, 1.82679259e+02, 1.82834995e+02, ...,\n",
       "          2.00067548e+02, 2.00056134e+02, 2.00072716e+02],\n",
       "         [8.86173304e+01, 8.81867486e+01, 8.81568449e+01, ...,\n",
       "          8.41982566e+01, 8.42035081e+01, 8.42023987e+01],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411594e+12, 1.50411594e+12, 1.50411594e+12, ...,\n",
       "          1.50411610e+12, 1.50411610e+12, 1.50411610e+12],\n",
       "         [0.00000000e+00, 1.78530413e+02, 1.77749233e+02, ...,\n",
       "          1.77955970e+02, 1.77859140e+02, 1.77880077e+02],\n",
       "         [0.00000000e+00, 9.18873498e+01, 9.20574969e+01, ...,\n",
       "          8.23676431e+01, 8.23702565e+01, 8.23749899e+01],\n",
       "         ...,\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          1.73611683e+02, 1.73571281e+02, 1.73559638e+02],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          8.26353261e+01, 8.26419929e+01, 8.26528371e+01],\n",
       "         [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411815e+12, 1.50411815e+12, 1.50411815e+12, ...,\n",
       "          1.50411831e+12, 1.50411831e+12, 1.50411831e+12],\n",
       "         [1.85375375e+02, 1.83458512e+02, 1.83312145e+02, ...,\n",
       "          2.28401263e+02, 2.28843979e+02, 2.29338643e+02],\n",
       "         [8.85529608e+01, 9.84053761e+01, 9.85564596e+01, ...,\n",
       "          8.00838462e+01, 8.00191947e+01, 7.99677822e+01],\n",
       "         ...,\n",
       "         [1.87271116e+02, 1.85473305e+02, 1.85508095e+02, ...,\n",
       "          2.27911322e+02, 2.28356810e+02, 2.28709322e+02],\n",
       "         [8.85617722e+01, 9.83434543e+01, 9.84410083e+01, ...,\n",
       "          8.01094895e+01, 8.00421274e+01, 7.99805924e+01],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411657e+12, 1.50411657e+12, 1.50411657e+12, ...,\n",
       "          1.50411665e+12, 1.50411665e+12, 1.50411665e+12],\n",
       "         [1.85099264e+02, 1.84818989e+02, 1.84881152e+02, ...,\n",
       "          1.49584656e+02, 1.49305904e+02, 1.49207065e+02],\n",
       "         [8.85118256e+01, 9.68704068e+01, 9.69199717e+01, ...,\n",
       "          9.40991218e+01, 9.42114618e+01, 9.42739123e+01],\n",
       "         ...,\n",
       "         [1.86999575e+02, 1.86196285e+02, 1.86267109e+02, ...,\n",
       "          1.53688025e+02, 1.53754674e+02, 1.53727872e+02],\n",
       "         [8.85213645e+01, 9.70575656e+01, 9.71071461e+01, ...,\n",
       "          9.50252432e+01, 9.50912571e+01, 9.51164312e+01],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]),\n",
       "  array([[1.50411772e+12, 1.50411772e+12, 1.50411772e+12, ...,\n",
       "          1.50411780e+12, 1.50411780e+12, 1.50411780e+12],\n",
       "         [1.94578654e+02, 1.64204257e+02, 1.64067545e+02, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "         [8.70278139e+01, 9.99334786e+01, 9.99438580e+01, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "         ...,\n",
       "         [1.96073466e+02, 1.66102773e+02, 1.65965918e+02, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "         [8.71021995e+01, 9.99322838e+01, 9.99429127e+01, ...,\n",
       "          0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "         [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "          1.00000000e+00, 1.00000000e+00, 1.00000000e+00]])]}"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat=list(zip(dat['Labels'], dat['Data']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id = get_id_from_path('05_ECG-GSR-Data/01 ECG-GSR Data (Pre-Processed)/101_ECG_GSR_PreProcessed.dat')\n",
    "id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing one .dat file\n",
    "for exp_id, entry in enumerate(dat):\n",
    "    label = entry[0]\n",
    "    ecg = entry[1][:,0]\n",
    "    gsr = entry[1][:,1]\n",
    "    print(label, ecg)\n",
    "    plt.plot(ecg)\n",
    "    break\n",
    "    #print(label, id, exp_id, compute_gsr_stats(gsr, sampling_rate=2000))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.50411953e+12 1.50411953e+12 1.50411953e+12 ... 1.50411971e+12\n",
      "  1.50411971e+12 1.50411971e+12]\n",
      " [1.88055790e+02 1.80763041e+02 1.80786310e+02 ... 2.91429446e+02\n",
      "  2.91361128e+02 2.91287595e+02]\n",
      " [8.76797455e+01 9.14254262e+01 9.14155081e+01 ... 7.67016716e+01\n",
      "  7.68751151e+01 7.70447519e+01]\n",
      " ...\n",
      " [1.89871401e+02 1.82091391e+02 1.82118575e+02 ... 2.91394500e+02\n",
      "  2.91332822e+02 2.91264055e+02]\n",
      " [8.77176552e+01 9.12346093e+01 9.12243324e+01 ... 7.61893631e+01\n",
      "  7.63615894e+01 7.65420271e+01]\n",
      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "for exp_id, entry in enumerate(dat):\n",
    "    label = entry[0]\n",
    "    print(entry[1])\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_gsr_stats(arr, sampling_rate):\n",
    "    mean = np.mean(arr)\n",
    "    std = np.std(arr)\n",
    "    var = np.var(arr)\n",
    "    min = np.min(arr)\n",
    "    max = np.max(arr)\n",
    "    duration = int(len(arr) / sampling_rate)\n",
    "    peaks, _ = find_peaks(arr) # All local maxima \n",
    "    valleys, _ = find_peaks(1/arr) # All local minima\n",
    "    peaks_per_second = len(peaks)/duration # Number of local maxima in one second \n",
    "    valleys_per_second = len(valleys)/duration # Number of local minima in one second \n",
    "    return duration, np.array([mean, std, var, min, max, peaks_per_second, valleys_per_second])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_ecg_stats(arr, sampling_rate):\n",
    "    mean_amplitude = np.mean(arr)\n",
    "    min = np.min(arr)\n",
    "    max = np.max(arr)\n",
    "    mean_rr\n",
    "    median_rr\n",
    "    min_rr\n",
    "    max_rr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dat['Data'][5])\n",
    "df['ID'] = 101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
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       "      <th>11985</th>\n",
       "      <th>11986</th>\n",
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       "      <th>11990</th>\n",
       "      <th>11991</th>\n",
       "      <th>11992</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>1.504114e+12</td>\n",
       "      <td>101</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.852478e+02</td>\n",
       "      <td>1.700625e+02</td>\n",
       "      <td>1.679254e+02</td>\n",
       "      <td>1.684343e+02</td>\n",
       "      <td>1.683247e+02</td>\n",
       "      <td>1.677700e+02</td>\n",
       "      <td>1.676353e+02</td>\n",
       "      <td>1.675656e+02</td>\n",
       "      <td>1.674539e+02</td>\n",
       "      <td>1.674454e+02</td>\n",
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       "      <td>2.476302e+02</td>\n",
       "      <td>2.476510e+02</td>\n",
       "      <td>2.476440e+02</td>\n",
       "      <td>2.476107e+02</td>\n",
       "      <td>2.475819e+02</td>\n",
       "      <td>2.475293e+02</td>\n",
       "      <td>2.475141e+02</td>\n",
       "      <td>2.475326e+02</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.852372e+01</td>\n",
       "      <td>9.637903e+01</td>\n",
       "      <td>9.664245e+01</td>\n",
       "      <td>9.696890e+01</td>\n",
       "      <td>9.713634e+01</td>\n",
       "      <td>9.725888e+01</td>\n",
       "      <td>9.732244e+01</td>\n",
       "      <td>9.737534e+01</td>\n",
       "      <td>9.742540e+01</td>\n",
       "      <td>9.739308e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.928507e+01</td>\n",
       "      <td>8.921934e+01</td>\n",
       "      <td>8.920180e+01</td>\n",
       "      <td>8.916994e+01</td>\n",
       "      <td>8.919552e+01</td>\n",
       "      <td>8.920529e+01</td>\n",
       "      <td>8.919801e+01</td>\n",
       "      <td>8.915902e+01</td>\n",
       "      <td>8.913019e+01</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.871465e+02</td>\n",
       "      <td>1.706077e+02</td>\n",
       "      <td>1.681313e+02</td>\n",
       "      <td>1.685476e+02</td>\n",
       "      <td>1.684336e+02</td>\n",
       "      <td>1.681568e+02</td>\n",
       "      <td>1.682263e+02</td>\n",
       "      <td>1.682537e+02</td>\n",
       "      <td>1.681818e+02</td>\n",
       "      <td>1.682268e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>2.479917e+02</td>\n",
       "      <td>2.479944e+02</td>\n",
       "      <td>2.479828e+02</td>\n",
       "      <td>2.479829e+02</td>\n",
       "      <td>2.479952e+02</td>\n",
       "      <td>2.479425e+02</td>\n",
       "      <td>2.479172e+02</td>\n",
       "      <td>2.478974e+02</td>\n",
       "      <td>2.479117e+02</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.853282e+01</td>\n",
       "      <td>9.685079e+01</td>\n",
       "      <td>9.747047e+01</td>\n",
       "      <td>9.772723e+01</td>\n",
       "      <td>9.774976e+01</td>\n",
       "      <td>9.796170e+01</td>\n",
       "      <td>9.790539e+01</td>\n",
       "      <td>9.792452e+01</td>\n",
       "      <td>9.790239e+01</td>\n",
       "      <td>9.789346e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>8.918468e+01</td>\n",
       "      <td>8.912301e+01</td>\n",
       "      <td>8.909399e+01</td>\n",
       "      <td>8.907644e+01</td>\n",
       "      <td>8.909401e+01</td>\n",
       "      <td>8.910110e+01</td>\n",
       "      <td>8.909547e+01</td>\n",
       "      <td>8.907124e+01</td>\n",
       "      <td>8.903823e+01</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>101</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 11994 columns</p>\n",
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       "              0             1             2             3             4  \\\n",
       "0  1.504114e+12  1.504114e+12  1.504114e+12  1.504114e+12  1.504114e+12   \n",
       "1  1.852478e+02  1.700625e+02  1.679254e+02  1.684343e+02  1.683247e+02   \n",
       "2  8.852372e+01  9.637903e+01  9.664245e+01  9.696890e+01  9.713634e+01   \n",
       "3  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "4  1.871465e+02  1.706077e+02  1.681313e+02  1.685476e+02  1.684336e+02   \n",
       "5  8.853282e+01  9.685079e+01  9.747047e+01  9.772723e+01  9.774976e+01   \n",
       "6  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "              5             6             7             8             9  ...  \\\n",
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       "1  1.677700e+02  1.676353e+02  1.675656e+02  1.674539e+02  1.674454e+02  ...   \n",
       "2  9.725888e+01  9.732244e+01  9.737534e+01  9.742540e+01  9.739308e+01  ...   \n",
       "3  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  ...   \n",
       "4  1.681568e+02  1.682263e+02  1.682537e+02  1.681818e+02  1.682268e+02  ...   \n",
       "5  9.796170e+01  9.790539e+01  9.792452e+01  9.790239e+01  9.789346e+01  ...   \n",
       "6  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  ...   \n",
       "\n",
       "          11984         11985         11986         11987         11988  \\\n",
       "0  1.504114e+12  1.504114e+12  1.504114e+12  1.504114e+12  1.504114e+12   \n",
       "1  2.476668e+02  2.476302e+02  2.476510e+02  2.476440e+02  2.476107e+02   \n",
       "2  8.928507e+01  8.921934e+01  8.920180e+01  8.916994e+01  8.919552e+01   \n",
       "3  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "4  2.479917e+02  2.479944e+02  2.479828e+02  2.479829e+02  2.479952e+02   \n",
       "5  8.918468e+01  8.912301e+01  8.909399e+01  8.907644e+01  8.909401e+01   \n",
       "6  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "          11989         11990         11991         11992   ID  \n",
       "0  1.504114e+12  1.504114e+12  1.504114e+12  1.504114e+12  101  \n",
       "1  2.475819e+02  2.475293e+02  2.475141e+02  2.475326e+02  101  \n",
       "2  8.920529e+01  8.919801e+01  8.915902e+01  8.913019e+01  101  \n",
       "3  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  101  \n",
       "4  2.479425e+02  2.479172e+02  2.478974e+02  2.479117e+02  101  \n",
       "5  8.910110e+01  8.909547e+01  8.907124e+01  8.903823e+01  101  \n",
       "6  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  101  \n",
       "\n",
       "[7 rows x 11994 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_data_to_df(dir):\n",
    "    for dat in os.listdir(dir):\n",
    "        filepath = os.path.join(dir, dat)\n",
    "        file = np.load(filepath, allow_pickle=True) \n",
    "        id = get_id_from_path(filepath)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_id_from_path(path):\n",
    "    return int(path.split('/')[-1].split('_')[0])"
   ]
  }
 ],
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