02a - ML Classifiers_Chest_Device.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
    "from sklearn.model_selection import cross_validate, LeaveOneGroupOut\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import sklearn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Subject Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_PATH = 'segmented_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subjects_data = [pd.read_feather(os.path.join(BASE_PATH, subject)) for subject in os.listdir(BASE_PATH)]\n",
    "# df = pd.concat(subjects_data)\n",
    "# df.drop(columns=['index'], inplace=True)\n",
    "# df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv('segmented_data/all_subjects.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(f'{BASE_PATH}/all_subjects.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121813, 79)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACC_x_absint</th>\n",
       "      <th>ACC_x_mean</th>\n",
       "      <th>ACC_x_peak</th>\n",
       "      <th>ACC_x_std</th>\n",
       "      <th>ACC_xyz_absint</th>\n",
       "      <th>ACC_xyz_std</th>\n",
       "      <th>ACC_xzy_mean</th>\n",
       "      <th>ACC_y_absint</th>\n",
       "      <th>ACC_y_mean</th>\n",
       "      <th>ACC_y_peak</th>\n",
       "      <th>...</th>\n",
       "      <th>RESP_stretch</th>\n",
       "      <th>RESP_volume</th>\n",
       "      <th>TEMP_drange</th>\n",
       "      <th>TEMP_max</th>\n",
       "      <th>TEMP_mean</th>\n",
       "      <th>TEMP_min</th>\n",
       "      <th>TEMP_slope</th>\n",
       "      <th>TEMP_std</th>\n",
       "      <th>label</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33018.685611</td>\n",
       "      <td>0.786178</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.030389</td>\n",
       "      <td>13233.532886</td>\n",
       "      <td>21266.015893</td>\n",
       "      <td>6736.963395</td>\n",
       "      <td>6256.039785</td>\n",
       "      <td>0.148957</td>\n",
       "      <td>0.2504</td>\n",
       "      <td>...</td>\n",
       "      <td>10.783828</td>\n",
       "      <td>125.194862</td>\n",
       "      <td>1.010598</td>\n",
       "      <td>35.171936</td>\n",
       "      <td>34.988750</td>\n",
       "      <td>34.803101</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.054536</td>\n",
       "      <td>3</td>\n",
       "      <td>S8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33012.188012</td>\n",
       "      <td>0.786023</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.030383</td>\n",
       "      <td>13228.857486</td>\n",
       "      <td>21267.760722</td>\n",
       "      <td>6732.487596</td>\n",
       "      <td>6260.111985</td>\n",
       "      <td>0.149054</td>\n",
       "      <td>0.2504</td>\n",
       "      <td>...</td>\n",
       "      <td>10.783828</td>\n",
       "      <td>125.194862</td>\n",
       "      <td>1.010598</td>\n",
       "      <td>35.171936</td>\n",
       "      <td>34.988785</td>\n",
       "      <td>34.803101</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.054524</td>\n",
       "      <td>3</td>\n",
       "      <td>S8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33006.168612</td>\n",
       "      <td>0.785880</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.030367</td>\n",
       "      <td>13225.533786</td>\n",
       "      <td>21268.840449</td>\n",
       "      <td>6728.895262</td>\n",
       "      <td>6264.232385</td>\n",
       "      <td>0.149152</td>\n",
       "      <td>0.2504</td>\n",
       "      <td>...</td>\n",
       "      <td>10.783828</td>\n",
       "      <td>125.194862</td>\n",
       "      <td>1.010598</td>\n",
       "      <td>35.171936</td>\n",
       "      <td>34.988869</td>\n",
       "      <td>34.803101</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.054485</td>\n",
       "      <td>3</td>\n",
       "      <td>S8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33005.078112</td>\n",
       "      <td>0.785855</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.030379</td>\n",
       "      <td>13228.145587</td>\n",
       "      <td>21269.264878</td>\n",
       "      <td>6729.222863</td>\n",
       "      <td>6268.463386</td>\n",
       "      <td>0.149253</td>\n",
       "      <td>0.2504</td>\n",
       "      <td>...</td>\n",
       "      <td>10.783828</td>\n",
       "      <td>125.194862</td>\n",
       "      <td>1.010598</td>\n",
       "      <td>35.171936</td>\n",
       "      <td>34.988983</td>\n",
       "      <td>34.803101</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.054432</td>\n",
       "      <td>3</td>\n",
       "      <td>S8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33008.020712</td>\n",
       "      <td>0.785926</td>\n",
       "      <td>0.9390</td>\n",
       "      <td>0.030564</td>\n",
       "      <td>13234.658287</td>\n",
       "      <td>21268.410145</td>\n",
       "      <td>6732.741796</td>\n",
       "      <td>6270.908585</td>\n",
       "      <td>0.149312</td>\n",
       "      <td>0.2504</td>\n",
       "      <td>...</td>\n",
       "      <td>10.783828</td>\n",
       "      <td>119.178477</td>\n",
       "      <td>1.010598</td>\n",
       "      <td>35.171936</td>\n",
       "      <td>34.989079</td>\n",
       "      <td>34.803101</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.054387</td>\n",
       "      <td>3</td>\n",
       "      <td>S8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACC_x_absint  ACC_x_mean  ACC_x_peak  ACC_x_std  ACC_xyz_absint  \\\n",
       "0  33018.685611    0.786178      0.8766   0.030389    13233.532886   \n",
       "1  33012.188012    0.786023      0.8766   0.030383    13228.857486   \n",
       "2  33006.168612    0.785880      0.8766   0.030367    13225.533786   \n",
       "3  33005.078112    0.785855      0.8766   0.030379    13228.145587   \n",
       "4  33008.020712    0.785926      0.9390   0.030564    13234.658287   \n",
       "\n",
       "    ACC_xyz_std  ACC_xzy_mean  ACC_y_absint  ACC_y_mean  ACC_y_peak  ...  \\\n",
       "0  21266.015893   6736.963395   6256.039785    0.148957      0.2504  ...   \n",
       "1  21267.760722   6732.487596   6260.111985    0.149054      0.2504  ...   \n",
       "2  21268.840449   6728.895262   6264.232385    0.149152      0.2504  ...   \n",
       "3  21269.264878   6729.222863   6268.463386    0.149253      0.2504  ...   \n",
       "4  21268.410145   6732.741796   6270.908585    0.149312      0.2504  ...   \n",
       "\n",
       "   RESP_stretch  RESP_volume  TEMP_drange   TEMP_max  TEMP_mean   TEMP_min  \\\n",
       "0     10.783828   125.194862     1.010598  35.171936  34.988750  34.803101   \n",
       "1     10.783828   125.194862     1.010598  35.171936  34.988785  34.803101   \n",
       "2     10.783828   125.194862     1.010598  35.171936  34.988869  34.803101   \n",
       "3     10.783828   125.194862     1.010598  35.171936  34.988983  34.803101   \n",
       "4     10.783828   119.178477     1.010598  35.171936  34.989079  34.803101   \n",
       "\n",
       "   TEMP_slope  TEMP_std  label  subject  \n",
       "0   -0.000002  0.054536      3       S8  \n",
       "1   -0.000002  0.054524      3       S8  \n",
       "2   -0.000002  0.054485      3       S8  \n",
       "3   -0.000002  0.054432      3       S8  \n",
       "4   -0.000002  0.054387      3       S8  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ACC_x_absint', 'ACC_x_mean', 'ACC_x_peak', 'ACC_x_std',\n",
       "       'ACC_xyz_absint', 'ACC_xyz_std', 'ACC_xzy_mean', 'ACC_y_absint',\n",
       "       'ACC_y_mean', 'ACC_y_peak', 'ACC_y_std', 'ACC_z_absint', 'ACC_z_mean',\n",
       "       'ACC_z_peak', 'ACC_z_std', 'ECG_hr_mean', 'ECG_hr_std', 'ECG_hrv_HF',\n",
       "       'ECG_hrv_HFn', 'ECG_hrv_LF', 'ECG_hrv_LFHF_ratio', 'ECG_hrv_LFn',\n",
       "       'ECG_hrv_NN50', 'ECG_hrv_RMS', 'ECG_hrv_TINN', 'ECG_hrv_ULF',\n",
       "       'ECG_hrv_VHF', 'ECG_hrv_f_sum', 'ECG_hrv_pNN50', 'ECG_hrv_rel_HF',\n",
       "       'ECG_hrv_rel_LF', 'ECG_hrv_rel_ULF', 'ECG_hrv_rel_VHF', 'EDA_SCL_mean',\n",
       "       'EDA_SCL_std', 'EDA_SCR_mean', 'EDA_SCR_no', 'EDA_SCR_std',\n",
       "       'EDA_ampl_sum', 'EDA_corr_SCL_t', 'EDA_drange', 'EDA_max', 'EDA_mean',\n",
       "       'EDA_min', 'EDA_scr_area', 'EDA_slope', 'EDA_std', 'EMG_absint',\n",
       "       'EMG_drange', 'EMG_mean', 'EMG_mean_freq', 'EMG_median',\n",
       "       'EMG_median_freq', 'EMG_peak_amp_mean', 'EMG_peak_amp_norm_sum',\n",
       "       'EMG_peak_amp_std', 'EMG_peak_amp_sum', 'EMG_peak_freq', 'EMG_peak_no',\n",
       "       'EMG_perc_10', 'EMG_perc_90', 'EMG_std', 'RESP_breath_rate',\n",
       "       'RESP_duration', 'RESP_exhal_mean', 'RESP_exhal_std', 'RESP_ie_ratio',\n",
       "       'RESP_inhal_mean', 'RESP_inhal_std', 'RESP_stretch', 'RESP_volume',\n",
       "       'TEMP_drange', 'TEMP_max', 'TEMP_mean', 'TEMP_min', 'TEMP_slope',\n",
       "       'TEMP_std', 'label', 'subject'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACC_x_absint</th>\n",
       "      <td>33018.7</td>\n",
       "      <td>33012.2</td>\n",
       "      <td>33006.2</td>\n",
       "      <td>33005.1</td>\n",
       "      <td>33008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_x_mean</th>\n",
       "      <td>0.786178</td>\n",
       "      <td>0.786023</td>\n",
       "      <td>0.78588</td>\n",
       "      <td>0.785855</td>\n",
       "      <td>0.785926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_x_peak</th>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_x_std</th>\n",
       "      <td>0.0303892</td>\n",
       "      <td>0.0303835</td>\n",
       "      <td>0.0303673</td>\n",
       "      <td>0.0303795</td>\n",
       "      <td>0.0305639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_xyz_absint</th>\n",
       "      <td>13233.5</td>\n",
       "      <td>13228.9</td>\n",
       "      <td>13225.5</td>\n",
       "      <td>13228.1</td>\n",
       "      <td>13234.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_xyz_std</th>\n",
       "      <td>21266</td>\n",
       "      <td>21267.8</td>\n",
       "      <td>21268.8</td>\n",
       "      <td>21269.3</td>\n",
       "      <td>21268.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_xzy_mean</th>\n",
       "      <td>6736.96</td>\n",
       "      <td>6732.49</td>\n",
       "      <td>6728.9</td>\n",
       "      <td>6729.22</td>\n",
       "      <td>6732.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_y_absint</th>\n",
       "      <td>6256.04</td>\n",
       "      <td>6260.11</td>\n",
       "      <td>6264.23</td>\n",
       "      <td>6268.46</td>\n",
       "      <td>6270.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_y_mean</th>\n",
       "      <td>0.148957</td>\n",
       "      <td>0.149054</td>\n",
       "      <td>0.149152</td>\n",
       "      <td>0.149253</td>\n",
       "      <td>0.149312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_y_peak</th>\n",
       "      <td>0.2504</td>\n",
       "      <td>0.2504</td>\n",
       "      <td>0.2504</td>\n",
       "      <td>0.2504</td>\n",
       "      <td>0.2504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_y_std</th>\n",
       "      <td>0.0165969</td>\n",
       "      <td>0.0165417</td>\n",
       "      <td>0.0164938</td>\n",
       "      <td>0.0164861</td>\n",
       "      <td>0.0166095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_z_absint</th>\n",
       "      <td>19064.3</td>\n",
       "      <td>19075.3</td>\n",
       "      <td>19084.2</td>\n",
       "      <td>19086.4</td>\n",
       "      <td>19081.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_z_mean</th>\n",
       "      <td>-0.453923</td>\n",
       "      <td>-0.454185</td>\n",
       "      <td>-0.454396</td>\n",
       "      <td>-0.454449</td>\n",
       "      <td>-0.454327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_z_peak</th>\n",
       "      <td>-0.2802</td>\n",
       "      <td>-0.2802</td>\n",
       "      <td>-0.2802</td>\n",
       "      <td>-0.2802</td>\n",
       "      <td>-0.2802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_z_std</th>\n",
       "      <td>0.0546233</td>\n",
       "      <td>0.0546473</td>\n",
       "      <td>0.0546595</td>\n",
       "      <td>0.0546621</td>\n",
       "      <td>0.0548029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hr_mean</th>\n",
       "      <td>95.6814</td>\n",
       "      <td>95.6814</td>\n",
       "      <td>95.4562</td>\n",
       "      <td>95.4562</td>\n",
       "      <td>95.3082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hr_std</th>\n",
       "      <td>13.1067</td>\n",
       "      <td>13.1067</td>\n",
       "      <td>13.0829</td>\n",
       "      <td>13.0829</td>\n",
       "      <td>13.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_HF</th>\n",
       "      <td>3438.78</td>\n",
       "      <td>3438.78</td>\n",
       "      <td>3403.76</td>\n",
       "      <td>3403.76</td>\n",
       "      <td>3333.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_HFn</th>\n",
       "      <td>11.9074</td>\n",
       "      <td>11.9074</td>\n",
       "      <td>11.7633</td>\n",
       "      <td>11.7633</td>\n",
       "      <td>11.5949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_LF</th>\n",
       "      <td>4323.31</td>\n",
       "      <td>4323.31</td>\n",
       "      <td>4345.51</td>\n",
       "      <td>4345.51</td>\n",
       "      <td>4275.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_LFHF_ratio</th>\n",
       "      <td>7.39811</td>\n",
       "      <td>7.39811</td>\n",
       "      <td>7.50098</td>\n",
       "      <td>7.50098</td>\n",
       "      <td>7.62448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_LFn</th>\n",
       "      <td>88.0926</td>\n",
       "      <td>88.0926</td>\n",
       "      <td>88.2367</td>\n",
       "      <td>88.2367</td>\n",
       "      <td>88.4051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_NN50</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_RMS</th>\n",
       "      <td>60.5512</td>\n",
       "      <td>60.5512</td>\n",
       "      <td>60.9725</td>\n",
       "      <td>60.9725</td>\n",
       "      <td>60.6523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_TINN</th>\n",
       "      <td>510</td>\n",
       "      <td>510</td>\n",
       "      <td>510</td>\n",
       "      <td>510</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_ULF</th>\n",
       "      <td>682.533</td>\n",
       "      <td>682.533</td>\n",
       "      <td>727.434</td>\n",
       "      <td>727.434</td>\n",
       "      <td>740.907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_VHF</th>\n",
       "      <td>464.819</td>\n",
       "      <td>464.819</td>\n",
       "      <td>453.775</td>\n",
       "      <td>453.775</td>\n",
       "      <td>437.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_f_sum</th>\n",
       "      <td>8909.44</td>\n",
       "      <td>8909.44</td>\n",
       "      <td>8930.47</td>\n",
       "      <td>8930.47</td>\n",
       "      <td>8786.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_pNN50</th>\n",
       "      <td>37.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>38.0952</td>\n",
       "      <td>38.0952</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_rel_HF</th>\n",
       "      <td>38.597</td>\n",
       "      <td>38.597</td>\n",
       "      <td>38.114</td>\n",
       "      <td>38.114</td>\n",
       "      <td>37.9371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_rel_LF</th>\n",
       "      <td>48.525</td>\n",
       "      <td>48.525</td>\n",
       "      <td>48.6593</td>\n",
       "      <td>48.6593</td>\n",
       "      <td>48.6553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_rel_ULF</th>\n",
       "      <td>7.66078</td>\n",
       "      <td>7.66078</td>\n",
       "      <td>8.14552</td>\n",
       "      <td>8.14552</td>\n",
       "      <td>8.4319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_rel_VHF</th>\n",
       "      <td>5.21715</td>\n",
       "      <td>5.21715</td>\n",
       "      <td>5.0812</td>\n",
       "      <td>5.0812</td>\n",
       "      <td>4.97569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCL_mean</th>\n",
       "      <td>1.3092</td>\n",
       "      <td>1.30695</td>\n",
       "      <td>1.30468</td>\n",
       "      <td>1.30238</td>\n",
       "      <td>1.30006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCL_std</th>\n",
       "      <td>1.3092</td>\n",
       "      <td>1.30695</td>\n",
       "      <td>1.30468</td>\n",
       "      <td>1.30238</td>\n",
       "      <td>1.30006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCR_mean</th>\n",
       "      <td>0.00136043</td>\n",
       "      <td>0.000329373</td>\n",
       "      <td>-0.000563776</td>\n",
       "      <td>-0.00111515</td>\n",
       "      <td>-0.000790051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCR_no</th>\n",
       "      <td>53</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCR_std</th>\n",
       "      <td>0.0719615</td>\n",
       "      <td>0.072439</td>\n",
       "      <td>0.0731532</td>\n",
       "      <td>0.0735171</td>\n",
       "      <td>0.0741443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_ampl_sum</th>\n",
       "      <td>38.0272</td>\n",
       "      <td>24.3994</td>\n",
       "      <td>9.40536</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_corr_SCL_t</th>\n",
       "      <td>-0.9638</td>\n",
       "      <td>-0.962739</td>\n",
       "      <td>-0.961514</td>\n",
       "      <td>-0.960136</td>\n",
       "      <td>-0.958618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_drange</th>\n",
       "      <td>2.38644</td>\n",
       "      <td>2.38644</td>\n",
       "      <td>2.38644</td>\n",
       "      <td>2.38644</td>\n",
       "      <td>2.38644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_max</th>\n",
       "      <td>1.96266</td>\n",
       "      <td>1.96266</td>\n",
       "      <td>1.96266</td>\n",
       "      <td>1.96266</td>\n",
       "      <td>1.96266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_mean</th>\n",
       "      <td>1.31056</td>\n",
       "      <td>1.30728</td>\n",
       "      <td>1.30412</td>\n",
       "      <td>1.30127</td>\n",
       "      <td>1.29927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_min</th>\n",
       "      <td>0.822423</td>\n",
       "      <td>0.822423</td>\n",
       "      <td>0.822423</td>\n",
       "      <td>0.822423</td>\n",
       "      <td>0.822423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_scr_area</th>\n",
       "      <td>0.643946</td>\n",
       "      <td>0.63607</td>\n",
       "      <td>0.635052</td>\n",
       "      <td>0.63368</td>\n",
       "      <td>0.649258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_slope</th>\n",
       "      <td>-2.23147e-05</td>\n",
       "      <td>-2.23061e-05</td>\n",
       "      <td>-2.23127e-05</td>\n",
       "      <td>-2.2286e-05</td>\n",
       "      <td>-2.20705e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_std</th>\n",
       "      <td>0.284947</td>\n",
       "      <td>0.284846</td>\n",
       "      <td>0.284908</td>\n",
       "      <td>0.284703</td>\n",
       "      <td>0.283462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_absint</th>\n",
       "      <td>0.00848961</td>\n",
       "      <td>0.0225237</td>\n",
       "      <td>0.00990211</td>\n",
       "      <td>0.00222574</td>\n",
       "      <td>0.00491277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_drange</th>\n",
       "      <td>-0.696487</td>\n",
       "      <td>-0.696487</td>\n",
       "      <td>-0.696487</td>\n",
       "      <td>-0.696487</td>\n",
       "      <td>-0.696487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_mean</th>\n",
       "      <td>9.46268e-08</td>\n",
       "      <td>-5.51795e-07</td>\n",
       "      <td>-2.15491e-07</td>\n",
       "      <td>4.2745e-08</td>\n",
       "      <td>-2.71029e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_mean_freq</th>\n",
       "      <td>0.19713</td>\n",
       "      <td>0.197005</td>\n",
       "      <td>0.197063</td>\n",
       "      <td>0.197021</td>\n",
       "      <td>0.197171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_median</th>\n",
       "      <td>0.000312396</td>\n",
       "      <td>0.000309889</td>\n",
       "      <td>0.000312938</td>\n",
       "      <td>0.000316162</td>\n",
       "      <td>0.000317644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_median_freq</th>\n",
       "      <td>0.0072619</td>\n",
       "      <td>0.0072619</td>\n",
       "      <td>0.0072619</td>\n",
       "      <td>0.0072619</td>\n",
       "      <td>0.00728571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_amp_mean</th>\n",
       "      <td>0.00388589</td>\n",
       "      <td>0.00389075</td>\n",
       "      <td>0.00388678</td>\n",
       "      <td>0.00387143</td>\n",
       "      <td>0.00388455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_amp_norm_sum</th>\n",
       "      <td>114.664</td>\n",
       "      <td>114.738</td>\n",
       "      <td>114.759</td>\n",
       "      <td>114.444</td>\n",
       "      <td>115.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_amp_std</th>\n",
       "      <td>0.00470975</td>\n",
       "      <td>0.00471004</td>\n",
       "      <td>0.0047147</td>\n",
       "      <td>0.00469738</td>\n",
       "      <td>0.00471994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_amp_sum</th>\n",
       "      <td>6.45835</td>\n",
       "      <td>6.46253</td>\n",
       "      <td>6.46372</td>\n",
       "      <td>6.44593</td>\n",
       "      <td>6.47942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_freq</th>\n",
       "      <td>0.102762</td>\n",
       "      <td>0.107214</td>\n",
       "      <td>0.107214</td>\n",
       "      <td>0.107214</td>\n",
       "      <td>0.107214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_no</th>\n",
       "      <td>1662</td>\n",
       "      <td>1661</td>\n",
       "      <td>1663</td>\n",
       "      <td>1665</td>\n",
       "      <td>1668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_perc_10</th>\n",
       "      <td>-0.00890824</td>\n",
       "      <td>-0.00890614</td>\n",
       "      <td>-0.00891277</td>\n",
       "      <td>-0.00891807</td>\n",
       "      <td>-0.00892934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_perc_90</th>\n",
       "      <td>0.00838726</td>\n",
       "      <td>0.00838751</td>\n",
       "      <td>0.00838751</td>\n",
       "      <td>0.00839636</td>\n",
       "      <td>0.00840537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_std</th>\n",
       "      <td>0.00725867</td>\n",
       "      <td>0.00725618</td>\n",
       "      <td>0.00725731</td>\n",
       "      <td>0.00726457</td>\n",
       "      <td>0.00727645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_breath_rate</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_duration</th>\n",
       "      <td>40107</td>\n",
       "      <td>40282</td>\n",
       "      <td>40457</td>\n",
       "      <td>40632</td>\n",
       "      <td>40807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_exhal_mean</th>\n",
       "      <td>1473.08</td>\n",
       "      <td>1473.08</td>\n",
       "      <td>1473.08</td>\n",
       "      <td>1473.08</td>\n",
       "      <td>1473.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_exhal_std</th>\n",
       "      <td>616.487</td>\n",
       "      <td>616.487</td>\n",
       "      <td>616.487</td>\n",
       "      <td>616.487</td>\n",
       "      <td>616.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_ie_ratio</th>\n",
       "      <td>1.0811</td>\n",
       "      <td>1.07137</td>\n",
       "      <td>1.06182</td>\n",
       "      <td>1.05243</td>\n",
       "      <td>1.04321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_inhal_mean</th>\n",
       "      <td>1488.14</td>\n",
       "      <td>1488.14</td>\n",
       "      <td>1488.14</td>\n",
       "      <td>1488.14</td>\n",
       "      <td>1488.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_inhal_std</th>\n",
       "      <td>510.762</td>\n",
       "      <td>510.762</td>\n",
       "      <td>510.762</td>\n",
       "      <td>510.762</td>\n",
       "      <td>510.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_stretch</th>\n",
       "      <td>10.7838</td>\n",
       "      <td>10.7838</td>\n",
       "      <td>10.7838</td>\n",
       "      <td>10.7838</td>\n",
       "      <td>10.7838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_volume</th>\n",
       "      <td>125.195</td>\n",
       "      <td>125.195</td>\n",
       "      <td>125.195</td>\n",
       "      <td>125.195</td>\n",
       "      <td>119.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_drange</th>\n",
       "      <td>1.0106</td>\n",
       "      <td>1.0106</td>\n",
       "      <td>1.0106</td>\n",
       "      <td>1.0106</td>\n",
       "      <td>1.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_max</th>\n",
       "      <td>35.1719</td>\n",
       "      <td>35.1719</td>\n",
       "      <td>35.1719</td>\n",
       "      <td>35.1719</td>\n",
       "      <td>35.1719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_mean</th>\n",
       "      <td>34.9888</td>\n",
       "      <td>34.9888</td>\n",
       "      <td>34.9889</td>\n",
       "      <td>34.989</td>\n",
       "      <td>34.9891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_min</th>\n",
       "      <td>34.8031</td>\n",
       "      <td>34.8031</td>\n",
       "      <td>34.8031</td>\n",
       "      <td>34.8031</td>\n",
       "      <td>34.8031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_slope</th>\n",
       "      <td>-1.89612e-06</td>\n",
       "      <td>-1.92166e-06</td>\n",
       "      <td>-1.94739e-06</td>\n",
       "      <td>-1.97413e-06</td>\n",
       "      <td>-1.99919e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_std</th>\n",
       "      <td>0.0545356</td>\n",
       "      <td>0.0545242</td>\n",
       "      <td>0.0544855</td>\n",
       "      <td>0.0544315</td>\n",
       "      <td>0.0543866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <td>S8</td>\n",
       "      <td>S8</td>\n",
       "      <td>S8</td>\n",
       "      <td>S8</td>\n",
       "      <td>S8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0            1            2            3  \\\n",
       "ACC_x_absint               33018.7      33012.2      33006.2      33005.1   \n",
       "ACC_x_mean                0.786178     0.786023      0.78588     0.785855   \n",
       "ACC_x_peak                  0.8766       0.8766       0.8766       0.8766   \n",
       "ACC_x_std                0.0303892    0.0303835    0.0303673    0.0303795   \n",
       "ACC_xyz_absint             13233.5      13228.9      13225.5      13228.1   \n",
       "ACC_xyz_std                  21266      21267.8      21268.8      21269.3   \n",
       "ACC_xzy_mean               6736.96      6732.49       6728.9      6729.22   \n",
       "ACC_y_absint               6256.04      6260.11      6264.23      6268.46   \n",
       "ACC_y_mean                0.148957     0.149054     0.149152     0.149253   \n",
       "ACC_y_peak                  0.2504       0.2504       0.2504       0.2504   \n",
       "ACC_y_std                0.0165969    0.0165417    0.0164938    0.0164861   \n",
       "ACC_z_absint               19064.3      19075.3      19084.2      19086.4   \n",
       "ACC_z_mean               -0.453923    -0.454185    -0.454396    -0.454449   \n",
       "ACC_z_peak                 -0.2802      -0.2802      -0.2802      -0.2802   \n",
       "ACC_z_std                0.0546233    0.0546473    0.0546595    0.0546621   \n",
       "ECG_hr_mean                95.6814      95.6814      95.4562      95.4562   \n",
       "ECG_hr_std                 13.1067      13.1067      13.0829      13.0829   \n",
       "ECG_hrv_HF                 3438.78      3438.78      3403.76      3403.76   \n",
       "ECG_hrv_HFn                11.9074      11.9074      11.7633      11.7633   \n",
       "ECG_hrv_LF                 4323.31      4323.31      4345.51      4345.51   \n",
       "ECG_hrv_LFHF_ratio         7.39811      7.39811      7.50098      7.50098   \n",
       "ECG_hrv_LFn                88.0926      88.0926      88.2367      88.2367   \n",
       "ECG_hrv_NN50                    24           24           24           24   \n",
       "ECG_hrv_RMS                60.5512      60.5512      60.9725      60.9725   \n",
       "ECG_hrv_TINN                   510          510          510          510   \n",
       "ECG_hrv_ULF                682.533      682.533      727.434      727.434   \n",
       "ECG_hrv_VHF                464.819      464.819      453.775      453.775   \n",
       "ECG_hrv_f_sum              8909.44      8909.44      8930.47      8930.47   \n",
       "ECG_hrv_pNN50                 37.5         37.5      38.0952      38.0952   \n",
       "ECG_hrv_rel_HF              38.597       38.597       38.114       38.114   \n",
       "ECG_hrv_rel_LF              48.525       48.525      48.6593      48.6593   \n",
       "ECG_hrv_rel_ULF            7.66078      7.66078      8.14552      8.14552   \n",
       "ECG_hrv_rel_VHF            5.21715      5.21715       5.0812       5.0812   \n",
       "EDA_SCL_mean                1.3092      1.30695      1.30468      1.30238   \n",
       "EDA_SCL_std                 1.3092      1.30695      1.30468      1.30238   \n",
       "EDA_SCR_mean            0.00136043  0.000329373 -0.000563776  -0.00111515   \n",
       "EDA_SCR_no                      53           52           52           52   \n",
       "EDA_SCR_std              0.0719615     0.072439    0.0731532    0.0735171   \n",
       "EDA_ampl_sum               38.0272      24.3994      9.40536            0   \n",
       "EDA_corr_SCL_t             -0.9638    -0.962739    -0.961514    -0.960136   \n",
       "EDA_drange                 2.38644      2.38644      2.38644      2.38644   \n",
       "EDA_max                    1.96266      1.96266      1.96266      1.96266   \n",
       "EDA_mean                   1.31056      1.30728      1.30412      1.30127   \n",
       "EDA_min                   0.822423     0.822423     0.822423     0.822423   \n",
       "EDA_scr_area              0.643946      0.63607     0.635052      0.63368   \n",
       "EDA_slope             -2.23147e-05 -2.23061e-05 -2.23127e-05  -2.2286e-05   \n",
       "EDA_std                   0.284947     0.284846     0.284908     0.284703   \n",
       "EMG_absint              0.00848961    0.0225237   0.00990211   0.00222574   \n",
       "EMG_drange               -0.696487    -0.696487    -0.696487    -0.696487   \n",
       "EMG_mean               9.46268e-08 -5.51795e-07 -2.15491e-07   4.2745e-08   \n",
       "EMG_mean_freq              0.19713     0.197005     0.197063     0.197021   \n",
       "EMG_median             0.000312396  0.000309889  0.000312938  0.000316162   \n",
       "EMG_median_freq          0.0072619    0.0072619    0.0072619    0.0072619   \n",
       "EMG_peak_amp_mean       0.00388589   0.00389075   0.00388678   0.00387143   \n",
       "EMG_peak_amp_norm_sum      114.664      114.738      114.759      114.444   \n",
       "EMG_peak_amp_std        0.00470975   0.00471004    0.0047147   0.00469738   \n",
       "EMG_peak_amp_sum           6.45835      6.46253      6.46372      6.44593   \n",
       "EMG_peak_freq             0.102762     0.107214     0.107214     0.107214   \n",
       "EMG_peak_no                   1662         1661         1663         1665   \n",
       "EMG_perc_10            -0.00890824  -0.00890614  -0.00891277  -0.00891807   \n",
       "EMG_perc_90             0.00838726   0.00838751   0.00838751   0.00839636   \n",
       "EMG_std                 0.00725867   0.00725618   0.00725731   0.00726457   \n",
       "RESP_breath_rate                14           14           14           14   \n",
       "RESP_duration                40107        40282        40457        40632   \n",
       "RESP_exhal_mean            1473.08      1473.08      1473.08      1473.08   \n",
       "RESP_exhal_std             616.487      616.487      616.487      616.487   \n",
       "RESP_ie_ratio               1.0811      1.07137      1.06182      1.05243   \n",
       "RESP_inhal_mean            1488.14      1488.14      1488.14      1488.14   \n",
       "RESP_inhal_std             510.762      510.762      510.762      510.762   \n",
       "RESP_stretch               10.7838      10.7838      10.7838      10.7838   \n",
       "RESP_volume                125.195      125.195      125.195      125.195   \n",
       "TEMP_drange                 1.0106       1.0106       1.0106       1.0106   \n",
       "TEMP_max                   35.1719      35.1719      35.1719      35.1719   \n",
       "TEMP_mean                  34.9888      34.9888      34.9889       34.989   \n",
       "TEMP_min                   34.8031      34.8031      34.8031      34.8031   \n",
       "TEMP_slope            -1.89612e-06 -1.92166e-06 -1.94739e-06 -1.97413e-06   \n",
       "TEMP_std                 0.0545356    0.0545242    0.0544855    0.0544315   \n",
       "label                            3            3            3            3   \n",
       "subject                         S8           S8           S8           S8   \n",
       "\n",
       "                                 4  \n",
       "ACC_x_absint                 33008  \n",
       "ACC_x_mean                0.785926  \n",
       "ACC_x_peak                   0.939  \n",
       "ACC_x_std                0.0305639  \n",
       "ACC_xyz_absint             13234.7  \n",
       "ACC_xyz_std                21268.4  \n",
       "ACC_xzy_mean               6732.74  \n",
       "ACC_y_absint               6270.91  \n",
       "ACC_y_mean                0.149312  \n",
       "ACC_y_peak                  0.2504  \n",
       "ACC_y_std                0.0166095  \n",
       "ACC_z_absint               19081.3  \n",
       "ACC_z_mean               -0.454327  \n",
       "ACC_z_peak                 -0.2802  \n",
       "ACC_z_std                0.0548029  \n",
       "ECG_hr_mean                95.3082  \n",
       "ECG_hr_std                  13.035  \n",
       "ECG_hrv_HF                 3333.51  \n",
       "ECG_hrv_HFn                11.5949  \n",
       "ECG_hrv_LF                 4275.32  \n",
       "ECG_hrv_LFHF_ratio         7.62448  \n",
       "ECG_hrv_LFn                88.4051  \n",
       "ECG_hrv_NN50                    24  \n",
       "ECG_hrv_RMS                60.6523  \n",
       "ECG_hrv_TINN                   510  \n",
       "ECG_hrv_ULF                740.907  \n",
       "ECG_hrv_VHF                437.212  \n",
       "ECG_hrv_f_sum              8786.95  \n",
       "ECG_hrv_pNN50                 37.5  \n",
       "ECG_hrv_rel_HF             37.9371  \n",
       "ECG_hrv_rel_LF             48.6553  \n",
       "ECG_hrv_rel_ULF             8.4319  \n",
       "ECG_hrv_rel_VHF            4.97569  \n",
       "EDA_SCL_mean               1.30006  \n",
       "EDA_SCL_std                1.30006  \n",
       "EDA_SCR_mean          -0.000790051  \n",
       "EDA_SCR_no                      51  \n",
       "EDA_SCR_std              0.0741443  \n",
       "EDA_ampl_sum                     0  \n",
       "EDA_corr_SCL_t           -0.958618  \n",
       "EDA_drange                 2.38644  \n",
       "EDA_max                    1.96266  \n",
       "EDA_mean                   1.29927  \n",
       "EDA_min                   0.822423  \n",
       "EDA_scr_area              0.649258  \n",
       "EDA_slope             -2.20705e-05  \n",
       "EDA_std                   0.283462  \n",
       "EMG_absint              0.00491277  \n",
       "EMG_drange               -0.696487  \n",
       "EMG_mean              -2.71029e-08  \n",
       "EMG_mean_freq             0.197171  \n",
       "EMG_median             0.000317644  \n",
       "EMG_median_freq         0.00728571  \n",
       "EMG_peak_amp_mean       0.00388455  \n",
       "EMG_peak_amp_norm_sum      115.038  \n",
       "EMG_peak_amp_std        0.00471994  \n",
       "EMG_peak_amp_sum           6.47942  \n",
       "EMG_peak_freq             0.107214  \n",
       "EMG_peak_no                   1668  \n",
       "EMG_perc_10            -0.00892934  \n",
       "EMG_perc_90             0.00840537  \n",
       "EMG_std                 0.00727645  \n",
       "RESP_breath_rate                14  \n",
       "RESP_duration                40807  \n",
       "RESP_exhal_mean            1473.08  \n",
       "RESP_exhal_std             616.487  \n",
       "RESP_ie_ratio              1.04321  \n",
       "RESP_inhal_mean            1488.14  \n",
       "RESP_inhal_std             510.762  \n",
       "RESP_stretch               10.7838  \n",
       "RESP_volume                119.178  \n",
       "TEMP_drange                 1.0106  \n",
       "TEMP_max                   35.1719  \n",
       "TEMP_mean                  34.9891  \n",
       "TEMP_min                   34.8031  \n",
       "TEMP_slope            -1.99919e-06  \n",
       "TEMP_std                 0.0543866  \n",
       "label                            3  \n",
       "subject                         S8  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context(\"display.max_rows\", df.columns.shape[0]): \n",
    "        display(df.head().T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACC_x_absint</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>3.373182e+04</td>\n",
       "      <td>5.442559e+03</td>\n",
       "      <td>1.115059e+04</td>\n",
       "      <td>3.046235e+04</td>\n",
       "      <td>3.594657e+04</td>\n",
       "      <td>3.770159e+04</td>\n",
       "      <td>38482.839411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_x_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>8.031577e-01</td>\n",
       "      <td>1.295878e-01</td>\n",
       "      <td>2.654967e-01</td>\n",
       "      <td>7.253115e-01</td>\n",
       "      <td>8.558914e-01</td>\n",
       "      <td>8.976773e-01</td>\n",
       "      <td>0.916280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_x_peak</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>9.008852e-01</td>\n",
       "      <td>1.670471e-01</td>\n",
       "      <td>2.905999e-01</td>\n",
       "      <td>8.162000e-01</td>\n",
       "      <td>9.318000e-01</td>\n",
       "      <td>9.940000e-01</td>\n",
       "      <td>1.553400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_x_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.576536e-02</td>\n",
       "      <td>2.159889e-02</td>\n",
       "      <td>2.540918e-03</td>\n",
       "      <td>6.727452e-03</td>\n",
       "      <td>1.019764e-02</td>\n",
       "      <td>1.733832e-02</td>\n",
       "      <td>0.334104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_xyz_absint</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.126388e+04</td>\n",
       "      <td>6.171864e+03</td>\n",
       "      <td>6.333725e-01</td>\n",
       "      <td>6.368842e+03</td>\n",
       "      <td>1.208748e+04</td>\n",
       "      <td>1.549643e+04</td>\n",
       "      <td>31365.086834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_xyz_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.083812e+04</td>\n",
       "      <td>1.923747e+03</td>\n",
       "      <td>1.066439e+04</td>\n",
       "      <td>1.977699e+04</td>\n",
       "      <td>2.109936e+04</td>\n",
       "      <td>2.246112e+04</td>\n",
       "      <td>23359.812435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_xzy_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>5.946100e+03</td>\n",
       "      <td>5.767697e+03</td>\n",
       "      <td>-1.066388e+04</td>\n",
       "      <td>1.433468e+03</td>\n",
       "      <td>7.286843e+03</td>\n",
       "      <td>1.010301e+04</td>\n",
       "      <td>19538.685476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_y_absint</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.875874e+03</td>\n",
       "      <td>3.553315e+03</td>\n",
       "      <td>9.700954e-04</td>\n",
       "      <td>8.820891e+02</td>\n",
       "      <td>1.919441e+03</td>\n",
       "      <td>3.333147e+03</td>\n",
       "      <td>20173.308396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_y_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-3.171673e-02</td>\n",
       "      <td>1.041193e-01</td>\n",
       "      <td>-4.803281e-01</td>\n",
       "      <td>-5.821665e-02</td>\n",
       "      <td>-2.075289e-02</td>\n",
       "      <td>2.162790e-02</td>\n",
       "      <td>0.206044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_y_peak</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>6.079692e-02</td>\n",
       "      <td>1.348027e-01</td>\n",
       "      <td>-4.514000e-01</td>\n",
       "      <td>1.000047e-03</td>\n",
       "      <td>6.659997e-02</td>\n",
       "      <td>1.274000e-01</td>\n",
       "      <td>0.517600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_y_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.231234e-02</td>\n",
       "      <td>2.630981e-02</td>\n",
       "      <td>2.437846e-03</td>\n",
       "      <td>7.400205e-03</td>\n",
       "      <td>1.410219e-02</td>\n",
       "      <td>2.699352e-02</td>\n",
       "      <td>0.297557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_z_absint</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.524902e+04</td>\n",
       "      <td>9.934453e+03</td>\n",
       "      <td>2.211138e-01</td>\n",
       "      <td>7.232294e+03</td>\n",
       "      <td>1.363166e+04</td>\n",
       "      <td>2.284682e+04</td>\n",
       "      <td>39573.054387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_z_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-3.467196e-01</td>\n",
       "      <td>2.599314e-01</td>\n",
       "      <td>-9.422378e-01</td>\n",
       "      <td>-5.421536e-01</td>\n",
       "      <td>-3.188847e-01</td>\n",
       "      <td>-1.662460e-01</td>\n",
       "      <td>0.649590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_z_peak</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-1.734836e-01</td>\n",
       "      <td>3.374804e-01</td>\n",
       "      <td>-9.218000e-01</td>\n",
       "      <td>-4.204000e-01</td>\n",
       "      <td>-1.326000e-01</td>\n",
       "      <td>5.579996e-02</td>\n",
       "      <td>1.041800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC_z_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>3.790791e-02</td>\n",
       "      <td>4.562463e-02</td>\n",
       "      <td>4.217744e-03</td>\n",
       "      <td>1.332436e-02</td>\n",
       "      <td>2.389932e-02</td>\n",
       "      <td>4.342554e-02</td>\n",
       "      <td>0.416549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hr_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.150156e+02</td>\n",
       "      <td>2.596134e+01</td>\n",
       "      <td>7.283340e+01</td>\n",
       "      <td>9.793210e+01</td>\n",
       "      <td>1.086545e+02</td>\n",
       "      <td>1.245342e+02</td>\n",
       "      <td>212.975472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hr_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>9.631362e+00</td>\n",
       "      <td>3.932930e+00</td>\n",
       "      <td>2.702037e+00</td>\n",
       "      <td>6.948382e+00</td>\n",
       "      <td>8.916820e+00</td>\n",
       "      <td>1.134984e+01</td>\n",
       "      <td>36.254226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_HF</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>8.892800e+02</td>\n",
       "      <td>1.265845e+03</td>\n",
       "      <td>5.400580e-01</td>\n",
       "      <td>1.800907e+02</td>\n",
       "      <td>4.626218e+02</td>\n",
       "      <td>1.045669e+03</td>\n",
       "      <td>15434.314292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_HFn</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.142626e+01</td>\n",
       "      <td>1.704996e+01</td>\n",
       "      <td>9.798405e-01</td>\n",
       "      <td>8.521370e+00</td>\n",
       "      <td>1.604173e+01</td>\n",
       "      <td>2.956955e+01</td>\n",
       "      <td>93.041165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_LF</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>9.177515e+02</td>\n",
       "      <td>9.918280e+02</td>\n",
       "      <td>4.913167e+00</td>\n",
       "      <td>2.957927e+02</td>\n",
       "      <td>6.174729e+02</td>\n",
       "      <td>1.161405e+03</td>\n",
       "      <td>11597.641522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_LFHF_ratio</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>8.357228e+00</td>\n",
       "      <td>9.675399e+00</td>\n",
       "      <td>7.479307e-02</td>\n",
       "      <td>2.381858e+00</td>\n",
       "      <td>5.233740e+00</td>\n",
       "      <td>1.073520e+01</td>\n",
       "      <td>101.057431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_LFn</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>7.857374e+01</td>\n",
       "      <td>1.704996e+01</td>\n",
       "      <td>6.958835e+00</td>\n",
       "      <td>7.043045e+01</td>\n",
       "      <td>8.395827e+01</td>\n",
       "      <td>9.147863e+01</td>\n",
       "      <td>99.020160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_NN50</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>8.576523e+00</td>\n",
       "      <td>9.065345e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_RMS</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>3.535209e+01</td>\n",
       "      <td>2.451819e+01</td>\n",
       "      <td>1.394433e+00</td>\n",
       "      <td>1.903777e+01</td>\n",
       "      <td>2.910283e+01</td>\n",
       "      <td>4.421071e+01</td>\n",
       "      <td>174.578322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_TINN</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>3.178878e+02</td>\n",
       "      <td>1.549591e+02</td>\n",
       "      <td>4.571429e+01</td>\n",
       "      <td>2.157143e+02</td>\n",
       "      <td>2.900000e+02</td>\n",
       "      <td>3.957143e+02</td>\n",
       "      <td>1082.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_ULF</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>4.506362e+02</td>\n",
       "      <td>7.065871e+02</td>\n",
       "      <td>1.021278e+00</td>\n",
       "      <td>7.635542e+01</td>\n",
       "      <td>2.284820e+02</td>\n",
       "      <td>5.452221e+02</td>\n",
       "      <td>11459.380293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_VHF</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.540961e+02</td>\n",
       "      <td>5.686612e+02</td>\n",
       "      <td>3.479956e-01</td>\n",
       "      <td>2.934363e+01</td>\n",
       "      <td>9.153784e+01</td>\n",
       "      <td>2.731946e+02</td>\n",
       "      <td>9325.178318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_f_sum</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.511764e+03</td>\n",
       "      <td>2.557679e+03</td>\n",
       "      <td>2.532833e+01</td>\n",
       "      <td>8.974504e+02</td>\n",
       "      <td>1.775331e+03</td>\n",
       "      <td>3.151953e+03</td>\n",
       "      <td>27297.870278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_pNN50</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.315435e+01</td>\n",
       "      <td>1.548097e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.492537e+00</td>\n",
       "      <td>6.666667e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>69.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_rel_HF</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>3.151221e+01</td>\n",
       "      <td>1.771518e+01</td>\n",
       "      <td>4.915866e-01</td>\n",
       "      <td>1.750589e+01</td>\n",
       "      <td>2.882322e+01</td>\n",
       "      <td>4.280783e+01</td>\n",
       "      <td>88.155671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_rel_LF</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>4.004594e+01</td>\n",
       "      <td>1.868238e+01</td>\n",
       "      <td>8.653701e-01</td>\n",
       "      <td>2.507784e+01</td>\n",
       "      <td>3.899708e+01</td>\n",
       "      <td>5.379465e+01</td>\n",
       "      <td>94.736023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_rel_ULF</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.910078e+01</td>\n",
       "      <td>1.608598e+01</td>\n",
       "      <td>1.033621e-01</td>\n",
       "      <td>6.384687e+00</td>\n",
       "      <td>1.464047e+01</td>\n",
       "      <td>2.789209e+01</td>\n",
       "      <td>93.581365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECG_hrv_rel_VHF</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>9.341071e+00</td>\n",
       "      <td>1.070147e+01</td>\n",
       "      <td>1.558650e-01</td>\n",
       "      <td>2.483533e+00</td>\n",
       "      <td>4.939138e+00</td>\n",
       "      <td>1.195069e+01</td>\n",
       "      <td>75.871393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCL_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-1.903416e-02</td>\n",
       "      <td>8.781849e-01</td>\n",
       "      <td>-1.948759e+00</td>\n",
       "      <td>-6.761268e-01</td>\n",
       "      <td>-1.740805e-01</td>\n",
       "      <td>6.310920e-01</td>\n",
       "      <td>3.255024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCL_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-1.903416e-02</td>\n",
       "      <td>8.781849e-01</td>\n",
       "      <td>-1.948759e+00</td>\n",
       "      <td>-6.761268e-01</td>\n",
       "      <td>-1.740805e-01</td>\n",
       "      <td>6.310920e-01</td>\n",
       "      <td>3.255024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCR_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-1.199862e-06</td>\n",
       "      <td>4.290778e-03</td>\n",
       "      <td>-6.342619e-02</td>\n",
       "      <td>-8.577593e-04</td>\n",
       "      <td>5.842032e-07</td>\n",
       "      <td>8.588986e-04</td>\n",
       "      <td>0.071839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCR_no</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.869170e+01</td>\n",
       "      <td>3.747932e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>176.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_SCR_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>7.800268e-02</td>\n",
       "      <td>8.693728e-02</td>\n",
       "      <td>1.347313e-03</td>\n",
       "      <td>2.098119e-02</td>\n",
       "      <td>5.242651e-02</td>\n",
       "      <td>1.046312e-01</td>\n",
       "      <td>0.690004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_ampl_sum</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>7.393511e-02</td>\n",
       "      <td>4.119272e+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>488.265512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_corr_SCL_t</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.782612e-02</td>\n",
       "      <td>8.229942e-01</td>\n",
       "      <td>-9.999664e-01</td>\n",
       "      <td>-9.163881e-01</td>\n",
       "      <td>2.213031e-01</td>\n",
       "      <td>8.815104e-01</td>\n",
       "      <td>0.999548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_drange</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.768199e+00</td>\n",
       "      <td>1.238774e+02</td>\n",
       "      <td>-3.967831e+03</td>\n",
       "      <td>-2.964488e-01</td>\n",
       "      <td>6.062892e-01</td>\n",
       "      <td>1.574950e+00</td>\n",
       "      <td>11106.050509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_max</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>5.493528e-01</td>\n",
       "      <td>1.166038e+00</td>\n",
       "      <td>-1.738216e+00</td>\n",
       "      <td>-4.386713e-01</td>\n",
       "      <td>3.681331e-01</td>\n",
       "      <td>1.286859e+00</td>\n",
       "      <td>7.193766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-1.903162e-02</td>\n",
       "      <td>8.782353e-01</td>\n",
       "      <td>-1.945009e+00</td>\n",
       "      <td>-6.761643e-01</td>\n",
       "      <td>-1.746478e-01</td>\n",
       "      <td>6.312192e-01</td>\n",
       "      <td>3.256583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_min</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-4.145182e-01</td>\n",
       "      <td>9.309273e-01</td>\n",
       "      <td>-7.598910e+00</td>\n",
       "      <td>-9.663767e-01</td>\n",
       "      <td>-5.459753e-01</td>\n",
       "      <td>1.790925e-01</td>\n",
       "      <td>2.561550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_scr_area</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.125531e+00</td>\n",
       "      <td>2.750544e+00</td>\n",
       "      <td>3.232345e-04</td>\n",
       "      <td>4.697535e-02</td>\n",
       "      <td>1.972812e-01</td>\n",
       "      <td>1.061966e+00</td>\n",
       "      <td>30.107660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_slope</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>8.680248e-07</td>\n",
       "      <td>1.674013e-05</td>\n",
       "      <td>-1.079305e-04</td>\n",
       "      <td>-6.689605e-06</td>\n",
       "      <td>7.607842e-07</td>\n",
       "      <td>7.292923e-06</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDA_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.993713e-01</td>\n",
       "      <td>1.941195e-01</td>\n",
       "      <td>5.691243e-03</td>\n",
       "      <td>7.269518e-02</td>\n",
       "      <td>1.423018e-01</td>\n",
       "      <td>2.564714e-01</td>\n",
       "      <td>1.763312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_absint</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.140037e-02</td>\n",
       "      <td>1.243380e-02</td>\n",
       "      <td>3.760722e-08</td>\n",
       "      <td>3.657774e-03</td>\n",
       "      <td>8.015328e-03</td>\n",
       "      <td>1.491604e-02</td>\n",
       "      <td>0.342385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_drange</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-1.014171e+00</td>\n",
       "      <td>2.568502e-01</td>\n",
       "      <td>-2.213083e+00</td>\n",
       "      <td>-1.150374e+00</td>\n",
       "      <td>-9.668116e-01</td>\n",
       "      <td>-8.343992e-01</td>\n",
       "      <td>-0.310230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-2.132572e-10</td>\n",
       "      <td>4.395814e-07</td>\n",
       "      <td>-8.242197e-06</td>\n",
       "      <td>-2.133219e-07</td>\n",
       "      <td>-4.385435e-10</td>\n",
       "      <td>2.140062e-07</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_mean_freq</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.136757e-01</td>\n",
       "      <td>3.412440e-02</td>\n",
       "      <td>1.493851e-01</td>\n",
       "      <td>1.860345e-01</td>\n",
       "      <td>2.137755e-01</td>\n",
       "      <td>2.373458e-01</td>\n",
       "      <td>0.311637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_median</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.327240e-04</td>\n",
       "      <td>2.445275e-04</td>\n",
       "      <td>-8.887516e-04</td>\n",
       "      <td>7.678846e-05</td>\n",
       "      <td>2.630443e-04</td>\n",
       "      <td>4.206593e-04</td>\n",
       "      <td>0.001482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_median_freq</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>9.769595e-03</td>\n",
       "      <td>4.114953e-03</td>\n",
       "      <td>5.047619e-03</td>\n",
       "      <td>7.095238e-03</td>\n",
       "      <td>8.666667e-03</td>\n",
       "      <td>1.097619e-02</td>\n",
       "      <td>0.044643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_amp_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>5.049228e-03</td>\n",
       "      <td>2.060131e-03</td>\n",
       "      <td>1.735037e-03</td>\n",
       "      <td>3.600132e-03</td>\n",
       "      <td>4.527581e-03</td>\n",
       "      <td>5.982619e-03</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_amp_norm_sum</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>2.257264e+02</td>\n",
       "      <td>1.028546e+02</td>\n",
       "      <td>2.543861e+01</td>\n",
       "      <td>1.495391e+02</td>\n",
       "      <td>2.149861e+02</td>\n",
       "      <td>2.878847e+02</td>\n",
       "      <td>718.847777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_amp_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>5.042387e-03</td>\n",
       "      <td>2.248901e-03</td>\n",
       "      <td>1.753844e-03</td>\n",
       "      <td>3.471532e-03</td>\n",
       "      <td>4.711380e-03</td>\n",
       "      <td>5.945361e-03</td>\n",
       "      <td>0.026791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_amp_sum</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>8.804994e+00</td>\n",
       "      <td>5.532621e+00</td>\n",
       "      <td>1.248847e+00</td>\n",
       "      <td>5.072997e+00</td>\n",
       "      <td>7.309870e+00</td>\n",
       "      <td>1.073504e+01</td>\n",
       "      <td>47.760020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_freq</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.256571e-01</td>\n",
       "      <td>5.718963e-02</td>\n",
       "      <td>6.100000e-02</td>\n",
       "      <td>9.945238e-02</td>\n",
       "      <td>1.125714e-01</td>\n",
       "      <td>1.318810e-01</td>\n",
       "      <td>0.499976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_peak_no</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.651546e+03</td>\n",
       "      <td>4.293873e+02</td>\n",
       "      <td>3.100000e+02</td>\n",
       "      <td>1.439000e+03</td>\n",
       "      <td>1.683000e+03</td>\n",
       "      <td>1.911000e+03</td>\n",
       "      <td>2710.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_perc_10</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>-1.161736e-02</td>\n",
       "      <td>4.083110e-03</td>\n",
       "      <td>-4.159827e-02</td>\n",
       "      <td>-1.265936e-02</td>\n",
       "      <td>-1.056720e-02</td>\n",
       "      <td>-9.076794e-03</td>\n",
       "      <td>-0.006444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_perc_90</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.116668e-02</td>\n",
       "      <td>4.251662e-03</td>\n",
       "      <td>6.184561e-03</td>\n",
       "      <td>8.360369e-03</td>\n",
       "      <td>1.016214e-02</td>\n",
       "      <td>1.241528e-02</td>\n",
       "      <td>0.040787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMG_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>9.769588e-03</td>\n",
       "      <td>4.114905e-03</td>\n",
       "      <td>5.047376e-03</td>\n",
       "      <td>7.106887e-03</td>\n",
       "      <td>8.658870e-03</td>\n",
       "      <td>1.097158e-02</td>\n",
       "      <td>0.044644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_breath_rate</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.484241e+01</td>\n",
       "      <td>2.719323e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_duration</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>4.197226e+04</td>\n",
       "      <td>2.924766e+02</td>\n",
       "      <td>3.419800e+04</td>\n",
       "      <td>4.200000e+04</td>\n",
       "      <td>4.200000e+04</td>\n",
       "      <td>4.200000e+04</td>\n",
       "      <td>42000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_exhal_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.426550e+03</td>\n",
       "      <td>2.938229e+02</td>\n",
       "      <td>8.695217e+02</td>\n",
       "      <td>1.222765e+03</td>\n",
       "      <td>1.355467e+03</td>\n",
       "      <td>1.569769e+03</td>\n",
       "      <td>3218.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_exhal_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>4.146157e+02</td>\n",
       "      <td>2.916481e+02</td>\n",
       "      <td>2.598544e+01</td>\n",
       "      <td>1.703066e+02</td>\n",
       "      <td>3.358667e+02</td>\n",
       "      <td>6.038271e+02</td>\n",
       "      <td>1864.398251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_ie_ratio</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.021084e+00</td>\n",
       "      <td>1.218813e-01</td>\n",
       "      <td>4.952472e-01</td>\n",
       "      <td>9.586812e-01</td>\n",
       "      <td>1.014485e+00</td>\n",
       "      <td>1.079929e+00</td>\n",
       "      <td>2.030084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_inhal_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.444406e+03</td>\n",
       "      <td>2.890134e+02</td>\n",
       "      <td>8.788261e+02</td>\n",
       "      <td>1.235412e+03</td>\n",
       "      <td>1.390600e+03</td>\n",
       "      <td>1.607462e+03</td>\n",
       "      <td>3135.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_inhal_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>4.274283e+02</td>\n",
       "      <td>2.691726e+02</td>\n",
       "      <td>3.442137e+01</td>\n",
       "      <td>2.016383e+02</td>\n",
       "      <td>3.731933e+02</td>\n",
       "      <td>6.175455e+02</td>\n",
       "      <td>2140.917428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_stretch</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.082744e+01</td>\n",
       "      <td>7.026491e+00</td>\n",
       "      <td>3.995015e-01</td>\n",
       "      <td>5.363267e+00</td>\n",
       "      <td>9.501897e+00</td>\n",
       "      <td>1.447040e+01</td>\n",
       "      <td>45.375329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RESP_volume</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.149319e+02</td>\n",
       "      <td>1.107390e+02</td>\n",
       "      <td>3.581510e-01</td>\n",
       "      <td>4.896074e+01</td>\n",
       "      <td>8.260654e+01</td>\n",
       "      <td>1.418458e+02</td>\n",
       "      <td>1035.275390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_drange</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.008641e+00</td>\n",
       "      <td>5.121114e-02</td>\n",
       "      <td>-1.237100e-01</td>\n",
       "      <td>1.008075e+00</td>\n",
       "      <td>1.009397e+00</td>\n",
       "      <td>1.011384e+00</td>\n",
       "      <td>1.197949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_max</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>3.396902e+01</td>\n",
       "      <td>1.299388e+00</td>\n",
       "      <td>2.841458e+01</td>\n",
       "      <td>3.373526e+01</td>\n",
       "      <td>3.430771e+01</td>\n",
       "      <td>3.470312e+01</td>\n",
       "      <td>35.778046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_mean</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>3.376256e+01</td>\n",
       "      <td>1.331234e+00</td>\n",
       "      <td>2.820180e+01</td>\n",
       "      <td>3.354780e+01</td>\n",
       "      <td>3.412833e+01</td>\n",
       "      <td>3.453360e+01</td>\n",
       "      <td>35.631836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_min</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>3.300103e+01</td>\n",
       "      <td>1.369866e+01</td>\n",
       "      <td>-2.731500e+02</td>\n",
       "      <td>3.341599e+01</td>\n",
       "      <td>3.400568e+01</td>\n",
       "      <td>3.439948e+01</td>\n",
       "      <td>35.516296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_slope</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>6.747549e-07</td>\n",
       "      <td>7.263960e-06</td>\n",
       "      <td>-1.290544e-04</td>\n",
       "      <td>-3.701148e-07</td>\n",
       "      <td>5.957498e-07</td>\n",
       "      <td>1.631870e-06</td>\n",
       "      <td>0.000149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP_std</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>7.238136e-02</td>\n",
       "      <td>6.077758e-01</td>\n",
       "      <td>1.443778e-02</td>\n",
       "      <td>2.762968e-02</td>\n",
       "      <td>3.219721e-02</td>\n",
       "      <td>4.211922e-02</td>\n",
       "      <td>13.619300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>121813.0</td>\n",
       "      <td>1.604771e+00</td>\n",
       "      <td>7.389779e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count          mean           std           min  \\\n",
       "ACC_x_absint           121813.0  3.373182e+04  5.442559e+03  1.115059e+04   \n",
       "ACC_x_mean             121813.0  8.031577e-01  1.295878e-01  2.654967e-01   \n",
       "ACC_x_peak             121813.0  9.008852e-01  1.670471e-01  2.905999e-01   \n",
       "ACC_x_std              121813.0  1.576536e-02  2.159889e-02  2.540918e-03   \n",
       "ACC_xyz_absint         121813.0  1.126388e+04  6.171864e+03  6.333725e-01   \n",
       "ACC_xyz_std            121813.0  2.083812e+04  1.923747e+03  1.066439e+04   \n",
       "ACC_xzy_mean           121813.0  5.946100e+03  5.767697e+03 -1.066388e+04   \n",
       "ACC_y_absint           121813.0  2.875874e+03  3.553315e+03  9.700954e-04   \n",
       "ACC_y_mean             121813.0 -3.171673e-02  1.041193e-01 -4.803281e-01   \n",
       "ACC_y_peak             121813.0  6.079692e-02  1.348027e-01 -4.514000e-01   \n",
       "ACC_y_std              121813.0  2.231234e-02  2.630981e-02  2.437846e-03   \n",
       "ACC_z_absint           121813.0  1.524902e+04  9.934453e+03  2.211138e-01   \n",
       "ACC_z_mean             121813.0 -3.467196e-01  2.599314e-01 -9.422378e-01   \n",
       "ACC_z_peak             121813.0 -1.734836e-01  3.374804e-01 -9.218000e-01   \n",
       "ACC_z_std              121813.0  3.790791e-02  4.562463e-02  4.217744e-03   \n",
       "ECG_hr_mean            121813.0  1.150156e+02  2.596134e+01  7.283340e+01   \n",
       "ECG_hr_std             121813.0  9.631362e+00  3.932930e+00  2.702037e+00   \n",
       "ECG_hrv_HF             121813.0  8.892800e+02  1.265845e+03  5.400580e-01   \n",
       "ECG_hrv_HFn            121813.0  2.142626e+01  1.704996e+01  9.798405e-01   \n",
       "ECG_hrv_LF             121813.0  9.177515e+02  9.918280e+02  4.913167e+00   \n",
       "ECG_hrv_LFHF_ratio     121813.0  8.357228e+00  9.675399e+00  7.479307e-02   \n",
       "ECG_hrv_LFn            121813.0  7.857374e+01  1.704996e+01  6.958835e+00   \n",
       "ECG_hrv_NN50           121813.0  8.576523e+00  9.065345e+00  0.000000e+00   \n",
       "ECG_hrv_RMS            121813.0  3.535209e+01  2.451819e+01  1.394433e+00   \n",
       "ECG_hrv_TINN           121813.0  3.178878e+02  1.549591e+02  4.571429e+01   \n",
       "ECG_hrv_ULF            121813.0  4.506362e+02  7.065871e+02  1.021278e+00   \n",
       "ECG_hrv_VHF            121813.0  2.540961e+02  5.686612e+02  3.479956e-01   \n",
       "ECG_hrv_f_sum          121813.0  2.511764e+03  2.557679e+03  2.532833e+01   \n",
       "ECG_hrv_pNN50          121813.0  1.315435e+01  1.548097e+01  0.000000e+00   \n",
       "ECG_hrv_rel_HF         121813.0  3.151221e+01  1.771518e+01  4.915866e-01   \n",
       "ECG_hrv_rel_LF         121813.0  4.004594e+01  1.868238e+01  8.653701e-01   \n",
       "ECG_hrv_rel_ULF        121813.0  1.910078e+01  1.608598e+01  1.033621e-01   \n",
       "ECG_hrv_rel_VHF        121813.0  9.341071e+00  1.070147e+01  1.558650e-01   \n",
       "EDA_SCL_mean           121813.0 -1.903416e-02  8.781849e-01 -1.948759e+00   \n",
       "EDA_SCL_std            121813.0 -1.903416e-02  8.781849e-01 -1.948759e+00   \n",
       "EDA_SCR_mean           121813.0 -1.199862e-06  4.290778e-03 -6.342619e-02   \n",
       "EDA_SCR_no             121813.0  2.869170e+01  3.747932e+01  0.000000e+00   \n",
       "EDA_SCR_std            121813.0  7.800268e-02  8.693728e-02  1.347313e-03   \n",
       "EDA_ampl_sum           121813.0  7.393511e-02  4.119272e+00  0.000000e+00   \n",
       "EDA_corr_SCL_t         121813.0  2.782612e-02  8.229942e-01 -9.999664e-01   \n",
       "EDA_drange             121813.0  2.768199e+00  1.238774e+02 -3.967831e+03   \n",
       "EDA_max                121813.0  5.493528e-01  1.166038e+00 -1.738216e+00   \n",
       "EDA_mean               121813.0 -1.903162e-02  8.782353e-01 -1.945009e+00   \n",
       "EDA_min                121813.0 -4.145182e-01  9.309273e-01 -7.598910e+00   \n",
       "EDA_scr_area           121813.0  1.125531e+00  2.750544e+00  3.232345e-04   \n",
       "EDA_slope              121813.0  8.680248e-07  1.674013e-05 -1.079305e-04   \n",
       "EDA_std                121813.0  1.993713e-01  1.941195e-01  5.691243e-03   \n",
       "EMG_absint             121813.0  1.140037e-02  1.243380e-02  3.760722e-08   \n",
       "EMG_drange             121813.0 -1.014171e+00  2.568502e-01 -2.213083e+00   \n",
       "EMG_mean               121813.0 -2.132572e-10  4.395814e-07 -8.242197e-06   \n",
       "EMG_mean_freq          121813.0  2.136757e-01  3.412440e-02  1.493851e-01   \n",
       "EMG_median             121813.0  2.327240e-04  2.445275e-04 -8.887516e-04   \n",
       "EMG_median_freq        121813.0  9.769595e-03  4.114953e-03  5.047619e-03   \n",
       "EMG_peak_amp_mean      121813.0  5.049228e-03  2.060131e-03  1.735037e-03   \n",
       "EMG_peak_amp_norm_sum  121813.0  2.257264e+02  1.028546e+02  2.543861e+01   \n",
       "EMG_peak_amp_std       121813.0  5.042387e-03  2.248901e-03  1.753844e-03   \n",
       "EMG_peak_amp_sum       121813.0  8.804994e+00  5.532621e+00  1.248847e+00   \n",
       "EMG_peak_freq          121813.0  1.256571e-01  5.718963e-02  6.100000e-02   \n",
       "EMG_peak_no            121813.0  1.651546e+03  4.293873e+02  3.100000e+02   \n",
       "EMG_perc_10            121813.0 -1.161736e-02  4.083110e-03 -4.159827e-02   \n",
       "EMG_perc_90            121813.0  1.116668e-02  4.251662e-03  6.184561e-03   \n",
       "EMG_std                121813.0  9.769588e-03  4.114905e-03  5.047376e-03   \n",
       "RESP_breath_rate       121813.0  1.484241e+01  2.719323e+00  7.000000e+00   \n",
       "RESP_duration          121813.0  4.197226e+04  2.924766e+02  3.419800e+04   \n",
       "RESP_exhal_mean        121813.0  1.426550e+03  2.938229e+02  8.695217e+02   \n",
       "RESP_exhal_std         121813.0  4.146157e+02  2.916481e+02  2.598544e+01   \n",
       "RESP_ie_ratio          121813.0  1.021084e+00  1.218813e-01  4.952472e-01   \n",
       "RESP_inhal_mean        121813.0  1.444406e+03  2.890134e+02  8.788261e+02   \n",
       "RESP_inhal_std         121813.0  4.274283e+02  2.691726e+02  3.442137e+01   \n",
       "RESP_stretch           121813.0  1.082744e+01  7.026491e+00  3.995015e-01   \n",
       "RESP_volume            121813.0  1.149319e+02  1.107390e+02  3.581510e-01   \n",
       "TEMP_drange            121813.0  1.008641e+00  5.121114e-02 -1.237100e-01   \n",
       "TEMP_max               121813.0  3.396902e+01  1.299388e+00  2.841458e+01   \n",
       "TEMP_mean              121813.0  3.376256e+01  1.331234e+00  2.820180e+01   \n",
       "TEMP_min               121813.0  3.300103e+01  1.369866e+01 -2.731500e+02   \n",
       "TEMP_slope             121813.0  6.747549e-07  7.263960e-06 -1.290544e-04   \n",
       "TEMP_std               121813.0  7.238136e-02  6.077758e-01  1.443778e-02   \n",
       "label                  121813.0  1.604771e+00  7.389779e-01  1.000000e+00   \n",
       "\n",
       "                                25%           50%           75%           max  \n",
       "ACC_x_absint           3.046235e+04  3.594657e+04  3.770159e+04  38482.839411  \n",
       "ACC_x_mean             7.253115e-01  8.558914e-01  8.976773e-01      0.916280  \n",
       "ACC_x_peak             8.162000e-01  9.318000e-01  9.940000e-01      1.553400  \n",
       "ACC_x_std              6.727452e-03  1.019764e-02  1.733832e-02      0.334104  \n",
       "ACC_xyz_absint         6.368842e+03  1.208748e+04  1.549643e+04  31365.086834  \n",
       "ACC_xyz_std            1.977699e+04  2.109936e+04  2.246112e+04  23359.812435  \n",
       "ACC_xzy_mean           1.433468e+03  7.286843e+03  1.010301e+04  19538.685476  \n",
       "ACC_y_absint           8.820891e+02  1.919441e+03  3.333147e+03  20173.308396  \n",
       "ACC_y_mean            -5.821665e-02 -2.075289e-02  2.162790e-02      0.206044  \n",
       "ACC_y_peak             1.000047e-03  6.659997e-02  1.274000e-01      0.517600  \n",
       "ACC_y_std              7.400205e-03  1.410219e-02  2.699352e-02      0.297557  \n",
       "ACC_z_absint           7.232294e+03  1.363166e+04  2.284682e+04  39573.054387  \n",
       "ACC_z_mean            -5.421536e-01 -3.188847e-01 -1.662460e-01      0.649590  \n",
       "ACC_z_peak            -4.204000e-01 -1.326000e-01  5.579996e-02      1.041800  \n",
       "ACC_z_std              1.332436e-02  2.389932e-02  4.342554e-02      0.416549  \n",
       "ECG_hr_mean            9.793210e+01  1.086545e+02  1.245342e+02    212.975472  \n",
       "ECG_hr_std             6.948382e+00  8.916820e+00  1.134984e+01     36.254226  \n",
       "ECG_hrv_HF             1.800907e+02  4.626218e+02  1.045669e+03  15434.314292  \n",
       "ECG_hrv_HFn            8.521370e+00  1.604173e+01  2.956955e+01     93.041165  \n",
       "ECG_hrv_LF             2.957927e+02  6.174729e+02  1.161405e+03  11597.641522  \n",
       "ECG_hrv_LFHF_ratio     2.381858e+00  5.233740e+00  1.073520e+01    101.057431  \n",
       "ECG_hrv_LFn            7.043045e+01  8.395827e+01  9.147863e+01     99.020160  \n",
       "ECG_hrv_NN50           1.000000e+00  5.000000e+00  1.400000e+01     39.000000  \n",
       "ECG_hrv_RMS            1.903777e+01  2.910283e+01  4.421071e+01    174.578322  \n",
       "ECG_hrv_TINN           2.157143e+02  2.900000e+02  3.957143e+02   1082.857143  \n",
       "ECG_hrv_ULF            7.635542e+01  2.284820e+02  5.452221e+02  11459.380293  \n",
       "ECG_hrv_VHF            2.934363e+01  9.153784e+01  2.731946e+02   9325.178318  \n",
       "ECG_hrv_f_sum          8.974504e+02  1.775331e+03  3.151953e+03  27297.870278  \n",
       "ECG_hrv_pNN50          1.492537e+00  6.666667e+00  2.000000e+01     69.230769  \n",
       "ECG_hrv_rel_HF         1.750589e+01  2.882322e+01  4.280783e+01     88.155671  \n",
       "ECG_hrv_rel_LF         2.507784e+01  3.899708e+01  5.379465e+01     94.736023  \n",
       "ECG_hrv_rel_ULF        6.384687e+00  1.464047e+01  2.789209e+01     93.581365  \n",
       "ECG_hrv_rel_VHF        2.483533e+00  4.939138e+00  1.195069e+01     75.871393  \n",
       "EDA_SCL_mean          -6.761268e-01 -1.740805e-01  6.310920e-01      3.255024  \n",
       "EDA_SCL_std           -6.761268e-01 -1.740805e-01  6.310920e-01      3.255024  \n",
       "EDA_SCR_mean          -8.577593e-04  5.842032e-07  8.588986e-04      0.071839  \n",
       "EDA_SCR_no             4.000000e+00  1.000000e+01  4.600000e+01    176.000000  \n",
       "EDA_SCR_std            2.098119e-02  5.242651e-02  1.046312e-01      0.690004  \n",
       "EDA_ampl_sum           0.000000e+00  0.000000e+00  0.000000e+00    488.265512  \n",
       "EDA_corr_SCL_t        -9.163881e-01  2.213031e-01  8.815104e-01      0.999548  \n",
       "EDA_drange            -2.964488e-01  6.062892e-01  1.574950e+00  11106.050509  \n",
       "EDA_max               -4.386713e-01  3.681331e-01  1.286859e+00      7.193766  \n",
       "EDA_mean              -6.761643e-01 -1.746478e-01  6.312192e-01      3.256583  \n",
       "EDA_min               -9.663767e-01 -5.459753e-01  1.790925e-01      2.561550  \n",
       "EDA_scr_area           4.697535e-02  1.972812e-01  1.061966e+00     30.107660  \n",
       "EDA_slope             -6.689605e-06  7.607842e-07  7.292923e-06      0.000137  \n",
       "EDA_std                7.269518e-02  1.423018e-01  2.564714e-01      1.763312  \n",
       "EMG_absint             3.657774e-03  8.015328e-03  1.491604e-02      0.342385  \n",
       "EMG_drange            -1.150374e+00 -9.668116e-01 -8.343992e-01     -0.310230  \n",
       "EMG_mean              -2.133219e-07 -4.385435e-10  2.140062e-07      0.000008  \n",
       "EMG_mean_freq          1.860345e-01  2.137755e-01  2.373458e-01      0.311637  \n",
       "EMG_median             7.678846e-05  2.630443e-04  4.206593e-04      0.001482  \n",
       "EMG_median_freq        7.095238e-03  8.666667e-03  1.097619e-02      0.044643  \n",
       "EMG_peak_amp_mean      3.600132e-03  4.527581e-03  5.982619e-03      0.020000  \n",
       "EMG_peak_amp_norm_sum  1.495391e+02  2.149861e+02  2.878847e+02    718.847777  \n",
       "EMG_peak_amp_std       3.471532e-03  4.711380e-03  5.945361e-03      0.026791  \n",
       "EMG_peak_amp_sum       5.072997e+00  7.309870e+00  1.073504e+01     47.760020  \n",
       "EMG_peak_freq          9.945238e-02  1.125714e-01  1.318810e-01      0.499976  \n",
       "EMG_peak_no            1.439000e+03  1.683000e+03  1.911000e+03   2710.000000  \n",
       "EMG_perc_10           -1.265936e-02 -1.056720e-02 -9.076794e-03     -0.006444  \n",
       "EMG_perc_90            8.360369e-03  1.016214e-02  1.241528e-02      0.040787  \n",
       "EMG_std                7.106887e-03  8.658870e-03  1.097158e-02      0.044644  \n",
       "RESP_breath_rate       1.300000e+01  1.500000e+01  1.700000e+01     23.000000  \n",
       "RESP_duration          4.200000e+04  4.200000e+04  4.200000e+04  42000.000000  \n",
       "RESP_exhal_mean        1.222765e+03  1.355467e+03  1.569769e+03   3218.500000  \n",
       "RESP_exhal_std         1.703066e+02  3.358667e+02  6.038271e+02   1864.398251  \n",
       "RESP_ie_ratio          9.586812e-01  1.014485e+00  1.079929e+00      2.030084  \n",
       "RESP_inhal_mean        1.235412e+03  1.390600e+03  1.607462e+03   3135.571429  \n",
       "RESP_inhal_std         2.016383e+02  3.731933e+02  6.175455e+02   2140.917428  \n",
       "RESP_stretch           5.363267e+00  9.501897e+00  1.447040e+01     45.375329  \n",
       "RESP_volume            4.896074e+01  8.260654e+01  1.418458e+02   1035.275390  \n",
       "TEMP_drange            1.008075e+00  1.009397e+00  1.011384e+00      1.197949  \n",
       "TEMP_max               3.373526e+01  3.430771e+01  3.470312e+01     35.778046  \n",
       "TEMP_mean              3.354780e+01  3.412833e+01  3.453360e+01     35.631836  \n",
       "TEMP_min               3.341599e+01  3.400568e+01  3.439948e+01     35.516296  \n",
       "TEMP_slope            -3.701148e-07  5.957498e-07  1.631870e-06      0.000149  \n",
       "TEMP_std               2.762968e-02  3.219721e-02  4.211922e-02     13.619300  \n",
       "label                  1.000000e+00  1.000000e+00  2.000000e+00      3.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context(\"display.max_rows\", df.columns.shape[0], \"display.max_columns\", 100):\n",
    "    display(df.describe().T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting data for CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113425,)\n",
      "(113621,)\n",
      "(113628,)\n",
      "(113624,)\n",
      "(113601,)\n",
      "(113648,)\n",
      "(113429,)\n",
      "(114049,)\n",
      "(113913,)\n",
      "(113872,)\n",
      "(113665,)\n",
      "(113725,)\n",
      "(113740,)\n",
      "(113697,)\n",
      "(113745,)\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns=['label', 'subject'])\n",
    "filter_cols = df.columns[df.columns.str.startswith('ACC')]\n",
    "X_p = X.drop(columns=df.columns[df.columns.str.startswith('ACC')].values)  # Physiological modalities only\n",
    "y = df['label']\n",
    "y_b = y.copy().apply(lambda x: 1 if x == 2 else 0)  # stress vs non-stress\n",
    "loso_cv = LeaveOneGroupOut()  # just to analyze training set sizes\n",
    "for x_idxs, y_idxs in loso_cv.split(X, y, groups=df['subject']):\n",
    "    print(x_idxs.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = 100\n",
    "min_samples_split = 20\n",
    "criterion = 'entropy'  # information gain\n",
    "subjects = df['subject'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_cv(clf, X, y, groups, cv, scoring=['accuracy', 'f1_macro'], return_train_score=True,\n",
    "          return_estimator=True, n_jobs=-1):\n",
    "    \"\"\"\n",
    "        More on cross validation: https://scikit-learn.org/stable/modules/cross_validation.html#\n",
    "        More on scoring: https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter\n",
    "    \"\"\"\n",
    "    return cross_validate(clf, X, y, groups=groups, cv=cv, scoring=scoring, return_train_score=return_train_score,\n",
    "                          return_estimator=return_estimator, n_jobs=n_jobs)\n",
    "\n",
    "def raw_scores(cv):\n",
    "    return np.mean(cv['test_accuracy']), np.mean(cv['test_f1_macro'])\n",
    "\n",
    "def test_score(cv):\n",
    "    return {\n",
    "        'accuracy': f\"{np.mean(cv['test_accuracy']) * 100} +-{np.std(cv['test_accuracy']) * 100}\",\n",
    "        'f1_macro': f\"{np.mean(cv['test_f1_macro']) * 100} +-{np.std(cv['test_f1_macro']) * 100}\"\n",
    "    }\n",
    "\n",
    "def min_max(acc_scores):\n",
    "    print(np.argmax(acc_scores), np.amax(acc_scores))\n",
    "    print(np.argmin(acc_scores), np.amin(acc_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimental setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_report_all = pd.DataFrame()\n",
    "df_report_phy = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Experiment No 5'"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_idx = df_report_all.shape[0]\n",
    "# df_report_all.loc[experiment_idx, :] = []\n",
    "# df_report_phy.loc[experiment_idx, :] = []\n",
    "# df_report_all = df_report_all.append([[]])\n",
    "# df_report_phy = df_report_phy.append([[]])\n",
    "f'Experiment No {experiment_idx + 1}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_rf_pipeline(n_estimators=100, min_samples_split=20, criterion='entropy', n_jobs=-1):\n",
    "    model_rf =  RandomForestClassifier(n_estimators=n_estimators, min_samples_split=min_samples_split, \n",
    "                                  criterion=criterion, n_jobs=-1)\n",
    "    return make_pipeline(StandardScaler(), model_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline vs Stress vs Amusement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '68.16513325419396 +-16.60036819042444',\n",
       " 'f1_macro': '56.52126032888619 +-21.21037134783233'}"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_rf_all = create_rf_pipeline(n_estimators=n_estimators, min_samples_split=min_samples_split,\n",
    "                                criterion=criterion, n_jobs=-1)\n",
    "cv_rf_all = run_cv(clf_rf_all, X, y, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_rf_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_rf_all['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.9959499263622975\n",
      "2 0.3513744654856445\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_rf_all['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_rf_all['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.9936861528385794\n",
      "2 0.2281960896273186\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_rf_all['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Physiological modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '71.14370431773794 +-16.073039533797033',\n",
       " 'f1_macro': '61.76761351290884 +-20.355669500929636'}"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_rf_phy = create_rf_pipeline(n_estimators=n_estimators, min_samples_split=min_samples_split,\n",
    "                                criterion=criterion, n_jobs=-1)\n",
    "cv_rf_phy = run_cv(clf_rf_phy, X_p, y, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_rf_phy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_rf_phy['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_rf_phy['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stress vs Non-Stress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    85548\n",
       "1    36265\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_b.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': '84.33349928064499 +-12.596472511220755', 'f1_macro': '75.95840616270004 +-21.16801693089855'}\n"
     ]
    }
   ],
   "source": [
    "clf_b_rf_all = create_rf_pipeline(n_estimators=n_estimators, min_samples_split=min_samples_split,\n",
    "                              criterion=criterion, n_jobs=-1)\n",
    "cv_b_rf_all = run_cv(clf_b_rf_all, X, y_b, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "print(test_score(cv_b_rf_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_rf_all['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0.9819963257807716\n",
      "2 0.5584605986560782\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_rf_all['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_rf_all['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0.9782034917887934\n",
      "9 0.4153291120600795\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_rf_all['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Physiological modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': '84.93218852223181 +-12.05799327084041', 'f1_macro': '77.82266406484575 +-20.250290686304183'}\n"
     ]
    }
   ],
   "source": [
    "clf_b_rf_phy = create_rf_pipeline(n_estimators=n_estimators, min_samples_split=min_samples_split,\n",
    "                              criterion=criterion, n_jobs=-1)\n",
    "cv_b_rf_phy = run_cv(clf_b_rf_phy, X_p, y_b, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "print(test_score(cv_b_rf_phy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_rf_phy['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 1.0\n",
      "2 0.5675015271838729\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_rf_phy['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_rf_phy['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 1.0\n",
      "7 0.4165915238954013\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_rf_phy['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Reporting results\n",
    "rf_acc, rf_f1 = raw_scores(cv_rf_all)\n",
    "rf_b_acc, rf_b_f1 = raw_scores(cv_b_rf_all)\n",
    "df_report_all.loc[experiment_idx, 'rf_f1'] = rf_f1\n",
    "df_report_all.loc[experiment_idx, 'rf_acc'] = rf_acc\n",
    "df_report_all.loc[experiment_idx, 'rf_b_f1'] = rf_b_f1\n",
    "df_report_all.loc[experiment_idx, 'rf_b_acc'] = rf_b_acc\n",
    "\n",
    "rf_acc, rf_f1 = raw_scores(cv_rf_phy)\n",
    "rf_b_acc, rf_b_f1 = raw_scores(cv_b_rf_phy)\n",
    "df_report_phy.loc[experiment_idx, 'rf_f1'] = rf_f1\n",
    "df_report_phy.loc[experiment_idx, 'rf_acc'] = rf_acc\n",
    "df_report_phy.loc[experiment_idx, 'rf_b_f1'] = rf_b_f1\n",
    "df_report_phy.loc[experiment_idx, 'rf_b_acc'] = rf_b_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_ab_pipeline(n_estimators=100, min_samples_split=20, criterion='entropy'):\n",
    "    base_estimator = DecisionTreeClassifier(criterion=criterion, min_samples_split=min_samples_split)\n",
    "    model_ab = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=n_estimators)\n",
    "    return make_pipeline(StandardScaler(), model_ab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline vs Stress vs Amusement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '64.47869291637963 +-16.511607169619165',\n",
       " 'f1_macro': '55.298969054932826 +-19.095631337940592'}"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_ab_all = create_ab_pipeline()\n",
    "cv_ab_all = run_cv(clf_ab_all, X, y, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_ab_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_ab_all['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 0.8756348321565713\n",
      "10 0.28902798232695137\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_ab_all['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_ab_all['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 0.8592926331190952\n",
      "8 0.20870798058241313\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_ab_all['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Physiological modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '67.65107081856117 +-14.003524080288468',\n",
       " 'f1_macro': '59.83875538767948 +-15.898217501732168'}"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_ab_phy = create_ab_pipeline()\n",
    "cv_ab_phy = run_cv(clf_ab_phy, X_p, y, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_ab_phy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_ab_phy['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 0.8649851632047477\n",
      "2 0.3908368967623702\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_ab_phy['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_ab_phy['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 0.8193138567746395\n",
      "2 0.3004404972552602\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_ab_phy['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stress vs Non-stress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '80.67466917928397 +-14.153738869653523',\n",
       " 'f1_macro': '74.08500045240444 +-18.58292930086182'}"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_b_ab_all = create_ab_pipeline()\n",
    "cv_b_ab_all = run_cv(clf_b_ab_all, X, y_b, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_b_ab_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_ab_all['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 1.0\n",
      "0 0.5046494992846924\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_ab_all['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_ab_all['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 1.0\n",
      "11 0.41866109784641026\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_ab_all['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Physiological modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '83.45565130962734 +-13.519274301391304',\n",
       " 'f1_macro': '77.61923834025146 +-18.391765936674876'}"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_b_ab_phy = create_ab_pipeline()\n",
    "cv_b_ab_phy = run_cv(clf_b_ab_phy, X_p, y_b, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_b_ab_phy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_ab_phy['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0.9985303123086344\n",
      "0 0.4661421077730091\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_ab_phy['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_ab_phy['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0.9982493500189322\n",
      "7 0.4161088967436264\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_ab_phy['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Reporting results\n",
    "ab_acc, ab_f1 = raw_scores(cv_ab_all)\n",
    "ab_b_acc, ab_b_f1 = raw_scores(cv_b_ab_all)\n",
    "df_report_all.loc[experiment_idx, 'ab_f1'] = ab_f1\n",
    "df_report_all.loc[experiment_idx, 'ab_acc'] = ab_acc\n",
    "df_report_all.loc[experiment_idx, 'ab_b_f1'] = ab_b_f1\n",
    "df_report_all.loc[experiment_idx, 'ab_b_acc'] = ab_b_acc\n",
    "\n",
    "ab_acc, ab_f1 = raw_scores(cv_ab_phy)\n",
    "ab_b_acc, ab_b_f1 = raw_scores(cv_b_ab_phy)\n",
    "df_report_phy.loc[experiment_idx, 'ab_f1'] = ab_f1\n",
    "df_report_phy.loc[experiment_idx, 'ab_acc'] = ab_acc\n",
    "df_report_phy.loc[experiment_idx, 'ab_b_f1'] = ab_b_f1\n",
    "df_report_phy.loc[experiment_idx, 'ab_b_acc'] = ab_b_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lda_pipeline():\n",
    "    return make_pipeline(StandardScaler(), LDA())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline vs Stress vs Non-stress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '71.62674387682108 +-11.33211766138123',\n",
       " 'f1_macro': '65.30661915803545 +-12.535242577794905'}"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lda_all = create_lda_pipeline()\n",
    "cv_lda_all = run_cv(model_lda_all, X, y, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_lda_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_lda_all['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.9207167403043691\n",
      "5 0.5733006736068585\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_lda_all['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_lda_all['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.8889149709505785\n",
      "7 0.42365088765629805\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_lda_all['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Physiological modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '74.02462925388204 +-13.077506856399717',\n",
       " 'f1_macro': '66.61675817610353 +-15.285442746968817'}"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lda_phy = create_lda_pipeline()\n",
    "cv_lda_phy = run_cv(model_lda_phy, X_p, y, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_lda_phy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_lda_phy['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.9130859375\n",
      "5 0.500796080832823\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_lda_phy['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_lda_phy['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.862461807316382\n",
      "7 0.2921918988479306\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_lda_phy['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stress vs Non-Stress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '89.0251616455377 +-9.608988218953025',\n",
       " 'f1_macro': '86.07048596185419 +-12.733815270677093'}"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_b_lda_all = create_lda_pipeline()\n",
    "cv_b_lda_all = run_cv(model_b_lda_all, X, y_b, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_b_lda_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_lda_all['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0.9998775260257196\n",
      "4 0.7021432050657574\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_lda_all['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_lda_all['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0.9998542995246666\n",
      "4 0.5792608046316486\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_lda_all['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Physiological modalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': '89.85557568780152 +-9.53372978861513',\n",
       " 'f1_macro': '86.43388559424253 +-14.678529317649266'}"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_b_lda_phy = create_lda_pipeline()\n",
    "cv_b_lda_phy = run_cv(model_b_lda_phy, X_p, y_b, groups=df['subject'], cv=LeaveOneGroupOut())\n",
    "test_score(cv_b_lda_phy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_lda_phy['test_accuracy']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 0.9995045212436516\n",
      "7 0.6563626996393611\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_lda_phy['test_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8Y6aZSV4M7KyqG2Y8wft7XlUdz+iTOl+T5PmNcucz+pX13VV1HPC/jA4HNDHcQLcK+GijvEcz2tM9FngScGSSM1pkV9UWRocVrgY+C9wE/KxF9h5eq2j0W+HBkOQ84D7gklaZVXVeVS0ZMs8aN34Sww/iv6DBD4jDvdAn+diBw0qSBzMq80uq6uOz8RrD4YVrgJUN4p4LrEpyB6NDWr+Z5MMNcnfvmVJVO4ErGR1Ca2E7sH3KbyhXMCr4Vk4GvlJVdzXKewHwraraVVU/BT4O/FqjbKrqvVX1K1X1fOCHjM7dtHJXkicCDH/ubJg9a5K8Angx8LLhB1FrlwAvbZT1y4x+2N88vA8XA19J8oT9DTrcC32Sjx04bCQJo+O6W6rqnY2zF+4+W5/kYcALga/NNLeq3lhVi6tqKaOv7xeqasZ7j0mOTPLI3cuMTlQ1uaqoqu4EtiV52rDpJGBzi+zBGhodbhl8B3h2kocP3yMnMTq/0kSSxw1/HsPo+PlHWmXzix/r8XLgnxtmz4okKxkdQlxVVfc0zF02ZXU1Dd5/AFX11ap6XFUtHd6H2xldXHHngYQd1g9GxwS/zuhql/MaZV7K6FjmT4cv3qsa5T6P0a+ktzD61fcm4JRG2c8EbhyybwXOn4Wv9Yk0usqF0ZVJNw+PTa3+7abkPwvYOHw9PgE8ulHukYw+WO6oxvN9M6MCuBX4EPCQhtn/xugH2s3ASTPIud/7Angs8HngG4yuoHlMw+xTh+WfAHcBVzXK3cro3Nvu9+B+X42yl9yPDf9+twCfBBa1+lpMe/4ODvAqF2/9l6ROHO6HXCRJE7LQJakTFrokdcJCl6ROWOiS1AkLXZI6YaFLUif+D7x2LlxZmdCNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=list(range(len(subjects))), y=cv_b_lda_phy['test_f1_macro']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 0.9993954539756483\n",
      "7 0.4064358295080224\n"
     ]
    }
   ],
   "source": [
    "min_max(cv_b_lda_phy['test_f1_macro'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Reporting results\n",
    "lda_acc, lda_f1 = raw_scores(cv_lda_all)\n",
    "lda_b_acc, lda_b_f1 = raw_scores(cv_b_lda_all)\n",
    "df_report_all.loc[experiment_idx, 'lda_f1'] = lda_f1\n",
    "df_report_all.loc[experiment_idx, 'lda_acc'] = lda_acc\n",
    "df_report_all.loc[experiment_idx, 'lda_b_f1'] = lda_b_f1\n",
    "df_report_all.loc[experiment_idx, 'lda_b_acc'] = lda_b_acc\n",
    "\n",
    "lda_acc, lda_f1 = raw_scores(cv_lda_phy)\n",
    "lda_b_acc, lda_b_f1 = raw_scores(cv_b_lda_phy)\n",
    "df_report_phy.loc[experiment_idx, 'lda_f1'] = lda_f1\n",
    "df_report_phy.loc[experiment_idx, 'lda_acc'] = lda_acc\n",
    "df_report_phy.loc[experiment_idx, 'lda_b_f1'] = lda_b_f1\n",
    "df_report_phy.loc[experiment_idx, 'lda_b_acc'] = lda_b_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rf_f1</th>\n",
       "      <th>rf_acc</th>\n",
       "      <th>rf_b_f1</th>\n",
       "      <th>rf_b_acc</th>\n",
       "      <th>ab_f1</th>\n",
       "      <th>ab_acc</th>\n",
       "      <th>ab_b_f1</th>\n",
       "      <th>ab_b_acc</th>\n",
       "      <th>lda_f1</th>\n",
       "      <th>lda_acc</th>\n",
       "      <th>lda_b_f1</th>\n",
       "      <th>lda_b_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.569973</td>\n",
       "      <td>0.694808</td>\n",
       "      <td>0.764492</td>\n",
       "      <td>0.848803</td>\n",
       "      <td>0.531372</td>\n",
       "      <td>0.628832</td>\n",
       "      <td>0.730374</td>\n",
       "      <td>0.799242</td>\n",
       "      <td>0.653066</td>\n",
       "      <td>0.716267</td>\n",
       "      <td>0.860705</td>\n",
       "      <td>0.890252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.569680</td>\n",
       "      <td>0.691200</td>\n",
       "      <td>0.760651</td>\n",
       "      <td>0.843797</td>\n",
       "      <td>0.539400</td>\n",
       "      <td>0.633503</td>\n",
       "      <td>0.731361</td>\n",
       "      <td>0.800324</td>\n",
       "      <td>0.653066</td>\n",
       "      <td>0.716267</td>\n",
       "      <td>0.860705</td>\n",
       "      <td>0.890252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.564980</td>\n",
       "      <td>0.688050</td>\n",
       "      <td>0.758422</td>\n",
       "      <td>0.845627</td>\n",
       "      <td>0.553703</td>\n",
       "      <td>0.641779</td>\n",
       "      <td>0.737756</td>\n",
       "      <td>0.800519</td>\n",
       "      <td>0.653066</td>\n",
       "      <td>0.716267</td>\n",
       "      <td>0.860705</td>\n",
       "      <td>0.890252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.587290</td>\n",
       "      <td>0.697717</td>\n",
       "      <td>0.760592</td>\n",
       "      <td>0.845984</td>\n",
       "      <td>0.541359</td>\n",
       "      <td>0.631875</td>\n",
       "      <td>0.727363</td>\n",
       "      <td>0.794404</td>\n",
       "      <td>0.653066</td>\n",
       "      <td>0.716267</td>\n",
       "      <td>0.860705</td>\n",
       "      <td>0.890252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.565213</td>\n",
       "      <td>0.681651</td>\n",
       "      <td>0.759584</td>\n",
       "      <td>0.843335</td>\n",
       "      <td>0.552990</td>\n",
       "      <td>0.644787</td>\n",
       "      <td>0.740850</td>\n",
       "      <td>0.806747</td>\n",
       "      <td>0.653066</td>\n",
       "      <td>0.716267</td>\n",
       "      <td>0.860705</td>\n",
       "      <td>0.890252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rf_f1    rf_acc   rf_b_f1  rf_b_acc     ab_f1    ab_acc   ab_b_f1  \\\n",
       "0  0.569973  0.694808  0.764492  0.848803  0.531372  0.628832  0.730374   \n",
       "1  0.569680  0.691200  0.760651  0.843797  0.539400  0.633503  0.731361   \n",
       "2  0.564980  0.688050  0.758422  0.845627  0.553703  0.641779  0.737756   \n",
       "3  0.587290  0.697717  0.760592  0.845984  0.541359  0.631875  0.727363   \n",
       "4  0.565213  0.681651  0.759584  0.843335  0.552990  0.644787  0.740850   \n",
       "\n",
       "   ab_b_acc    lda_f1   lda_acc  lda_b_f1  lda_b_acc  \n",
       "0  0.799242  0.653066  0.716267  0.860705   0.890252  \n",
       "1  0.800324  0.653066  0.716267  0.860705   0.890252  \n",
       "2  0.800519  0.653066  0.716267  0.860705   0.890252  \n",
       "3  0.794404  0.653066  0.716267  0.860705   0.890252  \n",
       "4  0.806747  0.653066  0.716267  0.860705   0.890252  "
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_report_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rf_f1</th>\n",
       "      <td>57.14</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_acc</th>\n",
       "      <td>69.07</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_b_f1</th>\n",
       "      <td>76.07</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_b_acc</th>\n",
       "      <td>84.55</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab_f1</th>\n",
       "      <td>54.38</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab_acc</th>\n",
       "      <td>63.62</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab_b_f1</th>\n",
       "      <td>73.35</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab_b_acc</th>\n",
       "      <td>80.02</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda_f1</th>\n",
       "      <td>65.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda_acc</th>\n",
       "      <td>71.63</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda_b_f1</th>\n",
       "      <td>86.07</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda_b_acc</th>\n",
       "      <td>89.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean   std\n",
       "rf_f1      57.14  0.92\n",
       "rf_acc     69.07  0.62\n",
       "rf_b_f1    76.07  0.23\n",
       "rf_b_acc   84.55  0.22\n",
       "ab_f1      54.38  0.95\n",
       "ab_acc     63.62  0.68\n",
       "ab_b_f1    73.35  0.56\n",
       "ab_b_acc   80.02  0.44\n",
       "lda_f1     65.31  0.00\n",
       "lda_acc    71.63  0.00\n",
       "lda_b_f1   86.07  0.00\n",
       "lda_b_acc  89.03  0.00"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_all = df_report_all.describe().T[['mean', 'std']].apply(lambda x: x * 100).round(decimals=2)\n",
    "eval_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eval_all.sort_values(by='mean', ascending=False).to_markdown()\n",
    "# eval_all.to_markdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rf_f1</th>\n",
       "      <th>rf_acc</th>\n",
       "      <th>rf_b_f1</th>\n",
       "      <th>rf_b_acc</th>\n",
       "      <th>ab_f1</th>\n",
       "      <th>ab_acc</th>\n",
       "      <th>ab_b_f1</th>\n",
       "      <th>ab_b_acc</th>\n",
       "      <th>lda_f1</th>\n",
       "      <th>lda_acc</th>\n",
       "      <th>lda_b_f1</th>\n",
       "      <th>lda_b_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.611613</td>\n",
       "      <td>0.712413</td>\n",
       "      <td>0.792816</td>\n",
       "      <td>0.858259</td>\n",
       "      <td>0.600025</td>\n",
       "      <td>0.677185</td>\n",
       "      <td>0.765867</td>\n",
       "      <td>0.824575</td>\n",
       "      <td>0.666168</td>\n",
       "      <td>0.740246</td>\n",
       "      <td>0.864339</td>\n",
       "      <td>0.898556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.595537</td>\n",
       "      <td>0.707775</td>\n",
       "      <td>0.789591</td>\n",
       "      <td>0.860137</td>\n",
       "      <td>0.610302</td>\n",
       "      <td>0.687882</td>\n",
       "      <td>0.768598</td>\n",
       "      <td>0.824075</td>\n",
       "      <td>0.666168</td>\n",
       "      <td>0.740246</td>\n",
       "      <td>0.864339</td>\n",
       "      <td>0.898556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.605814</td>\n",
       "      <td>0.706488</td>\n",
       "      <td>0.787350</td>\n",
       "      <td>0.857363</td>\n",
       "      <td>0.579471</td>\n",
       "      <td>0.661721</td>\n",
       "      <td>0.772506</td>\n",
       "      <td>0.828806</td>\n",
       "      <td>0.666168</td>\n",
       "      <td>0.740246</td>\n",
       "      <td>0.864339</td>\n",
       "      <td>0.898556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.617625</td>\n",
       "      <td>0.715526</td>\n",
       "      <td>0.780956</td>\n",
       "      <td>0.851126</td>\n",
       "      <td>0.594276</td>\n",
       "      <td>0.670094</td>\n",
       "      <td>0.775233</td>\n",
       "      <td>0.832660</td>\n",
       "      <td>0.666168</td>\n",
       "      <td>0.740246</td>\n",
       "      <td>0.864339</td>\n",
       "      <td>0.898556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.617676</td>\n",
       "      <td>0.711437</td>\n",
       "      <td>0.778227</td>\n",
       "      <td>0.849322</td>\n",
       "      <td>0.598388</td>\n",
       "      <td>0.676511</td>\n",
       "      <td>0.776192</td>\n",
       "      <td>0.834557</td>\n",
       "      <td>0.666168</td>\n",
       "      <td>0.740246</td>\n",
       "      <td>0.864339</td>\n",
       "      <td>0.898556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rf_f1    rf_acc   rf_b_f1  rf_b_acc     ab_f1    ab_acc   ab_b_f1  \\\n",
       "0  0.611613  0.712413  0.792816  0.858259  0.600025  0.677185  0.765867   \n",
       "1  0.595537  0.707775  0.789591  0.860137  0.610302  0.687882  0.768598   \n",
       "2  0.605814  0.706488  0.787350  0.857363  0.579471  0.661721  0.772506   \n",
       "3  0.617625  0.715526  0.780956  0.851126  0.594276  0.670094  0.775233   \n",
       "4  0.617676  0.711437  0.778227  0.849322  0.598388  0.676511  0.776192   \n",
       "\n",
       "   ab_b_acc    lda_f1   lda_acc  lda_b_f1  lda_b_acc  \n",
       "0  0.824575  0.666168  0.740246  0.864339   0.898556  \n",
       "1  0.824075  0.666168  0.740246  0.864339   0.898556  \n",
       "2  0.828806  0.666168  0.740246  0.864339   0.898556  \n",
       "3  0.832660  0.666168  0.740246  0.864339   0.898556  \n",
       "4  0.834557  0.666168  0.740246  0.864339   0.898556  "
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_report_phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rf_f1</th>\n",
       "      <td>60.97</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_acc</th>\n",
       "      <td>71.07</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_b_f1</th>\n",
       "      <td>78.58</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_b_acc</th>\n",
       "      <td>85.52</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab_f1</th>\n",
       "      <td>59.65</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab_acc</th>\n",
       "      <td>67.47</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab_b_f1</th>\n",
       "      <td>77.17</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ab_b_acc</th>\n",
       "      <td>82.89</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda_f1</th>\n",
       "      <td>66.62</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda_acc</th>\n",
       "      <td>74.02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda_b_f1</th>\n",
       "      <td>86.43</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lda_b_acc</th>\n",
       "      <td>89.86</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean   std\n",
       "rf_f1      60.97  0.93\n",
       "rf_acc     71.07  0.36\n",
       "rf_b_f1    78.58  0.61\n",
       "rf_b_acc   85.52  0.47\n",
       "ab_f1      59.65  1.12\n",
       "ab_acc     67.47  0.97\n",
       "ab_b_f1    77.17  0.44\n",
       "ab_b_acc   82.89  0.47\n",
       "lda_f1     66.62  0.00\n",
       "lda_acc    74.02  0.00\n",
       "lda_b_f1   86.43  0.00\n",
       "lda_b_acc  89.86  0.00"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_phy = df_report_phy.describe().T[['mean', 'std']].apply(lambda x: x * 100).round(decimals=2)\n",
    "eval_phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eval_phy.to_markdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline vs Stress vs Amusement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "fi_3 = DecisionTreeClassifier()\n",
    "fi_3.fit(X, y)\n",
    "df_3_fi = pd.DataFrame({'cols': X.columns, 'imp': fi_3.feature_importances_}).sort_values('imp', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "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",
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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>cols</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>RESP_exhal_mean</td>\n",
       "      <td>0.239524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>EDA_SCR_no</td>\n",
       "      <td>0.135226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ECG_hr_mean</td>\n",
       "      <td>0.106965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>EMG_peak_amp_sum</td>\n",
       "      <td>0.084956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ACC_xyz_std</td>\n",
       "      <td>0.068314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>TEMP_min</td>\n",
       "      <td>0.057586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>EDA_std</td>\n",
       "      <td>0.039229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>EMG_peak_no</td>\n",
       "      <td>0.024425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ACC_xzy_mean</td>\n",
       "      <td>0.023665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACC_x_mean</td>\n",
       "      <td>0.022358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                cols       imp\n",
       "64   RESP_exhal_mean  0.239524\n",
       "36        EDA_SCR_no  0.135226\n",
       "15       ECG_hr_mean  0.106965\n",
       "56  EMG_peak_amp_sum  0.084956\n",
       "5        ACC_xyz_std  0.068314\n",
       "74          TEMP_min  0.057586\n",
       "46           EDA_std  0.039229\n",
       "58       EMG_peak_no  0.024425\n",
       "6       ACC_xzy_mean  0.023665\n",
       "1         ACC_x_mean  0.022358"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3_fi[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_3_fi.iloc[:10].to_markdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stress vs Non-stress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cols</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>RESP_exhal_mean</td>\n",
       "      <td>0.395155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ECG_hr_mean</td>\n",
       "      <td>0.144775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>EDA_SCR_no</td>\n",
       "      <td>0.138381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACC_x_mean</td>\n",
       "      <td>0.042426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>TEMP_max</td>\n",
       "      <td>0.037903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>EMG_peak_no</td>\n",
       "      <td>0.037576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>EDA_scr_area</td>\n",
       "      <td>0.034910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>RESP_exhal_std</td>\n",
       "      <td>0.023892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ACC_y_mean</td>\n",
       "      <td>0.016804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>EMG_peak_amp_sum</td>\n",
       "      <td>0.016447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                cols       imp\n",
       "64   RESP_exhal_mean  0.395155\n",
       "15       ECG_hr_mean  0.144775\n",
       "36        EDA_SCR_no  0.138381\n",
       "1         ACC_x_mean  0.042426\n",
       "72          TEMP_max  0.037903\n",
       "58       EMG_peak_no  0.037576\n",
       "44      EDA_scr_area  0.034910\n",
       "65    RESP_exhal_std  0.023892\n",
       "8         ACC_y_mean  0.016804\n",
       "56  EMG_peak_amp_sum  0.016447"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi_b = DecisionTreeClassifier()\n",
    "fi_b.fit(X, y_b)\n",
    "df_b_fi = pd.DataFrame({'cols': X.columns, 'imp': fi_b.feature_importances_}).sort_values('imp', ascending=False)\n",
    "df_b_fi[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_b_fi.iloc[:10].to_markdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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