auto_ml_stress_simplified_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "from tpot import TPOTClassifier\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal \n",
    "import pickle\n",
    "\n",
    "\n",
    "import sklearn.metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import svm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import precision_recall_fscore_support \n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataframe_hrv = pd.read_csv(\"dataset/dataframe_hrv.csv\")\n",
    "dataframe_hrv = dataframe_hrv.reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ECG</th>\n",
       "      <th>EMG</th>\n",
       "      <th>HR</th>\n",
       "      <th>RESP</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>footGSR</th>\n",
       "      <th>handGSR</th>\n",
       "      <th>interval in seconds</th>\n",
       "      <th>marker</th>\n",
       "      <th>newtime</th>\n",
       "      <th>stress</th>\n",
       "      <th>time</th>\n",
       "      <th>NNRR</th>\n",
       "      <th>AVNN</th>\n",
       "      <th>SDNN</th>\n",
       "      <th>RMSSD</th>\n",
       "      <th>pNN50</th>\n",
       "      <th>TP</th>\n",
       "      <th>ULF</th>\n",
       "      <th>VLF</th>\n",
       "      <th>LF</th>\n",
       "      <th>HF</th>\n",
       "      <th>LF_HF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.001974</td>\n",
       "      <td>-0.004737</td>\n",
       "      <td>77.815789</td>\n",
       "      <td>10.801842</td>\n",
       "      <td>12.529684</td>\n",
       "      <td>2.417132</td>\n",
       "      <td>10.889447</td>\n",
       "      <td>0.614632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.529684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.529684</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.617297</td>\n",
       "      <td>3.558630e-02</td>\n",
       "      <td>0.015203</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>3.00200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002935</td>\n",
       "      <td>-0.004457</td>\n",
       "      <td>101.978261</td>\n",
       "      <td>10.750609</td>\n",
       "      <td>30.503500</td>\n",
       "      <td>2.417109</td>\n",
       "      <td>11.251065</td>\n",
       "      <td>0.647826</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.503500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.503500</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>0.647889</td>\n",
       "      <td>1.354660e-02</td>\n",
       "      <td>0.013858</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.79371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006745</td>\n",
       "      <td>-0.003426</td>\n",
       "      <td>104.957447</td>\n",
       "      <td>10.557234</td>\n",
       "      <td>52.523021</td>\n",
       "      <td>2.226872</td>\n",
       "      <td>11.379638</td>\n",
       "      <td>0.646383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.523021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.523021</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.645000</td>\n",
       "      <td>2.240000e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.004043</td>\n",
       "      <td>-0.002532</td>\n",
       "      <td>87.702128</td>\n",
       "      <td>10.640128</td>\n",
       "      <td>74.402170</td>\n",
       "      <td>2.173021</td>\n",
       "      <td>11.470830</td>\n",
       "      <td>0.645000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.402170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.402170</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.645000</td>\n",
       "      <td>2.240000e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.012745</td>\n",
       "      <td>-0.004426</td>\n",
       "      <td>88.829787</td>\n",
       "      <td>10.699319</td>\n",
       "      <td>96.219617</td>\n",
       "      <td>2.017106</td>\n",
       "      <td>11.135255</td>\n",
       "      <td>0.645000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.219617</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.219617</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.645000</td>\n",
       "      <td>2.240000e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ECG       EMG          HR       RESP    Seconds   footGSR    handGSR  \\\n",
       "0 -0.001974 -0.004737  77.815789   10.801842  12.529684  2.417132  10.889447   \n",
       "1  0.002935 -0.004457  101.978261  10.750609  30.503500  2.417109  11.251065   \n",
       "2  0.006745 -0.003426  104.957447  10.557234  52.523021  2.226872  11.379638   \n",
       "3 -0.004043 -0.002532  87.702128   10.640128  74.402170  2.173021  11.470830   \n",
       "4  0.012745 -0.004426  88.829787   10.699319  96.219617  2.017106  11.135255   \n",
       "\n",
       "   interval in seconds  marker    newtime  stress       time      NNRR  \\\n",
       "0  0.614632            NaN      12.529684  0.0     12.529684  0.973684   \n",
       "1  0.647826            NaN      30.503500  0.0     30.503500  0.978261   \n",
       "2  0.646383            NaN      52.523021  0.0     52.523021  0.978723   \n",
       "3  0.645000            NaN      74.402170  0.0     74.402170  0.978723   \n",
       "4  0.645000            NaN      96.219617  0.0     96.219617  0.978723   \n",
       "\n",
       "       AVNN          SDNN     RMSSD     pNN50        TP  ULF       VLF  \\\n",
       "0  0.617297  3.558630e-02  0.015203  0.055556  0.001238  0.0  0.000696   \n",
       "1  0.647889  1.354660e-02  0.013858  0.045455  0.000144  0.0  0.000009   \n",
       "2  0.645000  2.240000e-08  0.000000  0.000000 NaN        0.0 NaN         \n",
       "3  0.645000  2.240000e-08  0.000000  0.000000 NaN        0.0 NaN         \n",
       "4  0.645000  2.240000e-08  0.000000  0.000000 NaN        0.0 NaN         \n",
       "\n",
       "         LF        HF    LF_HF  \n",
       "0  0.000407  0.000135  3.00200  \n",
       "1  0.000060  0.000075  0.79371  \n",
       "2 NaN       NaN       NaN       \n",
       "3 NaN       NaN       NaN       \n",
       "4 NaN       NaN       NaN       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dataframe_hrv.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ECG</th>\n",
       "      <th>EMG</th>\n",
       "      <th>HR</th>\n",
       "      <th>RESP</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>footGSR</th>\n",
       "      <th>handGSR</th>\n",
       "      <th>interval in seconds</th>\n",
       "      <th>marker</th>\n",
       "      <th>newtime</th>\n",
       "      <th>stress</th>\n",
       "      <th>time</th>\n",
       "      <th>NNRR</th>\n",
       "      <th>AVNN</th>\n",
       "      <th>SDNN</th>\n",
       "      <th>RMSSD</th>\n",
       "      <th>pNN50</th>\n",
       "      <th>TP</th>\n",
       "      <th>ULF</th>\n",
       "      <th>VLF</th>\n",
       "      <th>LF</th>\n",
       "      <th>HF</th>\n",
       "      <th>LF_HF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4023.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4056.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>3722.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4.007000e+03</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>3830.000000</td>\n",
       "      <td>3957.000000</td>\n",
       "      <td>4002.000000</td>\n",
       "      <td>4002.000000</td>\n",
       "      <td>4002.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.170927</td>\n",
       "      <td>0.604475</td>\n",
       "      <td>83.136251</td>\n",
       "      <td>29.846928</td>\n",
       "      <td>2278.789710</td>\n",
       "      <td>6.487689</td>\n",
       "      <td>11.523244</td>\n",
       "      <td>0.788440</td>\n",
       "      <td>12.057030</td>\n",
       "      <td>35171.533570</td>\n",
       "      <td>0.516078</td>\n",
       "      <td>2278.789710</td>\n",
       "      <td>0.975377</td>\n",
       "      <td>0.788020</td>\n",
       "      <td>5.781201e-02</td>\n",
       "      <td>0.027223</td>\n",
       "      <td>0.034246</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.000728</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>3.555695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.137222</td>\n",
       "      <td>0.807892</td>\n",
       "      <td>17.633319</td>\n",
       "      <td>14.024560</td>\n",
       "      <td>1313.496347</td>\n",
       "      <td>4.307487</td>\n",
       "      <td>6.739658</td>\n",
       "      <td>0.109768</td>\n",
       "      <td>2.612543</td>\n",
       "      <td>19946.879538</td>\n",
       "      <td>0.431116</td>\n",
       "      <td>1313.496347</td>\n",
       "      <td>0.002469</td>\n",
       "      <td>0.109363</td>\n",
       "      <td>3.401004e-02</td>\n",
       "      <td>0.016964</td>\n",
       "      <td>0.025153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003244</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>2.080121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.699585</td>\n",
       "      <td>-0.697800</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>-12.606244</td>\n",
       "      <td>12.529684</td>\n",
       "      <td>0.971111</td>\n",
       "      <td>-28.381533</td>\n",
       "      <td>0.527944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.529684</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.529684</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.528170</td>\n",
       "      <td>7.840000e-09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.084500</td>\n",
       "      <td>0.108651</td>\n",
       "      <td>72.861111</td>\n",
       "      <td>28.565064</td>\n",
       "      <td>1114.941256</td>\n",
       "      <td>2.800841</td>\n",
       "      <td>6.713688</td>\n",
       "      <td>0.718930</td>\n",
       "      <td>10.350161</td>\n",
       "      <td>18158.311120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1114.941256</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.720548</td>\n",
       "      <td>3.187020e-02</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.014617</td>\n",
       "      <td>0.009989</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.220580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.144171</td>\n",
       "      <td>0.302947</td>\n",
       "      <td>79.926829</td>\n",
       "      <td>32.916500</td>\n",
       "      <td>2295.794860</td>\n",
       "      <td>5.770098</td>\n",
       "      <td>9.651417</td>\n",
       "      <td>0.789732</td>\n",
       "      <td>11.070972</td>\n",
       "      <td>34158.425430</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>2295.794860</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.790125</td>\n",
       "      <td>4.637430e-02</td>\n",
       "      <td>0.022889</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.034332</td>\n",
       "      <td>0.028017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.169260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.207457</td>\n",
       "      <td>0.822690</td>\n",
       "      <td>89.400000</td>\n",
       "      <td>38.627444</td>\n",
       "      <td>3425.276711</td>\n",
       "      <td>8.837477</td>\n",
       "      <td>16.647151</td>\n",
       "      <td>0.869053</td>\n",
       "      <td>12.879199</td>\n",
       "      <td>52300.114070</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>3425.276711</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>0.868135</td>\n",
       "      <td>8.941850e-02</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.105621</td>\n",
       "      <td>0.105178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.865465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.681714</td>\n",
       "      <td>9.491700</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>52.089590</td>\n",
       "      <td>5005.373902</td>\n",
       "      <td>22.582463</td>\n",
       "      <td>31.219800</td>\n",
       "      <td>1.040088</td>\n",
       "      <td>35.624000</td>\n",
       "      <td>70173.851200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5005.373902</td>\n",
       "      <td>0.981481</td>\n",
       "      <td>1.038360</td>\n",
       "      <td>1.645780e-01</td>\n",
       "      <td>0.080981</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.035841</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>9.117240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ECG          EMG           HR         RESP      Seconds  \\\n",
       "count  4129.000000  4023.000000  4129.000000  4129.000000  4129.000000   \n",
       "mean   0.170927     0.604475     83.136251    29.846928    2278.789710   \n",
       "std    0.137222     0.807892     17.633319    14.024560    1313.496347   \n",
       "min   -0.699585    -0.697800     11.800000   -12.606244    12.529684     \n",
       "25%    0.084500     0.108651     72.861111    28.565064    1114.941256   \n",
       "50%    0.144171     0.302947     79.926829    32.916500    2295.794860   \n",
       "75%    0.207457     0.822690     89.400000    38.627444    3425.276711   \n",
       "max    0.681714     9.491700     372.000000   52.089590    5005.373902   \n",
       "\n",
       "           footGSR      handGSR  interval in seconds       marker  \\\n",
       "count  4129.000000  4056.000000  4129.000000          3722.000000   \n",
       "mean   6.487689     11.523244    0.788440             12.057030     \n",
       "std    4.307487     6.739658     0.109768             2.612543      \n",
       "min    0.971111    -28.381533    0.527944             0.000000      \n",
       "25%    2.800841     6.713688     0.718930             10.350161     \n",
       "50%    5.770098     9.651417     0.789732             11.070972     \n",
       "75%    8.837477     16.647151    0.869053             12.879199     \n",
       "max    22.582463    31.219800    1.040088             35.624000     \n",
       "\n",
       "            newtime       stress         time         NNRR         AVNN  \\\n",
       "count  4129.000000   4129.000000  4129.000000  4129.000000  4129.000000   \n",
       "mean   35171.533570  0.516078     2278.789710  0.975377     0.788020      \n",
       "std    19946.879538  0.431116     1313.496347  0.002469     0.109363      \n",
       "min    12.529684     0.000000     12.529684    0.970588     0.528170      \n",
       "25%    18158.311120  0.000000     1114.941256  0.973684     0.720548      \n",
       "50%    34158.425430  0.631579     2295.794860  0.975000     0.790125      \n",
       "75%    52300.114070  0.974359     3425.276711  0.977273     0.868135      \n",
       "max    70173.851200  1.000000     5005.373902  0.981481     1.038360      \n",
       "\n",
       "               SDNN        RMSSD        pNN50           TP          ULF  \\\n",
       "count  4.007000e+03  4129.000000  4129.000000  3830.000000  3957.000000   \n",
       "mean   5.781201e-02  0.027223     0.034246     inf          inf           \n",
       "std    3.401004e-02  0.016964     0.025153    NaN          NaN            \n",
       "min    7.840000e-09  0.000000     0.000000     0.000008     0.000000      \n",
       "25%    3.187020e-02  0.013743     0.025641     0.014617     0.009989      \n",
       "50%    4.637430e-02  0.022889     0.027027     0.034332     0.028017      \n",
       "75%    8.941850e-02  0.043000     0.030303     0.105621     0.105178      \n",
       "max    1.645780e-01  0.080981     0.257143     inf          inf           \n",
       "\n",
       "               VLF           LF           HF      LF_HF  \n",
       "count  4002.000000  4002.000000  4002.000000  58.000000  \n",
       "mean   0.000728     0.000003     0.000001     3.555695   \n",
       "std    0.003244     0.000031     0.000010     2.080121   \n",
       "min    0.000000     0.000000     0.000000     0.418526   \n",
       "25%    0.000000     0.000000     0.000000     2.220580   \n",
       "50%    0.000000     0.000000     0.000000     3.169260   \n",
       "75%    0.000000     0.000000     0.000000     4.865465   \n",
       "max    0.035841     0.000616     0.000261     9.117240   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ECG', 'EMG', 'HR', 'RESP', 'Seconds', 'footGSR', 'handGSR',\n",
      "       'interval in seconds', 'marker', 'newtime', 'stress', 'time', 'NNRR',\n",
      "       'AVNN', 'SDNN', 'RMSSD', 'pNN50', 'TP', 'ULF', 'VLF', 'LF', 'HF',\n",
      "       'LF_HF'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "display(dataframe_hrv.describe())\n",
    "print(dataframe_hrv.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fix_stress_labels(df='',label_column='stress'):\n",
    "    df['stress'] = np.where(df['stress']>=0.5, 1, 0)\n",
    "    display(df[\"stress\"].unique())\n",
    "    return df\n",
    "dataframe_hrv = fix_stress_labels(df=dataframe_hrv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def missing_values(df):\n",
    "    df = df.reset_index()\n",
    "    df = df.replace([np.inf, -np.inf], np.nan)\n",
    "    df[~np.isfinite(df)] = np.nan\n",
    "#     df.plot( y=[\"HR\"])\n",
    "    df['HR'].fillna((df['HR'].mean()), inplace=True)\n",
    "    df['HR'] = signal.medfilt(df['HR'],13) \n",
    "#     df.plot( y=[\"HR\"])\n",
    "\n",
    "    df=df.fillna(df.mean(),inplace=True)\n",
    "    return df\n",
    "\n",
    "dataframe_hrv = missing_values(dataframe_hrv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HR', 'interval in seconds', 'AVNN', 'RMSSD', 'pNN50', 'TP', 'ULF',\n",
       "       'VLF', 'LF', 'HF', 'LF_HF'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HR</th>\n",
       "      <th>interval in seconds</th>\n",
       "      <th>AVNN</th>\n",
       "      <th>RMSSD</th>\n",
       "      <th>pNN50</th>\n",
       "      <th>TP</th>\n",
       "      <th>ULF</th>\n",
       "      <th>VLF</th>\n",
       "      <th>LF</th>\n",
       "      <th>HF</th>\n",
       "      <th>LF_HF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "      <td>4129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>81.066143</td>\n",
       "      <td>0.788440</td>\n",
       "      <td>0.788020</td>\n",
       "      <td>0.027223</td>\n",
       "      <td>0.034246</td>\n",
       "      <td>0.060604</td>\n",
       "      <td>0.057902</td>\n",
       "      <td>0.000728</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>3.555695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.666652</td>\n",
       "      <td>0.109768</td>\n",
       "      <td>0.109363</td>\n",
       "      <td>0.016964</td>\n",
       "      <td>0.025153</td>\n",
       "      <td>0.056901</td>\n",
       "      <td>0.057757</td>\n",
       "      <td>0.003194</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.244431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60.558824</td>\n",
       "      <td>0.527944</td>\n",
       "      <td>0.528170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>73.512821</td>\n",
       "      <td>0.718930</td>\n",
       "      <td>0.720548</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.019485</td>\n",
       "      <td>0.012480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.555695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.842105</td>\n",
       "      <td>0.789732</td>\n",
       "      <td>0.790125</td>\n",
       "      <td>0.022889</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.047369</td>\n",
       "      <td>0.035371</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.555695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>87.400000</td>\n",
       "      <td>0.869053</td>\n",
       "      <td>0.868135</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.100771</td>\n",
       "      <td>0.098881</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.555695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>114.893617</td>\n",
       "      <td>1.040088</td>\n",
       "      <td>1.038360</td>\n",
       "      <td>0.080981</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>0.329891</td>\n",
       "      <td>0.329891</td>\n",
       "      <td>0.035841</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>9.117240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                HR  interval in seconds         AVNN        RMSSD  \\\n",
       "count  4129.000000  4129.000000          4129.000000  4129.000000   \n",
       "mean   81.066143    0.788440             0.788020     0.027223      \n",
       "std    10.666652    0.109768             0.109363     0.016964      \n",
       "min    60.558824    0.527944             0.528170     0.000000      \n",
       "25%    73.512821    0.718930             0.720548     0.013743      \n",
       "50%    78.842105    0.789732             0.790125     0.022889      \n",
       "75%    87.400000    0.869053             0.868135     0.043000      \n",
       "max    114.893617   1.040088             1.038360     0.080981      \n",
       "\n",
       "             pNN50           TP          ULF          VLF           LF  \\\n",
       "count  4129.000000  4129.000000  4129.000000  4129.000000  4129.000000   \n",
       "mean   0.034246     0.060604     0.057902     0.000728     0.000003      \n",
       "std    0.025153     0.056901     0.057757     0.003194     0.000030      \n",
       "min    0.000000     0.000008     0.000000     0.000000     0.000000      \n",
       "25%    0.025641     0.019485     0.012480     0.000000     0.000000      \n",
       "50%    0.027027     0.047369     0.035371     0.000000     0.000000      \n",
       "75%    0.030303     0.100771     0.098881     0.000000     0.000000      \n",
       "max    0.257143     0.329891     0.329891     0.035841     0.000616      \n",
       "\n",
       "                HF        LF_HF  \n",
       "count  4129.000000  4129.000000  \n",
       "mean   0.000001     3.555695     \n",
       "std    0.000010     0.244431     \n",
       "min    0.000000     0.418526     \n",
       "25%    0.000000     3.555695     \n",
       "50%    0.000000     3.555695     \n",
       "75%    0.000000     3.555695     \n",
       "max    0.000261     9.117240     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(4129, 11)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_x_columns = ['HR','interval in seconds','AVNN', 'RMSSD', 'pNN50', 'TP', 'ULF', 'VLF', 'LF', 'HF','LF_HF']\n",
    "\n",
    "X = dataframe_hrv[selected_x_columns]\n",
    "y = dataframe_hrv['stress']\n",
    "\n",
    "display(X.columns)\n",
    "display(X.describe())\n",
    "display(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  17%|█▋        | 38/220 [00:17<01:14,  2.46pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.7359913799971757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  26%|██▌       | 57/220 [00:26<01:18,  2.09pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 2 - Current best internal CV score: 0.76929638292857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  34%|███▎      | 74/220 [00:43<01:56,  1.25pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 3 - Current best internal CV score: 0.76929638292857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  41%|████▏     | 91/220 [00:59<01:43,  1.25pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 4 - Current best internal CV score: 0.76929638292857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  50%|█████     | 110/220 [01:39<04:11,  2.29s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 5 - Current best internal CV score: 0.7774748988273074\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  58%|█████▊    | 127/220 [02:01<01:59,  1.29s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 6 - Current best internal CV score: 0.7838352359049431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  67%|██████▋   | 147/220 [02:19<00:55,  1.32pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 7 - Current best internal CV score: 0.7838352359049431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  75%|███████▌  | 166/220 [02:53<00:56,  1.05s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 8 - Current best internal CV score: 0.7838352359049431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  85%|████████▍ | 186/220 [03:07<00:24,  1.41pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 9 - Current best internal CV score: 0.7914052141106741\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 10 - Current best internal CV score: 0.7920090778568057\n",
      "\n",
      "Best pipeline: KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=4, KNeighborsClassifier__p=DEFAULT, KNeighborsClassifier__weights=distance)\n",
      "0.805084745763\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "def do_tpot(generations=5, population_size=10,X='',y=''):\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                        train_size=0.80, test_size=0.20)\n",
    "\n",
    "    tpot = TPOTClassifier(generations=generations, population_size=population_size, verbosity=2,cv=3)\n",
    "    tpot.fit(X_train, y_train)\n",
    "    print(tpot.score(X_test, y_test))\n",
    "    tpot.export('tpot_pipeline.py')\n",
    "    return tpot\n",
    "\n",
    "tpot_classifer = do_tpot(generations=10, population_size=20,X=X,y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def plotFitBitReading(dfnewHRV='', predictor = \"none\",selected_x_columns=''):\n",
    "    dfnewHRV = missing_values(dfnewHRV)\n",
    "    dfnewPol = dfnewHRV[selected_x_columns].fillna(0)\n",
    "\n",
    "    print(dfnewPol.columns)\n",
    "    print(dfnewPol.shape)\n",
    "    pred = predictor.predict_proba(dfnewPol)\n",
    "    \n",
    "    dfpred = pd.DataFrame(pred)\n",
    "\n",
    "    dfpred.columns = [[\"FALSE\",\"TRUE\"]]\n",
    "    dfpred['stress'] = np.where(dfpred[\"TRUE\"] > 0.5, 1, np.nan)\n",
    "\n",
    "    \n",
    "    dfnewHRV[\"stress\"] = dfpred[\"stress\"]\n",
    "    dfnewHRV.loc[dfnewHRV[\"steps\"] > 0, 'stress'] = np.nan\n",
    "    #mark is to mark the RR peaks as stress\n",
    "    dfnewHRV.loc[dfnewHRV[\"stress\"] == 1, 'stress'] = dfnewHRV['interval in seconds'] \n",
    "    dfnewHRV.loc[dfnewHRV[\"steps\"] > 0, 'moving'] = dfnewHRV['interval in seconds'] \n",
    "    dfnewHRV[\"minutes\"] = (dfnewHRV['newtime']/60)/1000\n",
    "    \n",
    "    from itertools import cycle, islice\n",
    "    my_colors = list(islice(cycle(['b', 'r', 'y', 'k']), None, len(dfnewHRV)))\n",
    "    plot = dfnewHRV.plot(x=\"minutes\", y=['interval in seconds',\"stress\", \"moving\"],color=my_colors)\n",
    "    \n",
    "    fig = plot.get_figure()\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset/Vikings/Female_33_years_old/dfnewHRV_Female_33_years_old_vikings.csv\n",
      "dataset/Vikings/male_22years/dfnewHRVVIKINGSFULLSERIES.csv\n",
      "dataset/Vikings/Male_23_years_old/dfnewHRVMale_23_years_old.csv\n",
      "dataset/Vikings/Male_21_years/dfnewHRV.csv\n",
      "dataset/Vikings/Female_24_years_old/dfnewHRV.csv\n",
      "dataset/American Horror Story/male_22years_american_horror_story/dfnewHRVmale_22yearsAmericanHorrorStory.csv\n",
      "dataset/American Horror Story/Male_25_years_american_horror_story/dfnewHRV_Male_25_years_american_horror_story.csv\n",
      "dataset/American Horror Story/female_24_years_american_horror_story/dfnewHRV.csv\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "for filename in glob.iglob('dataset/**/*.csv', recursive=True):\n",
    "    if 'dfnew' in filename:\n",
    "        print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['HR', 'interval in seconds', 'AVNN', 'RMSSD', 'pNN50', 'TP', 'ULF',\n",
      "       'VLF', 'LF', 'HF', 'LF_HF'],\n",
      "      dtype='object')\n",
      "(185, 11)\n"
     ]
    },
    {
     "data": {
      "image/png": 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U7uqD4exZy0aWs2lz85Xl7kvNkvQ10XiRTO65l04B5eV+zTfeh3j8cQBSDj/u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\nJnellPJC/w8k44uUrD+otgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed6f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_df = pd.read_csv('dataset/Vikings/Female_24_years_old/dfnewHRV.csv')\n",
    "plotFitBitReading(input_df,tpot_classifer,selected_x_columns)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}