model - ecg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from keras import layers\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, recall_score, f1_score, confusion_matrix\n",
    "from sklearn.model_selection import LeaveOneGroupOut, LeavePGroupsOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>pid</th>\n",
       "      <th>lab</th>\n",
       "      <th>bpm</th>\n",
       "      <th>ibi</th>\n",
       "      <th>sdnn</th>\n",
       "      <th>sdsd</th>\n",
       "      <th>rmssd</th>\n",
       "      <th>pnn20</th>\n",
       "      <th>pnn50</th>\n",
       "      <th>hr_mad</th>\n",
       "      <th>sd1</th>\n",
       "      <th>sd2</th>\n",
       "      <th>s</th>\n",
       "      <th>sd1/sd2</th>\n",
       "      <th>breathingrate</th>\n",
       "      <th>lf</th>\n",
       "      <th>hf</th>\n",
       "      <th>lf/hf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S10_000</th>\n",
       "      <td>S10</td>\n",
       "      <td>Base</td>\n",
       "      <td>100.351586</td>\n",
       "      <td>597.897874</td>\n",
       "      <td>20.918173</td>\n",
       "      <td>3.974686</td>\n",
       "      <td>6.208745</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.62500</td>\n",
       "      <td>4.371335</td>\n",
       "      <td>29.151830</td>\n",
       "      <td>400.340724</td>\n",
       "      <td>0.149951</td>\n",
       "      <td>0.069023</td>\n",
       "      <td>4317.830834</td>\n",
       "      <td>886.033184</td>\n",
       "      <td>4.873216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S10_001</th>\n",
       "      <td>S10</td>\n",
       "      <td>Base</td>\n",
       "      <td>101.107492</td>\n",
       "      <td>593.427835</td>\n",
       "      <td>19.516506</td>\n",
       "      <td>4.361267</td>\n",
       "      <td>6.585369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.71875</td>\n",
       "      <td>4.647473</td>\n",
       "      <td>27.295107</td>\n",
       "      <td>398.521312</td>\n",
       "      <td>0.170268</td>\n",
       "      <td>0.121707</td>\n",
       "      <td>1633.377329</td>\n",
       "      <td>646.859113</td>\n",
       "      <td>2.525090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S10_002</th>\n",
       "      <td>S10</td>\n",
       "      <td>Base</td>\n",
       "      <td>101.610857</td>\n",
       "      <td>590.488080</td>\n",
       "      <td>18.094553</td>\n",
       "      <td>5.043338</td>\n",
       "      <td>7.379010</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.71875</td>\n",
       "      <td>5.211182</td>\n",
       "      <td>25.070505</td>\n",
       "      <td>410.439547</td>\n",
       "      <td>0.207861</td>\n",
       "      <td>0.069896</td>\n",
       "      <td>4289.566142</td>\n",
       "      <td>420.117395</td>\n",
       "      <td>10.210399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S10_003</th>\n",
       "      <td>S10</td>\n",
       "      <td>Base</td>\n",
       "      <td>102.648846</td>\n",
       "      <td>584.517045</td>\n",
       "      <td>18.080210</td>\n",
       "      <td>4.874242</td>\n",
       "      <td>6.949337</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.71875</td>\n",
       "      <td>4.907236</td>\n",
       "      <td>25.190906</td>\n",
       "      <td>388.356480</td>\n",
       "      <td>0.194802</td>\n",
       "      <td>0.069183</td>\n",
       "      <td>11034.009898</td>\n",
       "      <td>871.615284</td>\n",
       "      <td>12.659266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S10_004</th>\n",
       "      <td>S10</td>\n",
       "      <td>Base</td>\n",
       "      <td>103.052318</td>\n",
       "      <td>582.228535</td>\n",
       "      <td>17.769968</td>\n",
       "      <td>4.959666</td>\n",
       "      <td>7.270157</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.71875</td>\n",
       "      <td>5.140778</td>\n",
       "      <td>24.741109</td>\n",
       "      <td>399.574576</td>\n",
       "      <td>0.207783</td>\n",
       "      <td>0.069453</td>\n",
       "      <td>14116.895674</td>\n",
       "      <td>1392.987428</td>\n",
       "      <td>10.134259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pid   lab         bpm         ibi       sdnn      sdsd     rmssd  \\\n",
       "S10_000  S10  Base  100.351586  597.897874  20.918173  3.974686  6.208745   \n",
       "S10_001  S10  Base  101.107492  593.427835  19.516506  4.361267  6.585369   \n",
       "S10_002  S10  Base  101.610857  590.488080  18.094553  5.043338  7.379010   \n",
       "S10_003  S10  Base  102.648846  584.517045  18.080210  4.874242  6.949337   \n",
       "S10_004  S10  Base  103.052318  582.228535  17.769968  4.959666  7.270157   \n",
       "\n",
       "            pnn20  pnn50    hr_mad       sd1        sd2           s   sd1/sd2  \\\n",
       "S10_000  0.000000    0.0  15.62500  4.371335  29.151830  400.340724  0.149951   \n",
       "S10_001  0.000000    0.0  11.71875  4.647473  27.295107  398.521312  0.170268   \n",
       "S10_002  0.010526    0.0  11.71875  5.211182  25.070505  410.439547  0.207861   \n",
       "S10_003  0.010309    0.0  11.71875  4.907236  25.190906  388.356480  0.194802   \n",
       "S10_004  0.010309    0.0  11.71875  5.140778  24.741109  399.574576  0.207783   \n",
       "\n",
       "         breathingrate            lf           hf      lf/hf  \n",
       "S10_000       0.069023   4317.830834   886.033184   4.873216  \n",
       "S10_001       0.121707   1633.377329   646.859113   2.525090  \n",
       "S10_002       0.069896   4289.566142   420.117395  10.210399  \n",
       "S10_003       0.069183  11034.009898   871.615284  12.659266  \n",
       "S10_004       0.069453  14116.895674  1392.987428  10.134259  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecg_expert = pd.read_csv('ECG_expert_features.csv', index_col=0)\n",
    "ecg_expert.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3199 entries, S10_000 to S9_211\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   pid            3199 non-null   object \n",
      " 1   lab            3199 non-null   object \n",
      " 2   bpm            3199 non-null   float64\n",
      " 3   ibi            3199 non-null   float64\n",
      " 4   sdnn           3199 non-null   float64\n",
      " 5   sdsd           3199 non-null   float64\n",
      " 6   rmssd          3199 non-null   float64\n",
      " 7   pnn20          3199 non-null   float64\n",
      " 8   pnn50          3199 non-null   float64\n",
      " 9   hr_mad         3199 non-null   float64\n",
      " 10  sd1            3199 non-null   float64\n",
      " 11  sd2            3199 non-null   float64\n",
      " 12  s              3199 non-null   float64\n",
      " 13  sd1/sd2        3199 non-null   float64\n",
      " 14  breathingrate  3199 non-null   float64\n",
      " 15  lf             3199 non-null   float64\n",
      " 16  hf             3199 non-null   float64\n",
      " 17  lf/hf          3199 non-null   float64\n",
      "dtypes: float64(16), object(2)\n",
      "memory usage: 474.9+ KB\n"
     ]
    }
   ],
   "source": [
    "ecg_expert.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>bpm</th>\n",
       "      <th>ibi</th>\n",
       "      <th>sdnn</th>\n",
       "      <th>sdsd</th>\n",
       "      <th>rmssd</th>\n",
       "      <th>pnn20</th>\n",
       "      <th>pnn50</th>\n",
       "      <th>hr_mad</th>\n",
       "      <th>sd1</th>\n",
       "      <th>sd2</th>\n",
       "      <th>s</th>\n",
       "      <th>sd1/sd2</th>\n",
       "      <th>breathingrate</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>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>3.199000e+03</td>\n",
       "      <td>3.199000e+03</td>\n",
       "      <td>3199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>80.662209</td>\n",
       "      <td>778.932925</td>\n",
       "      <td>68.214313</td>\n",
       "      <td>30.405282</td>\n",
       "      <td>47.280005</td>\n",
       "      <td>0.506018</td>\n",
       "      <td>0.235460</td>\n",
       "      <td>45.479300</td>\n",
       "      <td>33.246691</td>\n",
       "      <td>88.592962</td>\n",
       "      <td>10932.294375</td>\n",
       "      <td>0.373138</td>\n",
       "      <td>0.120212</td>\n",
       "      <td>3.705577e+43</td>\n",
       "      <td>7.967254e+46</td>\n",
       "      <td>3.994220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.495826</td>\n",
       "      <td>155.005556</td>\n",
       "      <td>29.983841</td>\n",
       "      <td>19.748978</td>\n",
       "      <td>31.545604</td>\n",
       "      <td>0.235930</td>\n",
       "      <td>0.201243</td>\n",
       "      <td>23.294007</td>\n",
       "      <td>21.890124</td>\n",
       "      <td>38.379289</td>\n",
       "      <td>11800.780974</td>\n",
       "      <td>0.163020</td>\n",
       "      <td>0.106801</td>\n",
       "      <td>2.095863e+45</td>\n",
       "      <td>4.506255e+48</td>\n",
       "      <td>5.624331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.666315</td>\n",
       "      <td>248.263889</td>\n",
       "      <td>11.424330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.360596</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.812500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083261</td>\n",
       "      <td>0.040235</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>68.920100</td>\n",
       "      <td>688.181323</td>\n",
       "      <td>47.441935</td>\n",
       "      <td>17.641069</td>\n",
       "      <td>27.012776</td>\n",
       "      <td>0.338710</td>\n",
       "      <td>0.065789</td>\n",
       "      <td>29.296875</td>\n",
       "      <td>19.044313</td>\n",
       "      <td>61.241041</td>\n",
       "      <td>4017.152993</td>\n",
       "      <td>0.257849</td>\n",
       "      <td>0.068072</td>\n",
       "      <td>3.189891e+04</td>\n",
       "      <td>1.253283e+04</td>\n",
       "      <td>1.014948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.181029</td>\n",
       "      <td>787.597656</td>\n",
       "      <td>62.972445</td>\n",
       "      <td>25.520542</td>\n",
       "      <td>40.249355</td>\n",
       "      <td>0.539474</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>41.015625</td>\n",
       "      <td>28.430870</td>\n",
       "      <td>82.681692</td>\n",
       "      <td>7471.498163</td>\n",
       "      <td>0.333648</td>\n",
       "      <td>0.092625</td>\n",
       "      <td>6.763827e+04</td>\n",
       "      <td>3.046672e+04</td>\n",
       "      <td>2.352720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>87.186324</td>\n",
       "      <td>870.573317</td>\n",
       "      <td>83.935984</td>\n",
       "      <td>37.667085</td>\n",
       "      <td>57.918097</td>\n",
       "      <td>0.693333</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>58.593750</td>\n",
       "      <td>40.748777</td>\n",
       "      <td>109.284867</td>\n",
       "      <td>13641.033008</td>\n",
       "      <td>0.463232</td>\n",
       "      <td>0.119235</td>\n",
       "      <td>1.308992e+05</td>\n",
       "      <td>7.020529e+04</td>\n",
       "      <td>4.928540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>241.678322</td>\n",
       "      <td>1184.218750</td>\n",
       "      <td>233.234203</td>\n",
       "      <td>227.910152</td>\n",
       "      <td>333.036927</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>175.781250</td>\n",
       "      <td>233.695444</td>\n",
       "      <td>273.647490</td>\n",
       "      <td>175691.467479</td>\n",
       "      <td>1.817113</td>\n",
       "      <td>1.637018</td>\n",
       "      <td>1.185414e+47</td>\n",
       "      <td>2.548724e+50</td>\n",
       "      <td>86.421220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bpm          ibi         sdnn         sdsd        rmssd  \\\n",
       "count  3199.000000  3199.000000  3199.000000  3199.000000  3199.000000   \n",
       "mean     80.662209   778.932925    68.214313    30.405282    47.280005   \n",
       "std      19.495826   155.005556    29.983841    19.748978    31.545604   \n",
       "min      50.666315   248.263889    11.424330     0.000000     4.360596   \n",
       "25%      68.920100   688.181323    47.441935    17.641069    27.012776   \n",
       "50%      76.181029   787.597656    62.972445    25.520542    40.249355   \n",
       "75%      87.186324   870.573317    83.935984    37.667085    57.918097   \n",
       "max     241.678322  1184.218750   233.234203   227.910152   333.036927   \n",
       "\n",
       "             pnn20        pnn50       hr_mad          sd1          sd2  \\\n",
       "count  3199.000000  3199.000000  3199.000000  3199.000000  3199.000000   \n",
       "mean      0.506018     0.235460    45.479300    33.246691    88.592962   \n",
       "std       0.235930     0.201243    23.294007    21.890124    38.379289   \n",
       "min       0.000000     0.000000     7.812500     0.000000     0.000000   \n",
       "25%       0.338710     0.065789    29.296875    19.044313    61.241041   \n",
       "50%       0.539474     0.189189    41.015625    28.430870    82.681692   \n",
       "75%       0.693333     0.363636    58.593750    40.748777   109.284867   \n",
       "max       1.000000     1.000000   175.781250   233.695444   273.647490   \n",
       "\n",
       "                   s      sd1/sd2  breathingrate            lf            hf  \\\n",
       "count    3199.000000  3199.000000    3199.000000  3.199000e+03  3.199000e+03   \n",
       "mean    10932.294375     0.373138       0.120212  3.705577e+43  7.967254e+46   \n",
       "std     11800.780974     0.163020       0.106801  2.095863e+45  4.506255e+48   \n",
       "min         0.000000     0.083261       0.040235  0.000000e+00  0.000000e+00   \n",
       "25%      4017.152993     0.257849       0.068072  3.189891e+04  1.253283e+04   \n",
       "50%      7471.498163     0.333648       0.092625  6.763827e+04  3.046672e+04   \n",
       "75%     13641.033008     0.463232       0.119235  1.308992e+05  7.020529e+04   \n",
       "max    175691.467479     1.817113       1.637018  1.185414e+47  2.548724e+50   \n",
       "\n",
       "             lf/hf  \n",
       "count  3199.000000  \n",
       "mean      3.994220  \n",
       "std       5.624331  \n",
       "min       0.000000  \n",
       "25%       1.014948  \n",
       "50%       2.352720  \n",
       "75%       4.928540  \n",
       "max      86.421220  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecg_expert.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_to_number(labels):\n",
    "    # This function converts classes from categories to numbers, e.g. \"Base\" to 0, \"Fun\" to 1, etc.\n",
    "    classes = np.unique(labels)\n",
    "    for c in range(len(classes)):\n",
    "        labels[labels == classes[c]] = c    \n",
    "\n",
    "    return np.array(labels, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encoding(labels, num_classes):\n",
    "    # This function performs one hot encoding for labels\n",
    "    if type(labels[0]) == str:\n",
    "        labels = class_to_number(labels)\n",
    "    elif type(labels[0]) == float:\n",
    "        labels = int(labels)\n",
    "    Y = np.eye(num_classes)[labels]\n",
    "    \n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_participants = ['S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9', 'S10', 'S11', 'S13', 'S14']\n",
    "test_participants  = ['S15', 'S16', 'S17']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subsample indices for each set\n",
    "train_idx = ecg_expert[ecg_expert.pid.isin(train_participants)].index\n",
    "test_idx = ecg_expert[ecg_expert.pid.isin(test_participants)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data\n",
    "X_train = ecg_expert.loc[train_idx].iloc[:,2:].to_numpy()    # iloc[:,2:] drops first two columns (pid and lab)\n",
    "y_train = ecg_expert.loc[train_idx].lab.to_numpy()\n",
    "y_train = class_to_number(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_test  = ecg_expert.loc[test_idx].iloc[:,2:].to_numpy()\n",
    "y_test  = ecg_expert.loc[test_idx].lab.to_numpy()\n",
    "y_test  = class_to_number(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize data\n",
    "scaler = StandardScaler()\n",
    "X_train_norm = scaler.fit_transform(X_train)\n",
    "X_test_norm  = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define classifier (SVM for classification) with default parameters\n",
    "clf = SVC()\n",
    "\n",
    "# Fit it to train data\n",
    "clf.fit(X_train_norm, y_train)\n",
    "\n",
    "# Make predictions\n",
    "pred_classical = clf.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (SVM):     0.739\n",
      "F1 score (SVM):     0.552\n",
      "UAR (SVM):          0.525\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy (SVM):    ', (np.round(accuracy_score(pred_classical, y_test), 3)))\n",
    "print('F1 score (SVM):    ', (np.round(f1_score(pred_classical, y_test, average = 'macro'), 3)))\n",
    "print('UAR (SVM):         ', (np.round(recall_score(pred_classical, y_test, average = 'macro'), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define classifier (SVM for classification) with default parameters\n",
    "KNN = KNeighborsClassifier()\n",
    "\n",
    "# Fit it to train data\n",
    "KNN.fit(X_train_norm, y_train)\n",
    "\n",
    "# Make predictions\n",
    "pred_classical = KNN.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (KNN):     0.618\n",
      "F1 score (KNN):     0.51\n",
      "UAR (KNN):          0.502\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy (KNN):    ', (np.round(accuracy_score(pred_classical, y_test), 3)))\n",
    "print('F1 score (KNN):    ', (np.round(f1_score(pred_classical, y_test, average = 'macro'), 3)))\n",
    "print('UAR (KNN):         ', (np.round(recall_score(pred_classical, y_test, average = 'macro'), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define classifier (SVM for classification) with default parameters\n",
    "randomClassifier = RandomForestClassifier()\n",
    "\n",
    "# Fit it to train data\n",
    "randomClassifier.fit(X_train_norm, y_train)\n",
    "\n",
    "# Make predictions\n",
    "pred_classical = randomClassifier.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Random Forest):     0.594\n",
      "F1 score (Random Forest):     0.515\n",
      "UAR (Random Forest):          0.515\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy (Random Forest):    ', (np.round(accuracy_score(pred_classical, y_test), 3)))\n",
    "print('F1 score (Random Forest):    ', (np.round(f1_score(pred_classical, y_test, average = 'macro'), 3)))\n",
    "print('UAR (Random Forest):         ', (np.round(recall_score(pred_classical, y_test, average = 'macro'), 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fully Supervised Deep Learning Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using CNN to recognize affect. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function reads the data according to participants list taken as a parameter\n",
    "def read_data(participants):\n",
    "    X = []\n",
    "    \n",
    "    for filename in train_participants:\n",
    "        temp = pd.read_csv('data/{}.csv'.format(filename), index_col=0)\n",
    "        X.append(temp)\n",
    "        \n",
    "    X = pd.concat(X, axis = 0)\n",
    "    y = X.lab.to_numpy()\n",
    "    y = class_to_number(y)\n",
    "    X = X.iloc[:,2:].to_numpy()\n",
    "    \n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define train and test sets\n",
    "train_participants = ['S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9', 'S10']\n",
    "devel_participants = ['S11', 'S13', 'S14']\n",
    "test_participants  = ['S15', 'S16', 'S17']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read files for train and test participants\n",
    "X_train, y_train = read_data(train_participants)\n",
    "X_devel, y_devel = read_data(devel_participants)\n",
    "X_test, y_test = read_data(test_participants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize data\n",
    "scaler = StandardScaler()\n",
    "X_train_norm = scaler.fit_transform(X_train)\n",
    "X_devel_norm = scaler.transform(X_devel)\n",
    "X_test_norm  = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert labels to one-hot\n",
    "y_train_one_hot = one_hot_encoding(y_train, 3)\n",
    "y_devel_one_hot = one_hot_encoding(y_devel, 3)\n",
    "y_test_one_hot = one_hot_encoding(y_test, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_graph(input_shape = 2560, num_classes = 3):\n",
    "    inputs = keras.Input(shape=(input_shape, 1))\n",
    "\n",
    "    # Conv1\n",
    "    x = layers.Conv1D(filters = 32, kernel_size = 32, strides = 1)(inputs)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(layers.LeakyReLU())(x)\n",
    "\n",
    "    x = layers.Conv1D(filters = 32, kernel_size = 32, strides = 1)(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(layers.LeakyReLU())(x)\n",
    "\n",
    "    x = layers.MaxPooling1D(pool_size = 8, strides = 2)(x)\n",
    "\n",
    "    # Conv2\n",
    "    x = layers.Conv1D(filters = 64, kernel_size = 16, strides = 1)(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(layers.LeakyReLU())(x)\n",
    "\n",
    "    x = layers.Conv1D(filters = 64, kernel_size = 16, strides = 1)(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(layers.LeakyReLU())(x)\n",
    "\n",
    "    x = layers.GlobalMaxPooling1D()(x)\n",
    "    x = layers.Flatten()(x)\n",
    "\n",
    "    # Head\n",
    "    x = layers.Dense(128)(x)\n",
    "    x = layers.Dropout(0.5)(x)\n",
    "    x = layers.Activation(layers.LeakyReLU())(x)\n",
    "    outputs = layers.Dense(num_classes, 'softmax')(x)\n",
    "  \n",
    "    return inputs, outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a model\n",
    "inputs, outputs = create_graph(input_shape = 2560, num_classes = 3)\n",
    "model_fully_supervised = keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\laksh\\.conda\\envs\\GPU-ENV\\lib\\site-packages\\keras\\optimizer_v2\\adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Optimizer and callbacks \n",
    "# (to stop the training when the development loss does not decrease)\n",
    "opt = keras.optimizers.Adam(lr = 0.001)\n",
    "callback = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 10, restore_best_weights = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model_fully_supervised.compile(optimizer = opt, loss = 'categorical_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 2560, 1)]         0         \n",
      "                                                                 \n",
      " conv1d (Conv1D)             (None, 2529, 32)          1056      \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 2529, 32)         128       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " activation (Activation)     (None, 2529, 32)          0         \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 2498, 32)          32800     \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 2498, 32)         128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 2498, 32)          0         \n",
      "                                                                 \n",
      " max_pooling1d (MaxPooling1D  (None, 1246, 32)         0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv1d_2 (Conv1D)           (None, 1231, 64)          32832     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 1231, 64)         256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 1231, 64)          0         \n",
      "                                                                 \n",
      " conv1d_3 (Conv1D)           (None, 1216, 64)          65600     \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 1216, 64)         256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 1216, 64)          0         \n",
      "                                                                 \n",
      " global_max_pooling1d (Globa  (None, 64)               0         \n",
      " lMaxPooling1D)                                                  \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               8320      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " activation_4 (Activation)   (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 3)                 387       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 141,763\n",
      "Trainable params: 141,379\n",
      "Non-trainable params: 384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_fully_supervised.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "64/64 - 10s - loss: 1.3123 - val_loss: 0.9794 - 10s/epoch - 163ms/step\n",
      "Epoch 2/100\n",
      "64/64 - 2s - loss: 1.0255 - val_loss: 0.9392 - 2s/epoch - 32ms/step\n",
      "Epoch 3/100\n",
      "64/64 - 2s - loss: 0.9649 - val_loss: 0.8943 - 2s/epoch - 31ms/step\n",
      "Epoch 4/100\n",
      "64/64 - 2s - loss: 0.8893 - val_loss: 0.8428 - 2s/epoch - 31ms/step\n",
      "Epoch 5/100\n",
      "64/64 - 2s - loss: 0.7673 - val_loss: 0.7098 - 2s/epoch - 30ms/step\n",
      "Epoch 6/100\n",
      "64/64 - 2s - loss: 0.6612 - val_loss: 0.6783 - 2s/epoch - 29ms/step\n",
      "Epoch 7/100\n",
      "64/64 - 2s - loss: 0.6476 - val_loss: 0.4923 - 2s/epoch - 29ms/step\n",
      "Epoch 8/100\n",
      "64/64 - 2s - loss: 0.5303 - val_loss: 0.4655 - 2s/epoch - 29ms/step\n",
      "Epoch 9/100\n",
      "64/64 - 2s - loss: 0.5438 - val_loss: 0.5420 - 2s/epoch - 29ms/step\n",
      "Epoch 10/100\n",
      "64/64 - 2s - loss: 0.4984 - val_loss: 0.3964 - 2s/epoch - 29ms/step\n",
      "Epoch 11/100\n",
      "64/64 - 2s - loss: 0.4658 - val_loss: 0.5899 - 2s/epoch - 29ms/step\n",
      "Epoch 12/100\n",
      "64/64 - 2s - loss: 0.4481 - val_loss: 0.3140 - 2s/epoch - 29ms/step\n",
      "Epoch 13/100\n",
      "64/64 - 2s - loss: 0.4749 - val_loss: 0.3416 - 2s/epoch - 30ms/step\n",
      "Epoch 14/100\n",
      "64/64 - 2s - loss: 0.4085 - val_loss: 0.3101 - 2s/epoch - 31ms/step\n",
      "Epoch 15/100\n",
      "64/64 - 2s - loss: 0.3763 - val_loss: 0.3202 - 2s/epoch - 30ms/step\n",
      "Epoch 16/100\n",
      "64/64 - 2s - loss: 0.4164 - val_loss: 0.3614 - 2s/epoch - 30ms/step\n",
      "Epoch 17/100\n",
      "64/64 - 2s - loss: 0.3027 - val_loss: 0.3391 - 2s/epoch - 30ms/step\n",
      "Epoch 18/100\n",
      "64/64 - 2s - loss: 0.3201 - val_loss: 0.3166 - 2s/epoch - 30ms/step\n",
      "Epoch 19/100\n",
      "64/64 - 2s - loss: 0.3380 - val_loss: 0.3306 - 2s/epoch - 30ms/step\n",
      "Epoch 20/100\n",
      "64/64 - 2s - loss: 0.3421 - val_loss: 0.2089 - 2s/epoch - 32ms/step\n",
      "Epoch 21/100\n",
      "64/64 - 2s - loss: 0.2764 - val_loss: 0.3230 - 2s/epoch - 31ms/step\n",
      "Epoch 22/100\n",
      "64/64 - 2s - loss: 0.3232 - val_loss: 0.2297 - 2s/epoch - 30ms/step\n",
      "Epoch 23/100\n",
      "64/64 - 2s - loss: 0.3162 - val_loss: 0.2044 - 2s/epoch - 30ms/step\n",
      "Epoch 24/100\n",
      "64/64 - 2s - loss: 0.2261 - val_loss: 0.2781 - 2s/epoch - 30ms/step\n",
      "Epoch 25/100\n",
      "64/64 - 2s - loss: 0.5372 - val_loss: 0.3354 - 2s/epoch - 30ms/step\n",
      "Epoch 26/100\n",
      "64/64 - 2s - loss: 0.3417 - val_loss: 0.3731 - 2s/epoch - 30ms/step\n",
      "Epoch 27/100\n",
      "64/64 - 2s - loss: 0.3085 - val_loss: 0.2467 - 2s/epoch - 31ms/step\n",
      "Epoch 28/100\n",
      "64/64 - 2s - loss: 0.3326 - val_loss: 0.3312 - 2s/epoch - 31ms/step\n",
      "Epoch 29/100\n",
      "64/64 - 2s - loss: 0.3801 - val_loss: 0.2979 - 2s/epoch - 31ms/step\n",
      "Epoch 30/100\n",
      "64/64 - 2s - loss: 0.2773 - val_loss: 0.1740 - 2s/epoch - 33ms/step\n",
      "Epoch 31/100\n",
      "64/64 - 2s - loss: 0.2203 - val_loss: 0.2694 - 2s/epoch - 32ms/step\n",
      "Epoch 32/100\n",
      "64/64 - 2s - loss: 0.2618 - val_loss: 0.3817 - 2s/epoch - 33ms/step\n",
      "Epoch 33/100\n",
      "64/64 - 2s - loss: 0.2681 - val_loss: 0.2428 - 2s/epoch - 33ms/step\n",
      "Epoch 34/100\n",
      "64/64 - 2s - loss: 0.2162 - val_loss: 0.1361 - 2s/epoch - 33ms/step\n",
      "Epoch 35/100\n",
      "64/64 - 2s - loss: 0.1831 - val_loss: 0.2424 - 2s/epoch - 32ms/step\n",
      "Epoch 36/100\n",
      "64/64 - 2s - loss: 0.2135 - val_loss: 0.1298 - 2s/epoch - 33ms/step\n",
      "Epoch 37/100\n",
      "64/64 - 2s - loss: 0.1905 - val_loss: 0.1354 - 2s/epoch - 33ms/step\n",
      "Epoch 38/100\n",
      "64/64 - 2s - loss: 0.1966 - val_loss: 0.1696 - 2s/epoch - 33ms/step\n",
      "Epoch 39/100\n",
      "64/64 - 2s - loss: 0.2184 - val_loss: 0.2214 - 2s/epoch - 33ms/step\n",
      "Epoch 40/100\n",
      "64/64 - 2s - loss: 0.1886 - val_loss: 0.1185 - 2s/epoch - 35ms/step\n",
      "Epoch 41/100\n",
      "64/64 - 2s - loss: 0.1656 - val_loss: 0.0975 - 2s/epoch - 34ms/step\n",
      "Epoch 42/100\n",
      "64/64 - 2s - loss: 0.1598 - val_loss: 0.1760 - 2s/epoch - 34ms/step\n",
      "Epoch 43/100\n",
      "64/64 - 2s - loss: 0.1501 - val_loss: 0.0968 - 2s/epoch - 36ms/step\n",
      "Epoch 44/100\n",
      "64/64 - 2s - loss: 0.1581 - val_loss: 0.1521 - 2s/epoch - 34ms/step\n",
      "Epoch 45/100\n",
      "64/64 - 2s - loss: 0.1621 - val_loss: 0.1586 - 2s/epoch - 35ms/step\n",
      "Epoch 46/100\n",
      "64/64 - 2s - loss: 0.1109 - val_loss: 0.1784 - 2s/epoch - 34ms/step\n",
      "Epoch 47/100\n",
      "64/64 - 2s - loss: 0.1494 - val_loss: 0.1527 - 2s/epoch - 35ms/step\n",
      "Epoch 48/100\n",
      "64/64 - 2s - loss: 0.3767 - val_loss: 0.2319 - 2s/epoch - 35ms/step\n",
      "Epoch 49/100\n",
      "64/64 - 2s - loss: 0.2016 - val_loss: 0.2083 - 2s/epoch - 36ms/step\n",
      "Epoch 50/100\n",
      "64/64 - 2s - loss: 0.1511 - val_loss: 0.0933 - 2s/epoch - 36ms/step\n",
      "Epoch 51/100\n",
      "64/64 - 2s - loss: 0.1262 - val_loss: 0.2100 - 2s/epoch - 36ms/step\n",
      "Epoch 52/100\n",
      "64/64 - 2s - loss: 0.2257 - val_loss: 0.0970 - 2s/epoch - 36ms/step\n",
      "Epoch 53/100\n",
      "64/64 - 2s - loss: 0.1630 - val_loss: 0.1166 - 2s/epoch - 35ms/step\n",
      "Epoch 54/100\n",
      "64/64 - 2s - loss: 0.1298 - val_loss: 0.0981 - 2s/epoch - 35ms/step\n",
      "Epoch 55/100\n",
      "64/64 - 2s - loss: 0.1226 - val_loss: 0.0950 - 2s/epoch - 35ms/step\n",
      "Epoch 56/100\n",
      "64/64 - 2s - loss: 0.1144 - val_loss: 0.0866 - 2s/epoch - 35ms/step\n",
      "Epoch 57/100\n",
      "64/64 - 2s - loss: 0.2928 - val_loss: 0.2825 - 2s/epoch - 35ms/step\n",
      "Epoch 58/100\n",
      "64/64 - 2s - loss: 0.2029 - val_loss: 0.1780 - 2s/epoch - 38ms/step\n",
      "Epoch 59/100\n",
      "64/64 - 2s - loss: 0.1708 - val_loss: 0.0911 - 2s/epoch - 36ms/step\n",
      "Epoch 60/100\n",
      "64/64 - 2s - loss: 0.1251 - val_loss: 0.0957 - 2s/epoch - 36ms/step\n",
      "Epoch 61/100\n",
      "64/64 - 2s - loss: 0.0945 - val_loss: 0.0714 - 2s/epoch - 36ms/step\n",
      "Epoch 62/100\n",
      "64/64 - 2s - loss: 0.1116 - val_loss: 0.2240 - 2s/epoch - 36ms/step\n",
      "Epoch 63/100\n",
      "64/64 - 2s - loss: 0.1083 - val_loss: 0.0772 - 2s/epoch - 36ms/step\n",
      "Epoch 64/100\n",
      "64/64 - 2s - loss: 0.2529 - val_loss: 0.0967 - 2s/epoch - 36ms/step\n",
      "Epoch 65/100\n",
      "64/64 - 2s - loss: 0.2114 - val_loss: 0.0958 - 2s/epoch - 36ms/step\n",
      "Epoch 66/100\n",
      "64/64 - 2s - loss: 0.1006 - val_loss: 0.1076 - 2s/epoch - 36ms/step\n",
      "Epoch 67/100\n",
      "64/64 - 2s - loss: 0.1571 - val_loss: 0.1686 - 2s/epoch - 36ms/step\n",
      "Epoch 68/100\n",
      "64/64 - 2s - loss: 0.1854 - val_loss: 0.1280 - 2s/epoch - 36ms/step\n",
      "Epoch 69/100\n",
      "64/64 - 2s - loss: 0.2322 - val_loss: 0.1161 - 2s/epoch - 37ms/step\n",
      "Epoch 70/100\n",
      "64/64 - 2s - loss: 0.1565 - val_loss: 0.0817 - 2s/epoch - 36ms/step\n",
      "Epoch 71/100\n",
      "64/64 - 2s - loss: 0.1060 - val_loss: 0.1032 - 2s/epoch - 36ms/step\n"
     ]
    }
   ],
   "source": [
    "history_fs = model_fully_supervised.fit(X_train_norm, \n",
    "                                        y_train_one_hot, \n",
    "                                        validation_data = (X_devel_norm, y_devel_one_hot), \n",
    "                                        callbacks = [callback], \n",
    "                                        epochs = 100, \n",
    "                                        batch_size = 32, \n",
    "                                        verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_fs.history['loss'])\n",
    "plt.plot(history_fs.history['val_loss'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_fully_supervised = model_fully_supervised.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:     0.977\n",
      "F1 score:     0.971\n",
      "UAR:          0.969\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy:    ', (np.round(accuracy_score(np.argmax(pred_fully_supervised, axis = 1), y_test), 3)))\n",
    "print('F1 score:    ', (np.round(f1_score(np.argmax(pred_fully_supervised, axis = 1), y_test, average = 'macro'), 3)))\n",
    "print('UAR:         ', (np.round(recall_score(np.argmax(pred_fully_supervised, axis = 1), y_test, average = 'macro'), 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Self supervised + Transfer learning\n",
    "\n",
    "Using self-supervised approach to learn representations from data (pretext model) and then apply transfer learning to use these representations for the target task (downstream model). \n",
    "\n",
    "First, we will need a way to create a proxy task for the model to train on. In our case it will be classification of transformation type applied to the signal (if any). \n",
    "\n",
    "We apply one of these transormations: horizontal flip, addition of noise, scaling by a certain factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Horizontal Flipping\n",
    "def hor_filp(signal):\n",
    "    hor_flipped = np.flip(signal)\n",
    "    return hor_flipped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding noise    \n",
    "# created using: https://stackoverflow.com/a/53688043/10700812 \n",
    "\n",
    "def add_noise_with_SNR(signal, noise_amount):\n",
    "    target_snr_db = noise_amount #20\n",
    "    x_watts = signal ** 2                       # Calculate signal power and convert to dB \n",
    "    \n",
    "    sig_avg_watts = np.mean(x_watts)\n",
    "    sig_avg_db = 10 * np.log10(sig_avg_watts)   # Calculate noise then convert to watts\n",
    "    \n",
    "    noise_avg_db = sig_avg_db - target_snr_db\n",
    "    noise_avg_watts = 10 ** (noise_avg_db / 10)\n",
    "    mean_noise = 0\n",
    "    \n",
    "    noise_volts = np.random.normal(mean_noise, np.sqrt(noise_avg_watts), len(x_watts))     # Generate an sample of white noise\n",
    "    \n",
    "    noised_signal = signal + noise_volts        # noise added signal\n",
    "\n",
    "    return noised_signal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Signal Scaling\n",
    "def scaled(signal, factor):\n",
    "    scaled_signal = signal * factor\n",
    "    return scaled_signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performs transformations and creates artificial labels.\n",
    "def transform_data(X):\n",
    "    X_noised = np.zeros(X.shape)\n",
    "    X_scaled = np.zeros(X.shape)\n",
    "    X_flipped = np.zeros(X.shape)\n",
    "\n",
    "    for i in range(X.shape[0]):\n",
    "        X_noised[i,:] = add_noise_with_SNR(X[i,:], 20)\n",
    "        X_scaled[i,:] = scaled(X[i,:], 1.1)\n",
    "        X_flipped[i,:] = hor_filp(X[i,:])\n",
    "    \n",
    "    full_data = np.concatenate((X, X_noised, X_scaled, X_flipped))\n",
    "    full_labels = np.concatenate((np.full(X.shape[0], 0),\n",
    "                                  np.full(X.shape[0], 1),\n",
    "                                  np.full(X.shape[0], 2),\n",
    "                                  np.full(X.shape[0], 3)))\n",
    "    \n",
    "    return full_data, full_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform data and get artificial labels\n",
    "X_train_transformed, y_train_transformed = transform_data(X_train_norm)\n",
    "X_devel_transformed, y_devel_transformed = transform_data(X_devel_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform one hot encoding for labels\n",
    "y_train_transformed_one_hot = one_hot_encoding(y_train_transformed, 4)\n",
    "y_devel_transformed_one_hot = one_hot_encoding(y_devel_transformed, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model\n",
    "inputs, outputs = create_graph(input_shape = 2560, num_classes = 4)\n",
    "model_pretext = keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimizer and callbacks (to stop the training when the development loss does not decrease)\n",
    "opt = keras.optimizers.Adam(learning_rate = 0.001)\n",
    "callback = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', \n",
    "                                            patience = 10, \n",
    "                                            restore_best_weights = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model_pretext.compile(optimizer = opt, loss = 'categorical_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 2560, 1)]         0         \n",
      "                                                                 \n",
      " conv1d_4 (Conv1D)           (None, 2529, 32)          1056      \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 2529, 32)         128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_5 (Activation)   (None, 2529, 32)          0         \n",
      "                                                                 \n",
      " conv1d_5 (Conv1D)           (None, 2498, 32)          32800     \n",
      "                                                                 \n",
      " batch_normalization_5 (Batc  (None, 2498, 32)         128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_6 (Activation)   (None, 2498, 32)          0         \n",
      "                                                                 \n",
      " max_pooling1d_1 (MaxPooling  (None, 1246, 32)         0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " conv1d_6 (Conv1D)           (None, 1231, 64)          32832     \n",
      "                                                                 \n",
      " batch_normalization_6 (Batc  (None, 1231, 64)         256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_7 (Activation)   (None, 1231, 64)          0         \n",
      "                                                                 \n",
      " conv1d_7 (Conv1D)           (None, 1216, 64)          65600     \n",
      "                                                                 \n",
      " batch_normalization_7 (Batc  (None, 1216, 64)         256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_8 (Activation)   (None, 1216, 64)          0         \n",
      "                                                                 \n",
      " global_max_pooling1d_1 (Glo  (None, 64)               0         \n",
      " balMaxPooling1D)                                                \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 128)               8320      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " activation_9 (Activation)   (None, 128)               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 4)                 516       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 141,892\n",
      "Trainable params: 141,508\n",
      "Non-trainable params: 384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_pretext.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "253/253 [==============================] - 12s 40ms/step - loss: 1.0356 - val_loss: 1.2647\n",
      "Epoch 2/100\n",
      "253/253 [==============================] - 9s 36ms/step - loss: 0.4035 - val_loss: 0.4058\n",
      "Epoch 3/100\n",
      "253/253 [==============================] - 10s 38ms/step - loss: 0.3525 - val_loss: 0.3703\n",
      "Epoch 4/100\n",
      "253/253 [==============================] - 10s 38ms/step - loss: 0.3209 - val_loss: 0.4606\n",
      "Epoch 5/100\n",
      "253/253 [==============================] - 10s 40ms/step - loss: 0.3113 - val_loss: 0.3641\n",
      "Epoch 6/100\n",
      "253/253 [==============================] - 10s 40ms/step - loss: 0.3216 - val_loss: 0.3783\n",
      "Epoch 7/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.2685 - val_loss: 0.2638\n",
      "Epoch 8/100\n",
      "253/253 [==============================] - 9s 37ms/step - loss: 0.2427 - val_loss: 0.5398\n",
      "Epoch 9/100\n",
      "253/253 [==============================] - 10s 38ms/step - loss: 0.2132 - val_loss: 0.3198\n",
      "Epoch 10/100\n",
      "253/253 [==============================] - 9s 38ms/step - loss: 0.2064 - val_loss: 1.2131\n",
      "Epoch 11/100\n",
      "253/253 [==============================] - 9s 37ms/step - loss: 0.1724 - val_loss: 0.1243\n",
      "Epoch 12/100\n",
      "253/253 [==============================] - 10s 38ms/step - loss: 0.1440 - val_loss: 0.1080\n",
      "Epoch 13/100\n",
      "253/253 [==============================] - 11s 43ms/step - loss: 0.1401 - val_loss: 0.9121\n",
      "Epoch 14/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.1255 - val_loss: 0.2041\n",
      "Epoch 15/100\n",
      "253/253 [==============================] - 10s 40ms/step - loss: 0.1296 - val_loss: 0.4158\n",
      "Epoch 16/100\n",
      "253/253 [==============================] - 11s 42ms/step - loss: 0.1155 - val_loss: 0.7738\n",
      "Epoch 17/100\n",
      "253/253 [==============================] - 11s 44ms/step - loss: 0.0956 - val_loss: 0.0565\n",
      "Epoch 18/100\n",
      "253/253 [==============================] - 11s 42ms/step - loss: 0.1181 - val_loss: 0.2068\n",
      "Epoch 19/100\n",
      "253/253 [==============================] - 11s 42ms/step - loss: 0.1094 - val_loss: 0.1585\n",
      "Epoch 20/100\n",
      "253/253 [==============================] - 10s 40ms/step - loss: 0.1075 - val_loss: 0.7525\n",
      "Epoch 21/100\n",
      "253/253 [==============================] - 10s 39ms/step - loss: 0.1005 - val_loss: 0.1038\n",
      "Epoch 22/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.0941 - val_loss: 0.0905\n",
      "Epoch 23/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.0780 - val_loss: 0.0496\n",
      "Epoch 24/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.0890 - val_loss: 0.2320\n",
      "Epoch 25/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.0907 - val_loss: 0.0464\n",
      "Epoch 26/100\n",
      "253/253 [==============================] - 11s 44ms/step - loss: 0.0774 - val_loss: 0.0402\n",
      "Epoch 27/100\n",
      "253/253 [==============================] - 11s 43ms/step - loss: 0.0596 - val_loss: 0.0281\n",
      "Epoch 28/100\n",
      "253/253 [==============================] - 11s 43ms/step - loss: 0.0654 - val_loss: 0.0657\n",
      "Epoch 29/100\n",
      "253/253 [==============================] - 11s 45ms/step - loss: 0.0756 - val_loss: 0.0652\n",
      "Epoch 30/100\n",
      "253/253 [==============================] - 16s 62ms/step - loss: 0.0682 - val_loss: 0.0460\n",
      "Epoch 31/100\n",
      "253/253 [==============================] - 9s 36ms/step - loss: 0.0667 - val_loss: 0.0289\n",
      "Epoch 32/100\n",
      "253/253 [==============================] - 9s 37ms/step - loss: 0.0624 - val_loss: 0.1206\n",
      "Epoch 33/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.0742 - val_loss: 0.0305\n",
      "Epoch 34/100\n",
      "253/253 [==============================] - 10s 38ms/step - loss: 0.0690 - val_loss: 0.0582\n",
      "Epoch 35/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.0776 - val_loss: 0.0746\n",
      "Epoch 36/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.0563 - val_loss: 0.0180\n",
      "Epoch 37/100\n",
      "253/253 [==============================] - 10s 40ms/step - loss: 0.0627 - val_loss: 0.0990\n",
      "Epoch 38/100\n",
      "253/253 [==============================] - 10s 39ms/step - loss: 0.0612 - val_loss: 0.3897\n",
      "Epoch 39/100\n",
      "253/253 [==============================] - 10s 41ms/step - loss: 0.0713 - val_loss: 0.1442\n",
      "Epoch 40/100\n",
      "253/253 [==============================] - 10s 39ms/step - loss: 0.0703 - val_loss: 0.0601\n",
      "Epoch 41/100\n",
      "253/253 [==============================] - 10s 39ms/step - loss: 0.0605 - val_loss: 0.6586\n",
      "Epoch 42/100\n",
      "253/253 [==============================] - 10s 39ms/step - loss: 0.0455 - val_loss: 0.0327\n",
      "Epoch 43/100\n",
      "253/253 [==============================] - 10s 39ms/step - loss: 0.0564 - val_loss: 0.0188\n",
      "Epoch 44/100\n",
      "253/253 [==============================] - 10s 40ms/step - loss: 0.0626 - val_loss: 0.0315\n",
      "Epoch 45/100\n",
      "253/253 [==============================] - 10s 39ms/step - loss: 0.0575 - val_loss: 0.0599\n",
      "Epoch 46/100\n",
      "253/253 [==============================] - 10s 39ms/step - loss: 0.0551 - val_loss: 0.0359\n"
     ]
    }
   ],
   "source": [
    "history = model_pretext.fit(X_train_transformed, y_train_transformed_one_hot, \n",
    "                            validation_data = (X_devel_transformed, y_devel_transformed_one_hot),\n",
    "                            callbacks = [callback], \n",
    "                            epochs = 100, \n",
    "                            batch_size = 32, \n",
    "                            verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stacking the layers\n",
    "x = layers.Dense(128)(model_pretext.layers[-5].output)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "x = layers.Activation(layers.LeakyReLU())(x)\n",
    "outputs_downstream = layers.Dense(3, 'softmax')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model\n",
    "model_downstream = keras.Model(inputs, outputs_downstream)\n",
    "for i in range(len(model_downstream.layers) - 5):\n",
    "    model_downstream.layers[i].trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimizer and callbacks (to stop the training when the development loss does not decrease)\n",
    "opt = keras.optimizers.Adam(learning_rate = 0.001)\n",
    "callback = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', \n",
    "                                            patience = 10, \n",
    "                                            restore_best_weights = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model_downstream.compile(optimizer = opt, loss = 'categorical_crossentropy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 2560, 1)]         0         \n",
      "                                                                 \n",
      " conv1d_4 (Conv1D)           (None, 2529, 32)          1056      \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 2529, 32)         128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_5 (Activation)   (None, 2529, 32)          0         \n",
      "                                                                 \n",
      " conv1d_5 (Conv1D)           (None, 2498, 32)          32800     \n",
      "                                                                 \n",
      " batch_normalization_5 (Batc  (None, 2498, 32)         128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_6 (Activation)   (None, 2498, 32)          0         \n",
      "                                                                 \n",
      " max_pooling1d_1 (MaxPooling  (None, 1246, 32)         0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " conv1d_6 (Conv1D)           (None, 1231, 64)          32832     \n",
      "                                                                 \n",
      " batch_normalization_6 (Batc  (None, 1231, 64)         256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_7 (Activation)   (None, 1231, 64)          0         \n",
      "                                                                 \n",
      " conv1d_7 (Conv1D)           (None, 1216, 64)          65600     \n",
      "                                                                 \n",
      " batch_normalization_7 (Batc  (None, 1216, 64)         256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " activation_8 (Activation)   (None, 1216, 64)          0         \n",
      "                                                                 \n",
      " global_max_pooling1d_1 (Glo  (None, 64)               0         \n",
      " balMaxPooling1D)                                                \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 128)               8320      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " activation_10 (Activation)  (None, 128)               0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 3)                 387       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 141,763\n",
      "Trainable params: 8,707\n",
      "Non-trainable params: 133,056\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_downstream.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "64/64 - 2s - loss: 1.2116 - val_loss: 0.9943 - 2s/epoch - 37ms/step\n",
      "Epoch 2/100\n",
      "64/64 - 1s - loss: 1.0331 - val_loss: 0.9459 - 1s/epoch - 16ms/step\n",
      "Epoch 3/100\n",
      "64/64 - 1s - loss: 0.9988 - val_loss: 0.9388 - 1s/epoch - 16ms/step\n",
      "Epoch 4/100\n",
      "64/64 - 1s - loss: 0.9674 - val_loss: 0.9216 - 1s/epoch - 16ms/step\n",
      "Epoch 5/100\n",
      "64/64 - 1s - loss: 0.9458 - val_loss: 0.9508 - 1s/epoch - 16ms/step\n",
      "Epoch 6/100\n",
      "64/64 - 1s - loss: 0.9302 - val_loss: 0.9055 - 1s/epoch - 16ms/step\n",
      "Epoch 7/100\n",
      "64/64 - 1s - loss: 0.9226 - val_loss: 0.8715 - 1s/epoch - 16ms/step\n",
      "Epoch 8/100\n",
      "64/64 - 1s - loss: 0.9144 - val_loss: 0.8757 - 1s/epoch - 16ms/step\n",
      "Epoch 9/100\n",
      "64/64 - 1s - loss: 0.8842 - val_loss: 0.8488 - 1s/epoch - 16ms/step\n",
      "Epoch 10/100\n",
      "64/64 - 1s - loss: 0.8816 - val_loss: 0.8296 - 989ms/epoch - 15ms/step\n",
      "Epoch 11/100\n",
      "64/64 - 1s - loss: 0.8717 - val_loss: 0.8397 - 981ms/epoch - 15ms/step\n",
      "Epoch 12/100\n",
      "64/64 - 1s - loss: 0.8621 - val_loss: 0.8023 - 991ms/epoch - 15ms/step\n",
      "Epoch 13/100\n",
      "64/64 - 1s - loss: 0.8413 - val_loss: 0.8122 - 994ms/epoch - 16ms/step\n",
      "Epoch 14/100\n",
      "64/64 - 1s - loss: 0.8232 - val_loss: 0.7637 - 1s/epoch - 16ms/step\n",
      "Epoch 15/100\n",
      "64/64 - 1s - loss: 0.8259 - val_loss: 0.8418 - 981ms/epoch - 15ms/step\n",
      "Epoch 16/100\n",
      "64/64 - 1s - loss: 0.8245 - val_loss: 0.7591 - 991ms/epoch - 15ms/step\n",
      "Epoch 17/100\n",
      "64/64 - 1s - loss: 0.7912 - val_loss: 0.7387 - 985ms/epoch - 15ms/step\n",
      "Epoch 18/100\n",
      "64/64 - 1s - loss: 0.7850 - val_loss: 0.7405 - 990ms/epoch - 15ms/step\n",
      "Epoch 19/100\n",
      "64/64 - 1s - loss: 0.7823 - val_loss: 0.7123 - 985ms/epoch - 15ms/step\n",
      "Epoch 20/100\n",
      "64/64 - 1s - loss: 0.7647 - val_loss: 0.7122 - 981ms/epoch - 15ms/step\n",
      "Epoch 21/100\n",
      "64/64 - 1s - loss: 0.7671 - val_loss: 0.7277 - 979ms/epoch - 15ms/step\n",
      "Epoch 22/100\n",
      "64/64 - 1s - loss: 0.7715 - val_loss: 0.6990 - 984ms/epoch - 15ms/step\n",
      "Epoch 23/100\n",
      "64/64 - 1s - loss: 0.7585 - val_loss: 0.6764 - 989ms/epoch - 15ms/step\n",
      "Epoch 24/100\n",
      "64/64 - 1s - loss: 0.7296 - val_loss: 0.6655 - 982ms/epoch - 15ms/step\n",
      "Epoch 25/100\n",
      "64/64 - 1s - loss: 0.7315 - val_loss: 0.6701 - 983ms/epoch - 15ms/step\n",
      "Epoch 26/100\n",
      "64/64 - 1s - loss: 0.7231 - val_loss: 0.6579 - 988ms/epoch - 15ms/step\n",
      "Epoch 27/100\n",
      "64/64 - 1s - loss: 0.7171 - val_loss: 0.6554 - 975ms/epoch - 15ms/step\n",
      "Epoch 28/100\n",
      "64/64 - 1s - loss: 0.7252 - val_loss: 0.6458 - 985ms/epoch - 15ms/step\n",
      "Epoch 29/100\n",
      "64/64 - 1s - loss: 0.7420 - val_loss: 0.6444 - 984ms/epoch - 15ms/step\n",
      "Epoch 30/100\n",
      "64/64 - 1s - loss: 0.7046 - val_loss: 0.6294 - 989ms/epoch - 15ms/step\n",
      "Epoch 31/100\n",
      "64/64 - 1s - loss: 0.6913 - val_loss: 0.6256 - 984ms/epoch - 15ms/step\n",
      "Epoch 32/100\n",
      "64/64 - 1s - loss: 0.6964 - val_loss: 0.6433 - 982ms/epoch - 15ms/step\n",
      "Epoch 33/100\n",
      "64/64 - 1s - loss: 0.6889 - val_loss: 0.6267 - 981ms/epoch - 15ms/step\n",
      "Epoch 34/100\n",
      "64/64 - 1s - loss: 0.7015 - val_loss: 0.6054 - 985ms/epoch - 15ms/step\n",
      "Epoch 35/100\n",
      "64/64 - 1s - loss: 0.6871 - val_loss: 0.6186 - 975ms/epoch - 15ms/step\n",
      "Epoch 36/100\n",
      "64/64 - 1s - loss: 0.6762 - val_loss: 0.5886 - 985ms/epoch - 15ms/step\n",
      "Epoch 37/100\n",
      "64/64 - 1s - loss: 0.6776 - val_loss: 0.5848 - 982ms/epoch - 15ms/step\n",
      "Epoch 38/100\n",
      "64/64 - 1s - loss: 0.6610 - val_loss: 0.5801 - 988ms/epoch - 15ms/step\n",
      "Epoch 39/100\n",
      "64/64 - 1s - loss: 0.6775 - val_loss: 0.6093 - 981ms/epoch - 15ms/step\n",
      "Epoch 40/100\n",
      "64/64 - 1s - loss: 0.6574 - val_loss: 0.6057 - 976ms/epoch - 15ms/step\n",
      "Epoch 41/100\n",
      "64/64 - 1s - loss: 0.6640 - val_loss: 0.6423 - 979ms/epoch - 15ms/step\n",
      "Epoch 42/100\n",
      "64/64 - 1s - loss: 0.6515 - val_loss: 0.5941 - 979ms/epoch - 15ms/step\n",
      "Epoch 43/100\n",
      "64/64 - 1s - loss: 0.6361 - val_loss: 0.5965 - 989ms/epoch - 15ms/step\n",
      "Epoch 44/100\n",
      "64/64 - 1s - loss: 0.6462 - val_loss: 0.5724 - 984ms/epoch - 15ms/step\n",
      "Epoch 45/100\n",
      "64/64 - 1s - loss: 0.6276 - val_loss: 0.5726 - 979ms/epoch - 15ms/step\n",
      "Epoch 46/100\n",
      "64/64 - 1s - loss: 0.6301 - val_loss: 0.5715 - 995ms/epoch - 16ms/step\n",
      "Epoch 47/100\n",
      "64/64 - 1s - loss: 0.6338 - val_loss: 0.5917 - 990ms/epoch - 15ms/step\n",
      "Epoch 48/100\n",
      "64/64 - 1s - loss: 0.6181 - val_loss: 0.5546 - 991ms/epoch - 15ms/step\n",
      "Epoch 49/100\n",
      "64/64 - 1s - loss: 0.6353 - val_loss: 0.5577 - 983ms/epoch - 15ms/step\n",
      "Epoch 50/100\n",
      "64/64 - 1s - loss: 0.6240 - val_loss: 0.5353 - 990ms/epoch - 15ms/step\n",
      "Epoch 51/100\n",
      "64/64 - 1s - loss: 0.6128 - val_loss: 0.5378 - 983ms/epoch - 15ms/step\n",
      "Epoch 52/100\n",
      "64/64 - 1s - loss: 0.6025 - val_loss: 0.5269 - 985ms/epoch - 15ms/step\n",
      "Epoch 53/100\n",
      "64/64 - 1s - loss: 0.6073 - val_loss: 0.5912 - 984ms/epoch - 15ms/step\n",
      "Epoch 54/100\n",
      "64/64 - 1s - loss: 0.6217 - val_loss: 0.5276 - 982ms/epoch - 15ms/step\n",
      "Epoch 55/100\n",
      "64/64 - 1s - loss: 0.6118 - val_loss: 0.5229 - 985ms/epoch - 15ms/step\n",
      "Epoch 56/100\n",
      "64/64 - 1s - loss: 0.5871 - val_loss: 0.5233 - 976ms/epoch - 15ms/step\n",
      "Epoch 57/100\n",
      "64/64 - 1s - loss: 0.6022 - val_loss: 0.5280 - 983ms/epoch - 15ms/step\n",
      "Epoch 58/100\n",
      "64/64 - 1s - loss: 0.5981 - val_loss: 0.5182 - 983ms/epoch - 15ms/step\n",
      "Epoch 59/100\n",
      "64/64 - 1s - loss: 0.5956 - val_loss: 0.5226 - 980ms/epoch - 15ms/step\n",
      "Epoch 60/100\n",
      "64/64 - 1s - loss: 0.6191 - val_loss: 0.5421 - 979ms/epoch - 15ms/step\n",
      "Epoch 61/100\n",
      "64/64 - 1s - loss: 0.6056 - val_loss: 0.5417 - 992ms/epoch - 16ms/step\n",
      "Epoch 62/100\n",
      "64/64 - 1s - loss: 0.5841 - val_loss: 0.5074 - 986ms/epoch - 15ms/step\n",
      "Epoch 63/100\n",
      "64/64 - 1s - loss: 0.6491 - val_loss: 0.5177 - 988ms/epoch - 15ms/step\n",
      "Epoch 64/100\n",
      "64/64 - 1s - loss: 0.5963 - val_loss: 0.5006 - 988ms/epoch - 15ms/step\n",
      "Epoch 65/100\n",
      "64/64 - 1s - loss: 0.6041 - val_loss: 0.5317 - 979ms/epoch - 15ms/step\n",
      "Epoch 66/100\n",
      "64/64 - 1s - loss: 0.5730 - val_loss: 0.4915 - 989ms/epoch - 15ms/step\n",
      "Epoch 67/100\n",
      "64/64 - 1s - loss: 0.5926 - val_loss: 0.5011 - 985ms/epoch - 15ms/step\n",
      "Epoch 68/100\n",
      "64/64 - 1s - loss: 0.5680 - val_loss: 0.5096 - 981ms/epoch - 15ms/step\n",
      "Epoch 69/100\n",
      "64/64 - 1s - loss: 0.5923 - val_loss: 0.4872 - 992ms/epoch - 15ms/step\n",
      "Epoch 70/100\n",
      "64/64 - 1s - loss: 0.5907 - val_loss: 0.4874 - 980ms/epoch - 15ms/step\n",
      "Epoch 71/100\n",
      "64/64 - 1s - loss: 0.5606 - val_loss: 0.4831 - 987ms/epoch - 15ms/step\n",
      "Epoch 72/100\n",
      "64/64 - 1s - loss: 0.5697 - val_loss: 0.5059 - 975ms/epoch - 15ms/step\n",
      "Epoch 73/100\n",
      "64/64 - 1s - loss: 0.5544 - val_loss: 0.5503 - 979ms/epoch - 15ms/step\n",
      "Epoch 74/100\n",
      "64/64 - 1s - loss: 0.5663 - val_loss: 0.5008 - 982ms/epoch - 15ms/step\n",
      "Epoch 75/100\n",
      "64/64 - 1s - loss: 0.5668 - val_loss: 0.4979 - 979ms/epoch - 15ms/step\n",
      "Epoch 76/100\n",
      "64/64 - 1s - loss: 0.5573 - val_loss: 0.5023 - 979ms/epoch - 15ms/step\n",
      "Epoch 77/100\n",
      "64/64 - 1s - loss: 0.5649 - val_loss: 0.4776 - 991ms/epoch - 15ms/step\n",
      "Epoch 78/100\n",
      "64/64 - 1s - loss: 0.5778 - val_loss: 0.5283 - 973ms/epoch - 15ms/step\n",
      "Epoch 79/100\n",
      "64/64 - 1s - loss: 0.5770 - val_loss: 0.4833 - 981ms/epoch - 15ms/step\n",
      "Epoch 80/100\n",
      "64/64 - 1s - loss: 0.5599 - val_loss: 0.4654 - 987ms/epoch - 15ms/step\n",
      "Epoch 81/100\n",
      "64/64 - 1s - loss: 0.5630 - val_loss: 0.4966 - 993ms/epoch - 16ms/step\n",
      "Epoch 82/100\n",
      "64/64 - 1s - loss: 0.5557 - val_loss: 0.4912 - 1s/epoch - 16ms/step\n",
      "Epoch 83/100\n",
      "64/64 - 1s - loss: 0.5399 - val_loss: 0.4677 - 1s/epoch - 16ms/step\n",
      "Epoch 84/100\n",
      "64/64 - 1s - loss: 0.5450 - val_loss: 0.4724 - 1s/epoch - 16ms/step\n",
      "Epoch 85/100\n",
      "64/64 - 1s - loss: 0.5756 - val_loss: 0.4835 - 1s/epoch - 16ms/step\n",
      "Epoch 86/100\n",
      "64/64 - 1s - loss: 0.6174 - val_loss: 0.4732 - 984ms/epoch - 15ms/step\n",
      "Epoch 87/100\n",
      "64/64 - 1s - loss: 0.5399 - val_loss: 0.4662 - 985ms/epoch - 15ms/step\n",
      "Epoch 88/100\n",
      "64/64 - 1s - loss: 0.5504 - val_loss: 0.4783 - 981ms/epoch - 15ms/step\n",
      "Epoch 89/100\n",
      "64/64 - 1s - loss: 0.5362 - val_loss: 0.5486 - 982ms/epoch - 15ms/step\n",
      "Epoch 90/100\n",
      "64/64 - 1s - loss: 0.5536 - val_loss: 0.5309 - 1s/epoch - 16ms/step\n"
     ]
    }
   ],
   "source": [
    "history = model_downstream.fit(X_train_norm, \n",
    "                               y_train_one_hot, \n",
    "                               validation_data = (X_devel_norm, y_devel_one_hot), \n",
    "                               callbacks = [callback], \n",
    "                               epochs = 100,\n",
    "                               batch_size = 32, \n",
    "                               verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_downstream = model_downstream.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:     0.813\n",
      "F1 score:     0.716\n",
      "UAR:          0.812\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy:    ', (np.round(accuracy_score(np.argmax(pred_downstream, axis = 1), y_test), 3)))\n",
    "print('F1 score:    ', (np.round(f1_score(np.argmax(pred_downstream, axis = 1), y_test, average = 'macro'), 3)))\n",
    "print('UAR:         ', (np.round(recall_score(np.argmax(pred_downstream, axis = 1), y_test, average = 'macro'), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Self supervised UAR:      0.812\n",
      "Fully supervised UAR:     0.969\n"
     ]
    }
   ],
   "source": [
    "print('Self supervised UAR:     ', (np.round(recall_score(np.argmax(pred_downstream, axis = 1), y_test, average = 'macro'), 3)))\n",
    "print('Fully supervised UAR:    ', (np.round(recall_score(np.argmax(pred_fully_supervised, axis = 1), y_test, average = 'macro'), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "self_supervised = pd.read_csv('results_cv/self_supervised.csv', index_col = 0)\n",
    "fully_supervised = pd.read_csv('results_cv/fully_supervised.csv', index_col = 0)\n",
    "classical = pd.read_csv('results_cv/classical.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Mean over Cross Validation Partitions\n",
    "self_supervised_mean = np.mean(self_supervised, axis = 0)\n",
    "fully_supervised_mean = np.mean(fully_supervised, axis = 0)\n",
    "classical_mean = np.mean(classical, axis = 0)\n",
    "\n",
    "x = np.arange(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.bar(x - 0.2, classical_mean, width = 0.2, color = \"skyblue\", label = 'Classical')\n",
    "plt.bar(x, fully_supervised_mean, width = 0.2, color = \"red\", label = 'Fully supervised')\n",
    "plt.bar(x + 0.2, self_supervised_mean, width = 0.2, color = \"green\", label = 'Self supervised')\n",
    "\n",
    "plt.ylim([0.4, 1])\n",
    "plt.xticks([0, 1, 2], ['Accuracy', 'F1 score', 'UAR'], size = 12)\n",
    "\n",
    "plt.legend(loc = 'upper center', bbox_to_anchor=(0.5, -0.05), ncol=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('GPU-ENV')",
   "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": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "49ac5221da2eb58e4661dbfaf9c49835db5af697a89f602a9deef4b00ab51f2a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}