HML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "org_BECTS_path = 'BECTS_TLE_Data_File/Fig3_BECTS.txt'\n",
    "org_TLE_path = 'BECTS_TLE_Data_File/Fig3_TLE.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IMF1</th>\n",
       "      <th>IMF2</th>\n",
       "      <th>IMF3</th>\n",
       "      <th>IMF4</th>\n",
       "      <th>IMF5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.574485</td>\n",
       "      <td>3.807585</td>\n",
       "      <td>5.505068</td>\n",
       "      <td>-0.570404</td>\n",
       "      <td>1.445255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.208113</td>\n",
       "      <td>3.452560</td>\n",
       "      <td>3.561308</td>\n",
       "      <td>-0.736854</td>\n",
       "      <td>1.482468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.893457</td>\n",
       "      <td>2.060365</td>\n",
       "      <td>1.597659</td>\n",
       "      <td>-0.882304</td>\n",
       "      <td>1.515563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.779090</td>\n",
       "      <td>-0.246792</td>\n",
       "      <td>-0.306272</td>\n",
       "      <td>-1.003735</td>\n",
       "      <td>1.544298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.182468</td>\n",
       "      <td>-2.973308</td>\n",
       "      <td>-2.070880</td>\n",
       "      <td>-1.098125</td>\n",
       "      <td>1.568432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IMF1      IMF2      IMF3      IMF4      IMF5\n",
       "0  0.574485  3.807585  5.505068 -0.570404  1.445255\n",
       "1  6.208113  3.452560  3.561308 -0.736854  1.482468\n",
       "2  0.893457  2.060365  1.597659 -0.882304  1.515563\n",
       "3 -6.779090 -0.246792 -0.306272 -1.003735  1.544298\n",
       "4  5.182468 -2.973308 -2.070880 -1.098125  1.568432"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['IMF1', 'IMF2', 'IMF3', 'IMF4', 'IMF5']\n",
    "org_BECTS_df = pd.read_csv(org_BECTS_path, sep='\\t', header=None, names=column_names)\n",
    "\n",
    "org_BECTS_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_BECTS_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IMF1</th>\n",
       "      <th>IMF2</th>\n",
       "      <th>IMF3</th>\n",
       "      <th>IMF4</th>\n",
       "      <th>IMF5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.207402</td>\n",
       "      <td>-0.372624</td>\n",
       "      <td>3.318712</td>\n",
       "      <td>0.917063</td>\n",
       "      <td>-0.489797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-10.408658</td>\n",
       "      <td>-1.372764</td>\n",
       "      <td>3.103425</td>\n",
       "      <td>0.798438</td>\n",
       "      <td>-0.495173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.439281</td>\n",
       "      <td>-2.194279</td>\n",
       "      <td>2.854599</td>\n",
       "      <td>0.679851</td>\n",
       "      <td>-0.500306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.554512</td>\n",
       "      <td>-2.818793</td>\n",
       "      <td>2.577714</td>\n",
       "      <td>0.561672</td>\n",
       "      <td>-0.505180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.644915</td>\n",
       "      <td>-3.235437</td>\n",
       "      <td>2.278253</td>\n",
       "      <td>0.444267</td>\n",
       "      <td>-0.509781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        IMF1      IMF2      IMF3      IMF4      IMF5\n",
       "0  -3.207402 -0.372624  3.318712  0.917063 -0.489797\n",
       "1 -10.408658 -1.372764  3.103425  0.798438 -0.495173\n",
       "2  -6.439281 -2.194279  2.854599  0.679851 -0.500306\n",
       "3  -2.554512 -2.818793  2.577714  0.561672 -0.505180\n",
       "4   3.644915 -3.235437  2.278253  0.444267 -0.509781"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_TLE_df = pd.read_csv(org_TLE_path, sep='\\t', header=None, names=column_names)\n",
    "\n",
    "org_TLE_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_TLE_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decomposed Datasets After EMD and Statistical Indice Calculations\n",
    "\n",
    "T3-C3 and T4-C4 channel data from 20-10 EEG system is used.\n",
    "\n",
    "Sub-band signals are derived using the EMD method:\n",
    "- IMF1: gamma band neuronal oscillation (> 30 Hz)\n",
    "- IMF2: beta band oscillation (13-30 Hz)\n",
    "- IMF3: alpha band oscillation (8-12 Hz)\n",
    "- IMF4: theta band oscillation (3.5-8 Hz)\n",
    "- IMF5: delta band oscillation (0.5-3.5 Hz)\n",
    "\n",
    "Label 1 -> BECTS\n",
    "\n",
    "Label 0 -> TLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "T3_BECTS_path = 'BECTS_TLE_Data_File/Fig4_BECTS.txt'\n",
    "T4_BECTS_path = 'BECTS_TLE_Data_File/Fig5_BECTS.txt'\n",
    "\n",
    "T3_TLE_path = 'BECTS_TLE_Data_File/Fig4_TLE.txt'\n",
    "T4_TLE_path = 'BECTS_TLE_Data_File/Fig5_TLE.txt'\n",
    "\n",
    "T3_BECTS_df = pd.read_csv(T3_BECTS_path, sep='\\t')\n",
    "y_T3_BECTS = np.ones(T3_BECTS_df.shape[0])\n",
    "\n",
    "T4_BECTS_df = pd.read_csv(T4_BECTS_path, sep='\\t')\n",
    "y_T4_BECTS = np.ones(T4_BECTS_df.shape[0])\n",
    "\n",
    "T3_TLE_df = pd.read_csv(T3_TLE_path, sep='\\t')\n",
    "y_T3_TLE = np.zeros(T3_TLE_df.shape[0])\n",
    "\n",
    "T4_TLE_df = pd.read_csv(T4_TLE_path, sep='\\t')\n",
    "y_T4_TLE = np.zeros(T4_TLE_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CV_IMF1</th>\n",
       "      <th>CV_IMF2</th>\n",
       "      <th>CV_IMF3</th>\n",
       "      <th>CV_IMF4</th>\n",
       "      <th>CV_IMF5</th>\n",
       "      <th>FI_IMF1</th>\n",
       "      <th>FI_IMF2</th>\n",
       "      <th>FI_IMF3</th>\n",
       "      <th>FI_IMF4</th>\n",
       "      <th>FI_IMF5</th>\n",
       "      <th>SK_IMF1</th>\n",
       "      <th>SK_IMF2</th>\n",
       "      <th>SK_IMF3</th>\n",
       "      <th>SK_IMF4</th>\n",
       "      <th>SK_IMF5</th>\n",
       "      <th>KU_IMF1</th>\n",
       "      <th>KU_IMF2</th>\n",
       "      <th>KU_IMF3</th>\n",
       "      <th>KU_IMF4</th>\n",
       "      <th>KU_IMF5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.179958</td>\n",
       "      <td>-293.393923</td>\n",
       "      <td>-250833.76110</td>\n",
       "      <td>-34125.604220</td>\n",
       "      <td>-9.372835e+03</td>\n",
       "      <td>1.624712</td>\n",
       "      <td>0.454270</td>\n",
       "      <td>0.068414</td>\n",
       "      <td>0.026660</td>\n",
       "      <td>0.010508</td>\n",
       "      <td>-0.222586</td>\n",
       "      <td>-0.029176</td>\n",
       "      <td>0.255986</td>\n",
       "      <td>0.496558</td>\n",
       "      <td>1.061101</td>\n",
       "      <td>2.258220</td>\n",
       "      <td>2.401026</td>\n",
       "      <td>3.501249</td>\n",
       "      <td>3.745766</td>\n",
       "      <td>6.079500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>439.917103</td>\n",
       "      <td>-3176.723727</td>\n",
       "      <td>-22346.13834</td>\n",
       "      <td>-3921.508032</td>\n",
       "      <td>-9.126214e+04</td>\n",
       "      <td>1.795715</td>\n",
       "      <td>0.380108</td>\n",
       "      <td>0.066567</td>\n",
       "      <td>0.026646</td>\n",
       "      <td>0.007862</td>\n",
       "      <td>0.031469</td>\n",
       "      <td>-0.000340</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.011156</td>\n",
       "      <td>-0.097538</td>\n",
       "      <td>2.100341</td>\n",
       "      <td>2.333814</td>\n",
       "      <td>2.863083</td>\n",
       "      <td>2.507800</td>\n",
       "      <td>2.094273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.185534</td>\n",
       "      <td>-2693.026027</td>\n",
       "      <td>-12042.86543</td>\n",
       "      <td>-12861.344350</td>\n",
       "      <td>-1.091637e+09</td>\n",
       "      <td>1.584948</td>\n",
       "      <td>0.452024</td>\n",
       "      <td>0.081185</td>\n",
       "      <td>0.027115</td>\n",
       "      <td>0.008891</td>\n",
       "      <td>-0.264721</td>\n",
       "      <td>-0.027211</td>\n",
       "      <td>-0.234781</td>\n",
       "      <td>0.222110</td>\n",
       "      <td>0.045542</td>\n",
       "      <td>2.403527</td>\n",
       "      <td>2.516279</td>\n",
       "      <td>3.400286</td>\n",
       "      <td>3.743209</td>\n",
       "      <td>1.978074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.683381</td>\n",
       "      <td>-51230.959970</td>\n",
       "      <td>-10061.32963</td>\n",
       "      <td>-2520.874182</td>\n",
       "      <td>-4.071085e+04</td>\n",
       "      <td>1.732353</td>\n",
       "      <td>0.450553</td>\n",
       "      <td>0.070310</td>\n",
       "      <td>0.021753</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>-0.139238</td>\n",
       "      <td>0.046150</td>\n",
       "      <td>-0.023084</td>\n",
       "      <td>-0.128933</td>\n",
       "      <td>0.016697</td>\n",
       "      <td>2.171342</td>\n",
       "      <td>2.369437</td>\n",
       "      <td>3.599581</td>\n",
       "      <td>2.935491</td>\n",
       "      <td>2.834916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202.617867</td>\n",
       "      <td>-1563.083064</td>\n",
       "      <td>-27660.10828</td>\n",
       "      <td>-107258.165800</td>\n",
       "      <td>-1.420668e+04</td>\n",
       "      <td>1.771042</td>\n",
       "      <td>0.368516</td>\n",
       "      <td>0.071656</td>\n",
       "      <td>0.024274</td>\n",
       "      <td>0.005628</td>\n",
       "      <td>-0.093936</td>\n",
       "      <td>-0.035272</td>\n",
       "      <td>-0.431327</td>\n",
       "      <td>-0.059919</td>\n",
       "      <td>0.120010</td>\n",
       "      <td>2.208219</td>\n",
       "      <td>2.244567</td>\n",
       "      <td>4.833915</td>\n",
       "      <td>3.037845</td>\n",
       "      <td>2.734746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CV_IMF1       CV_IMF2       CV_IMF3        CV_IMF4       CV_IMF5  \\\n",
       "0    9.179958   -293.393923 -250833.76110  -34125.604220 -9.372835e+03   \n",
       "1  439.917103  -3176.723727  -22346.13834   -3921.508032 -9.126214e+04   \n",
       "2   16.185534  -2693.026027  -12042.86543  -12861.344350 -1.091637e+09   \n",
       "3   29.683381 -51230.959970  -10061.32963   -2520.874182 -4.071085e+04   \n",
       "4  202.617867  -1563.083064  -27660.10828 -107258.165800 -1.420668e+04   \n",
       "\n",
       "    FI_IMF1   FI_IMF2   FI_IMF3   FI_IMF4   FI_IMF5   SK_IMF1   SK_IMF2  \\\n",
       "0  1.624712  0.454270  0.068414  0.026660  0.010508 -0.222586 -0.029176   \n",
       "1  1.795715  0.380108  0.066567  0.026646  0.007862  0.031469 -0.000340   \n",
       "2  1.584948  0.452024  0.081185  0.027115  0.008891 -0.264721 -0.027211   \n",
       "3  1.732353  0.450553  0.070310  0.021753  0.005186 -0.139238  0.046150   \n",
       "4  1.771042  0.368516  0.071656  0.024274  0.005628 -0.093936 -0.035272   \n",
       "\n",
       "    SK_IMF3   SK_IMF4   SK_IMF5   KU_IMF1   KU_IMF2   KU_IMF3   KU_IMF4  \\\n",
       "0  0.255986  0.496558  1.061101  2.258220  2.401026  3.501249  3.745766   \n",
       "1  0.188000  0.011156 -0.097538  2.100341  2.333814  2.863083  2.507800   \n",
       "2 -0.234781  0.222110  0.045542  2.403527  2.516279  3.400286  3.743209   \n",
       "3 -0.023084 -0.128933  0.016697  2.171342  2.369437  3.599581  2.935491   \n",
       "4 -0.431327 -0.059919  0.120010  2.208219  2.244567  4.833915  3.037845   \n",
       "\n",
       "    KU_IMF5  \n",
       "0  6.079500  \n",
       "1  2.094273  \n",
       "2  1.978074  \n",
       "3  2.834916  \n",
       "4  2.734746  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T3_BECTS_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30, 20), (30,), 30.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T3_BECTS_df.shape, y_T3_BECTS.shape, y_T3_BECTS.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CV_IMF1</th>\n",
       "      <th>CV_IMF2</th>\n",
       "      <th>CV_IMF3</th>\n",
       "      <th>CV_IMF4</th>\n",
       "      <th>CV_IMF5</th>\n",
       "      <th>FI_IMF1</th>\n",
       "      <th>FI_IMF2</th>\n",
       "      <th>FI_IMF3</th>\n",
       "      <th>FI_IMF4</th>\n",
       "      <th>FI_IMF5</th>\n",
       "      <th>SK_IMF1</th>\n",
       "      <th>SK_IMF2</th>\n",
       "      <th>SK_IMF3</th>\n",
       "      <th>SK_IMF4</th>\n",
       "      <th>SK_IMF5</th>\n",
       "      <th>KU_IMF1</th>\n",
       "      <th>KU_IMF2</th>\n",
       "      <th>KU_IMF3</th>\n",
       "      <th>KU_IMF4</th>\n",
       "      <th>KU_IMF5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.535809</td>\n",
       "      <td>65.551214</td>\n",
       "      <td>1085.331045</td>\n",
       "      <td>118.586408</td>\n",
       "      <td>824.632619</td>\n",
       "      <td>4.552376</td>\n",
       "      <td>1.968427</td>\n",
       "      <td>0.819671</td>\n",
       "      <td>0.274602</td>\n",
       "      <td>0.046190</td>\n",
       "      <td>-1.356812</td>\n",
       "      <td>0.010818</td>\n",
       "      <td>0.309529</td>\n",
       "      <td>0.045989</td>\n",
       "      <td>0.045849</td>\n",
       "      <td>19.219749</td>\n",
       "      <td>4.721998</td>\n",
       "      <td>3.531598</td>\n",
       "      <td>2.278504</td>\n",
       "      <td>2.706229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>477.462396</td>\n",
       "      <td>1831.463515</td>\n",
       "      <td>5.297635</td>\n",
       "      <td>138.912359</td>\n",
       "      <td>83.976649</td>\n",
       "      <td>4.756564</td>\n",
       "      <td>1.961597</td>\n",
       "      <td>0.822662</td>\n",
       "      <td>0.309518</td>\n",
       "      <td>0.033252</td>\n",
       "      <td>-0.407724</td>\n",
       "      <td>0.074973</td>\n",
       "      <td>0.198885</td>\n",
       "      <td>0.060686</td>\n",
       "      <td>0.002312</td>\n",
       "      <td>9.434578</td>\n",
       "      <td>6.193978</td>\n",
       "      <td>4.005002</td>\n",
       "      <td>4.039002</td>\n",
       "      <td>1.933780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3165.091734</td>\n",
       "      <td>376.236370</td>\n",
       "      <td>255.032133</td>\n",
       "      <td>24.816572</td>\n",
       "      <td>120.933195</td>\n",
       "      <td>4.927435</td>\n",
       "      <td>2.433717</td>\n",
       "      <td>0.823418</td>\n",
       "      <td>0.368325</td>\n",
       "      <td>0.083129</td>\n",
       "      <td>-0.322455</td>\n",
       "      <td>-0.018617</td>\n",
       "      <td>0.563046</td>\n",
       "      <td>0.003779</td>\n",
       "      <td>0.218962</td>\n",
       "      <td>19.054786</td>\n",
       "      <td>4.013581</td>\n",
       "      <td>8.643616</td>\n",
       "      <td>4.150999</td>\n",
       "      <td>3.319012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3796.883005</td>\n",
       "      <td>25609.517150</td>\n",
       "      <td>45.084127</td>\n",
       "      <td>109.318101</td>\n",
       "      <td>218.206571</td>\n",
       "      <td>4.847519</td>\n",
       "      <td>2.166501</td>\n",
       "      <td>0.785491</td>\n",
       "      <td>0.247453</td>\n",
       "      <td>0.065799</td>\n",
       "      <td>-0.060620</td>\n",
       "      <td>0.124137</td>\n",
       "      <td>0.148358</td>\n",
       "      <td>-0.136466</td>\n",
       "      <td>-0.026793</td>\n",
       "      <td>4.762491</td>\n",
       "      <td>2.892872</td>\n",
       "      <td>5.329845</td>\n",
       "      <td>2.605486</td>\n",
       "      <td>2.519372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.712649</td>\n",
       "      <td>126.654547</td>\n",
       "      <td>139.460328</td>\n",
       "      <td>87.711662</td>\n",
       "      <td>2.673805</td>\n",
       "      <td>5.403557</td>\n",
       "      <td>2.636420</td>\n",
       "      <td>0.909673</td>\n",
       "      <td>0.335976</td>\n",
       "      <td>0.081627</td>\n",
       "      <td>-2.142009</td>\n",
       "      <td>0.134482</td>\n",
       "      <td>0.260396</td>\n",
       "      <td>-0.173283</td>\n",
       "      <td>-0.098706</td>\n",
       "      <td>19.385660</td>\n",
       "      <td>10.803049</td>\n",
       "      <td>4.587521</td>\n",
       "      <td>2.660095</td>\n",
       "      <td>3.525226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CV_IMF1       CV_IMF2      CV_IMF3     CV_IMF4     CV_IMF5   FI_IMF1  \\\n",
       "0    18.535809     65.551214  1085.331045  118.586408  824.632619  4.552376   \n",
       "1   477.462396   1831.463515     5.297635  138.912359   83.976649  4.756564   \n",
       "2  3165.091734    376.236370   255.032133   24.816572  120.933195  4.927435   \n",
       "3  3796.883005  25609.517150    45.084127  109.318101  218.206571  4.847519   \n",
       "4    16.712649    126.654547   139.460328   87.711662    2.673805  5.403557   \n",
       "\n",
       "    FI_IMF2   FI_IMF3   FI_IMF4   FI_IMF5   SK_IMF1   SK_IMF2   SK_IMF3  \\\n",
       "0  1.968427  0.819671  0.274602  0.046190 -1.356812  0.010818  0.309529   \n",
       "1  1.961597  0.822662  0.309518  0.033252 -0.407724  0.074973  0.198885   \n",
       "2  2.433717  0.823418  0.368325  0.083129 -0.322455 -0.018617  0.563046   \n",
       "3  2.166501  0.785491  0.247453  0.065799 -0.060620  0.124137  0.148358   \n",
       "4  2.636420  0.909673  0.335976  0.081627 -2.142009  0.134482  0.260396   \n",
       "\n",
       "    SK_IMF4   SK_IMF5    KU_IMF1    KU_IMF2   KU_IMF3   KU_IMF4   KU_IMF5  \n",
       "0  0.045989  0.045849  19.219749   4.721998  3.531598  2.278504  2.706229  \n",
       "1  0.060686  0.002312   9.434578   6.193978  4.005002  4.039002  1.933780  \n",
       "2  0.003779  0.218962  19.054786   4.013581  8.643616  4.150999  3.319012  \n",
       "3 -0.136466 -0.026793   4.762491   2.892872  5.329845  2.605486  2.519372  \n",
       "4 -0.173283 -0.098706  19.385660  10.803049  4.587521  2.660095  3.525226  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T4_BECTS_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30, 20), (30,), 30.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T4_BECTS_df.shape, y_T4_BECTS.shape, y_T4_BECTS.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CV_IMF1</th>\n",
       "      <th>CV_IMF2</th>\n",
       "      <th>CV_IMF3</th>\n",
       "      <th>CV_IMF4</th>\n",
       "      <th>CV_IMF5</th>\n",
       "      <th>FI_IMF1</th>\n",
       "      <th>FI_IMF2</th>\n",
       "      <th>FI_IMF3</th>\n",
       "      <th>FI_IMF4</th>\n",
       "      <th>FI_IMF5</th>\n",
       "      <th>SK_IMF1</th>\n",
       "      <th>SK_IMF2</th>\n",
       "      <th>SK_IMF3</th>\n",
       "      <th>SK_IMF4</th>\n",
       "      <th>SK_IMF5</th>\n",
       "      <th>KU_IMF1</th>\n",
       "      <th>KU_IMF2</th>\n",
       "      <th>KU_IMF3</th>\n",
       "      <th>KU_IMF4</th>\n",
       "      <th>KU_IMF5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1920.272288</td>\n",
       "      <td>-5.970019e+04</td>\n",
       "      <td>-4.286777e+04</td>\n",
       "      <td>-3.614313e+05</td>\n",
       "      <td>-9.451908e+04</td>\n",
       "      <td>0.937283</td>\n",
       "      <td>0.166059</td>\n",
       "      <td>0.037621</td>\n",
       "      <td>0.011254</td>\n",
       "      <td>0.002321</td>\n",
       "      <td>-0.037673</td>\n",
       "      <td>-0.006755</td>\n",
       "      <td>-0.218101</td>\n",
       "      <td>-0.067952</td>\n",
       "      <td>-0.179224</td>\n",
       "      <td>2.232315</td>\n",
       "      <td>3.138410</td>\n",
       "      <td>3.988448</td>\n",
       "      <td>3.093332</td>\n",
       "      <td>3.246055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-899.874683</td>\n",
       "      <td>-3.604890e+05</td>\n",
       "      <td>-8.638742e+05</td>\n",
       "      <td>-7.903127e+06</td>\n",
       "      <td>-1.549710e+06</td>\n",
       "      <td>1.001675</td>\n",
       "      <td>0.157632</td>\n",
       "      <td>0.047958</td>\n",
       "      <td>0.016947</td>\n",
       "      <td>0.003121</td>\n",
       "      <td>-0.028868</td>\n",
       "      <td>-0.040164</td>\n",
       "      <td>0.030988</td>\n",
       "      <td>0.056385</td>\n",
       "      <td>-0.059385</td>\n",
       "      <td>2.181564</td>\n",
       "      <td>2.845839</td>\n",
       "      <td>2.931379</td>\n",
       "      <td>2.103790</td>\n",
       "      <td>1.895251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-369.647682</td>\n",
       "      <td>-2.079902e+05</td>\n",
       "      <td>-1.720080e+07</td>\n",
       "      <td>-1.025000e+05</td>\n",
       "      <td>-9.940670e+07</td>\n",
       "      <td>0.968746</td>\n",
       "      <td>0.162315</td>\n",
       "      <td>0.045171</td>\n",
       "      <td>0.013832</td>\n",
       "      <td>0.002391</td>\n",
       "      <td>-0.081406</td>\n",
       "      <td>-0.009580</td>\n",
       "      <td>0.082314</td>\n",
       "      <td>-0.116298</td>\n",
       "      <td>0.071031</td>\n",
       "      <td>2.343241</td>\n",
       "      <td>2.654483</td>\n",
       "      <td>3.320616</td>\n",
       "      <td>2.644230</td>\n",
       "      <td>2.468872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1367.310685</td>\n",
       "      <td>-8.969106e+04</td>\n",
       "      <td>-6.734212e+05</td>\n",
       "      <td>-2.343064e+05</td>\n",
       "      <td>-2.833691e+07</td>\n",
       "      <td>0.968280</td>\n",
       "      <td>0.171960</td>\n",
       "      <td>0.045319</td>\n",
       "      <td>0.012499</td>\n",
       "      <td>0.002072</td>\n",
       "      <td>-0.052963</td>\n",
       "      <td>-0.123357</td>\n",
       "      <td>-0.010089</td>\n",
       "      <td>-0.234165</td>\n",
       "      <td>0.156449</td>\n",
       "      <td>2.285585</td>\n",
       "      <td>2.790033</td>\n",
       "      <td>3.043828</td>\n",
       "      <td>3.472967</td>\n",
       "      <td>3.454131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-565.556321</td>\n",
       "      <td>-1.445096e+07</td>\n",
       "      <td>-1.126861e+05</td>\n",
       "      <td>-2.954158e+04</td>\n",
       "      <td>-2.086832e+05</td>\n",
       "      <td>0.933376</td>\n",
       "      <td>0.167101</td>\n",
       "      <td>0.035138</td>\n",
       "      <td>0.011157</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>-0.071167</td>\n",
       "      <td>-0.023937</td>\n",
       "      <td>0.196301</td>\n",
       "      <td>0.129571</td>\n",
       "      <td>-0.199983</td>\n",
       "      <td>2.406741</td>\n",
       "      <td>2.650097</td>\n",
       "      <td>2.964650</td>\n",
       "      <td>2.895810</td>\n",
       "      <td>3.009463</td>\n",
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       "       CV_IMF1       CV_IMF2       CV_IMF3       CV_IMF4       CV_IMF5  \\\n",
       "0 -1920.272288 -5.970019e+04 -4.286777e+04 -3.614313e+05 -9.451908e+04   \n",
       "1  -899.874683 -3.604890e+05 -8.638742e+05 -7.903127e+06 -1.549710e+06   \n",
       "2  -369.647682 -2.079902e+05 -1.720080e+07 -1.025000e+05 -9.940670e+07   \n",
       "3 -1367.310685 -8.969106e+04 -6.734212e+05 -2.343064e+05 -2.833691e+07   \n",
       "4  -565.556321 -1.445096e+07 -1.126861e+05 -2.954158e+04 -2.086832e+05   \n",
       "\n",
       "    FI_IMF1   FI_IMF2   FI_IMF3   FI_IMF4   FI_IMF5   SK_IMF1   SK_IMF2  \\\n",
       "0  0.937283  0.166059  0.037621  0.011254  0.002321 -0.037673 -0.006755   \n",
       "1  1.001675  0.157632  0.047958  0.016947  0.003121 -0.028868 -0.040164   \n",
       "2  0.968746  0.162315  0.045171  0.013832  0.002391 -0.081406 -0.009580   \n",
       "3  0.968280  0.171960  0.045319  0.012499  0.002072 -0.052963 -0.123357   \n",
       "4  0.933376  0.167101  0.035138  0.011157  0.002338 -0.071167 -0.023937   \n",
       "\n",
       "    SK_IMF3   SK_IMF4   SK_IMF5   KU_IMF1   KU_IMF2   KU_IMF3   KU_IMF4  \\\n",
       "0 -0.218101 -0.067952 -0.179224  2.232315  3.138410  3.988448  3.093332   \n",
       "1  0.030988  0.056385 -0.059385  2.181564  2.845839  2.931379  2.103790   \n",
       "2  0.082314 -0.116298  0.071031  2.343241  2.654483  3.320616  2.644230   \n",
       "3 -0.010089 -0.234165  0.156449  2.285585  2.790033  3.043828  3.472967   \n",
       "4  0.196301  0.129571 -0.199983  2.406741  2.650097  2.964650  2.895810   \n",
       "\n",
       "    KU_IMF5  \n",
       "0  3.246055  \n",
       "1  1.895251  \n",
       "2  2.468872  \n",
       "3  3.454131  \n",
       "4  3.009463  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T3_TLE_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30, 20), (30,), 0.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T3_TLE_df.shape, y_T3_TLE.shape, y_T3_TLE.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>71.679507</td>\n",
       "      <td>29933.433180</td>\n",
       "      <td>985.994979</td>\n",
       "      <td>108.694405</td>\n",
       "      <td>-12.774529</td>\n",
       "      <td>4.418576</td>\n",
       "      <td>1.135345</td>\n",
       "      <td>0.380331</td>\n",
       "      <td>0.102658</td>\n",
       "      <td>0.019498</td>\n",
       "      <td>-0.053783</td>\n",
       "      <td>-0.051486</td>\n",
       "      <td>0.007683</td>\n",
       "      <td>0.066290</td>\n",
       "      <td>0.176168</td>\n",
       "      <td>3.028698</td>\n",
       "      <td>3.442976</td>\n",
       "      <td>3.342342</td>\n",
       "      <td>3.634604</td>\n",
       "      <td>2.088887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76.379052</td>\n",
       "      <td>217.653303</td>\n",
       "      <td>26.502862</td>\n",
       "      <td>59.831595</td>\n",
       "      <td>-34.587066</td>\n",
       "      <td>3.731739</td>\n",
       "      <td>1.209921</td>\n",
       "      <td>0.382103</td>\n",
       "      <td>0.086880</td>\n",
       "      <td>0.007101</td>\n",
       "      <td>0.063953</td>\n",
       "      <td>0.073784</td>\n",
       "      <td>0.051455</td>\n",
       "      <td>0.250202</td>\n",
       "      <td>1.638330</td>\n",
       "      <td>4.711460</td>\n",
       "      <td>3.835815</td>\n",
       "      <td>2.371646</td>\n",
       "      <td>2.984776</td>\n",
       "      <td>5.378381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.001986</td>\n",
       "      <td>899.981606</td>\n",
       "      <td>6117.135756</td>\n",
       "      <td>346.026368</td>\n",
       "      <td>-919.009977</td>\n",
       "      <td>3.528960</td>\n",
       "      <td>1.332443</td>\n",
       "      <td>0.471470</td>\n",
       "      <td>0.148245</td>\n",
       "      <td>0.008421</td>\n",
       "      <td>-0.368923</td>\n",
       "      <td>0.160869</td>\n",
       "      <td>0.128644</td>\n",
       "      <td>-0.049136</td>\n",
       "      <td>0.234171</td>\n",
       "      <td>5.845145</td>\n",
       "      <td>3.431300</td>\n",
       "      <td>2.569457</td>\n",
       "      <td>2.582910</td>\n",
       "      <td>3.850313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.059346</td>\n",
       "      <td>1649.589673</td>\n",
       "      <td>378.349685</td>\n",
       "      <td>547.350965</td>\n",
       "      <td>-161.043882</td>\n",
       "      <td>4.024080</td>\n",
       "      <td>1.461708</td>\n",
       "      <td>0.359327</td>\n",
       "      <td>0.102186</td>\n",
       "      <td>0.015408</td>\n",
       "      <td>-0.050621</td>\n",
       "      <td>0.150515</td>\n",
       "      <td>-0.215766</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>0.396355</td>\n",
       "      <td>5.276968</td>\n",
       "      <td>3.077956</td>\n",
       "      <td>3.558666</td>\n",
       "      <td>2.831150</td>\n",
       "      <td>3.026044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>268.266935</td>\n",
       "      <td>128.450178</td>\n",
       "      <td>45047.834960</td>\n",
       "      <td>82.468813</td>\n",
       "      <td>-200.117164</td>\n",
       "      <td>4.281105</td>\n",
       "      <td>1.340876</td>\n",
       "      <td>0.367898</td>\n",
       "      <td>0.102772</td>\n",
       "      <td>0.010601</td>\n",
       "      <td>0.045670</td>\n",
       "      <td>-0.139799</td>\n",
       "      <td>-0.049070</td>\n",
       "      <td>-0.162660</td>\n",
       "      <td>-0.129068</td>\n",
       "      <td>3.839943</td>\n",
       "      <td>3.221957</td>\n",
       "      <td>2.504929</td>\n",
       "      <td>2.736096</td>\n",
       "      <td>3.962818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CV_IMF1       CV_IMF2       CV_IMF3     CV_IMF4     CV_IMF5   FI_IMF1  \\\n",
       "0   71.679507  29933.433180    985.994979  108.694405  -12.774529  4.418576   \n",
       "1   76.379052    217.653303     26.502862   59.831595  -34.587066  3.731739   \n",
       "2   63.001986    899.981606   6117.135756  346.026368 -919.009977  3.528960   \n",
       "3   71.059346   1649.589673    378.349685  547.350965 -161.043882  4.024080   \n",
       "4  268.266935    128.450178  45047.834960   82.468813 -200.117164  4.281105   \n",
       "\n",
       "    FI_IMF2   FI_IMF3   FI_IMF4   FI_IMF5   SK_IMF1   SK_IMF2   SK_IMF3  \\\n",
       "0  1.135345  0.380331  0.102658  0.019498 -0.053783 -0.051486  0.007683   \n",
       "1  1.209921  0.382103  0.086880  0.007101  0.063953  0.073784  0.051455   \n",
       "2  1.332443  0.471470  0.148245  0.008421 -0.368923  0.160869  0.128644   \n",
       "3  1.461708  0.359327  0.102186  0.015408 -0.050621  0.150515 -0.215766   \n",
       "4  1.340876  0.367898  0.102772  0.010601  0.045670 -0.139799 -0.049070   \n",
       "\n",
       "    SK_IMF4   SK_IMF5   KU_IMF1   KU_IMF2   KU_IMF3   KU_IMF4   KU_IMF5  \n",
       "0  0.066290  0.176168  3.028698  3.442976  3.342342  3.634604  2.088887  \n",
       "1  0.250202  1.638330  4.711460  3.835815  2.371646  2.984776  5.378381  \n",
       "2 -0.049136  0.234171  5.845145  3.431300  2.569457  2.582910  3.850313  \n",
       "3 -0.093318  0.396355  5.276968  3.077956  3.558666  2.831150  3.026044  \n",
       "4 -0.162660 -0.129068  3.839943  3.221957  2.504929  2.736096  3.962818  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T4_TLE_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30, 20), (30,), 0.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T4_TLE_df.shape, y_T4_TLE.shape, y_T4_TLE.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([T3_BECTS_df, T4_BECTS_df, T3_TLE_df, T4_TLE_df], axis=0)\n",
    "y = np.concatenate([y_T3_BECTS, y_T4_BECTS, y_T3_TLE, y_T4_TLE])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CV_IMF1</th>\n",
       "      <th>CV_IMF2</th>\n",
       "      <th>CV_IMF3</th>\n",
       "      <th>CV_IMF4</th>\n",
       "      <th>CV_IMF5</th>\n",
       "      <th>FI_IMF1</th>\n",
       "      <th>FI_IMF2</th>\n",
       "      <th>FI_IMF3</th>\n",
       "      <th>FI_IMF4</th>\n",
       "      <th>FI_IMF5</th>\n",
       "      <th>SK_IMF1</th>\n",
       "      <th>SK_IMF2</th>\n",
       "      <th>SK_IMF3</th>\n",
       "      <th>SK_IMF4</th>\n",
       "      <th>SK_IMF5</th>\n",
       "      <th>KU_IMF1</th>\n",
       "      <th>KU_IMF2</th>\n",
       "      <th>KU_IMF3</th>\n",
       "      <th>KU_IMF4</th>\n",
       "      <th>KU_IMF5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.179958</td>\n",
       "      <td>-293.393923</td>\n",
       "      <td>-250833.76110</td>\n",
       "      <td>-34125.604220</td>\n",
       "      <td>-9.372835e+03</td>\n",
       "      <td>1.624712</td>\n",
       "      <td>0.454270</td>\n",
       "      <td>0.068414</td>\n",
       "      <td>0.026660</td>\n",
       "      <td>0.010508</td>\n",
       "      <td>-0.222586</td>\n",
       "      <td>-0.029176</td>\n",
       "      <td>0.255986</td>\n",
       "      <td>0.496558</td>\n",
       "      <td>1.061101</td>\n",
       "      <td>2.258220</td>\n",
       "      <td>2.401026</td>\n",
       "      <td>3.501249</td>\n",
       "      <td>3.745766</td>\n",
       "      <td>6.079500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>439.917103</td>\n",
       "      <td>-3176.723727</td>\n",
       "      <td>-22346.13834</td>\n",
       "      <td>-3921.508032</td>\n",
       "      <td>-9.126214e+04</td>\n",
       "      <td>1.795715</td>\n",
       "      <td>0.380108</td>\n",
       "      <td>0.066567</td>\n",
       "      <td>0.026646</td>\n",
       "      <td>0.007862</td>\n",
       "      <td>0.031469</td>\n",
       "      <td>-0.000340</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.011156</td>\n",
       "      <td>-0.097538</td>\n",
       "      <td>2.100341</td>\n",
       "      <td>2.333814</td>\n",
       "      <td>2.863083</td>\n",
       "      <td>2.507800</td>\n",
       "      <td>2.094273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.185534</td>\n",
       "      <td>-2693.026027</td>\n",
       "      <td>-12042.86543</td>\n",
       "      <td>-12861.344350</td>\n",
       "      <td>-1.091637e+09</td>\n",
       "      <td>1.584948</td>\n",
       "      <td>0.452024</td>\n",
       "      <td>0.081185</td>\n",
       "      <td>0.027115</td>\n",
       "      <td>0.008891</td>\n",
       "      <td>-0.264721</td>\n",
       "      <td>-0.027211</td>\n",
       "      <td>-0.234781</td>\n",
       "      <td>0.222110</td>\n",
       "      <td>0.045542</td>\n",
       "      <td>2.403527</td>\n",
       "      <td>2.516279</td>\n",
       "      <td>3.400286</td>\n",
       "      <td>3.743209</td>\n",
       "      <td>1.978074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.683381</td>\n",
       "      <td>-51230.959970</td>\n",
       "      <td>-10061.32963</td>\n",
       "      <td>-2520.874182</td>\n",
       "      <td>-4.071085e+04</td>\n",
       "      <td>1.732353</td>\n",
       "      <td>0.450553</td>\n",
       "      <td>0.070310</td>\n",
       "      <td>0.021753</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>-0.139238</td>\n",
       "      <td>0.046150</td>\n",
       "      <td>-0.023084</td>\n",
       "      <td>-0.128933</td>\n",
       "      <td>0.016697</td>\n",
       "      <td>2.171342</td>\n",
       "      <td>2.369437</td>\n",
       "      <td>3.599581</td>\n",
       "      <td>2.935491</td>\n",
       "      <td>2.834916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202.617867</td>\n",
       "      <td>-1563.083064</td>\n",
       "      <td>-27660.10828</td>\n",
       "      <td>-107258.165800</td>\n",
       "      <td>-1.420668e+04</td>\n",
       "      <td>1.771042</td>\n",
       "      <td>0.368516</td>\n",
       "      <td>0.071656</td>\n",
       "      <td>0.024274</td>\n",
       "      <td>0.005628</td>\n",
       "      <td>-0.093936</td>\n",
       "      <td>-0.035272</td>\n",
       "      <td>-0.431327</td>\n",
       "      <td>-0.059919</td>\n",
       "      <td>0.120010</td>\n",
       "      <td>2.208219</td>\n",
       "      <td>2.244567</td>\n",
       "      <td>4.833915</td>\n",
       "      <td>3.037845</td>\n",
       "      <td>2.734746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CV_IMF1       CV_IMF2       CV_IMF3        CV_IMF4       CV_IMF5  \\\n",
       "0    9.179958   -293.393923 -250833.76110  -34125.604220 -9.372835e+03   \n",
       "1  439.917103  -3176.723727  -22346.13834   -3921.508032 -9.126214e+04   \n",
       "2   16.185534  -2693.026027  -12042.86543  -12861.344350 -1.091637e+09   \n",
       "3   29.683381 -51230.959970  -10061.32963   -2520.874182 -4.071085e+04   \n",
       "4  202.617867  -1563.083064  -27660.10828 -107258.165800 -1.420668e+04   \n",
       "\n",
       "    FI_IMF1   FI_IMF2   FI_IMF3   FI_IMF4   FI_IMF5   SK_IMF1   SK_IMF2  \\\n",
       "0  1.624712  0.454270  0.068414  0.026660  0.010508 -0.222586 -0.029176   \n",
       "1  1.795715  0.380108  0.066567  0.026646  0.007862  0.031469 -0.000340   \n",
       "2  1.584948  0.452024  0.081185  0.027115  0.008891 -0.264721 -0.027211   \n",
       "3  1.732353  0.450553  0.070310  0.021753  0.005186 -0.139238  0.046150   \n",
       "4  1.771042  0.368516  0.071656  0.024274  0.005628 -0.093936 -0.035272   \n",
       "\n",
       "    SK_IMF3   SK_IMF4   SK_IMF5   KU_IMF1   KU_IMF2   KU_IMF3   KU_IMF4  \\\n",
       "0  0.255986  0.496558  1.061101  2.258220  2.401026  3.501249  3.745766   \n",
       "1  0.188000  0.011156 -0.097538  2.100341  2.333814  2.863083  2.507800   \n",
       "2 -0.234781  0.222110  0.045542  2.403527  2.516279  3.400286  3.743209   \n",
       "3 -0.023084 -0.128933  0.016697  2.171342  2.369437  3.599581  2.935491   \n",
       "4 -0.431327 -0.059919  0.120010  2.208219  2.244567  4.833915  3.037845   \n",
       "\n",
       "    KU_IMF5  \n",
       "0  6.079500  \n",
       "1  2.094273  \n",
       "2  1.978074  \n",
       "3  2.834916  \n",
       "4  2.734746  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((120, 20), (120,), 60.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape, y.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of Signals\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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         "gridcolor": "LightGrey",
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     },
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   ],
   "source": [
    "fig = make_subplots(rows=5, cols=1, subplot_titles=(\"IMF1\", \"IMF2\", \"IMF3\", \"IMF4\", \"IMF5\"))\n",
    "\n",
    "# Add line plots for each IMF\n",
    "fig.add_trace(go.Scatter(y=org_BECTS_df['IMF1'], mode='lines', name='IMF1'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(y=org_BECTS_df['IMF2'], mode='lines', name='IMF2'), row=2, col=1)\n",
    "fig.add_trace(go.Scatter(y=org_BECTS_df['IMF3'], mode='lines', name='IMF3'), row=3, col=1)\n",
    "fig.add_trace(go.Scatter(y=org_BECTS_df['IMF4'], mode='lines', name='IMF4'), row=4, col=1)\n",
    "fig.add_trace(go.Scatter(y=org_BECTS_df['IMF5'], mode='lines', name='IMF5'), row=5, col=1)\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(height=1000, showlegend=False, title_text=\"BECTS Patient Signals\")\n",
    "\n",
    "for i in range(1, 6):\n",
    "    fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey', row=i, col=1)\n",
    "    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey', row=i, col=1)\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
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           "#636efa",
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          "font": {
           "color": "#2a3f5f"
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          "geo": {
           "bgcolor": "white",
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           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
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           "bgcolor": "#E5ECF6",
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           "line": {
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "TLE Patient Signals"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0,
          1
         ],
         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "xaxis3": {
         "anchor": "y3",
         "domain": [
          0,
          1
         ],
         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
          0,
          1
         ],
         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "xaxis5": {
         "anchor": "y5",
         "domain": [
          0,
          1
         ],
         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.88,
          1
         ],
         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0.66,
          0.78
         ],
         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0.44,
          0.56
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         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "yaxis4": {
         "anchor": "x4",
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          0.22,
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         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        },
        "yaxis5": {
         "anchor": "x5",
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          0,
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         "gridcolor": "LightGrey",
         "gridwidth": 1,
         "showgrid": true
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(rows=5, cols=1, subplot_titles=(\"IMF1\", \"IMF2\", \"IMF3\", \"IMF4\", \"IMF5\"))\n",
    "\n",
    "# Add line plots for each IMF\n",
    "fig.add_trace(go.Scatter(y=org_TLE_df['IMF1'], mode='lines', name='IMF1'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(y=org_TLE_df['IMF2'], mode='lines', name='IMF2'), row=2, col=1)\n",
    "fig.add_trace(go.Scatter(y=org_TLE_df['IMF3'], mode='lines', name='IMF3'), row=3, col=1)\n",
    "fig.add_trace(go.Scatter(y=org_TLE_df['IMF4'], mode='lines', name='IMF4'), row=4, col=1)\n",
    "fig.add_trace(go.Scatter(y=org_TLE_df['IMF5'], mode='lines', name='IMF5'), row=5, col=1)\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(height=1000, showlegend=False, title_text=\"TLE Patient Signals\")\n",
    "\n",
    "for i in range(1, 6):\n",
    "    fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey', row=i, col=1)\n",
    "    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey', row=i, col=1)\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, f1_score, cohen_kappa_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((96, 20), (96,), (24, 20), (24,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kayracoskun/miniconda3/envs/ML/lib/python3.10/site-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "LogisticRegression(C=0.5, penalty='l1', solver='liblinear')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LogisticRegression(C=0.5, penalty='l1', solver='liblinear')\n",
    "LR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "--------------------\n",
      "Accuracy: 66.67%\n",
      "Sensitivity: 76.92%\n",
      "Specificity: 54.55%\n",
      "F1-Score: 0.71\n",
      "Cohen's Kappa: 0.32\n"
     ]
    }
   ],
   "source": [
    "predictions_LR = LR.predict(X_test)\n",
    "accuracy_LR = accuracy_score(y_test, predictions_LR)\n",
    "\n",
    "conf_matrix_LR = confusion_matrix(y_test, predictions_LR)\n",
    "tn, fp, fn, tp = conf_matrix_LR.ravel()\n",
    "\n",
    "sensitivity_LR = tp / (tp + fn)\n",
    "specificity_LR = tn / (tn + fp)\n",
    "f1_LR = f1_score(y_test, predictions_LR)\n",
    "kappa_LR = cohen_kappa_score(y_test, predictions_LR)\n",
    "\n",
    "print(\"Logistic Regression\")\n",
    "print(\"-\"*20)\n",
    "print(f\"Accuracy: {accuracy_LR * 100:.2f}%\")\n",
    "print(f\"Sensitivity: {sensitivity_LR * 100:.2f}%\")\n",
    "print(f\"Specificity: {specificity_LR * 100:.2f}%\")\n",
    "print(f\"F1-Score: {f1_LR:.2f}\")\n",
    "print(f\"Cohen's Kappa: {kappa_LR:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors\n",
      "--------------------\n",
      "Accuracy: 70.83%\n",
      "Sensitivity: 76.92%\n",
      "Specificity: 63.64%\n",
      "F1-Score: 0.74\n",
      "Cohen's Kappa: 0.41\n"
     ]
    }
   ],
   "source": [
    "predictions_KNN = knn.predict(X_test)\n",
    "accuracy_KNN = accuracy_score(y_test, predictions_KNN)\n",
    "\n",
    "conf_matrix_KNN = confusion_matrix(y_test, predictions_KNN)\n",
    "tn, fp, fn, tp = conf_matrix_KNN.ravel()\n",
    "\n",
    "sensitivity_KNN = tp / (tp + fn)\n",
    "specificity_KNN = tn / (tn + fp)\n",
    "f1_KNN = f1_score(y_test, predictions_KNN)\n",
    "kappa_KNN = cohen_kappa_score(y_test, predictions_KNN)\n",
    "\n",
    "print(\"K-Nearest Neighbors\")\n",
    "print(\"-\"*20)\n",
    "print(f\"Accuracy: {accuracy_KNN * 100:.2f}%\")\n",
    "print(f\"Sensitivity: {sensitivity_KNN * 100:.2f}%\")\n",
    "print(f\"Specificity: {specificity_KNN * 100:.2f}%\")\n",
    "print(f\"F1-Score: {f1_KNN:.2f}\")\n",
    "print(f\"Cohen's Kappa: {kappa_KNN:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaling = MinMaxScaler(feature_range=(-1,1)).fit(X_train)\n",
    "X_train_scaled = scaling.transform(X_train)\n",
    "X_test_scaled = scaling.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.5, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.5, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.5, kernel='linear')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(kernel='linear', C=0.5)\n",
    "svm.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine\n",
      "--------------------\n",
      "Accuracy: 70.83%\n",
      "Sensitivity: 76.92%\n",
      "Specificity: 63.64%\n",
      "F1-Score: 0.74\n",
      "Cohen's Kappa: 0.41\n"
     ]
    }
   ],
   "source": [
    "predictions_SVM = knn.predict(X_test)\n",
    "accuracy_SVM = accuracy_score(y_test, predictions_SVM)\n",
    "\n",
    "conf_matrix_SVM = confusion_matrix(y_test, predictions_SVM)\n",
    "tn, fp, fn, tp = conf_matrix_SVM.ravel()\n",
    "\n",
    "sensitivity_SVM = tp / (tp + fn)\n",
    "specificity_SVM = tn / (tn + fp)\n",
    "f1_SVM = f1_score(y_test, predictions_SVM)\n",
    "kappa_SVM = cohen_kappa_score(y_test, predictions_SVM)\n",
    "\n",
    "print(\"Support Vector Machine\")\n",
    "print(\"-\"*20)\n",
    "print(f\"Accuracy: {accuracy_SVM * 100:.2f}%\")\n",
    "print(f\"Sensitivity: {sensitivity_SVM * 100:.2f}%\")\n",
    "print(f\"Specificity: {specificity_SVM * 100:.2f}%\")\n",
    "print(f\"F1-Score: {f1_SVM:.2f}\")\n",
    "print(f\"Cohen's Kappa: {kappa_SVM:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=4, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=4, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_features=4, random_state=42)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_features = int(np.sqrt(X.shape[1]))\n",
    "RFC = RandomForestClassifier(n_estimators=100, max_features=n_features, random_state=42)\n",
    "RFC.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 87.50%\n",
      "Sensitivity: 84.62%\n",
      "Specificity: 90.91%\n",
      "F1-Score: 0.88\n",
      "Cohen's Kappa: 0.75\n"
     ]
    }
   ],
   "source": [
    "predictions_RFC = RFC.predict(X_test)\n",
    "accuracy_RFC = accuracy_score(y_test, predictions_RFC)\n",
    "\n",
    "conf_matrix_RFC = confusion_matrix(y_test, predictions_RFC)\n",
    "tn, fp, fn, tp = conf_matrix_RFC.ravel()\n",
    "\n",
    "sensitivity_RFC = tp / (tp + fn)\n",
    "specificity_RFC = tn / (tn + fp)\n",
    "f1_RFC = f1_score(y_test, predictions_RFC)\n",
    "kappa_RFC = cohen_kappa_score(y_test, predictions_RFC)\n",
    "\n",
    "print(f\"Accuracy: {accuracy_RFC * 100:.2f}%\")\n",
    "print(f\"Sensitivity: {sensitivity_RFC * 100:.2f}%\")\n",
    "print(f\"Specificity: {specificity_RFC * 100:.2f}%\")\n",
    "print(f\"F1-Score: {f1_RFC:.2f}\")\n",
    "print(f\"Cohen's Kappa: {kappa_RFC:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MLP\n",
    "\n",
    "Not implemented in the original article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(activation=&#x27;logistic&#x27;, hidden_layer_sizes=(256, 256, 128),\n",
       "              max_iter=300, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(activation=&#x27;logistic&#x27;, hidden_layer_sizes=(256, 256, 128),\n",
       "              max_iter=300, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(activation='logistic', hidden_layer_sizes=(256, 256, 128),\n",
       "              max_iter=300, random_state=42)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(256, 256, 128), max_iter=300, activation='logistic', solver='adam', random_state=42)\n",
    "mlp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-Layer Perceptron\n",
      "--------------------\n",
      "Accuracy: 66.67%\n",
      "Sensitivity: 61.54%\n",
      "Specificity: 72.73%\n",
      "F1-Score: 0.67\n",
      "Cohen's Kappa: 0.34\n"
     ]
    }
   ],
   "source": [
    "predictions_MLP = mlp.predict(X_test)\n",
    "accuracy_MLP = accuracy_score(y_test, predictions_MLP)\n",
    "\n",
    "conf_matrix_MLP = confusion_matrix(y_test, predictions_MLP)\n",
    "tn, fp, fn, tp = conf_matrix_MLP.ravel()\n",
    "\n",
    "sensitivity_MLP = tp / (tp + fn)\n",
    "specificity_MLP = tn / (tn + fp)\n",
    "f1_MLP = f1_score(y_test, predictions_MLP)\n",
    "kappa_MLP = cohen_kappa_score(y_test, predictions_MLP)\n",
    "\n",
    "print(\"Multi-Layer Perceptron\")\n",
    "print(\"-\"*20)\n",
    "print(f\"Accuracy: {accuracy_MLP * 100:.2f}%\")\n",
    "print(f\"Sensitivity: {sensitivity_MLP * 100:.2f}%\")\n",
    "print(f\"Specificity: {specificity_MLP * 100:.2f}%\")\n",
    "print(f\"F1-Score: {f1_MLP:.2f}\")\n",
    "print(f\"Cohen's Kappa: {kappa_MLP:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM-RFE & Decision Tree\n",
    "\n",
    "SVM-RFE is used to remove some features that have little effect. \n",
    "\n",
    "Decision tree classifies the epilepsy type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='linear', C=1)\n",
    "rfe = RFE(estimator=svc, n_features_to_select=10)\n",
    "rfe = rfe.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Select the features\n",
    "X_train_rfe = rfe.transform(X_train_scaled)\n",
    "X_test_rfe = rfe.transform(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((96, 10), (24, 10))"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rfe.shape, X_test_rfe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-26 {color: black;background-color: white;}#sk-container-id-26 pre{padding: 0;}#sk-container-id-26 div.sk-toggleable {background-color: white;}#sk-container-id-26 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-26 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-26 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-26 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-26 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-26 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-26 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-26 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-26 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-26 div.sk-item {position: relative;z-index: 1;}#sk-container-id-26 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-26 div.sk-item::before, #sk-container-id-26 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-26 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-26 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-26 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-26 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-26 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-26 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-26 div.sk-label-container {text-align: center;}#sk-container-id-26 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-26 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a random forest classifier\n",
    "HML = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "HML.fit(X_train_rfe, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-Layer Perceptron\n",
      "--------------------\n",
      "Accuracy: 91.67%\n",
      "Sensitivity: 92.31%\n",
      "Specificity: 90.91%\n",
      "F1-Score: 0.92\n",
      "Cohen's Kappa: 0.83\n"
     ]
    }
   ],
   "source": [
    "predictions_HML= HML.predict(X_test_rfe)\n",
    "accuracy_HML = accuracy_score(y_test, predictions_HML)\n",
    "\n",
    "conf_matrix_HML = confusion_matrix(y_test, predictions_HML)\n",
    "tn, fp, fn, tp = conf_matrix_HML.ravel()\n",
    "\n",
    "sensitivity_HML = tp / (tp + fn)\n",
    "specificity_HML = tn / (tn + fp)\n",
    "f1_HML = f1_score(y_test, predictions_HML)\n",
    "kappa_HML = cohen_kappa_score(y_test, predictions_HML)\n",
    "\n",
    "print(\"Multi-Layer Perceptron\")\n",
    "print(\"-\"*20)\n",
    "print(f\"Accuracy: {accuracy_HML * 100:.2f}%\")\n",
    "print(f\"Sensitivity: {sensitivity_HML * 100:.2f}%\")\n",
    "print(f\"Specificity: {specificity_HML * 100:.2f}%\")\n",
    "print(f\"F1-Score: {f1_HML:.2f}\")\n",
    "print(f\"Cohen's Kappa: {kappa_HML:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots Comparing Scores of Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Accuracy",
         "text": [
          "0.6666666666666666",
          "0.7083333333333334",
          "0.7083333333333334",
          "0.875",
          "0.6666666666666666",
          "0.9166666666666666"
         ],
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Logistic Regression",
          "KNN",
          "SVM",
          "Decision Tree",
          "MLP",
          "HML"
         ],
         "xaxis": "x",
         "y": [
          0.6666666666666666,
          0.7083333333333334,
          0.7083333333333334,
          0.875,
          0.6666666666666666,
          0.9166666666666666
         ],
         "yaxis": "y"
        },
        {
         "name": "Sensitivity",
         "text": [
          "0.7692307692307693",
          "0.7692307692307693",
          "0.7692307692307693",
          "0.8461538461538461",
          "0.6153846153846154",
          "0.9230769230769231"
         ],
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Logistic Regression",
          "KNN",
          "SVM",
          "Decision Tree",
          "MLP",
          "HML"
         ],
         "xaxis": "x2",
         "y": [
          0.7692307692307693,
          0.7692307692307693,
          0.7692307692307693,
          0.8461538461538461,
          0.6153846153846154,
          0.9230769230769231
         ],
         "yaxis": "y2"
        },
        {
         "name": "Specificity",
         "text": [
          "0.5454545454545454",
          "0.6363636363636364",
          "0.6363636363636364",
          "0.9090909090909091",
          "0.7272727272727273",
          "0.9090909090909091"
         ],
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Logistic Regression",
          "KNN",
          "SVM",
          "Decision Tree",
          "MLP",
          "HML"
         ],
         "xaxis": "x3",
         "y": [
          0.5454545454545454,
          0.6363636363636364,
          0.6363636363636364,
          0.9090909090909091,
          0.7272727272727273,
          0.9090909090909091
         ],
         "yaxis": "y3"
        },
        {
         "name": "F1 Score",
         "text": [
          "0.7142857142857142",
          "0.7407407407407408",
          "0.7407407407407408",
          "0.8799999999999999",
          "0.6666666666666667",
          "0.9230769230769231"
         ],
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Logistic Regression",
          "KNN",
          "SVM",
          "Decision Tree",
          "MLP",
          "HML"
         ],
         "xaxis": "x4",
         "y": [
          0.7142857142857142,
          0.7407407407407408,
          0.7407407407407408,
          0.8799999999999999,
          0.6666666666666667,
          0.9230769230769231
         ],
         "yaxis": "y4"
        },
        {
         "name": "Cohen's Kappa",
         "text": [
          "0.3191489361702128",
          "0.4084507042253521",
          "0.4084507042253521",
          "0.75",
          "0.33793103448275863",
          "0.8321678321678322"
         ],
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Logistic Regression",
          "KNN",
          "SVM",
          "Decision Tree",
          "MLP",
          "HML"
         ],
         "xaxis": "x5",
         "y": [
          0.3191489361702128,
          0.4084507042253521,
          0.4084507042253521,
          0.75,
          0.33793103448275863,
          0.8321678321678322
         ],
         "yaxis": "y5"
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Model Accuracy Comparison",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": 1,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Model Sensitivity Comparison",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.78,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Model Specificity Comparison",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.56,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Model F1 Score Comparison",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.33999999999999997,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Model Cohen's Kappa Comparison",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.12,
          "yanchor": "bottom",
          "yref": "paper"
         }
        ],
        "height": 1500,
        "showlegend": false,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
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          "text": "F1 Score"
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        },
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       }
      }
     },
     "metadata": {},
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   ],
   "source": [
    "model_names = ['Logistic Regression', 'KNN', 'SVM', 'Decision Tree', 'MLP', 'HML']\n",
    "model_accuracies = [accuracy_LR, accuracy_KNN, accuracy_SVM, accuracy_RFC, accuracy_MLP, accuracy_HML]\n",
    "model_sensitivity = [sensitivity_LR, sensitivity_KNN, sensitivity_SVM, sensitivity_RFC, sensitivity_MLP, sensitivity_HML]\n",
    "model_specificity = [specificity_LR, specificity_KNN, specificity_SVM, specificity_RFC, specificity_MLP, specificity_HML]\n",
    "model_f1_scores = [f1_LR, f1_KNN, f1_SVM, f1_RFC, f1_MLP, f1_HML]\n",
    "model_kappa_scores = [kappa_LR, kappa_KNN, kappa_SVM, kappa_RFC, kappa_MLP, kappa_HML]\n",
    "\n",
    "fig = make_subplots(rows=5, cols=1, subplot_titles=(\"Model Accuracy Comparison\", \"Model Sensitivity Comparison\", \"Model Specificity Comparison\", \"Model F1 Score Comparison\", \"Model Cohen's Kappa Comparison\"))\n",
    "\n",
    "fig.add_trace(go.Bar(x=model_names, y=model_accuracies, text=model_accuracies, textposition='auto', name='Accuracy'), row=1, col=1)\n",
    "fig.add_trace(go.Bar(x=model_names, y=model_sensitivity, text=model_sensitivity, textposition='auto', name='Sensitivity'), row=2, col=1)\n",
    "fig.add_trace(go.Bar(x=model_names, y=model_specificity, text=model_specificity, textposition='auto', name='Specificity'), row=3, col=1)\n",
    "fig.add_trace(go.Bar(x=model_names, y=model_f1_scores, text=model_f1_scores, textposition='auto', name='F1 Score'), row=4, col=1)\n",
    "fig.add_trace(go.Bar(x=model_names, y=model_kappa_scores, text=model_kappa_scores, textposition='auto', name='Cohen\\'s Kappa'), row=5, col=1)\n",
    "\n",
    "fig.update_layout(height=1500, showlegend=False, title_text=\"Model Performance Comparison\")\n",
    "\n",
    "fig.update_yaxes(title_text=\"Accuracy\", row=1, col=1, range=[0, 1])\n",
    "fig.update_yaxes(title_text=\"Sensitivity\", row=2, col=1, range=[0, 1])\n",
    "fig.update_yaxes(title_text=\"Specificity\", row=3, col=1, range=[0, 1])\n",
    "fig.update_yaxes(title_text=\"F1 Score\", row=4, col=1, range=[0, 1])\n",
    "fig.update_yaxes(title_text=\"Cohen's Kappa\", row=5, col=1, range=[0, 1])\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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