Autoregressive.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto Regressive for Feature Extraction by Lags of time series \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mne.utils.use_log_level at 0x217c9cef550>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import mne\n",
    "mne.utils.use_log_level('error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Reading all files and selecting Channel 4 for each subject\n",
    "- Take 5 sec segment for 1 min data and using autoregressive with 17 lags of time series values for each subject\n",
    "- Build a dataframe of features according to subject\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "path = r'Preprocessed_Data'\n",
    "all_files = glob.glob(path + \"/*_VR.fif\")\n",
    "\n",
    "li, s = [],[]\n",
    "cols = []\n",
    "sub = 1\n",
    "col = ['Features']\n",
    "\n",
    "for filename in all_files:\n",
    "    raw =  mne.io.read_raw_fif(filename, preload=True, verbose='error');\n",
    "    \n",
    "    if 'CH 4' in raw.info['ch_names']:\n",
    "        raw.pick_channels([\"CH 4\"])\n",
    "        \n",
    "        picks = mne.pick_types(raw.info, eeg=True, exclude=[])\n",
    "        interval = 5\n",
    "        start = 60\n",
    "        stop = start + interval\n",
    "        while(stop <= 120):\n",
    "            start1, stop1 = raw.time_as_index([start, stop])\n",
    "            data, times = raw[picks[:], start1:stop1]\n",
    "            segment = pd.Series(data[0]).shift(17).dropna()\n",
    "        \n",
    "            li.append(segment)\n",
    "            for i in range(len(segment)):\n",
    "                s.append(sub)\n",
    "\n",
    "            start += interval\n",
    "            stop += interval\n",
    "\n",
    "            cols.append(s)\n",
    "    sub += 1\n",
    "    \n",
    "s = pd.Series(s)      \n",
    "frame = pd.concat(li, ignore_index=True, sort=False)\n",
    "\n",
    "df = pd.DataFrame([frame,s])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Features</th>\n",
       "      <th>Subjects</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.525848</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.133788</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-22.865923</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-40.482288</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-31.190411</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-35.478352</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-37.260670</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-15.887967</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-8.499919</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-11.071376</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-7.799063</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-6.571025</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-7.821908</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-20.077360</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-30.086840</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-21.136303</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-13.304882</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-7.971230</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-1.661466</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-5.228827</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.845920</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.862007</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-13.110352</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-6.234336</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.196968</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-11.666724</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-44.714001</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-70.763306</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-70.239983</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-50.225555</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216774</th>\n",
       "      <td>0.190056</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216775</th>\n",
       "      <td>-1.038849</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216776</th>\n",
       "      <td>-2.848111</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216777</th>\n",
       "      <td>1.546918</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216778</th>\n",
       "      <td>-3.205785</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216779</th>\n",
       "      <td>-11.459682</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216780</th>\n",
       "      <td>-5.521101</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216781</th>\n",
       "      <td>17.140558</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216782</th>\n",
       "      <td>26.450714</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216783</th>\n",
       "      <td>12.321572</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216784</th>\n",
       "      <td>1.615459</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216785</th>\n",
       "      <td>-9.631644</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216786</th>\n",
       "      <td>-13.756511</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216787</th>\n",
       "      <td>-3.090215</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216788</th>\n",
       "      <td>-3.970843</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216789</th>\n",
       "      <td>-10.218308</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216790</th>\n",
       "      <td>-5.909690</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216791</th>\n",
       "      <td>-0.420862</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216792</th>\n",
       "      <td>0.392522</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216793</th>\n",
       "      <td>2.969975</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216794</th>\n",
       "      <td>2.313870</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216795</th>\n",
       "      <td>-4.949295</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216796</th>\n",
       "      <td>0.607024</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216797</th>\n",
       "      <td>5.880373</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216798</th>\n",
       "      <td>0.461895</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216799</th>\n",
       "      <td>6.668212</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216800</th>\n",
       "      <td>3.156539</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216801</th>\n",
       "      <td>-6.978634</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216802</th>\n",
       "      <td>3.110620</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216803</th>\n",
       "      <td>6.660659</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216804 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Features  Subjects\n",
       "0       17.525848       1.0\n",
       "1        6.133788       1.0\n",
       "2      -22.865923       1.0\n",
       "3      -40.482288       1.0\n",
       "4      -31.190411       1.0\n",
       "5      -35.478352       1.0\n",
       "6      -37.260670       1.0\n",
       "7      -15.887967       1.0\n",
       "8       -8.499919       1.0\n",
       "9      -11.071376       1.0\n",
       "10      -7.799063       1.0\n",
       "11      -6.571025       1.0\n",
       "12      -7.821908       1.0\n",
       "13     -20.077360       1.0\n",
       "14     -30.086840       1.0\n",
       "15     -21.136303       1.0\n",
       "16     -13.304882       1.0\n",
       "17      -7.971230       1.0\n",
       "18      -1.661466       1.0\n",
       "19      -5.228827       1.0\n",
       "20      -0.845920       1.0\n",
       "21       1.862007       1.0\n",
       "22     -13.110352       1.0\n",
       "23      -6.234336       1.0\n",
       "24       8.196968       1.0\n",
       "25     -11.666724       1.0\n",
       "26     -44.714001       1.0\n",
       "27     -70.763306       1.0\n",
       "28     -70.239983       1.0\n",
       "29     -50.225555       1.0\n",
       "...           ...       ...\n",
       "216774   0.190056      29.0\n",
       "216775  -1.038849      29.0\n",
       "216776  -2.848111      29.0\n",
       "216777   1.546918      29.0\n",
       "216778  -3.205785      29.0\n",
       "216779 -11.459682      29.0\n",
       "216780  -5.521101      29.0\n",
       "216781  17.140558      29.0\n",
       "216782  26.450714      29.0\n",
       "216783  12.321572      29.0\n",
       "216784   1.615459      29.0\n",
       "216785  -9.631644      29.0\n",
       "216786 -13.756511      29.0\n",
       "216787  -3.090215      29.0\n",
       "216788  -3.970843      29.0\n",
       "216789 -10.218308      29.0\n",
       "216790  -5.909690      29.0\n",
       "216791  -0.420862      29.0\n",
       "216792   0.392522      29.0\n",
       "216793   2.969975      29.0\n",
       "216794   2.313870      29.0\n",
       "216795  -4.949295      29.0\n",
       "216796   0.607024      29.0\n",
       "216797   5.880373      29.0\n",
       "216798   0.461895      29.0\n",
       "216799   6.668212      29.0\n",
       "216800   3.156539      29.0\n",
       "216801  -6.978634      29.0\n",
       "216802   3.110620      29.0\n",
       "216803   6.660659      29.0\n",
       "\n",
       "[216804 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.T\n",
    "df1.columns = ['Features','Subjects']\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(216804, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vectore Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1.drop(['Subjects'], axis=1)\n",
    "y = df1['Subjects']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(C=1.0, kernel='rbf', degree=3, gamma='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.08      0.25      0.12      2274\n",
      "         2.0       0.09      0.00      0.00      2182\n",
      "         3.0       0.05      0.04      0.04      2255\n",
      "         4.0       0.05      0.05      0.05      2223\n",
      "         5.0       0.04      0.01      0.01      2236\n",
      "         6.0       0.06      0.03      0.04      2224\n",
      "         7.0       0.06      0.22      0.09      2232\n",
      "         8.0       0.04      0.02      0.02      2213\n",
      "         9.0       0.00      0.00      0.00      2200\n",
      "        10.0       0.32      0.31      0.31      2239\n",
      "        11.0       0.05      0.09      0.06      2259\n",
      "        12.0       0.05      0.03      0.04      2251\n",
      "        13.0       0.04      0.02      0.03      2332\n",
      "        14.0       0.70      0.47      0.57      2266\n",
      "        15.0       0.03      0.01      0.02      2227\n",
      "        16.0       0.04      0.01      0.02      2224\n",
      "        17.0       0.05      0.02      0.03      2188\n",
      "        18.0       0.00      0.00      0.00      2168\n",
      "        19.0       0.00      0.00      0.00      2174\n",
      "        20.0       0.13      0.20      0.16      2210\n",
      "        21.0       0.05      0.05      0.05      2230\n",
      "        22.0       0.05      0.01      0.02      2236\n",
      "        23.0       0.04      0.04      0.04      2223\n",
      "        24.0       0.04      0.04      0.04      2162\n",
      "        25.0       0.02      0.00      0.00      2210\n",
      "        26.0       0.06      0.05      0.06      2310\n",
      "        27.0       0.14      0.89      0.24      2239\n",
      "        28.0       0.04      0.02      0.03      2190\n",
      "        29.0       0.04      0.02      0.02      2247\n",
      "\n",
      "   micro avg       0.10      0.10      0.10     64624\n",
      "   macro avg       0.08      0.10      0.07     64624\n",
      "weighted avg       0.08      0.10      0.07     64624\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\sonal\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "c:\\users\\sonal\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "c:\\users\\sonal\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}