ECG_XGBoost(98_35%)_Ensemble(98_21%).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The first part of this code contains { TSFEL+SMOTE+Selection+RFC-Rfe+XGBoost }\n",
    "## The 2nd part of this code contains { TSFEL+SMOTE+Selection+Ensemble (RFC-Rfe+SVM) }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tsfel\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from imblearn.over_sampling import SMOTE\n",
    "#import wfdb\n",
    "\n",
    "\n",
    "ecg_labels = ['N','S','V','F','Q']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features extracted using TSFEL and stored in excel files\n",
    "## Reading those excel files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_N_total=pd.read_excel(\"X_N_total.xlsx\")\n",
    "X_S_total=pd.read_excel(\"X_S_total.xlsx\")\n",
    "X_V_total=pd.read_excel(\"X_V_total.xlsx\")\n",
    "X_F_total=pd.read_excel(\"X_F_total.xlsx\")\n",
    "X_Q_total=pd.read_excel(\"X_Q_total.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Train-Test Split (Not Required)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "y_N1=pd.Series(\"1\" for i in range(len(X_N_total)))\n",
    "y_S1=pd.Series(\"2\" for i in range(len(X_S_total)))\n",
    "y_V1=pd.Series(\"3\" for i in range(len(X_V_total)))\n",
    "y_F1=pd.Series(\"4\" for i in range(len(X_F_total)))\n",
    "y_Q1=pd.Series(\"5\" for i in range(len(X_Q_total)))\n",
    "\n",
    "X_train_N, X_test_N, y_train_N, y_test_N = train_test_split(X_N_total, y_N1,test_size=0.40,random_state=42)\n",
    "X_train_S, X_test_S, y_train_S, y_test_S = train_test_split(X_S_total, y_S1,test_size=0.40,random_state=42)\n",
    "X_train_V, X_test_V, y_train_V, y_test_V = train_test_split(X_V_total, y_V1,test_size=0.40,random_state=42)\n",
    "X_train_F, X_test_F, y_train_F, y_test_F = train_test_split(X_F_total, y_F1,test_size=0.40,random_state=42)\n",
    "X_train_Q, X_test_Q, y_train_Q, y_test_Q = train_test_split(X_Q_total, y_Q1,test_size=0.40,random_state=42)\n",
    "\n",
    "frames_X_train=[X_train_N,X_train_S,X_train_V,X_train_F,X_train_Q]\n",
    "frames_X_test=[X_test_N,X_test_S,X_test_V,X_test_F,X_test_Q]\n",
    "frames_y_train=[y_train_N,y_train_S,y_train_V,y_train_F,y_train_Q]\n",
    "frames_y_test=[y_test_N,y_test_S,y_test_V,y_test_F,y_test_Q]\n",
    "\n",
    "X_train_smt=pd.concat(frames_X_train, ignore_index=True)\n",
    "X_test=pd.concat(frames_X_test, ignore_index=True)\n",
    "y_train_smt=pd.concat(frames_y_train, ignore_index=True)\n",
    "y_test=pd.concat(frames_y_test, ignore_index=True)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balancing 'Train' portion by SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 13.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "smt=SMOTE(sampling_strategy='auto',random_state=42)\n",
    "X_train, y_train = smt.fit_resample(X_train_smt,y_train_smt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial no. of instances in 'Train' portion in each of five classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    54057\n",
       "3     4204\n",
       "2     1668\n",
       "4      481\n",
       "5        9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_smt.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No.of instances in 'Train' portion after balancing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    54057\n",
       "1    54057\n",
       "2    54057\n",
       "4    54057\n",
       "5    54057\n",
       "dtype: int64"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No. of instances in 'Test' portion, no balancing is applied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    36038\n",
       "3     2804\n",
       "2     1113\n",
       "4      321\n",
       "5        6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Feature Selection\n",
    "\n",
    "# Highly correlated features are removed\n",
    "corr_features = tsfel.correlated_features(X_train)\n",
    "X_train.drop(corr_features, axis=1, inplace=True)\n",
    "X_test.drop(corr_features, axis=1, inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "# Normalising Features\n",
    "min_max_scaler = preprocessing.StandardScaler()\n",
    "nX_train = min_max_scaler.fit_transform(X_train)\n",
    "nX_test = min_max_scaler.transform(X_test)\n",
    "\n",
    "# Remove low variance features\n",
    "selector = VarianceThreshold()\n",
    "X_train = selector.fit_transform(nX_train)\n",
    "X_test = selector.transform(nX_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0_Area under the curve',\n",
       " '0_Autocorrelation',\n",
       " '0_ECDF Percentile_1',\n",
       " '0_FFT mean coefficient_101',\n",
       " '0_FFT mean coefficient_102',\n",
       " '0_FFT mean coefficient_103',\n",
       " '0_FFT mean coefficient_104',\n",
       " '0_FFT mean coefficient_105',\n",
       " '0_FFT mean coefficient_106',\n",
       " '0_FFT mean coefficient_107',\n",
       " '0_FFT mean coefficient_108',\n",
       " '0_FFT mean coefficient_109',\n",
       " '0_FFT mean coefficient_110',\n",
       " '0_FFT mean coefficient_111',\n",
       " '0_FFT mean coefficient_112',\n",
       " '0_FFT mean coefficient_113',\n",
       " '0_FFT mean coefficient_114',\n",
       " '0_FFT mean coefficient_115',\n",
       " '0_FFT mean coefficient_116',\n",
       " '0_FFT mean coefficient_117',\n",
       " '0_FFT mean coefficient_118',\n",
       " '0_FFT mean coefficient_119',\n",
       " '0_FFT mean coefficient_120',\n",
       " '0_FFT mean coefficient_121',\n",
       " '0_FFT mean coefficient_122',\n",
       " '0_FFT mean coefficient_123',\n",
       " '0_FFT mean coefficient_124',\n",
       " '0_FFT mean coefficient_125',\n",
       " '0_FFT mean coefficient_126',\n",
       " '0_FFT mean coefficient_127',\n",
       " '0_FFT mean coefficient_128',\n",
       " '0_FFT mean coefficient_38',\n",
       " '0_FFT mean coefficient_40',\n",
       " '0_FFT mean coefficient_41',\n",
       " '0_FFT mean coefficient_43',\n",
       " '0_FFT mean coefficient_46',\n",
       " '0_FFT mean coefficient_50',\n",
       " '0_FFT mean coefficient_54',\n",
       " '0_FFT mean coefficient_55',\n",
       " '0_FFT mean coefficient_56',\n",
       " '0_FFT mean coefficient_57',\n",
       " '0_FFT mean coefficient_58',\n",
       " '0_FFT mean coefficient_59',\n",
       " '0_FFT mean coefficient_60',\n",
       " '0_FFT mean coefficient_61',\n",
       " '0_FFT mean coefficient_62',\n",
       " '0_FFT mean coefficient_63',\n",
       " '0_FFT mean coefficient_64',\n",
       " '0_FFT mean coefficient_65',\n",
       " '0_FFT mean coefficient_66',\n",
       " '0_FFT mean coefficient_67',\n",
       " '0_FFT mean coefficient_68',\n",
       " '0_FFT mean coefficient_69',\n",
       " '0_FFT mean coefficient_70',\n",
       " '0_FFT mean coefficient_71',\n",
       " '0_FFT mean coefficient_77',\n",
       " '0_FFT mean coefficient_78',\n",
       " '0_FFT mean coefficient_79',\n",
       " '0_FFT mean coefficient_80',\n",
       " '0_FFT mean coefficient_81',\n",
       " '0_FFT mean coefficient_84',\n",
       " '0_FFT mean coefficient_86',\n",
       " '0_FFT mean coefficient_87',\n",
       " '0_FFT mean coefficient_88',\n",
       " '0_FFT mean coefficient_89',\n",
       " '0_FFT mean coefficient_90',\n",
       " '0_FFT mean coefficient_91',\n",
       " '0_FFT mean coefficient_92',\n",
       " '0_FFT mean coefficient_93',\n",
       " '0_FFT mean coefficient_94',\n",
       " '0_FFT mean coefficient_95',\n",
       " '0_FFT mean coefficient_96',\n",
       " '0_FFT mean coefficient_97',\n",
       " '0_FFT mean coefficient_98',\n",
       " '0_FFT mean coefficient_99',\n",
       " '0_Histogram_1',\n",
       " '0_Histogram_2',\n",
       " '0_Histogram_4',\n",
       " '0_Histogram_7',\n",
       " '0_Histogram_8',\n",
       " '0_Histogram_9',\n",
       " '0_LPCC_11',\n",
       " '0_LPCC_2',\n",
       " '0_LPCC_7',\n",
       " '0_LPCC_8',\n",
       " '0_LPCC_9',\n",
       " '0_Mean',\n",
       " '0_Median',\n",
       " '0_Median frequency',\n",
       " '0_Positive turning points',\n",
       " '0_Root mean square',\n",
       " '0_Signal distance',\n",
       " '0_Slope',\n",
       " '0_Spectral centroid',\n",
       " '0_Spectral roll-off',\n",
       " '0_Spectral roll-on',\n",
       " '0_Spectral skewness',\n",
       " '0_Spectral slope',\n",
       " '0_Standard deviation',\n",
       " '0_Sum absolute diff',\n",
       " '0_Total energy',\n",
       " '0_Variance',\n",
       " '0_Wavelet absolute mean_1',\n",
       " '0_Wavelet absolute mean_2',\n",
       " '0_Wavelet absolute mean_3',\n",
       " '0_Wavelet absolute mean_4',\n",
       " '0_Wavelet absolute mean_5',\n",
       " '0_Wavelet absolute mean_6',\n",
       " '0_Wavelet absolute mean_7',\n",
       " '0_Wavelet absolute mean_8',\n",
       " '0_Wavelet energy_3',\n",
       " '0_Wavelet energy_4',\n",
       " '0_Wavelet energy_5',\n",
       " '0_Wavelet energy_6',\n",
       " '0_Wavelet energy_7',\n",
       " '0_Wavelet energy_8',\n",
       " '0_Wavelet standard deviation_0',\n",
       " '0_Wavelet standard deviation_1',\n",
       " '0_Wavelet standard deviation_2',\n",
       " '0_Wavelet standard deviation_3',\n",
       " '0_Wavelet standard deviation_4',\n",
       " '0_Wavelet standard deviation_5',\n",
       " '0_Wavelet standard deviation_6',\n",
       " '0_Wavelet standard deviation_7',\n",
       " '0_Wavelet standard deviation_8',\n",
       " '0_Wavelet variance_0',\n",
       " '0_Wavelet variance_1',\n",
       " '0_Wavelet variance_2',\n",
       " '0_Wavelet variance_3',\n",
       " '0_Wavelet variance_4',\n",
       " '0_Wavelet variance_5',\n",
       " '0_Wavelet variance_6',\n",
       " '0_Wavelet variance_7',\n",
       " '0_Wavelet variance_8',\n",
       " '0_Zero crossing rate']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Recursive Feature Elimination on Random Forest Classifier(RFC-RFE)\n",
    "## RFC-RFE has already been done and results noted, hence commenting this portion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "%%time\n",
    "# RFECV with Random Forest\n",
    "estimator = RandomForestClassifier(random_state=42)\n",
    "rfecv = RFECV(estimator, step=3, min_features_to_select=76, cv=StratifiedKFold(3), scoring='accuracy', n_jobs=-1)\n",
    "rfecv.fit(nX_train, y_train.ravel())\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "%matplotlib notebook\n",
    "# Plot number of features VS. cross-validation scores\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(\"Optimal number of features : %d\" % rfecv.n_features_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "nX_train=pd.DataFrame(nX_train)\n",
    "nX_test=pd.DataFrame(nX_test)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "print(\"Features/Columns to eliminate/drop :- \"np.where(rfecv.support_ == False)[0])\n",
    "nX_train.drop(nX_train.columns[np.where(rfecv.support_ == False)[0]], axis=1, inplace=True)\n",
    "nX_test.drop(nX_train.columns[np.where(rfecv.support_ == False)[0]], axis=1, inplace=True)\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing unimportant features obtained from RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nX_test=pd.DataFrame(X_test)\n",
    "nX_train=pd.DataFrame(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <td>0.175062</td>\n",
       "      <td>2.006897</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>-0.451157</td>\n",
       "      <td>-0.629537</td>\n",
       "      <td>0.529402</td>\n",
       "      <td>-0.093340</td>\n",
       "      <td>0.621032</td>\n",
       "      <td>...</td>\n",
       "      <td>1.369317</td>\n",
       "      <td>-0.615340</td>\n",
       "      <td>-0.645874</td>\n",
       "      <td>-0.491750</td>\n",
       "      <td>1.708244</td>\n",
       "      <td>-0.213311</td>\n",
       "      <td>-0.111830</td>\n",
       "      <td>0.507626</td>\n",
       "      <td>0.677190</td>\n",
       "      <td>0.730201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.215396</td>\n",
       "      <td>0.276131</td>\n",
       "      <td>0.169616</td>\n",
       "      <td>0.955713</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>-0.449860</td>\n",
       "      <td>-0.632757</td>\n",
       "      <td>0.631368</td>\n",
       "      <td>-0.123439</td>\n",
       "      <td>0.699766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.130346</td>\n",
       "      <td>-0.080871</td>\n",
       "      <td>-0.893533</td>\n",
       "      <td>-0.488306</td>\n",
       "      <td>1.838828</td>\n",
       "      <td>-0.218656</td>\n",
       "      <td>-0.216336</td>\n",
       "      <td>0.248899</td>\n",
       "      <td>0.504260</td>\n",
       "      <td>0.407741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40277</th>\n",
       "      <td>-0.638333</td>\n",
       "      <td>-0.857159</td>\n",
       "      <td>-0.518779</td>\n",
       "      <td>-0.501865</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>-0.254848</td>\n",
       "      <td>-0.326159</td>\n",
       "      <td>-0.942780</td>\n",
       "      <td>-0.103903</td>\n",
       "      <td>-1.071009</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.446760</td>\n",
       "      <td>-0.769161</td>\n",
       "      <td>1.583058</td>\n",
       "      <td>0.622436</td>\n",
       "      <td>-1.205382</td>\n",
       "      <td>-1.645734</td>\n",
       "      <td>-2.012150</td>\n",
       "      <td>-2.188508</td>\n",
       "      <td>-2.083777</td>\n",
       "      <td>1.084542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40278</th>\n",
       "      <td>-1.300196</td>\n",
       "      <td>2.407192</td>\n",
       "      <td>-1.097731</td>\n",
       "      <td>-0.409039</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>0.381655</td>\n",
       "      <td>1.470821</td>\n",
       "      <td>-0.439047</td>\n",
       "      <td>-0.058622</td>\n",
       "      <td>-0.334537</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.576385</td>\n",
       "      <td>-1.215563</td>\n",
       "      <td>-0.645874</td>\n",
       "      <td>0.522472</td>\n",
       "      <td>-1.452748</td>\n",
       "      <td>-0.596182</td>\n",
       "      <td>0.174619</td>\n",
       "      <td>0.526702</td>\n",
       "      <td>0.488496</td>\n",
       "      <td>0.257087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40279</th>\n",
       "      <td>0.050842</td>\n",
       "      <td>-0.888730</td>\n",
       "      <td>0.013669</td>\n",
       "      <td>0.006712</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>-0.456481</td>\n",
       "      <td>0.218744</td>\n",
       "      <td>-0.163656</td>\n",
       "      <td>-0.123212</td>\n",
       "      <td>-0.398440</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.499732</td>\n",
       "      <td>0.685707</td>\n",
       "      <td>-0.893533</td>\n",
       "      <td>-0.672094</td>\n",
       "      <td>-0.920857</td>\n",
       "      <td>0.142078</td>\n",
       "      <td>0.008940</td>\n",
       "      <td>-0.318067</td>\n",
       "      <td>-0.385769</td>\n",
       "      <td>-1.004748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40280</th>\n",
       "      <td>-0.686223</td>\n",
       "      <td>-0.495449</td>\n",
       "      <td>-0.109839</td>\n",
       "      <td>-0.211158</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>-0.454736</td>\n",
       "      <td>-0.586175</td>\n",
       "      <td>-1.034804</td>\n",
       "      <td>-0.124396</td>\n",
       "      <td>-1.085631</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.878342</td>\n",
       "      <td>-0.354510</td>\n",
       "      <td>-1.636510</td>\n",
       "      <td>0.415695</td>\n",
       "      <td>-1.411882</td>\n",
       "      <td>-0.527152</td>\n",
       "      <td>-0.785643</td>\n",
       "      <td>-1.217693</td>\n",
       "      <td>-1.449366</td>\n",
       "      <td>0.028117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40281</th>\n",
       "      <td>0.734791</td>\n",
       "      <td>-1.850340</td>\n",
       "      <td>-1.863803</td>\n",
       "      <td>-1.051123</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>22.268918</td>\n",
       "      <td>6.742839</td>\n",
       "      <td>1.709282</td>\n",
       "      <td>0.053033</td>\n",
       "      <td>-0.609382</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.734618</td>\n",
       "      <td>-1.579364</td>\n",
       "      <td>0.840081</td>\n",
       "      <td>2.567252</td>\n",
       "      <td>-2.038843</td>\n",
       "      <td>-4.130026</td>\n",
       "      <td>-3.349721</td>\n",
       "      <td>-1.752464</td>\n",
       "      <td>-0.514225</td>\n",
       "      <td>0.830206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40282 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4          5         6    \\\n",
       "0      0.165393  0.672773  0.750033 -0.305795  0.014233  -0.408106 -0.581977   \n",
       "1      0.190912 -0.344107  0.685728  1.194777  0.014233  -0.426247 -0.538709   \n",
       "2     -0.135130  0.018771 -0.156186 -0.406487  0.014233  -0.454034 -0.429770   \n",
       "3     -0.248351  0.279040  0.175062  2.006897  0.014233  -0.451157 -0.629537   \n",
       "4     -0.215396  0.276131  0.169616  0.955713  0.014233  -0.449860 -0.632757   \n",
       "...         ...       ...       ...       ...       ...        ...       ...   \n",
       "40277 -0.638333 -0.857159 -0.518779 -0.501865  0.014233  -0.254848 -0.326159   \n",
       "40278 -1.300196  2.407192 -1.097731 -0.409039  0.014233   0.381655  1.470821   \n",
       "40279  0.050842 -0.888730  0.013669  0.006712  0.014233  -0.456481  0.218744   \n",
       "40280 -0.686223 -0.495449 -0.109839 -0.211158  0.014233  -0.454736 -0.586175   \n",
       "40281  0.734791 -1.850340 -1.863803 -1.051123  0.014233  22.268918  6.742839   \n",
       "\n",
       "            7         8         9    ...       106       107       108  \\\n",
       "0     -0.822028 -0.123803 -0.815244  ...  0.639746 -0.366241 -0.645874   \n",
       "1     -0.885242 -0.124195 -1.046277  ... -0.282321  0.883597 -0.398214   \n",
       "2     -0.767930 -0.124309 -0.978362  ... -0.268330  1.798522 -1.636510   \n",
       "3      0.529402 -0.093340  0.621032  ...  1.369317 -0.615340 -0.645874   \n",
       "4      0.631368 -0.123439  0.699766  ...  1.130346 -0.080871 -0.893533   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "40277 -0.942780 -0.103903 -1.071009  ... -0.446760 -0.769161  1.583058   \n",
       "40278 -0.439047 -0.058622 -0.334537  ... -0.576385 -1.215563 -0.645874   \n",
       "40279 -0.163656 -0.123212 -0.398440  ... -0.499732  0.685707 -0.893533   \n",
       "40280 -1.034804 -0.124396 -1.085631  ... -0.878342 -0.354510 -1.636510   \n",
       "40281  1.709282  0.053033 -0.609382  ... -1.734618 -1.579364  0.840081   \n",
       "\n",
       "            109       110       111       112       113       114       115  \n",
       "0      0.377683 -0.266713  0.290674  0.033311 -0.194867 -0.264387  0.797216  \n",
       "1     -0.499781  0.355279  0.160288 -0.080614 -0.579466 -0.770962 -0.942882  \n",
       "2     -0.799853  0.356362  0.121587 -0.093916 -0.455166 -0.545705 -1.598911  \n",
       "3     -0.491750  1.708244 -0.213311 -0.111830  0.507626  0.677190  0.730201  \n",
       "4     -0.488306  1.838828 -0.218656 -0.216336  0.248899  0.504260  0.407741  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "40277  0.622436 -1.205382 -1.645734 -2.012150 -2.188508 -2.083777  1.084542  \n",
       "40278  0.522472 -1.452748 -0.596182  0.174619  0.526702  0.488496  0.257087  \n",
       "40279 -0.672094 -0.920857  0.142078  0.008940 -0.318067 -0.385769 -1.004748  \n",
       "40280  0.415695 -1.411882 -0.527152 -0.785643 -1.217693 -1.449366  0.028117  \n",
       "40281  2.567252 -2.038843 -4.130026 -3.349721 -1.752464 -0.514225  0.830206  \n",
       "\n",
       "[40282 rows x 116 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nX_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nX_train=nX_train.drop(columns=[4,27,28,30,31,32,35,41,43,44,49,54,55,58,59,63,64,65,66,71,73,75,100,108])\n",
    "nX_test=nX_test.drop(columns=[4,27,28,30,31,32,35,41,43,44,49,54,55,58,59,63,64,65,66,71,73,75,100,108])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-1.196409</td>\n",
       "      <td>0.302648</td>\n",
       "      <td>-0.912600</td>\n",
       "      <td>0.176102</td>\n",
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       "      <td>-0.626019</td>\n",
       "      <td>0.978419</td>\n",
       "      <td>-0.120253</td>\n",
       "      <td>1.101402</td>\n",
       "      <td>1.204142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.843739</td>\n",
       "      <td>1.407324</td>\n",
       "      <td>-0.830661</td>\n",
       "      <td>-0.713825</td>\n",
       "      <td>1.609373</td>\n",
       "      <td>-1.318097</td>\n",
       "      <td>-1.137723</td>\n",
       "      <td>0.035292</td>\n",
       "      <td>0.513124</td>\n",
       "      <td>1.125429</td>\n",
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       "      <th>3</th>\n",
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       "      <td>-0.170743</td>\n",
       "      <td>0.716441</td>\n",
       "      <td>3.031726</td>\n",
       "      <td>-0.455079</td>\n",
       "      <td>-0.553776</td>\n",
       "      <td>-0.545408</td>\n",
       "      <td>-0.124380</td>\n",
       "      <td>-0.656535</td>\n",
       "      <td>-0.653423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.405348</td>\n",
       "      <td>0.370649</td>\n",
       "      <td>1.893022</td>\n",
       "      <td>-1.106176</td>\n",
       "      <td>0.320562</td>\n",
       "      <td>0.387501</td>\n",
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       "      <td>-0.370586</td>\n",
       "      <td>-0.340366</td>\n",
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       "      <th>4</th>\n",
       "      <td>-1.431598</td>\n",
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       "      <td>-1.120660</td>\n",
       "      <td>0.373228</td>\n",
       "      <td>-0.422994</td>\n",
       "      <td>-0.624479</td>\n",
       "      <td>0.845879</td>\n",
       "      <td>-0.084588</td>\n",
       "      <td>0.837686</td>\n",
       "      <td>1.242848</td>\n",
       "      <td>...</td>\n",
       "      <td>1.106857</td>\n",
       "      <td>1.701929</td>\n",
       "      <td>-0.590851</td>\n",
       "      <td>-0.393388</td>\n",
       "      <td>1.411674</td>\n",
       "      <td>-1.745431</td>\n",
       "      <td>-1.382152</td>\n",
       "      <td>0.175373</td>\n",
       "      <td>0.658451</td>\n",
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       "      <td>0.317581</td>\n",
       "      <td>-0.751399</td>\n",
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       "      <td>-1.053674</td>\n",
       "      <td>-0.998503</td>\n",
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       "      <td>-0.097885</td>\n",
       "      <td>-1.702959</td>\n",
       "      <td>-0.399955</td>\n",
       "      <td>0.521826</td>\n",
       "      <td>-1.737651</td>\n",
       "      <td>-0.057287</td>\n",
       "      <td>-0.269989</td>\n",
       "      <td>-0.785592</td>\n",
       "      <td>-1.027564</td>\n",
       "      <td>-1.057695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270281</th>\n",
       "      <td>1.320444</td>\n",
       "      <td>-2.434880</td>\n",
       "      <td>-0.000153</td>\n",
       "      <td>-0.990855</td>\n",
       "      <td>6.227866</td>\n",
       "      <td>3.319430</td>\n",
       "      <td>-0.674292</td>\n",
       "      <td>-0.124403</td>\n",
       "      <td>-0.537415</td>\n",
       "      <td>-0.900349</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.258725</td>\n",
       "      <td>-2.433157</td>\n",
       "      <td>0.797798</td>\n",
       "      <td>-0.345953</td>\n",
       "      <td>-2.032818</td>\n",
       "      <td>-1.201947</td>\n",
       "      <td>-1.361394</td>\n",
       "      <td>-1.794409</td>\n",
       "      <td>-2.004334</td>\n",
       "      <td>0.434772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270282</th>\n",
       "      <td>0.061300</td>\n",
       "      <td>-1.568500</td>\n",
       "      <td>-0.717474</td>\n",
       "      <td>-0.802094</td>\n",
       "      <td>2.997316</td>\n",
       "      <td>1.393851</td>\n",
       "      <td>0.180296</td>\n",
       "      <td>-0.113747</td>\n",
       "      <td>0.048230</td>\n",
       "      <td>-0.080775</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.430336</td>\n",
       "      <td>-1.359367</td>\n",
       "      <td>0.384614</td>\n",
       "      <td>-0.562799</td>\n",
       "      <td>-1.121386</td>\n",
       "      <td>0.047143</td>\n",
       "      <td>0.089585</td>\n",
       "      <td>-0.036150</td>\n",
       "      <td>-0.052851</td>\n",
       "      <td>0.219346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270283</th>\n",
       "      <td>0.788231</td>\n",
       "      <td>-2.201977</td>\n",
       "      <td>-0.017145</td>\n",
       "      <td>-0.882577</td>\n",
       "      <td>2.871368</td>\n",
       "      <td>2.275761</td>\n",
       "      <td>-0.855275</td>\n",
       "      <td>-0.124380</td>\n",
       "      <td>-0.835592</td>\n",
       "      <td>-0.958333</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.761917</td>\n",
       "      <td>-2.183502</td>\n",
       "      <td>0.244932</td>\n",
       "      <td>-0.003765</td>\n",
       "      <td>-1.909527</td>\n",
       "      <td>-0.791218</td>\n",
       "      <td>-0.984170</td>\n",
       "      <td>-1.444172</td>\n",
       "      <td>-1.631475</td>\n",
       "      <td>-0.742672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270284</th>\n",
       "      <td>-2.616486</td>\n",
       "      <td>0.451071</td>\n",
       "      <td>-3.163698</td>\n",
       "      <td>-0.374537</td>\n",
       "      <td>-0.112729</td>\n",
       "      <td>-0.340904</td>\n",
       "      <td>-0.026739</td>\n",
       "      <td>5.063844</td>\n",
       "      <td>-0.926536</td>\n",
       "      <td>3.334636</td>\n",
       "      <td>...</td>\n",
       "      <td>1.670271</td>\n",
       "      <td>1.020464</td>\n",
       "      <td>-1.457142</td>\n",
       "      <td>1.880621</td>\n",
       "      <td>0.636119</td>\n",
       "      <td>-2.261436</td>\n",
       "      <td>-0.694298</td>\n",
       "      <td>1.687009</td>\n",
       "      <td>1.983367</td>\n",
       "      <td>3.254091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270285 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         5         6         7    \\\n",
       "0       0.382232 -0.008297  0.923910 -0.368306 -0.449964 -0.622421 -0.567739   \n",
       "1      -1.262938  0.252526 -0.898756  0.281099 -0.357370 -0.626105  0.891047   \n",
       "2      -1.196409  0.302648 -0.912600  0.176102 -0.438644 -0.626019  0.978419   \n",
       "3       0.224080 -0.170743  0.716441  3.031726 -0.455079 -0.553776 -0.545408   \n",
       "4      -1.431598 -0.080701 -1.120660  0.373228 -0.422994 -0.624479  0.845879   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "270280  0.489923 -1.689820  0.317581 -0.751399  0.259635  0.911631 -0.996654   \n",
       "270281  1.320444 -2.434880 -0.000153 -0.990855  6.227866  3.319430 -0.674292   \n",
       "270282  0.061300 -1.568500 -0.717474 -0.802094  2.997316  1.393851  0.180296   \n",
       "270283  0.788231 -2.201977 -0.017145 -0.882577  2.871368  2.275761 -0.855275   \n",
       "270284 -2.616486  0.451071 -3.163698 -0.374537 -0.112729 -0.340904 -0.026739   \n",
       "\n",
       "             8         9         10   ...       105       106       107  \\\n",
       "0      -0.120835 -0.517221 -0.408226  ...  0.176096  1.023803 -0.241642   \n",
       "1      -0.050376  1.032476  1.276194  ...  0.913258  1.716388 -0.645074   \n",
       "2      -0.120253  1.101402  1.204142  ...  0.843739  1.407324 -0.830661   \n",
       "3      -0.124380 -0.656535 -0.653423  ...  0.405348  0.370649  1.893022   \n",
       "4      -0.084588  0.837686  1.242848  ...  1.106857  1.701929 -0.590851   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "270280 -0.124381 -1.053674 -0.998503  ... -0.097885 -1.702959 -0.399955   \n",
       "270281 -0.124403 -0.537415 -0.900349  ... -1.258725 -2.433157  0.797798   \n",
       "270282 -0.113747  0.048230 -0.080775  ... -0.430336 -1.359367  0.384614   \n",
       "270283 -0.124380 -0.835592 -0.958333  ... -0.761917 -2.183502  0.244932   \n",
       "270284  5.063844 -0.926536  3.334636  ...  1.670271  1.020464 -1.457142   \n",
       "\n",
       "             109       110       111       112       113       114       115  \n",
       "0      -0.509733  1.334598  0.588311  0.645179  0.529795  0.231271  0.223028  \n",
       "1      -0.372410  1.367215 -1.665932 -1.254076  0.455371  0.863177  1.900779  \n",
       "2      -0.713825  1.609373 -1.318097 -1.137723  0.035292  0.513124  1.125429  \n",
       "3      -1.106176  0.320562  0.387501  0.147389 -0.206655 -0.370586 -0.340366  \n",
       "4      -0.393388  1.411674 -1.745431 -1.382152  0.175373  0.658451  1.888705  \n",
       "...          ...       ...       ...       ...       ...       ...       ...  \n",
       "270280  0.521826 -1.737651 -0.057287 -0.269989 -0.785592 -1.027564 -1.057695  \n",
       "270281 -0.345953 -2.032818 -1.201947 -1.361394 -1.794409 -2.004334  0.434772  \n",
       "270282 -0.562799 -1.121386  0.047143  0.089585 -0.036150 -0.052851  0.219346  \n",
       "270283 -0.003765 -1.909527 -0.791218 -0.984170 -1.444172 -1.631475 -0.742672  \n",
       "270284  1.880621  0.636119 -2.261436 -0.694298  1.687009  1.983367  3.254091  \n",
       "\n",
       "[270285 rows x 92 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nX_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-0.406487</td>\n",
       "      <td>-0.454034</td>\n",
       "      <td>-0.429770</td>\n",
       "      <td>-0.767930</td>\n",
       "      <td>-0.124309</td>\n",
       "      <td>-0.978362</td>\n",
       "      <td>-0.966997</td>\n",
       "      <td>...</td>\n",
       "      <td>0.139875</td>\n",
       "      <td>-0.268330</td>\n",
       "      <td>1.798522</td>\n",
       "      <td>-0.799853</td>\n",
       "      <td>0.356362</td>\n",
       "      <td>0.121587</td>\n",
       "      <td>-0.093916</td>\n",
       "      <td>-0.455166</td>\n",
       "      <td>-0.545705</td>\n",
       "      <td>-1.598911</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.248351</td>\n",
       "      <td>0.279040</td>\n",
       "      <td>0.175062</td>\n",
       "      <td>2.006897</td>\n",
       "      <td>-0.451157</td>\n",
       "      <td>-0.629537</td>\n",
       "      <td>0.529402</td>\n",
       "      <td>-0.093340</td>\n",
       "      <td>0.621032</td>\n",
       "      <td>0.826815</td>\n",
       "      <td>...</td>\n",
       "      <td>0.316058</td>\n",
       "      <td>1.369317</td>\n",
       "      <td>-0.615340</td>\n",
       "      <td>-0.491750</td>\n",
       "      <td>1.708244</td>\n",
       "      <td>-0.213311</td>\n",
       "      <td>-0.111830</td>\n",
       "      <td>0.507626</td>\n",
       "      <td>0.677190</td>\n",
       "      <td>0.730201</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.215396</td>\n",
       "      <td>0.276131</td>\n",
       "      <td>0.169616</td>\n",
       "      <td>0.955713</td>\n",
       "      <td>-0.449860</td>\n",
       "      <td>-0.632757</td>\n",
       "      <td>0.631368</td>\n",
       "      <td>-0.123439</td>\n",
       "      <td>0.699766</td>\n",
       "      <td>0.717879</td>\n",
       "      <td>...</td>\n",
       "      <td>0.299532</td>\n",
       "      <td>1.130346</td>\n",
       "      <td>-0.080871</td>\n",
       "      <td>-0.488306</td>\n",
       "      <td>1.838828</td>\n",
       "      <td>-0.218656</td>\n",
       "      <td>-0.216336</td>\n",
       "      <td>0.248899</td>\n",
       "      <td>0.504260</td>\n",
       "      <td>0.407741</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>40277</th>\n",
       "      <td>-0.638333</td>\n",
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       "      <td>-0.501865</td>\n",
       "      <td>-0.254848</td>\n",
       "      <td>-0.326159</td>\n",
       "      <td>-0.942780</td>\n",
       "      <td>-0.103903</td>\n",
       "      <td>-1.071009</td>\n",
       "      <td>-0.838392</td>\n",
       "      <td>...</td>\n",
       "      <td>1.078319</td>\n",
       "      <td>-0.446760</td>\n",
       "      <td>-0.769161</td>\n",
       "      <td>0.622436</td>\n",
       "      <td>-1.205382</td>\n",
       "      <td>-1.645734</td>\n",
       "      <td>-2.012150</td>\n",
       "      <td>-2.188508</td>\n",
       "      <td>-2.083777</td>\n",
       "      <td>1.084542</td>\n",
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       "    <tr>\n",
       "      <th>40278</th>\n",
       "      <td>-1.300196</td>\n",
       "      <td>2.407192</td>\n",
       "      <td>-1.097731</td>\n",
       "      <td>-0.409039</td>\n",
       "      <td>0.381655</td>\n",
       "      <td>1.470821</td>\n",
       "      <td>-0.439047</td>\n",
       "      <td>-0.058622</td>\n",
       "      <td>-0.334537</td>\n",
       "      <td>0.218240</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.070944</td>\n",
       "      <td>-0.576385</td>\n",
       "      <td>-1.215563</td>\n",
       "      <td>0.522472</td>\n",
       "      <td>-1.452748</td>\n",
       "      <td>-0.596182</td>\n",
       "      <td>0.174619</td>\n",
       "      <td>0.526702</td>\n",
       "      <td>0.488496</td>\n",
       "      <td>0.257087</td>\n",
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       "    <tr>\n",
       "      <th>40279</th>\n",
       "      <td>0.050842</td>\n",
       "      <td>-0.888730</td>\n",
       "      <td>0.013669</td>\n",
       "      <td>0.006712</td>\n",
       "      <td>-0.456481</td>\n",
       "      <td>0.218744</td>\n",
       "      <td>-0.163656</td>\n",
       "      <td>-0.123212</td>\n",
       "      <td>-0.398440</td>\n",
       "      <td>-0.570936</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.312085</td>\n",
       "      <td>-0.499732</td>\n",
       "      <td>0.685707</td>\n",
       "      <td>-0.672094</td>\n",
       "      <td>-0.920857</td>\n",
       "      <td>0.142078</td>\n",
       "      <td>0.008940</td>\n",
       "      <td>-0.318067</td>\n",
       "      <td>-0.385769</td>\n",
       "      <td>-1.004748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40280</th>\n",
       "      <td>-0.686223</td>\n",
       "      <td>-0.495449</td>\n",
       "      <td>-0.109839</td>\n",
       "      <td>-0.211158</td>\n",
       "      <td>-0.454736</td>\n",
       "      <td>-0.586175</td>\n",
       "      <td>-1.034804</td>\n",
       "      <td>-0.124396</td>\n",
       "      <td>-1.085631</td>\n",
       "      <td>-1.000745</td>\n",
       "      <td>...</td>\n",
       "      <td>1.878371</td>\n",
       "      <td>-0.878342</td>\n",
       "      <td>-0.354510</td>\n",
       "      <td>0.415695</td>\n",
       "      <td>-1.411882</td>\n",
       "      <td>-0.527152</td>\n",
       "      <td>-0.785643</td>\n",
       "      <td>-1.217693</td>\n",
       "      <td>-1.449366</td>\n",
       "      <td>0.028117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40281</th>\n",
       "      <td>0.734791</td>\n",
       "      <td>-1.850340</td>\n",
       "      <td>-1.863803</td>\n",
       "      <td>-1.051123</td>\n",
       "      <td>22.268918</td>\n",
       "      <td>6.742839</td>\n",
       "      <td>1.709282</td>\n",
       "      <td>0.053033</td>\n",
       "      <td>-0.609382</td>\n",
       "      <td>1.181629</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.929347</td>\n",
       "      <td>-1.734618</td>\n",
       "      <td>-1.579364</td>\n",
       "      <td>2.567252</td>\n",
       "      <td>-2.038843</td>\n",
       "      <td>-4.130026</td>\n",
       "      <td>-3.349721</td>\n",
       "      <td>-1.752464</td>\n",
       "      <td>-0.514225</td>\n",
       "      <td>0.830206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40282 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3          5         6         7    \\\n",
       "0      0.165393  0.672773  0.750033 -0.305795  -0.408106 -0.581977 -0.822028   \n",
       "1      0.190912 -0.344107  0.685728  1.194777  -0.426247 -0.538709 -0.885242   \n",
       "2     -0.135130  0.018771 -0.156186 -0.406487  -0.454034 -0.429770 -0.767930   \n",
       "3     -0.248351  0.279040  0.175062  2.006897  -0.451157 -0.629537  0.529402   \n",
       "4     -0.215396  0.276131  0.169616  0.955713  -0.449860 -0.632757  0.631368   \n",
       "...         ...       ...       ...       ...        ...       ...       ...   \n",
       "40277 -0.638333 -0.857159 -0.518779 -0.501865  -0.254848 -0.326159 -0.942780   \n",
       "40278 -1.300196  2.407192 -1.097731 -0.409039   0.381655  1.470821 -0.439047   \n",
       "40279  0.050842 -0.888730  0.013669  0.006712  -0.456481  0.218744 -0.163656   \n",
       "40280 -0.686223 -0.495449 -0.109839 -0.211158  -0.454736 -0.586175 -1.034804   \n",
       "40281  0.734791 -1.850340 -1.863803 -1.051123  22.268918  6.742839  1.709282   \n",
       "\n",
       "            8         9         10   ...       105       106       107  \\\n",
       "0     -0.123803 -0.815244 -0.765963  ...  0.785020  0.639746 -0.366241   \n",
       "1     -0.124195 -1.046277 -0.917471  ...  0.438164 -0.282321  0.883597   \n",
       "2     -0.124309 -0.978362 -0.966997  ...  0.139875 -0.268330  1.798522   \n",
       "3     -0.093340  0.621032  0.826815  ...  0.316058  1.369317 -0.615340   \n",
       "4     -0.123439  0.699766  0.717879  ...  0.299532  1.130346 -0.080871   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "40277 -0.103903 -1.071009 -0.838392  ...  1.078319 -0.446760 -0.769161   \n",
       "40278 -0.058622 -0.334537  0.218240  ... -0.070944 -0.576385 -1.215563   \n",
       "40279 -0.123212 -0.398440 -0.570936  ... -0.312085 -0.499732  0.685707   \n",
       "40280 -0.124396 -1.085631 -1.000745  ...  1.878371 -0.878342 -0.354510   \n",
       "40281  0.053033 -0.609382  1.181629  ... -2.929347 -1.734618 -1.579364   \n",
       "\n",
       "            109       110       111       112       113       114       115  \n",
       "0      0.377683 -0.266713  0.290674  0.033311 -0.194867 -0.264387  0.797216  \n",
       "1     -0.499781  0.355279  0.160288 -0.080614 -0.579466 -0.770962 -0.942882  \n",
       "2     -0.799853  0.356362  0.121587 -0.093916 -0.455166 -0.545705 -1.598911  \n",
       "3     -0.491750  1.708244 -0.213311 -0.111830  0.507626  0.677190  0.730201  \n",
       "4     -0.488306  1.838828 -0.218656 -0.216336  0.248899  0.504260  0.407741  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "40277  0.622436 -1.205382 -1.645734 -2.012150 -2.188508 -2.083777  1.084542  \n",
       "40278  0.522472 -1.452748 -0.596182  0.174619  0.526702  0.488496  0.257087  \n",
       "40279 -0.672094 -0.920857  0.142078  0.008940 -0.318067 -0.385769 -1.004748  \n",
       "40280  0.415695 -1.411882 -0.527152 -0.785643 -1.217693 -1.449366  0.028117  \n",
       "40281  2.567252 -2.038843 -4.130026 -3.349721 -1.752464 -0.514225  0.830206  \n",
       "\n",
       "[40282 rows x 92 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nX_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Applying XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 21min 19s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=-1, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "#xgboost classifier with 92 features\n",
    "\n",
    "import xgboost as xgb\n",
    "regressor = xgb.XGBClassifier(n_jobs=-1)\n",
    "regressor.fit(nX_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 824 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#xgboost classifier with 92 features\n",
    "\n",
    "y_test_predict = regressor.predict(nX_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           N     0.9885    0.9944    0.9915     36038\n",
      "           S     0.8955    0.7853    0.8368      1113\n",
      "           V     0.9630    0.9479    0.9554      2804\n",
      "           F     0.8517    0.7695    0.8085       321\n",
      "           Q     0.0000    0.0000    0.0000         6\n",
      "\n",
      "    accuracy                         0.9835     40282\n",
      "   macro avg     0.7398    0.6994    0.7184     40282\n",
      "weighted avg     0.9829    0.9835    0.9831     40282\n",
      "\n",
      "Accuracy:  98.35 %\n",
      "Wall time: 1.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#xgboost classifier with 92 features\n",
    "# Get the classification report\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_test_predict) * 100\n",
    "print(classification_report(y_test, y_test_predict, target_names=ecg_labels, zero_division=0, digits=4))\n",
    "print(\"Accuracy: \", round(accuracy,2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 0.9834665607467356\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "print('Sensitivity:',recall_score(y_test, y_test_predict, average = 'micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgboost classifier with 92 features\n",
    "# Confusion Matrix\n",
    "%matplotlib notebook\n",
    "cm = confusion_matrix(y_test, y_test_predict)\n",
    "df_cm = pd.DataFrame(cm, index=[i for i in ecg_labels], columns=[i for i in ecg_labels])\n",
    "plt.figure()\n",
    "ax= sns.heatmap(df_cm,  cbar=False, cmap=\"BuGn\", annot=True, fmt=\"d\")\n",
    "plt.setp(ax.get_xticklabels(), rotation=0)\n",
    "\n",
    "plt.ylabel('True Data', fontweight='bold', fontsize = 18)\n",
    "plt.xlabel('Predicted Data', fontweight='bold', fontsize = 18)\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Applying Ensemble of Random Forest Classifier and SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#random forest classifier with\n",
    "# Training\n",
    "\n",
    "#Random Forest Classifier\n",
    "classifier = RandomForestClassifier(random_state=42, n_jobs=-1)\n",
    "# Train the classifier\n",
    "classifier.fit(nX_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random forest classifier with\n",
    "y_test_predict = classifier.predict(nX_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '1', '1', ..., '3', '1', '1'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           N     0.9890    0.9908    0.9899     36038\n",
      "           S     0.8672    0.7628    0.8117      1113\n",
      "           V     0.9247    0.9597    0.9419      2804\n",
      "           F     0.8328    0.7445    0.7862       321\n",
      "           Q     0.0000    0.0000    0.0000         6\n",
      "\n",
      "    accuracy                         0.9802     40282\n",
      "   macro avg     0.7227    0.6916    0.7059     40282\n",
      "weighted avg     0.9797    0.9802    0.9798     40282\n",
      "\n",
      "Accuracy:  98.02 %\n"
     ]
    }
   ],
   "source": [
    "#random forest classifier with\n",
    "# Get the classification report\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_test_predict) * 100\n",
    "print(classification_report(y_test, y_test_predict, target_names=ecg_labels, zero_division=0, digits=4))\n",
    "print(\"Accuracy: \", round(accuracy,2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#svm\n",
    "from sklearn.svm import SVC\n",
    "clf = SVC().fit(nX_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#svm\n",
    "y_test_predict = clf.predict(nX_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           N     0.9909    0.9736    0.9822     36038\n",
      "           S     0.5628    0.8329    0.6717      1113\n",
      "           V     0.9377    0.9444    0.9410      2804\n",
      "           F     0.6952    0.8598    0.7688       321\n",
      "           Q     0.0000    0.0000    0.0000         6\n",
      "\n",
      "    accuracy                         0.9667     40282\n",
      "   macro avg     0.6373    0.7221    0.6728     40282\n",
      "weighted avg     0.9729    0.9667    0.9689     40282\n",
      "\n",
      "Accuracy:  96.67 %\n"
     ]
    }
   ],
   "source": [
    "#svm\n",
    "# Get the classification report\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_test_predict) * 100\n",
    "print(classification_report(y_test, y_test_predict, target_names=ecg_labels, zero_division=0, digits=4))\n",
    "print(\"Accuracy: \", round(accuracy,2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#svm\n",
    "# Confusion Matrix\n",
    "%matplotlib notebook\n",
    "cm = confusion_matrix(y_test, y_test_predict)\n",
    "df_cm = pd.DataFrame(cm, index=[i for i in ecg_labels], columns=[i for i in ecg_labels])\n",
    "plt.figure()\n",
    "ax= sns.heatmap(df_cm,  cbar=False, cmap=\"BuGn\", annot=True, fmt=\"d\")\n",
    "plt.setp(ax.get_xticklabels(), rotation=0)\n",
    "\n",
    "plt.ylabel('True Data', fontweight='bold', fontsize = 18)\n",
    "plt.xlabel('Predicted Data', fontweight='bold', fontsize = 18)\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for Random Forest and SVM is: 0.9821260116180924\n"
     ]
    }
   ],
   "source": [
    "#ensemble(rfc+svm)\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "ensemble=VotingClassifier(estimators=[('rfc', classifier), ('svm', clf)], voting='hard', n_jobs=-1).fit(nX_train, y_train)\n",
    "print('The accuracy for Random Forest and SVM is:',ensemble.score(nX_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('rfc',\n",
       "                              RandomForestClassifier(n_jobs=-1,\n",
       "                                                     random_state=42)),\n",
       "                             ('svm', SVC())],\n",
       "                 n_jobs=-1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hard'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble.voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ensemble(rfc+svm)\n",
    "y_test_predict = ensemble.predict(nX_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           N     0.9867    0.9950    0.9908     36038\n",
      "           S     0.9008    0.7421    0.8138      1113\n",
      "           V     0.9590    0.9422    0.9505      2804\n",
      "           F     0.8835    0.7321    0.8007       321\n",
      "           Q     0.0000    0.0000    0.0000         6\n",
      "\n",
      "    accuracy                         0.9821     40282\n",
      "   macro avg     0.7460    0.6823    0.7112     40282\n",
      "weighted avg     0.9814    0.9821    0.9815     40282\n",
      "\n",
      "Accuracy:  98.21 %\n"
     ]
    }
   ],
   "source": [
    "#ensemble(rfc+svm)\n",
    "# Get the classification report\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_test_predict) * 100\n",
    "print(classification_report(y_test, y_test_predict, target_names=ecg_labels, zero_division=0, digits=4))\n",
    "print(\"Accuracy: \", round(accuracy,2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ensemble(rfc+svm)\n",
    "# Confusion Matrix\n",
    "%matplotlib notebook\n",
    "cm = confusion_matrix(y_test, y_test_predict)\n",
    "df_cm = pd.DataFrame(cm, index=[i for i in ecg_labels], columns=[i for i in ecg_labels])\n",
    "plt.figure()\n",
    "ax= sns.heatmap(df_cm,  cbar=False, cmap=\"BuGn\", annot=True, fmt=\"d\")\n",
    "plt.setp(ax.get_xticklabels(), rotation=0)\n",
    "\n",
    "plt.ylabel('True Data', fontweight='bold', fontsize = 18)\n",
    "plt.xlabel('Predicted Data', fontweight='bold', fontsize = 18)\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}