Testing file-Copy_hybrid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "59496a3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd#\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib\n",
    "import os\n",
    "import sklearn\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from sklearn import model_selection, svm\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report, average_precision_score, mean_squared_error\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import accuracy_score\n",
    "#from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import preprocessing\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#SMOTE and IMBLearn\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "#Plotly Libraries\n",
    "import plotly\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objs as go\n",
    "import chart_studio\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a67949f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('final_one_for_class_weights_stnd_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14803655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26126, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e404b44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power</th>\n",
       "      <th>Spindle</th>\n",
       "      <th>vibration</th>\n",
       "      <th>roll_mean_Power</th>\n",
       "      <th>roll_std_Power</th>\n",
       "      <th>roll_min_Power</th>\n",
       "      <th>roll_max_Power</th>\n",
       "      <th>roll_EME_Power</th>\n",
       "      <th>roll_IQR_Power</th>\n",
       "      <th>roll_skew_Power</th>\n",
       "      <th>roll_mean_Spindle</th>\n",
       "      <th>roll_std_Spindle</th>\n",
       "      <th>roll_min_Spindle</th>\n",
       "      <th>roll_max_Spindle</th>\n",
       "      <th>roll_EME_Spindle</th>\n",
       "      <th>roll_IQR_Spindle</th>\n",
       "      <th>roll_skew_Spindle</th>\n",
       "      <th>roll_mean_vibration</th>\n",
       "      <th>roll_std_vibration</th>\n",
       "      <th>roll_min_vibration</th>\n",
       "      <th>roll_max_vibration</th>\n",
       "      <th>roll_EME_vibration</th>\n",
       "      <th>roll_IQR_vibration</th>\n",
       "      <th>roll_skew_vibration</th>\n",
       "      <th>Label_Numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.704504</td>\n",
       "      <td>-0.656656</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>-5.243308</td>\n",
       "      <td>3.549036</td>\n",
       "      <td>-8.273532</td>\n",
       "      <td>0.237851</td>\n",
       "      <td>-0.850778</td>\n",
       "      <td>-0.454735</td>\n",
       "      <td>1.763536</td>\n",
       "      <td>-0.894810</td>\n",
       "      <td>-0.794447</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.891522</td>\n",
       "      <td>-0.561806</td>\n",
       "      <td>-0.672802</td>\n",
       "      <td>-0.718196</td>\n",
       "      <td>-0.338410</td>\n",
       "      <td>0.153263</td>\n",
       "      <td>-0.362442</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.157717</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>1.940044</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.693592</td>\n",
       "      <td>-0.270631</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>-3.705873</td>\n",
       "      <td>4.412749</td>\n",
       "      <td>-8.273532</td>\n",
       "      <td>0.237851</td>\n",
       "      <td>-0.570332</td>\n",
       "      <td>13.084977</td>\n",
       "      <td>0.318055</td>\n",
       "      <td>-0.789580</td>\n",
       "      <td>-0.504153</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.617958</td>\n",
       "      <td>-0.550103</td>\n",
       "      <td>-0.672802</td>\n",
       "      <td>1.657976</td>\n",
       "      <td>-0.275950</td>\n",
       "      <td>0.243939</td>\n",
       "      <td>-0.362442</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.155242</td>\n",
       "      <td>0.617664</td>\n",
       "      <td>0.317541</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.705959</td>\n",
       "      <td>-0.259266</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>-2.165217</td>\n",
       "      <td>4.414593</td>\n",
       "      <td>-8.273532</td>\n",
       "      <td>0.238714</td>\n",
       "      <td>-0.288709</td>\n",
       "      <td>13.110000</td>\n",
       "      <td>-0.762990</td>\n",
       "      <td>-0.681253</td>\n",
       "      <td>-0.433614</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.609904</td>\n",
       "      <td>-0.537702</td>\n",
       "      <td>-0.252914</td>\n",
       "      <td>-0.069784</td>\n",
       "      <td>-0.213490</td>\n",
       "      <td>0.243939</td>\n",
       "      <td>-0.362442</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.152767</td>\n",
       "      <td>0.617664</td>\n",
       "      <td>-0.895892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.685590</td>\n",
       "      <td>-0.656656</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>-0.629865</td>\n",
       "      <td>3.544424</td>\n",
       "      <td>-8.273532</td>\n",
       "      <td>0.238714</td>\n",
       "      <td>-0.009022</td>\n",
       "      <td>-0.411362</td>\n",
       "      <td>-2.208437</td>\n",
       "      <td>-0.681253</td>\n",
       "      <td>-0.433614</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.609904</td>\n",
       "      <td>-0.537702</td>\n",
       "      <td>-0.252914</td>\n",
       "      <td>-0.069784</td>\n",
       "      <td>-0.151029</td>\n",
       "      <td>0.153263</td>\n",
       "      <td>-0.362442</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.150292</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-2.518395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.692137</td>\n",
       "      <td>-0.643987</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>0.907191</td>\n",
       "      <td>-0.300801</td>\n",
       "      <td>1.455817</td>\n",
       "      <td>0.238714</td>\n",
       "      <td>0.271287</td>\n",
       "      <td>-0.426376</td>\n",
       "      <td>-0.175717</td>\n",
       "      <td>-0.677799</td>\n",
       "      <td>-0.437396</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.609904</td>\n",
       "      <td>-0.537689</td>\n",
       "      <td>-0.252914</td>\n",
       "      <td>-0.072604</td>\n",
       "      <td>-0.088569</td>\n",
       "      <td>-0.250196</td>\n",
       "      <td>-0.012533</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.147817</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-0.289175</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26121</th>\n",
       "      <td>0.433161</td>\n",
       "      <td>1.635646</td>\n",
       "      <td>0.064349</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>-0.085733</td>\n",
       "      <td>0.482722</td>\n",
       "      <td>0.092613</td>\n",
       "      <td>0.123167</td>\n",
       "      <td>-0.424707</td>\n",
       "      <td>-2.186625</td>\n",
       "      <td>1.446901</td>\n",
       "      <td>1.115797</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>0.989496</td>\n",
       "      <td>1.002605</td>\n",
       "      <td>0.851926</td>\n",
       "      <td>-1.851743</td>\n",
       "      <td>0.067581</td>\n",
       "      <td>-0.250196</td>\n",
       "      <td>0.162422</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.132349</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-0.289175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26122</th>\n",
       "      <td>0.438496</td>\n",
       "      <td>-0.640820</td>\n",
       "      <td>0.064349</td>\n",
       "      <td>0.577024</td>\n",
       "      <td>-0.298003</td>\n",
       "      <td>1.039291</td>\n",
       "      <td>0.092613</td>\n",
       "      <td>0.153365</td>\n",
       "      <td>-0.431936</td>\n",
       "      <td>0.896485</td>\n",
       "      <td>0.763519</td>\n",
       "      <td>1.291478</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>0.989496</td>\n",
       "      <td>0.502827</td>\n",
       "      <td>1.803361</td>\n",
       "      <td>-0.560021</td>\n",
       "      <td>0.067581</td>\n",
       "      <td>-0.250196</td>\n",
       "      <td>0.162422</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.132349</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-0.289175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26123</th>\n",
       "      <td>0.451590</td>\n",
       "      <td>2.272624</td>\n",
       "      <td>0.064349</td>\n",
       "      <td>0.581002</td>\n",
       "      <td>-0.298183</td>\n",
       "      <td>1.039291</td>\n",
       "      <td>0.092613</td>\n",
       "      <td>0.154755</td>\n",
       "      <td>-0.424707</td>\n",
       "      <td>-0.097931</td>\n",
       "      <td>0.838480</td>\n",
       "      <td>1.431031</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>1.184371</td>\n",
       "      <td>0.623404</td>\n",
       "      <td>1.803361</td>\n",
       "      <td>-0.416993</td>\n",
       "      <td>0.067581</td>\n",
       "      <td>-0.250196</td>\n",
       "      <td>0.162422</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.132349</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-0.289175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26124</th>\n",
       "      <td>0.436071</td>\n",
       "      <td>-0.075569</td>\n",
       "      <td>-0.010030</td>\n",
       "      <td>0.574877</td>\n",
       "      <td>-0.302332</td>\n",
       "      <td>1.039291</td>\n",
       "      <td>0.087868</td>\n",
       "      <td>0.152614</td>\n",
       "      <td>-0.431380</td>\n",
       "      <td>0.311312</td>\n",
       "      <td>0.996883</td>\n",
       "      <td>1.230144</td>\n",
       "      <td>-0.394501</td>\n",
       "      <td>1.184371</td>\n",
       "      <td>0.649921</td>\n",
       "      <td>1.188526</td>\n",
       "      <td>-0.394843</td>\n",
       "      <td>0.051966</td>\n",
       "      <td>-0.149331</td>\n",
       "      <td>0.074945</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.134050</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-2.518395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26125</th>\n",
       "      <td>0.446498</td>\n",
       "      <td>2.333732</td>\n",
       "      <td>0.064349</td>\n",
       "      <td>0.574940</td>\n",
       "      <td>-0.302271</td>\n",
       "      <td>1.039291</td>\n",
       "      <td>0.087868</td>\n",
       "      <td>0.152636</td>\n",
       "      <td>-0.430824</td>\n",
       "      <td>0.291145</td>\n",
       "      <td>1.506474</td>\n",
       "      <td>1.522843</td>\n",
       "      <td>-0.394501</td>\n",
       "      <td>1.227676</td>\n",
       "      <td>1.253494</td>\n",
       "      <td>1.881381</td>\n",
       "      <td>-1.288836</td>\n",
       "      <td>0.051966</td>\n",
       "      <td>-0.149331</td>\n",
       "      <td>0.074945</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.134050</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-2.518395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26126 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Power   Spindle  vibration  roll_mean_Power  roll_std_Power  roll_min_Power  roll_max_Power  roll_EME_Power  roll_IQR_Power  roll_skew_Power  roll_mean_Spindle  roll_std_Spindle  roll_min_Spindle  roll_max_Spindle  roll_EME_Spindle  roll_IQR_Spindle  roll_skew_Spindle  roll_mean_vibration  roll_std_vibration  roll_min_vibration  roll_max_vibration  roll_EME_vibration  roll_IQR_vibration  roll_skew_vibration  Label_Numeric\n",
       "0      0.704504 -0.656656  -0.084409        -5.243308        3.549036       -8.273532        0.237851       -0.850778       -0.454735         1.763536          -0.894810         -0.794447         -0.436556         -0.891522         -0.561806         -0.672802          -0.718196            -0.338410            0.153263           -0.362442           -0.163626           -0.157717           -0.194306             1.940044              0\n",
       "1      0.693592 -0.270631  -0.084409        -3.705873        4.412749       -8.273532        0.237851       -0.570332       13.084977         0.318055          -0.789580         -0.504153         -0.436556         -0.617958         -0.550103         -0.672802           1.657976            -0.275950            0.243939           -0.362442           -0.163626           -0.155242            0.617664             0.317541              0\n",
       "2      0.705959 -0.259266  -0.084409        -2.165217        4.414593       -8.273532        0.238714       -0.288709       13.110000        -0.762990          -0.681253         -0.433614         -0.436556         -0.609904         -0.537702         -0.252914          -0.069784            -0.213490            0.243939           -0.362442           -0.163626           -0.152767            0.617664            -0.895892              0\n",
       "3      0.685590 -0.656656  -0.084409        -0.629865        3.544424       -8.273532        0.238714       -0.009022       -0.411362        -2.208437          -0.681253         -0.433614         -0.436556         -0.609904         -0.537702         -0.252914          -0.069784            -0.151029            0.153263           -0.362442           -0.163626           -0.150292           -0.194306            -2.518395              0\n",
       "4      0.692137 -0.643987  -0.084409         0.907191       -0.300801        1.455817        0.238714        0.271287       -0.426376        -0.175717          -0.677799         -0.437396         -0.436556         -0.609904         -0.537689         -0.252914          -0.072604            -0.088569           -0.250196           -0.012533           -0.163626           -0.147817           -0.194306            -0.289175              0\n",
       "...         ...       ...        ...              ...             ...             ...             ...             ...             ...              ...                ...               ...               ...               ...               ...               ...                ...                  ...                 ...                 ...                 ...                 ...                 ...                  ...            ...\n",
       "26121  0.433161  1.635646   0.064349         0.487805       -0.085733        0.482722        0.092613        0.123167       -0.424707        -2.186625           1.446901          1.115797         -0.436556          0.989496          1.002605          0.851926          -1.851743             0.067581           -0.250196            0.162422           -0.040807           -0.132349           -0.194306            -0.289175              0\n",
       "26122  0.438496 -0.640820   0.064349         0.577024       -0.298003        1.039291        0.092613        0.153365       -0.431936         0.896485           0.763519          1.291478         -0.436556          0.989496          0.502827          1.803361          -0.560021             0.067581           -0.250196            0.162422           -0.040807           -0.132349           -0.194306            -0.289175              0\n",
       "26123  0.451590  2.272624   0.064349         0.581002       -0.298183        1.039291        0.092613        0.154755       -0.424707        -0.097931           0.838480          1.431031         -0.436556          1.184371          0.623404          1.803361          -0.416993             0.067581           -0.250196            0.162422           -0.040807           -0.132349           -0.194306            -0.289175              0\n",
       "26124  0.436071 -0.075569  -0.010030         0.574877       -0.302332        1.039291        0.087868        0.152614       -0.431380         0.311312           0.996883          1.230144         -0.394501          1.184371          0.649921          1.188526          -0.394843             0.051966           -0.149331            0.074945           -0.040807           -0.134050           -0.194306            -2.518395              0\n",
       "26125  0.446498  2.333732   0.064349         0.574940       -0.302271        1.039291        0.087868        0.152636       -0.430824         0.291145           1.506474          1.522843         -0.394501          1.227676          1.253494          1.881381          -1.288836             0.051966           -0.149331            0.074945           -0.040807           -0.134050           -0.194306            -2.518395              0\n",
       "\n",
       "[26126 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "732dcb8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    16315\n",
       "1     9811\n",
       "Name: Label_Numeric, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Label_Numeric'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44a4edb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "frame_size = 30\n",
    "hop_size = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1ff4450",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_frames(df, frame_size, hop_size):\n",
    "    \n",
    "\n",
    "    N_FEATURES = 24\n",
    "\n",
    "    frames = []\n",
    "    labels = []\n",
    "    for i in range(0, len(df) - frame_size, hop_size):\n",
    "        N_AccX = df['Power'].values[i: i + frame_size]\n",
    "        N_AccY = df['Spindle'].values[i: i + frame_size]\n",
    "        N_AccZ = df['vibration'].values[i: i + frame_size]\n",
    "        N_GyroX = df['roll_mean_Power'].values[i: i + frame_size]\n",
    "        N_GyroY = df['roll_std_Power'].values[i: i + frame_size]\n",
    "        N_GyroZ = df['roll_min_Power'].values[i: i + frame_size]\n",
    "        T_AccX = df['roll_max_Power'].values[i: i + frame_size]\n",
    "        T_AccY = df['roll_EME_Power'].values[i: i + frame_size] \n",
    "        T_AccZ = df['roll_IQR_Power'].values[i: i + frame_size]\n",
    "        \n",
    "        roll_std_NAccX = df['roll_skew_Power'].values[i: i + frame_size]\n",
    "        roll_IQR_TGyroX = df['roll_mean_Spindle'].values[i: i + frame_size]\n",
    "        roll_EME_TGyrozz = df['roll_std_Spindle'].values[i: i + frame_size]\n",
    "        roll_EME_TGyroZ = df['roll_min_Spindle'].values[i: i + frame_size]\n",
    "        roll_EME_TGyroY = df['roll_max_Spindle'].values[i: i + frame_size]\n",
    "        roll_skew_NGyroZ = df['roll_EME_Spindle'].values[i: i + frame_size]\n",
    "        roll_max_NAccX = df['roll_IQR_Spindle'].values[i: i + frame_size]\n",
    "        roll_mad_NGyroZ = df['roll_skew_Spindle'].values[i: i + frame_size]\n",
    "        roll_mean_NGyroZ = df['roll_mean_vibration'].values[i: i + frame_size]\n",
    "        roll_max_NGyroX = df['roll_std_vibration'].values[i: i + frame_size]\n",
    "        roll_mean_NAccY = df['roll_min_vibration'].values[i: i + frame_size]\n",
    "        roll_mean_NAccX = df['roll_max_vibration'].values[i: i + frame_size]\n",
    "        roll_mad_NAccY = df['roll_EME_vibration'].values[i: i + frame_size]\n",
    "        roll_EME_TAccX = df['roll_IQR_vibration'].values[i: i + frame_size]\n",
    "        roll_kurt_NAccX = df['roll_skew_vibration'].values[i: i + frame_size]\n",
    "        \n",
    "        \n",
    "        # Retrieve the most often used label in this segment\n",
    "        label = stats.mode(df['Label_Numeric'][i: i + frame_size])[0][0]\n",
    "        frames.append([N_AccX,N_AccY,N_AccZ,N_GyroX,N_GyroY,N_GyroZ,T_AccX,T_AccY,T_AccZ,roll_std_NAccX,roll_IQR_TGyroX,roll_EME_TGyrozz,roll_EME_TGyroZ,roll_EME_TGyroY,roll_skew_NGyroZ, \n",
    "                       roll_max_NAccX,roll_mad_NGyroZ,roll_mean_NGyroZ,roll_max_NGyroX,roll_mean_NAccY,\n",
    "                       roll_mean_NAccX,roll_mad_NAccY,roll_EME_TAccX,roll_kurt_NAccX])\n",
    "        labels.append(label)\n",
    "\n",
    "    # Bring the segments into a better shape\n",
    "    frames = np.asarray(frames).reshape(-1, frame_size, N_FEATURES)\n",
    "    labels = np.asarray(labels)\n",
    "\n",
    "    return frames, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3db4bb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2366efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2610, 30, 24), (2610,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,y = get_frames(df, frame_size, hop_size)\n",
    "\n",
    "X.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ffe155d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2610\n"
     ]
    }
   ],
   "source": [
    "print(len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e6a10db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2610,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30e23a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, stratify = y)\n",
    "V_train, end_test, vl_train, test_label = train_test_split(X_test, y_test, test_size = 0.5, random_state = 0, stratify = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75c82440",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train Features (2088, 30, 24)\n",
      "Shape of Train Labels (2088,)\n",
      "Shape of validation Features (261, 30, 24)\n",
      "Shape of validation Labels (261,)\n",
      "Shape of Test Features (261, 30, 24)\n",
      "Shape of Test Labels (261,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of Train Features\",X_train.shape)\n",
    "print(\"Shape of Train Labels\",y_train.shape)\n",
    "\n",
    "print(\"Shape of validation Features\",V_train.shape)\n",
    "print(\"Shape of validation Labels\",vl_train.shape)\n",
    "\n",
    "print(\"Shape of Test Features\",end_test.shape)\n",
    "print(\"Shape of Test Labels\",test_label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ad8b2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM,Conv1D,Flatten\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import SGD, Adam\n",
    "from tensorflow.keras import optimizers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "719f4e3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_network():\n",
    "    model = Sequential()\n",
    "    model.add(Conv1D(filters=4, kernel_size=2, padding='same' ,strides=1, activation='relu', input_shape = X_train[0].shape))\n",
    "    model.add(Conv1D(filters=4, kernel_size=2, padding='same' ,strides=1, activation='relu'))\n",
    "    model.add(Conv1D(filters=4, kernel_size=2, padding='same' , strides=1, activation='relu'))\n",
    "    model.add(Conv1D(filters=8, kernel_size=2, padding='same' ,strides=1, activation='relu'))\n",
    "    model.add(Conv1D(filters=8, kernel_size=2, padding='same' ,strides=1, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(8, activation='relu'))\n",
    "    model.add(Dense(8, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    #stochastic gradient descent\n",
    "    sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "    adam = Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, name='Adam')\n",
    "    model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "81c84f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_network_lsmt(): \n",
    "    model = Sequential()\n",
    "    model.add(LSTM(16, return_sequences=True, input_shape= (X_train.shape[1], X_train.shape[2])))\n",
    "    model.add(LSTM(16, return_sequences=True))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(LSTM(8, return_sequences=True))\n",
    "    model.add(LSTM(8, return_sequences=True))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(LSTM(4, return_sequences=True))\n",
    "    model.add(LSTM(4, return_sequences=False))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(8))\n",
    "    model.add(Dense(4))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "    #adam = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, name='Adam')\n",
    "    model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6cf54b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Hybrid Model of 1D CNN and LSTM\n",
    " # LFLB1\n",
    "def creat_network_hybrid():\n",
    "    model = Sequential()\n",
    "    model.add(Conv1D(filters=4, kernel_size=2, padding='same' ,strides=1, activation='relu', input_shape = X_train[0].shape)) \n",
    "    model.add(Conv1D(filters=8, kernel_size=2, padding='same' ,strides=1, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "\n",
    "    model.add(LSTM(16, return_sequences=True, input_shape= (X_train.shape[1], X_train.shape[2])))\n",
    "    model.add(LSTM(16, return_sequences=True))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Flatten())\n",
    "#     model.add(Dense(8, activation='relu'))\n",
    "#     model.add(Dense(8, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "001b272a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Dense,TimeDistributed,MaxPooling1D, Reshape\n",
    "from tensorflow.keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b51be740",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, 30, 4)             196       \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 30, 4)             36        \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 30, 4)             36        \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 30, 8)             72        \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 30, 8)             136       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 30, 8)             0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 240)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 240)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 8)                 1928      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 2,485\n",
      "Trainable params: 2,485\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = create_network()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d73fc54d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 30, 16)            2624      \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 30, 16)            2112      \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 30, 16)            0         \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 30, 8)             800       \n",
      "_________________________________________________________________\n",
      "lstm_5 (LSTM)                (None, 30, 8)             544       \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 30, 8)             0         \n",
      "_________________________________________________________________\n",
      "lstm_6 (LSTM)                (None, 30, 4)             208       \n",
      "_________________________________________________________________\n",
      "lstm_7 (LSTM)                (None, 4)                 144       \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 4)                 0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 8)                 40        \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 4)                 36        \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 6,513\n",
      "Trainable params: 6,513\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model1 = create_network_lsmt()\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b1e24fd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_5 (Conv1D)            (None, 30, 4)             196       \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, 30, 8)             72        \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 30, 8)             0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 30, 16)            1600      \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 30, 16)            2112      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 30, 16)            0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 480)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 481       \n",
      "=================================================================\n",
      "Total params: 4,461\n",
      "Trainable params: 4,461\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model2 = creat_network_hybrid()\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3a148feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model, \"my_first_model.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c497dbc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weight = {0: 0.6686,\n",
    "            1: 1.3314\n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "879bb48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import livelossplot\n",
    "#from livelossplot import PlotLossesKeras\n",
    "from livelossplot import PlotLossesKerasTF\n",
    "# from livelossplot.tf_keras import PlotLossesCallback\n",
    "plot_losses = livelossplot.PlotLossesKerasTF()\n",
    "plot_losses_hybrid = livelossplot.PlotLossesKerasTF()\n",
    "plot_losses_lstm = livelossplot.PlotLossesKerasTF()\n",
    "# plot_losses = livelossplot.PlotLossesKeras()\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fb10ab14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "\ttraining         \t (min:    0.385, max:    0.931, cur:    0.924)\n",
      "\tvalidation       \t (min:    0.379, max:    0.883, cur:    0.870)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.146, max:    0.637, cur:    0.158)\n",
      "\tvalidation       \t (min:    0.296, max:    0.705, cur:    0.331)\n",
      "Training time:  841.6880402565002\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "history = model.fit(X_train, y_train,  validation_data=(X_test, y_test), shuffle=True, epochs=250,batch_size=4,verbose=1,class_weight=class_weight,callbacks=[plot_losses])\n",
    "\n",
    "end_time = time.time()\n",
    "training_time = end_time - start_time\n",
    "print(\"Training time: \", training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "957c4cad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "\ttraining         \t (min:    0.679, max:    0.877, cur:    0.870)\n",
      "\tvalidation       \t (min:    0.701, max:    0.851, cur:    0.847)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.237, max:    0.478, cur:    0.244)\n",
      "\tvalidation       \t (min:    0.335, max:    0.533, cur:    0.343)\n"
     ]
    }
   ],
   "source": [
    "history_hybrid = model2.fit(X_train, y_train,  validation_data=(V_train, vl_train), shuffle=True, epochs=200,batch_size=4,verbose=1,class_weight=class_weight,callbacks=[plot_losses_hybrid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ea235096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "\ttraining         \t (min:    0.375, max:    0.916, cur:    0.901)\n",
      "\tvalidation       \t (min:    0.375, max:    0.839, cur:    0.820)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.191, max:    0.635, cur:    0.210)\n",
      "\tvalidation       \t (min:    0.351, max:    0.714, cur:    0.473)\n"
     ]
    }
   ],
   "source": [
    "history_lstm = model1.fit(X_train, y_train,  validation_data=(V_train, vl_train), shuffle=True, epochs=250,batch_size=4,verbose=1,class_weight=class_weight,callbacks=[plot_losses_lstm])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91501e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('CNN_model_hybrid.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9ee05940",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.save('LSTM_model_hybrid.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "901e36f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.save('hybrid_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a4db04a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dfacdb2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c6b5ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('LSTM_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f5f5ae7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "model_cnn = keras.models.load_model('CNN_model_hybrid.h5')\n",
    "model_lstm = keras.models.load_model('LSTM_model_hybrid.h5')\n",
    "model_hybrid = keras.models.load_model('hybrid_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27203a62",
   "metadata": {},
   "source": [
    "# CNN _prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "15ec3d9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 46.9 ms\n",
      "Wall time: 49.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cnn_rounded_predictions = model_cnn.predict_proba(end_test)\n",
    "cnn_rounded_predictions = (cnn_rounded_predictions > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cc41cdc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.91      0.93       164\n",
      "           1       0.86      0.93      0.89        97\n",
      "\n",
      "    accuracy                           0.92       261\n",
      "   macro avg       0.91      0.92      0.91       261\n",
      "weighted avg       0.92      0.92      0.92       261\n",
      "\n",
      "0.9157088122605364\n",
      "[[149  15]\n",
      " [  7  90]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_label, cnn_rounded_predictions))\n",
    "print(accuracy_score(test_label, cnn_rounded_predictions))\n",
    "cf_matrix = confusion_matrix(test_label, cnn_rounded_predictions)\n",
    "print(confusion_matrix(test_label, cnn_rounded_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f28d72a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAEWCAYAAADy2YssAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdbklEQVR4nO3de7xVVbn/8c8XUC6KcpNLIKCGEFle4uc1PaSVUP7CzimvGRkesyw9lZlaR8zydE+ztA6liVreSlPDvESal7wBiYookiSCIHdTxHDDc/6Yc+tis/faay7W2mutyffta75Yc8y5xnz2Rh/HmHOMORQRmJnlUadaB2BmVi1OcGaWW05wZpZbTnBmlltOcGaWW05wZpZbTnA5I6m7pFslvSzphi2o53hJd1YytlqQ9EdJE2sdh9WGE1yNSDpO0gxJr0pakv6H+N4KVP0xYADQNyI+Xm4lEfHriPhgBeLZhKSxkkLSTS3K90zL7ymxnvMkXd3eeRExPiKmlhmuNTgnuBqQ9CXgIuB/SJLRUOBSYEIFqh8GzIuIpgrUVS3LgQMk9S0omwjMq9QFlPC/31u7iPDWgRuwI/Aq8PEi53QlSYAvpttFQNf02FhgEfBlYBmwBDgxPfYNYD3wRnqNScB5wNUFdQ8HAuiS7n8KeA54BVgAHF9Qfn/B9w4EHgVeTv88sODYPcA3gQfSeu4E+rXxszXH/3Pg1LSsM7AYOBe4p+DcHwMvAP8EZgIHp+XjWvycswviuCCNYx3w9rTspPT4z4DfFdT/XWA6oFr/e+GtOpv/D9fxDgC6ATcVOedrwP7AXsCewL7A1wuODyRJlINJktglknpHxGSSVuF1EbF9RFxWLBBJ2wEXA+MjoidJEnuslfP6ANPSc/sCPwKmtWiBHQecCPQHtgXOKHZt4Ergk+nnw4EnSZJ5oUdJfgd9gN8AN0jqFhG3t/g59yz4zgnAyUBP4PkW9X0ZeJekT0k6mOR3NzHSbGf54wTX8foCK6J4F/J44PyIWBYRy0laZicUHH8jPf5GRNxG0ooZWWY8G4E9JHWPiCURMaeVcz4MPBsRV0VEU0RcAzwN/P+Cc34VEfMiYh1wPUlialNE/BXoI2kkSaK7spVzro6Ilek1f0jSsm3v57wiIuak33mjRX2vkfwefwRcDXwhIha1U581MCe4jrcS6CepS5Fz3samrY/n07I362iRIF8Dts8aSESsBY4GTgGWSJomaVQJ8TTHNLhgf2kZ8VwFfB54H620aCWdIWlu+kR4DUmrtV87db5Q7GBEPEzSJRdJIrYcc4LreA8C/wKOLHLOiyQPC5oNZfPuW6nWAj0K9gcWHoyIOyLiA8AgklbZL0qIpzmmxWXG1Owq4HPAbWnr6k1pF/JM4Cigd0T0Irn/p+bQ26izaHdT0qkkLcEX0/otx5zgOlhEvExyM/0SSUdK6iFpG0njJX0vPe0a4OuSdpLULz2/3SERbXgMOETSUEk7Amc3H5A0QNKE9F7cv0i6uhtbqeM2YPd0aEsXSUcDo4E/lBkTABGxAPg3knuOLfUEmkieuHaRdC6wQ8Hxl4DhWZ6UStod+BbwCZKu6pmS9iovemsETnA1kN5P+hLJg4PlJN2qzwO/T0/5FjADeBx4ApiVlpVzrbuA69K6ZrJpUuqUxvEisIok2Xy2lTpWAkeQ3KRfSdLyOSIiVpQTU4u674+I1lqndwC3kwwdeR54nU27n82DmFdKmtXeddJbAlcD342I2RHxLHAOcJWkrlvyM1j9kh8gmVleuQVnZrnlBGdmueUEZ2a55QRnZrlVbLBph1OX7qFte9Y6DMvg3aN2rnUIlsELC59n5YoVav/MtnXeYVhE07qSzo11y++IiHFbcr0tUV8JbtuedB15VK3DsAym33tRrUOwDA47ZL8triOaXqfrqGNKOvf1v/2kvZknVVVXCc7MGoAAbVEjsMM4wZlZdg3yqj0nODPLzi04M8snQafOtQ6iJE5wZpaNcBfVzPJK7qKaWY65BWdmueUWnJnlkxqmBdcYUZpZ/RDJU9RStvaqki6XtEzSk60c+3K6GHi/dF+SLpY0X9LjkvZpr34nODPLKG3BlbK17wqSdW43vYK0M/BBYGFB8XhgRLqdTLLObVFOcGaWXSeVtrUjIu4leV1+SxeSvBq/8JXjE4ArI/EQ0EvSoGL1+x6cmWWTbRxcP0kzCvanRMSUotVLE4DFETFbmz7MGMym63IsSsuWtFWXE5yZZVf6U9QVETGm9GrVg2QxoA+WE1ZLTnBmllFVp2rtBuwCNLfehgCzJO1Lsg5v4QsIh9DO2ry+B2dm2VXuIcMmIuKJiOgfEcMjYjhJN3SfiFgK3AJ8Mn2auj/wckS02T0FJzgzy0oqfWu3Kl0DPAiMlLRI0qQip98GPAfMB34BfK69+t1FNbPsKjTQNyKObef48ILPAZyapX4nODPLzlO1zCyfGmeqlhOcmWXTPFWrATjBmVlGbsGZWZ75HpyZ5ZZbcGaWW27BmVkuyffgzCzH1MkJzsxySIDcRTWzXFK6NQAnODPLSG7BmVl+OcGZWW518kMGM8sl34Mzs7yS78GZWZ45wZlZbjnBmVluOcGZWT4JVMKq9fXACc7MMvFDBjPLtUZJcI0xWs/M6otK3NqrRrpc0jJJTxaUfV/S05Iel3STpF4Fx86WNF/SM5IOb69+Jzgzy0ZJC66UrQRXAONalN0F7BER7wbmAWcDSBoNHAO8M/3OpZKKrn7jBGdmmVUqwUXEvcCqFmV3RkRTuvsQMCT9PAG4NiL+FRELSFa437dY/b4HZ2aZCGWZi9pP0oyC/SkRMSXD5T4NXJd+HkyS8JotSsva5ARnZtmV/oxhRUSMKesS0teAJuDX5XwfnODMLCtV/ymqpE8BRwCHRUSkxYuBnQtOG5KWtcn34Mwsswo+ZGit7nHAmcBHIuK1gkO3AMdI6ippF2AE8EixutyCM7PMKtWCk3QNMJbkXt0iYDLJU9OuwF3pdR6KiFMiYo6k64GnSLqup0bEhmL1O8GZWWaVmqoVEce2UnxZkfMvAC4otX53USvg55OP5/np32bGDedsduz0Ew5l3d9+St9e2wHQq2d3rvvhf/LIdWdz31VnMHq3QR0drhU47bMnMWqXt/Heffd6s+y7/3M+e+w+jLEHvoexB76Hu+74Y+0CrEOldk/rYbZDVROcpHHpiOP5ks6q5rVq6apbH2LCqZdsVj5kQC8O2/8dLFzy1jCfMycdzuxnFrHv0d9m0n9fxQ++8rGODNVaOOb4iVx30x82Kz/l1NO5568zueevM/nA4eNrEFl92+oTXDrC+BJgPDAaODYdiZw7D8z6O6tefm2z8u+d8R987ce/562HQDBq14H85dF5AMz7x0sMe1sf+vfp2WGx2qYOfO/B9O7dp9ZhNJytPsGRjDCeHxHPRcR64FqSkchbhSPGvosXl63hiXmbPsV+Yt5iJhy6JwBj3jmMoYP6MHhArxpEaMVcNuVSDtl/b0777EmsWb261uHUnwrNRa22aia4wcALBfutjjqWdLKkGZJmRNO6KobTcbp324YzP3045/9s2mbHfvCru9ixZw8euvYsPnvMvzH7mUVs2LCxBlFaW0486TPMePwZ7vnrTAYMHMS553yl1iHVnUZpwdX8KWo6bWMKQKce/aOd0xvCrkN2Ytjgvjxy3dkADO7fiwd/81UOPuH7vLTyFT5z3tVvnvv0tG+wYPHKWoVqrejff8Cbn0/41CSO+/iRtQumDknQyS+8zD7qOC/mzH+RYYed/eb+09O+wUHHf4+Va9ay4/bdee319bzRtIETP3og98+azytrX69htNbS0qVLGDgwebo97dbfM2r0O2scUb2pj9ZZKaqZ4B4FRqQjjheTvObkuCper2amfvtTHPyeEfTrtT3zb/8m3/z5bUz9/YOtnjtq14H84vwTiAjm/n0Jp3yj7Gl2VgH/eeIneOC+v7Bq5QreNXI4Xz3nXB64/y88+fhsJLHz0OH88OJLax1m3WmQ/IYKn/BVvHLpQ8BFQGfg8nSQXps69egfXUceVbV4rPIW3XdRrUOwDA47ZD8emzVzi9JTt4G7x7CJPynp3HnfGzez3Mn2lVDVe3ARcRtwWzWvYWYdTI3Tgqv5QwYzayzCDxnMLMec4Mwsn9xFNbO8Eo2zbKATnJll5HFwZpZjDZLfnODMLCNP1TKzvPI9ODPLtQbJb05wZpadW3BmllsNkt+86IyZZaTKvfBS0uWSlkl6sqCsj6S7JD2b/tk7LZeki9M1Xh6XtE979TvBmVkmQnTqVNpWgiuAcS3KzgKmR8QIYHq6D8n6LiPS7WTgZ+1V7gRnZplJpW3tiYh7gVUtiicAU9PPU4EjC8qvjMRDQC9JRdfd9D04M8ssw0OGfpJmFOxPSZcpKGZARCxJPy8Fmt8h39Y6L0togxOcmWWTbbL9ii154WVEhKSy38rrLqqZZdI80LeKq2q91Nz1TP9clpZnXufFCc7MMqtygrsFmJh+ngjcXFD+yfRp6v7AywVd2Va5i2pmmVVqLqqka4CxJPfqFgGTge8A10uaBDwPNC/UchvwIWA+8BpwYnv1O8GZWTYVfOFlRBzbxqHDWjk3gFOz1O8EZ2aZyO+DM7M8a5D85gRnZtl1apAM5wRnZpnIL7w0szxrkPzmBGdm2TX8QwZJPwHanCIREadVJSIzq3sNkt+KtuBmFDlmZlspkQwVaQRtJriImFq4L6lHRLxW/ZDMrN41yj24dueiSjpA0lPA0+n+npIurXpkZlafVNEXXlZVKZPtLwIOB1YCRMRs4JAqxmRmdUwk4+BK2WqtpKeoEfFCi6cmG6oTjpk1gjrIXSUpJcG9IOlAICRtA5wOzK1uWGZWzxplmEgpXdRTSGbwDwZeBPYi44x+M8uPUtdjqIcc2G4LLiJWAMd3QCxm1iA610P2KkEpT1F3lXSrpOXp+oU3S9q1I4Izs/pU5Tf6VkwpXdTfANcDg4C3ATcA11QzKDOrX8lT1NK2WislwfWIiKsioindrga6VTswM6tTJbbe6qEFV2wuap/04x8lnQVcSzI39WiSd6Ob2VaqDnJXSYo9ZJhJktCaf5TPFBwL4OxqBWVm9a0eWmelKDYXdZeODMTMGoOAzvVwg60EJc1kkLQHMJqCe28RcWW1gjKz+tYY6a2EBCdpMsm6haNJ7r2NB+4HnODMtkJS5dZkkPRF4CSS215PkKx1Oojknn9fkltlJ0TE+nLqL+Up6sdI1ihcGhEnAnsCO5ZzMTPLh0rMZJA0GDgNGBMRewCdgWOA7wIXRsTbgdXApHLjLCXBrYuIjUCTpB2AZcDO5V7QzBpfBYeJdAG6S+oC9ACWAIcCv02PTwWOLDfOUu7BzZDUC/gFSXPxVeDBci9oZo0vQw+1n6TCt4NPiYgpABGxWNIPgIXAOuBOkhyzJiKa0vMXkcyDL0spc1E/l378uaTbgR0i4vFyL2hmjU1SlqeoKyJiTBv19AYmALsAa0hmSY2rRIzNig303afYsYiYVclAzKxxVGgc3PuBBRGxPK3zRuAgoJekLmkrbgiwuNwLFGvB/bDIsSDpJ1fU3u8YygMP/7TS1VoVHXOF1yZqJP9Yta4i9ZRy874EC4H9JfUg6aIeRrLY1d0kDzevBSYCN5d7gWIDfd9XbqVmll+iMi24iHhY0m+BWUAT8DdgCjANuFbSt9Kyy8q9hhd+NrPMKjWRISImA5NbFD8H7FuJ+p3gzCwTKWdTtczMCjVIfivpjb6S9AlJ56b7QyVVpPloZo2pUdZkKOVhyKXAAcCx6f4rwCVVi8jM6lre1kXdLyL2kfQ3gIhYLWnbKsdlZnWsQsNEqq6UBPeGpM4kY9+QtBOwsapRmVldq4PGWUlKSXAXAzcB/SVdQDIA7+tVjcrM6lbGqVo1Vcpc1F9LmkkyyljAkRHhle3NtmINkt9KeuHlUOA14NbCsohYWM3AzKw+NT9kaASldFGn8dbiM91IZv4/A7yzinGZWR1rkPxWUhf1XYX76VtGPtfG6WaWd3WyqHMpMs9kiIhZkvarRjBm1hjUIMvOlHIP7ksFu52AfYAXqxaRmdU1AV0aZCBcKS24ngWfm0juyf2uOuGYWSNo+IWfAdIBvj0j4owOisfM6lzyFLXWUZSm2CvLu0REk6SDOjIgM6tzdTKRvhTFWnCPkNxve0zSLSQLQqxtPhgRN1Y5NjOrU3kaB9cNWEmyBkPzeLgAnODMtkICOufgIUP/9Anqk7yV2JpFVaMyszomOuVgmEhnYHto9SdxgjPbSiWLztQ6itIUS3BLIuL8DovEzBpDA81kKNaTbpAfwcw6WqXe6Cupl6TfSnpa0lxJB0jqI+kuSc+mf/YuO84ixw4rt1Izy6/mLmqF1mT4MXB7RIwC9gTmAmcB0yNiBDA93S9LmwkuIlaVW6mZ5VvnTippK0bSjsAhpAs7R8T6iFgDTACmpqdNBY4sN84GedhrZvVCJImjlK0duwDLgV9J+pukX0raDhgQEUvSc5YCA8qN1QnOzLJRMhe1lA3oJ2lGwXZyQU1dSCYT/Cwi9iaZSLBJdzQigi0YteGFn80sswxPIFdExJg2ji0CFkXEw+n+b0kS3EuSBkXEEkmDgGXlxukWnJllUql1USNiKfCCpJFp0WHAU8AtwMS0bCJwc7mxugVnZplVcAzZF4Bfp2stPwecSNLwul7SJOB54KhyK3eCM7OMRKcKjfSNiMeA1rqwFRmm5gRnZpk0P0VtBE5wZpZZLt7oa2bWmsZIb05wZpaV3IIzs5wS0NkJzszyqjHSmxOcmZWhQRpwTnBmlk0yTKQxMpwTnJll5hacmeWUkFtwZpZHfopqZvmVk5Xtzcxa5QRnZrnle3BmlkvJCy9rHUVpnODMLLNS1jytB05wZpaZu6jGvGee4YTjjn5zf8GC5/jvyefzhdP/q3ZB2WaOeGd/PjByJyS46+nl3DpnGdt37cwZh+5G/+23Zdmr6/n+9L+zdv2GWodaF9xFBSRdDhwBLIuIPap1nXq2+8iRPDzzMQA2bNjAbsMG85EjP1rboGwTQ3t34wMjd+IrN8+laeNGJo/bnUdfeJnDR+3E44v/yY2PL+Xf3z2Q/9hzIFc+urjW4daJxhnoW803D18BjKti/Q3l7j9PZ5ddd2PYsGG1DsUKDOnVnWeXv8r6DRvZGDBnySscMLw3+w7txd3PrgTg7mdXst+w3jWOtI6k4+BK2WqtagkuIu4FVlWr/kZzw3XXctTRx9Y6DGth4ep1vGNgT3p27cy2nTuxz8470m+7bejVvQur170BwOp1b9Cru+/mFFKJW63V/G8tXen6ZICdhw6tcTTVsX79eqb94RbOv+DbtQ7FWli05nVumr2U88bvzutvbGTBqtfY2Mo66mUvrZ5DnqqVQURMAaYAvOc9Y3L579Edt/+RvfbehwEDBtQ6FGvFn+at4E/zVgDwiTGDWbl2PWvWNdG7+zasXvcGvbtvw8vrmmocZZ2pYH6T1BmYASyOiCMk7QJcC/QFZgInRMT6cupulNW/Gtr1113j7mkd27Fb8v/5fttty/7De3Hv31fxyMI1vG9EXwDeN6IvjyxcU8MI649K/KdEpwNzC/a/C1wYEW8HVgOTyo2z5i24vFu7di1//tNd/PTS/611KNaGr75/N3p27ULTxmDKXxeydv0Gbpy9hK8cuhvvH9mP5a+u5/t//nutw6wrleqhShoCfBi4APiSktVsDgWOS0+ZCpwH/Kyc+qs5TOQaYCzQT9IiYHJEXFat69Wr7bbbjsUvrax1GFbEOX94ZrOyV/61gXP/OK8G0TSGDPmtn6QZBftT0ttSzS4CzgR6pvt9gTUR0XxPYBEwuNw4q5bgIsJ9MrO8Kj3DrYiIMa1WITWPk50paWxlAtuUu6hmlolUsbmoBwEfkfQhoBuwA/BjoJekLmkrbghQ9ghrP2Qws8wqMQ4uIs6OiCERMRw4BvhzRBwP3A18LD1tInBzuXE6wZlZdtUd6ftVkgcO80nuyZV9795dVDPLqPJzUSPiHuCe9PNzwL6VqNcJzswya5CJDE5wZpaNcIIzsxxrlNclOcGZWWZuwZlZbjVIfnOCM7OM6uVlbyVwgjOzzHwPzsxyyYvOmFm+OcGZWV65i2pmueVhImaWWw2S35zgzKwMDZLhnODMLJMKvvCy6pzgzCyzxkhvTnBmVo4GyXBOcGaWUeVfeFktTnBmllmD3IJzgjOzbPzCSzPLNXdRzSy33IIzs9xqkPzmdVHNLCMlLbhStqLVSDtLulvSU5LmSDo9Le8j6S5Jz6Z/9i43VCc4MytDRVZ+bgK+HBGjgf2BUyWNBs4CpkfECGB6ul8WJzgzy6T5hZelbMVExJKImJV+fgWYCwwGJgBT09OmAkeWG6vvwZlZZhkeMvSTNKNgf0pETNm8Pg0H9gYeBgZExJL00FJgQLlxOsGZWWYZhomsiIgxReuStgd+B/xXRPxTBdkzIkJSlBunu6hmll1FbsGBpG1IktuvI+LGtPglSYPS44OAZeWG6QRnZplVIr8paapdBsyNiB8VHLoFmJh+ngjcXG6c7qKaWSalDAEp0UHACcATkh5Ly84BvgNcL2kS8DxwVLkXcIIzs8xUgQwXEffTdkPvsC2+AE5wZlaGRpnJ4ARnZpl5LqqZ5ZRfeGlmOeX3wZlZrjnBmVluuYtqZvlUuXFwVecEZ2aZlDgLqy44wZlZdg2S4ZzgzCwz34Mzs9xq72WW9cIJzsyyc4Izs7xyF9XMcqmRZjIoouy3AVecpOUk73/Km37AiloHYZnk9e9sWETstCUVSLqd5PdTihURMW5Lrrcl6irB5ZWkGe29l97qi//O8sGvLDez3HKCM7PccoLrGJutA2l1z39nOeB7cGaWW27BmVluOcGZWW45wVWRpHGSnpE0X9JZtY7H2ifpcknLJD1Z61hsyznBVYmkzsAlwHhgNHCspNG1jcpKcAVQs4GpVllOcNWzLzA/Ip6LiPXAtcCEGsdk7YiIe4FVtY7DKsMJrnoGAy8U7C9Ky8ysgzjBmVluOcFVz2Jg54L9IWmZmXUQJ7jqeRQYIWkXSdsCxwC31Dgms62KE1yVREQT8HngDmAucH1EzKltVNYeSdcADwIjJS2SNKnWMVn5PFXLzHLLLTgzyy0nODPLLSc4M8stJzgzyy0nODPLLSe4BiJpg6THJD0p6QZJPbagriskfSz9/MtiLwKQNFbSgWVc4x+SNlt9qa3yFue8mvFa50k6I2uMlm9OcI1lXUTsFRF7AOuBUwoPSiprnduIOCkinipyylggc4IzqzUnuMZ1H/D2tHV1n6RbgKckdZb0fUmPSnpc0mcAlPhp+n66PwH9myuSdI+kMenncZJmSZotabqk4SSJ9Itp6/FgSTtJ+l16jUclHZR+t6+kOyXNkfRLaH/5c0m/lzQz/c7JLY5dmJZPl7RTWrabpNvT79wnaVRFfpuWS17ZvgGlLbXxwO1p0T7AHhGxIE0SL0fE/5PUFXhA0p3A3sBIknfTDQCeAi5vUe9OwC+AQ9K6+kTEKkk/B16NiB+k5/0GuDAi7pc0lGS2xjuAycD9EXG+pA8DpcwC+HR6je7Ao5J+FxErge2AGRHxRUnnpnV/nmQxmFMi4llJ+wGXAoeW8Wu0rYATXGPpLumx9PN9wGUkXcdHImJBWv5B4N3N99eAHYERwCHANRGxAXhR0p9bqX9/4N7muiKirfeivR8YLb3ZQNtB0vbpNf49/e40SatL+JlOk/TR9PPOaawrgY3AdWn51cCN6TUOBG4ouHbXEq5hWyknuMayLiL2KixI/0NfW1gEfCEi7mhx3ocqGEcnYP+IeL2VWEomaSxJsjwgIl6TdA/QrY3TI73umpa/A7O2+B5c/twBfFbSNgCSdpe0HXAvcHR6j24Q8L5WvvsQcIikXdLv9knLXwF6Fpx3J/CF5h1Je6Uf7wWOS8vGA73biXVHYHWa3EaRtCCbdQKaW6HHkXR9/wkskPTx9BqStGc717CtmBNc/vyS5P7arHThlP8laanfBDybHruS5I0Zm4iI5cDJJN3B2bzVRbwV+GjzQwbgNGBM+hDjKd56mvsNkgQ5h6SrurCdWG8HukiaC3yHJME2Wwvsm/4MhwLnp+XHA5PS+Obg18BbEX6biJnllltwZpZbTnBmlltOcGaWW05wZpZbTnBmlltOcGaWW05wZpZb/weyfl0RamvWjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(test_label, cnn_rounded_predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff62f97",
   "metadata": {},
   "source": [
    "# LSTM prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ace5a8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_rounded_predictions = model_lstm.predict_proba(end_test)\n",
    "lstm_rounded_predictions = (lstm_rounded_predictions > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6510e6d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.92      0.93       164\n",
      "           1       0.87      0.89      0.88        97\n",
      "\n",
      "    accuracy                           0.91       261\n",
      "   macro avg       0.90      0.90      0.90       261\n",
      "weighted avg       0.91      0.91      0.91       261\n",
      "\n",
      "0.9080459770114943\n",
      "[[151  13]\n",
      " [ 11  86]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_label, lstm_rounded_predictions))\n",
    "print(accuracy_score(test_label, lstm_rounded_predictions))\n",
    "cf_matrix = confusion_matrix(test_label, lstm_rounded_predictions)\n",
    "print(confusion_matrix(test_label, lstm_rounded_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "efa9b08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(test_label, lstm_rounded_predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacb65d4",
   "metadata": {},
   "source": [
    "# Hybrid _prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "18b1fe19",
   "metadata": {},
   "outputs": [],
   "source": [
    "hybrid_rounded_predictions = model_hybrid.predict_proba(end_test)\n",
    "hybrid_rounded_predictions = (hybrid_rounded_predictions > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "58ac8330",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hybrid_rounded_predictions = hybrid_rounded_predictions*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "798c90de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.85      0.90       164\n",
      "           1       0.79      0.95      0.86        97\n",
      "\n",
      "    accuracy                           0.89       261\n",
      "   macro avg       0.88      0.90      0.88       261\n",
      "weighted avg       0.90      0.89      0.89       261\n",
      "\n",
      "0.8850574712643678\n",
      "[[139  25]\n",
      " [  5  92]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_label, hybrid_rounded_predictions))\n",
    "print(accuracy_score(test_label, hybrid_rounded_predictions))\n",
    "cf_matrix = confusion_matrix(test_label, hybrid_rounded_predictions)\n",
    "print(confusion_matrix(test_label, hybrid_rounded_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6a0d45a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(test_label, hybrid_rounded_predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "412acc6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40d9fa1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e4d5f10c",
   "metadata": {},
   "source": [
    "# ROC curver Deep learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d017a717",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "y_pred_keras = model_cnn.predict(end_test).ravel()\n",
    "fpr_keras, tpr_keras, thresholds_keras = roc_curve(test_label, y_pred_keras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0c6db7ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LS\n",
    "y_pred_rf = model_lstm.predict(end_test).ravel()\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(test_label, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b1e73e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #LS\n",
    "y_pred_hb = model_hybrid.predict(end_test).ravel()\n",
    "fpr_hb, tpr_hb, thresholds_hb = roc_curve(test_label, y_pred_hb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cedb2777",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import auc\n",
    "auc_keras = auc(fpr_keras, tpr_keras)\n",
    "auc_rf = auc(fpr_rf, tpr_rf)\n",
    "auc_hb = auc(fpr_hb, tpr_hb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e0eb3550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_keras, tpr_keras, label='CNN (area = {:.4f})'.format(auc_keras))\n",
    "plt.plot(fpr_rf, tpr_rf, label='LSTM (area = {:.4f})'.format(auc_rf))\n",
    "plt.plot(fpr_hb, tpr_hb, label='hybrid (area = {:.4f})'.format(auc_hb))\n",
    "\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9550551",
   "metadata": {},
   "source": [
    "# Machine Learning Implementation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "59ff87cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power</th>\n",
       "      <th>Spindle</th>\n",
       "      <th>vibration</th>\n",
       "      <th>roll_mean_Power</th>\n",
       "      <th>roll_std_Power</th>\n",
       "      <th>roll_min_Power</th>\n",
       "      <th>roll_max_Power</th>\n",
       "      <th>roll_EME_Power</th>\n",
       "      <th>roll_IQR_Power</th>\n",
       "      <th>roll_skew_Power</th>\n",
       "      <th>roll_mean_Spindle</th>\n",
       "      <th>roll_std_Spindle</th>\n",
       "      <th>roll_min_Spindle</th>\n",
       "      <th>roll_max_Spindle</th>\n",
       "      <th>roll_EME_Spindle</th>\n",
       "      <th>roll_IQR_Spindle</th>\n",
       "      <th>roll_skew_Spindle</th>\n",
       "      <th>roll_mean_vibration</th>\n",
       "      <th>roll_std_vibration</th>\n",
       "      <th>roll_min_vibration</th>\n",
       "      <th>roll_max_vibration</th>\n",
       "      <th>roll_EME_vibration</th>\n",
       "      <th>roll_IQR_vibration</th>\n",
       "      <th>roll_skew_vibration</th>\n",
       "      <th>Label_Numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.704504</td>\n",
       "      <td>-0.656656</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>-5.243308</td>\n",
       "      <td>3.549036</td>\n",
       "      <td>-8.273532</td>\n",
       "      <td>0.237851</td>\n",
       "      <td>-0.850778</td>\n",
       "      <td>-0.454735</td>\n",
       "      <td>1.763536</td>\n",
       "      <td>-0.894810</td>\n",
       "      <td>-0.794447</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.891522</td>\n",
       "      <td>-0.561806</td>\n",
       "      <td>-0.672802</td>\n",
       "      <td>-0.718196</td>\n",
       "      <td>-0.338410</td>\n",
       "      <td>0.153263</td>\n",
       "      <td>-0.362442</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.157717</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>1.940044</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.693592</td>\n",
       "      <td>-0.270631</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>-3.705873</td>\n",
       "      <td>4.412749</td>\n",
       "      <td>-8.273532</td>\n",
       "      <td>0.237851</td>\n",
       "      <td>-0.570332</td>\n",
       "      <td>13.084977</td>\n",
       "      <td>0.318055</td>\n",
       "      <td>-0.789580</td>\n",
       "      <td>-0.504153</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.617958</td>\n",
       "      <td>-0.550103</td>\n",
       "      <td>-0.672802</td>\n",
       "      <td>1.657976</td>\n",
       "      <td>-0.275950</td>\n",
       "      <td>0.243939</td>\n",
       "      <td>-0.362442</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.155242</td>\n",
       "      <td>0.617664</td>\n",
       "      <td>0.317541</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.705959</td>\n",
       "      <td>-0.259266</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>-2.165217</td>\n",
       "      <td>4.414593</td>\n",
       "      <td>-8.273532</td>\n",
       "      <td>0.238714</td>\n",
       "      <td>-0.288709</td>\n",
       "      <td>13.110000</td>\n",
       "      <td>-0.762990</td>\n",
       "      <td>-0.681253</td>\n",
       "      <td>-0.433614</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.609904</td>\n",
       "      <td>-0.537702</td>\n",
       "      <td>-0.252914</td>\n",
       "      <td>-0.069784</td>\n",
       "      <td>-0.213490</td>\n",
       "      <td>0.243939</td>\n",
       "      <td>-0.362442</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.152767</td>\n",
       "      <td>0.617664</td>\n",
       "      <td>-0.895892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.685590</td>\n",
       "      <td>-0.656656</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>-0.629865</td>\n",
       "      <td>3.544424</td>\n",
       "      <td>-8.273532</td>\n",
       "      <td>0.238714</td>\n",
       "      <td>-0.009022</td>\n",
       "      <td>-0.411362</td>\n",
       "      <td>-2.208437</td>\n",
       "      <td>-0.681253</td>\n",
       "      <td>-0.433614</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.609904</td>\n",
       "      <td>-0.537702</td>\n",
       "      <td>-0.252914</td>\n",
       "      <td>-0.069784</td>\n",
       "      <td>-0.151029</td>\n",
       "      <td>0.153263</td>\n",
       "      <td>-0.362442</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.150292</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-2.518395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.692137</td>\n",
       "      <td>-0.643987</td>\n",
       "      <td>-0.084409</td>\n",
       "      <td>0.907191</td>\n",
       "      <td>-0.300801</td>\n",
       "      <td>1.455817</td>\n",
       "      <td>0.238714</td>\n",
       "      <td>0.271287</td>\n",
       "      <td>-0.426376</td>\n",
       "      <td>-0.175717</td>\n",
       "      <td>-0.677799</td>\n",
       "      <td>-0.437396</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>-0.609904</td>\n",
       "      <td>-0.537689</td>\n",
       "      <td>-0.252914</td>\n",
       "      <td>-0.072604</td>\n",
       "      <td>-0.088569</td>\n",
       "      <td>-0.250196</td>\n",
       "      <td>-0.012533</td>\n",
       "      <td>-0.163626</td>\n",
       "      <td>-0.147817</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-0.289175</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26121</th>\n",
       "      <td>0.433161</td>\n",
       "      <td>1.635646</td>\n",
       "      <td>0.064349</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>-0.085733</td>\n",
       "      <td>0.482722</td>\n",
       "      <td>0.092613</td>\n",
       "      <td>0.123167</td>\n",
       "      <td>-0.424707</td>\n",
       "      <td>-2.186625</td>\n",
       "      <td>1.446901</td>\n",
       "      <td>1.115797</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>0.989496</td>\n",
       "      <td>1.002605</td>\n",
       "      <td>0.851926</td>\n",
       "      <td>-1.851743</td>\n",
       "      <td>0.067581</td>\n",
       "      <td>-0.250196</td>\n",
       "      <td>0.162422</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.132349</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-0.289175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26122</th>\n",
       "      <td>0.438496</td>\n",
       "      <td>-0.640820</td>\n",
       "      <td>0.064349</td>\n",
       "      <td>0.577024</td>\n",
       "      <td>-0.298003</td>\n",
       "      <td>1.039291</td>\n",
       "      <td>0.092613</td>\n",
       "      <td>0.153365</td>\n",
       "      <td>-0.431936</td>\n",
       "      <td>0.896485</td>\n",
       "      <td>0.763519</td>\n",
       "      <td>1.291478</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>0.989496</td>\n",
       "      <td>0.502827</td>\n",
       "      <td>1.803361</td>\n",
       "      <td>-0.560021</td>\n",
       "      <td>0.067581</td>\n",
       "      <td>-0.250196</td>\n",
       "      <td>0.162422</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.132349</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-0.289175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26123</th>\n",
       "      <td>0.451590</td>\n",
       "      <td>2.272624</td>\n",
       "      <td>0.064349</td>\n",
       "      <td>0.581002</td>\n",
       "      <td>-0.298183</td>\n",
       "      <td>1.039291</td>\n",
       "      <td>0.092613</td>\n",
       "      <td>0.154755</td>\n",
       "      <td>-0.424707</td>\n",
       "      <td>-0.097931</td>\n",
       "      <td>0.838480</td>\n",
       "      <td>1.431031</td>\n",
       "      <td>-0.436556</td>\n",
       "      <td>1.184371</td>\n",
       "      <td>0.623404</td>\n",
       "      <td>1.803361</td>\n",
       "      <td>-0.416993</td>\n",
       "      <td>0.067581</td>\n",
       "      <td>-0.250196</td>\n",
       "      <td>0.162422</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.132349</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-0.289175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26124</th>\n",
       "      <td>0.436071</td>\n",
       "      <td>-0.075569</td>\n",
       "      <td>-0.010030</td>\n",
       "      <td>0.574877</td>\n",
       "      <td>-0.302332</td>\n",
       "      <td>1.039291</td>\n",
       "      <td>0.087868</td>\n",
       "      <td>0.152614</td>\n",
       "      <td>-0.431380</td>\n",
       "      <td>0.311312</td>\n",
       "      <td>0.996883</td>\n",
       "      <td>1.230144</td>\n",
       "      <td>-0.394501</td>\n",
       "      <td>1.184371</td>\n",
       "      <td>0.649921</td>\n",
       "      <td>1.188526</td>\n",
       "      <td>-0.394843</td>\n",
       "      <td>0.051966</td>\n",
       "      <td>-0.149331</td>\n",
       "      <td>0.074945</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.134050</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-2.518395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26125</th>\n",
       "      <td>0.446498</td>\n",
       "      <td>2.333732</td>\n",
       "      <td>0.064349</td>\n",
       "      <td>0.574940</td>\n",
       "      <td>-0.302271</td>\n",
       "      <td>1.039291</td>\n",
       "      <td>0.087868</td>\n",
       "      <td>0.152636</td>\n",
       "      <td>-0.430824</td>\n",
       "      <td>0.291145</td>\n",
       "      <td>1.506474</td>\n",
       "      <td>1.522843</td>\n",
       "      <td>-0.394501</td>\n",
       "      <td>1.227676</td>\n",
       "      <td>1.253494</td>\n",
       "      <td>1.881381</td>\n",
       "      <td>-1.288836</td>\n",
       "      <td>0.051966</td>\n",
       "      <td>-0.149331</td>\n",
       "      <td>0.074945</td>\n",
       "      <td>-0.040807</td>\n",
       "      <td>-0.134050</td>\n",
       "      <td>-0.194306</td>\n",
       "      <td>-2.518395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26126 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Power   Spindle  vibration  roll_mean_Power  roll_std_Power  roll_min_Power  roll_max_Power  roll_EME_Power  roll_IQR_Power  roll_skew_Power  roll_mean_Spindle  roll_std_Spindle  roll_min_Spindle  roll_max_Spindle  roll_EME_Spindle  roll_IQR_Spindle  roll_skew_Spindle  roll_mean_vibration  roll_std_vibration  roll_min_vibration  roll_max_vibration  roll_EME_vibration  roll_IQR_vibration  roll_skew_vibration  Label_Numeric\n",
       "0      0.704504 -0.656656  -0.084409        -5.243308        3.549036       -8.273532        0.237851       -0.850778       -0.454735         1.763536          -0.894810         -0.794447         -0.436556         -0.891522         -0.561806         -0.672802          -0.718196            -0.338410            0.153263           -0.362442           -0.163626           -0.157717           -0.194306             1.940044              0\n",
       "1      0.693592 -0.270631  -0.084409        -3.705873        4.412749       -8.273532        0.237851       -0.570332       13.084977         0.318055          -0.789580         -0.504153         -0.436556         -0.617958         -0.550103         -0.672802           1.657976            -0.275950            0.243939           -0.362442           -0.163626           -0.155242            0.617664             0.317541              0\n",
       "2      0.705959 -0.259266  -0.084409        -2.165217        4.414593       -8.273532        0.238714       -0.288709       13.110000        -0.762990          -0.681253         -0.433614         -0.436556         -0.609904         -0.537702         -0.252914          -0.069784            -0.213490            0.243939           -0.362442           -0.163626           -0.152767            0.617664            -0.895892              0\n",
       "3      0.685590 -0.656656  -0.084409        -0.629865        3.544424       -8.273532        0.238714       -0.009022       -0.411362        -2.208437          -0.681253         -0.433614         -0.436556         -0.609904         -0.537702         -0.252914          -0.069784            -0.151029            0.153263           -0.362442           -0.163626           -0.150292           -0.194306            -2.518395              0\n",
       "4      0.692137 -0.643987  -0.084409         0.907191       -0.300801        1.455817        0.238714        0.271287       -0.426376        -0.175717          -0.677799         -0.437396         -0.436556         -0.609904         -0.537689         -0.252914          -0.072604            -0.088569           -0.250196           -0.012533           -0.163626           -0.147817           -0.194306            -0.289175              0\n",
       "...         ...       ...        ...              ...             ...             ...             ...             ...             ...              ...                ...               ...               ...               ...               ...               ...                ...                  ...                 ...                 ...                 ...                 ...                 ...                  ...            ...\n",
       "26121  0.433161  1.635646   0.064349         0.487805       -0.085733        0.482722        0.092613        0.123167       -0.424707        -2.186625           1.446901          1.115797         -0.436556          0.989496          1.002605          0.851926          -1.851743             0.067581           -0.250196            0.162422           -0.040807           -0.132349           -0.194306            -0.289175              0\n",
       "26122  0.438496 -0.640820   0.064349         0.577024       -0.298003        1.039291        0.092613        0.153365       -0.431936         0.896485           0.763519          1.291478         -0.436556          0.989496          0.502827          1.803361          -0.560021             0.067581           -0.250196            0.162422           -0.040807           -0.132349           -0.194306            -0.289175              0\n",
       "26123  0.451590  2.272624   0.064349         0.581002       -0.298183        1.039291        0.092613        0.154755       -0.424707        -0.097931           0.838480          1.431031         -0.436556          1.184371          0.623404          1.803361          -0.416993             0.067581           -0.250196            0.162422           -0.040807           -0.132349           -0.194306            -0.289175              0\n",
       "26124  0.436071 -0.075569  -0.010030         0.574877       -0.302332        1.039291        0.087868        0.152614       -0.431380         0.311312           0.996883          1.230144         -0.394501          1.184371          0.649921          1.188526          -0.394843             0.051966           -0.149331            0.074945           -0.040807           -0.134050           -0.194306            -2.518395              0\n",
       "26125  0.446498  2.333732   0.064349         0.574940       -0.302271        1.039291        0.087868        0.152636       -0.430824         0.291145           1.506474          1.522843         -0.394501          1.227676          1.253494          1.881381          -1.288836             0.051966           -0.149331            0.074945           -0.040807           -0.134050           -0.194306            -2.518395              0\n",
       "\n",
       "[26126 rows x 25 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ff04a5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df.iloc[:,0:-1]\n",
    "Y_test = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "43f7b554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power</th>\n",
       "      <th>Spindle</th>\n",
       "      <th>vibration</th>\n",
       "      <th>roll_mean_Power</th>\n",
       "      <th>roll_std_Power</th>\n",
       "      <th>roll_min_Power</th>\n",
       "      <th>roll_max_Power</th>\n",
       "      <th>roll_EME_Power</th>\n",
       "      <th>roll_IQR_Power</th>\n",
       "      <th>roll_skew_Power</th>\n",
       "      <th>roll_mean_Spindle</th>\n",
       "      <th>roll_std_Spindle</th>\n",
       "      <th>roll_min_Spindle</th>\n",
       "      <th>roll_max_Spindle</th>\n",
       "      <th>roll_EME_Spindle</th>\n",
       "      <th>roll_IQR_Spindle</th>\n",
       "      <th>roll_skew_Spindle</th>\n",
       "      <th>roll_mean_vibration</th>\n",
       "      <th>roll_std_vibration</th>\n",
       "      <th>roll_min_vibration</th>\n",
       "      <th>roll_max_vibration</th>\n",
       "      <th>roll_EME_vibration</th>\n",
       "      <th>roll_IQR_vibration</th>\n",
       "      <th>roll_skew_vibration</th>\n",
       "      <th>Label_Numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Power</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297077</td>\n",
       "      <td>0.136306</td>\n",
       "      <td>0.758871</td>\n",
       "      <td>0.416487</td>\n",
       "      <td>0.633756</td>\n",
       "      <td>0.615002</td>\n",
       "      <td>0.467822</td>\n",
       "      <td>0.330007</td>\n",
       "      <td>0.037827</td>\n",
       "      <td>0.403786</td>\n",
       "      <td>0.364195</td>\n",
       "      <td>0.190321</td>\n",
       "      <td>0.408533</td>\n",
       "      <td>0.316839</td>\n",
       "      <td>0.292084</td>\n",
       "      <td>-0.150479</td>\n",
       "      <td>0.144910</td>\n",
       "      <td>0.092484</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>0.148285</td>\n",
       "      <td>0.059751</td>\n",
       "      <td>0.080824</td>\n",
       "      <td>0.004756</td>\n",
       "      <td>0.332917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spindle</th>\n",
       "      <td>0.297077</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003516</td>\n",
       "      <td>0.382536</td>\n",
       "      <td>0.096732</td>\n",
       "      <td>0.370002</td>\n",
       "      <td>0.225524</td>\n",
       "      <td>0.137922</td>\n",
       "      <td>0.181011</td>\n",
       "      <td>-0.008789</td>\n",
       "      <td>0.758790</td>\n",
       "      <td>0.604224</td>\n",
       "      <td>0.389834</td>\n",
       "      <td>0.695199</td>\n",
       "      <td>0.621443</td>\n",
       "      <td>0.535481</td>\n",
       "      <td>-0.314127</td>\n",
       "      <td>-0.001377</td>\n",
       "      <td>0.003860</td>\n",
       "      <td>-0.004477</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>-0.035286</td>\n",
       "      <td>0.005344</td>\n",
       "      <td>-0.007330</td>\n",
       "      <td>0.294498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vibration</th>\n",
       "      <td>0.136306</td>\n",
       "      <td>-0.003516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170347</td>\n",
       "      <td>-0.014697</td>\n",
       "      <td>0.224232</td>\n",
       "      <td>0.066957</td>\n",
       "      <td>0.048836</td>\n",
       "      <td>-0.012929</td>\n",
       "      <td>-0.030977</td>\n",
       "      <td>-0.005253</td>\n",
       "      <td>0.021404</td>\n",
       "      <td>-0.034147</td>\n",
       "      <td>0.008885</td>\n",
       "      <td>-0.024602</td>\n",
       "      <td>0.020059</td>\n",
       "      <td>-0.050452</td>\n",
       "      <td>0.938803</td>\n",
       "      <td>0.371563</td>\n",
       "      <td>0.908012</td>\n",
       "      <td>0.867874</td>\n",
       "      <td>0.897019</td>\n",
       "      <td>0.324826</td>\n",
       "      <td>-0.012851</td>\n",
       "      <td>-0.030853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_mean_Power</th>\n",
       "      <td>0.758871</td>\n",
       "      <td>0.382536</td>\n",
       "      <td>0.170347</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.531835</td>\n",
       "      <td>0.828167</td>\n",
       "      <td>0.794073</td>\n",
       "      <td>0.609672</td>\n",
       "      <td>0.472869</td>\n",
       "      <td>0.033640</td>\n",
       "      <td>0.526090</td>\n",
       "      <td>0.469200</td>\n",
       "      <td>0.250655</td>\n",
       "      <td>0.527540</td>\n",
       "      <td>0.394682</td>\n",
       "      <td>0.387453</td>\n",
       "      <td>-0.193610</td>\n",
       "      <td>0.184598</td>\n",
       "      <td>0.106709</td>\n",
       "      <td>0.166065</td>\n",
       "      <td>0.184886</td>\n",
       "      <td>0.076344</td>\n",
       "      <td>0.090596</td>\n",
       "      <td>-0.005107</td>\n",
       "      <td>0.418211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_std_Power</th>\n",
       "      <td>0.416487</td>\n",
       "      <td>0.096732</td>\n",
       "      <td>-0.014697</td>\n",
       "      <td>0.531835</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019243</td>\n",
       "      <td>0.930927</td>\n",
       "      <td>0.864082</td>\n",
       "      <td>0.483590</td>\n",
       "      <td>0.169000</td>\n",
       "      <td>0.144681</td>\n",
       "      <td>0.158086</td>\n",
       "      <td>0.052256</td>\n",
       "      <td>0.173019</td>\n",
       "      <td>0.201067</td>\n",
       "      <td>0.078144</td>\n",
       "      <td>-0.007636</td>\n",
       "      <td>-0.014894</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>-0.020787</td>\n",
       "      <td>-0.013038</td>\n",
       "      <td>-0.015913</td>\n",
       "      <td>0.012580</td>\n",
       "      <td>0.003060</td>\n",
       "      <td>0.006901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_min_Power</th>\n",
       "      <td>0.633756</td>\n",
       "      <td>0.370002</td>\n",
       "      <td>0.224232</td>\n",
       "      <td>0.828167</td>\n",
       "      <td>0.019243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.379793</td>\n",
       "      <td>0.259100</td>\n",
       "      <td>0.100626</td>\n",
       "      <td>-0.020751</td>\n",
       "      <td>0.503891</td>\n",
       "      <td>0.435101</td>\n",
       "      <td>0.246138</td>\n",
       "      <td>0.488833</td>\n",
       "      <td>0.313931</td>\n",
       "      <td>0.393201</td>\n",
       "      <td>-0.228759</td>\n",
       "      <td>0.241538</td>\n",
       "      <td>0.131449</td>\n",
       "      <td>0.224069</td>\n",
       "      <td>0.240763</td>\n",
       "      <td>0.109682</td>\n",
       "      <td>0.099837</td>\n",
       "      <td>-0.008610</td>\n",
       "      <td>0.489088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_max_Power</th>\n",
       "      <td>0.615002</td>\n",
       "      <td>0.225524</td>\n",
       "      <td>0.066957</td>\n",
       "      <td>0.794073</td>\n",
       "      <td>0.930927</td>\n",
       "      <td>0.379793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.887861</td>\n",
       "      <td>0.474198</td>\n",
       "      <td>0.152586</td>\n",
       "      <td>0.319150</td>\n",
       "      <td>0.306844</td>\n",
       "      <td>0.138396</td>\n",
       "      <td>0.340395</td>\n",
       "      <td>0.302903</td>\n",
       "      <td>0.216677</td>\n",
       "      <td>-0.090066</td>\n",
       "      <td>0.073134</td>\n",
       "      <td>0.049222</td>\n",
       "      <td>0.061312</td>\n",
       "      <td>0.074682</td>\n",
       "      <td>0.024538</td>\n",
       "      <td>0.047534</td>\n",
       "      <td>0.000937</td>\n",
       "      <td>0.184882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_EME_Power</th>\n",
       "      <td>0.467822</td>\n",
       "      <td>0.137922</td>\n",
       "      <td>0.048836</td>\n",
       "      <td>0.609672</td>\n",
       "      <td>0.864082</td>\n",
       "      <td>0.259100</td>\n",
       "      <td>0.887861</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.222218</td>\n",
       "      <td>0.037445</td>\n",
       "      <td>0.192216</td>\n",
       "      <td>0.179260</td>\n",
       "      <td>0.072814</td>\n",
       "      <td>0.196200</td>\n",
       "      <td>0.168746</td>\n",
       "      <td>0.138141</td>\n",
       "      <td>-0.072530</td>\n",
       "      <td>0.053419</td>\n",
       "      <td>0.031489</td>\n",
       "      <td>0.047164</td>\n",
       "      <td>0.053228</td>\n",
       "      <td>0.020370</td>\n",
       "      <td>0.027945</td>\n",
       "      <td>-0.016413</td>\n",
       "      <td>0.115464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_IQR_Power</th>\n",
       "      <td>0.330007</td>\n",
       "      <td>0.181011</td>\n",
       "      <td>-0.012929</td>\n",
       "      <td>0.472869</td>\n",
       "      <td>0.483590</td>\n",
       "      <td>0.100626</td>\n",
       "      <td>0.474198</td>\n",
       "      <td>0.222218</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060439</td>\n",
       "      <td>0.244960</td>\n",
       "      <td>0.208491</td>\n",
       "      <td>0.140289</td>\n",
       "      <td>0.249621</td>\n",
       "      <td>0.228959</td>\n",
       "      <td>0.144664</td>\n",
       "      <td>-0.022325</td>\n",
       "      <td>-0.011272</td>\n",
       "      <td>0.002426</td>\n",
       "      <td>-0.019036</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>-0.019103</td>\n",
       "      <td>0.024800</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>0.078263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_skew_Power</th>\n",
       "      <td>0.037827</td>\n",
       "      <td>-0.008789</td>\n",
       "      <td>-0.030977</td>\n",
       "      <td>0.033640</td>\n",
       "      <td>0.169000</td>\n",
       "      <td>-0.020751</td>\n",
       "      <td>0.152586</td>\n",
       "      <td>0.037445</td>\n",
       "      <td>0.060439</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010715</td>\n",
       "      <td>-0.014143</td>\n",
       "      <td>0.008709</td>\n",
       "      <td>-0.007752</td>\n",
       "      <td>-0.003927</td>\n",
       "      <td>-0.028404</td>\n",
       "      <td>0.015117</td>\n",
       "      <td>-0.034600</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>-0.043546</td>\n",
       "      <td>-0.026378</td>\n",
       "      <td>-0.018521</td>\n",
       "      <td>0.012383</td>\n",
       "      <td>0.067985</td>\n",
       "      <td>-0.052913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_mean_Spindle</th>\n",
       "      <td>0.403786</td>\n",
       "      <td>0.758790</td>\n",
       "      <td>-0.005253</td>\n",
       "      <td>0.526090</td>\n",
       "      <td>0.144681</td>\n",
       "      <td>0.503891</td>\n",
       "      <td>0.319150</td>\n",
       "      <td>0.192216</td>\n",
       "      <td>0.244960</td>\n",
       "      <td>-0.010715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.821479</td>\n",
       "      <td>0.520174</td>\n",
       "      <td>0.940448</td>\n",
       "      <td>0.812817</td>\n",
       "      <td>0.727781</td>\n",
       "      <td>-0.377205</td>\n",
       "      <td>-0.003686</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>-0.007394</td>\n",
       "      <td>-0.001529</td>\n",
       "      <td>-0.048700</td>\n",
       "      <td>0.003257</td>\n",
       "      <td>-0.021656</td>\n",
       "      <td>0.398150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_std_Spindle</th>\n",
       "      <td>0.364195</td>\n",
       "      <td>0.604224</td>\n",
       "      <td>0.021404</td>\n",
       "      <td>0.469200</td>\n",
       "      <td>0.158086</td>\n",
       "      <td>0.435101</td>\n",
       "      <td>0.306844</td>\n",
       "      <td>0.179260</td>\n",
       "      <td>0.208491</td>\n",
       "      <td>-0.014143</td>\n",
       "      <td>0.821479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004222</td>\n",
       "      <td>0.951005</td>\n",
       "      <td>0.868592</td>\n",
       "      <td>0.901156</td>\n",
       "      <td>-0.262064</td>\n",
       "      <td>0.024305</td>\n",
       "      <td>0.028782</td>\n",
       "      <td>0.017109</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>-0.016212</td>\n",
       "      <td>0.015175</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.377007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_min_Spindle</th>\n",
       "      <td>0.190321</td>\n",
       "      <td>0.389834</td>\n",
       "      <td>-0.034147</td>\n",
       "      <td>0.250655</td>\n",
       "      <td>0.052256</td>\n",
       "      <td>0.246138</td>\n",
       "      <td>0.138396</td>\n",
       "      <td>0.072814</td>\n",
       "      <td>0.140289</td>\n",
       "      <td>0.008709</td>\n",
       "      <td>0.520174</td>\n",
       "      <td>0.004222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286385</td>\n",
       "      <td>0.234215</td>\n",
       "      <td>-0.050757</td>\n",
       "      <td>-0.108837</td>\n",
       "      <td>-0.034835</td>\n",
       "      <td>-0.031469</td>\n",
       "      <td>-0.031179</td>\n",
       "      <td>-0.040462</td>\n",
       "      <td>-0.056224</td>\n",
       "      <td>-0.014336</td>\n",
       "      <td>-0.042863</td>\n",
       "      <td>0.144077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_max_Spindle</th>\n",
       "      <td>0.408533</td>\n",
       "      <td>0.695199</td>\n",
       "      <td>0.008885</td>\n",
       "      <td>0.527540</td>\n",
       "      <td>0.173019</td>\n",
       "      <td>0.488833</td>\n",
       "      <td>0.340395</td>\n",
       "      <td>0.196200</td>\n",
       "      <td>0.249621</td>\n",
       "      <td>-0.007752</td>\n",
       "      <td>0.940448</td>\n",
       "      <td>0.951005</td>\n",
       "      <td>0.286385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.894171</td>\n",
       "      <td>0.804379</td>\n",
       "      <td>-0.279457</td>\n",
       "      <td>0.011135</td>\n",
       "      <td>0.017352</td>\n",
       "      <td>0.005019</td>\n",
       "      <td>0.016202</td>\n",
       "      <td>-0.033182</td>\n",
       "      <td>0.010900</td>\n",
       "      <td>-0.010878</td>\n",
       "      <td>0.395365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_EME_Spindle</th>\n",
       "      <td>0.316839</td>\n",
       "      <td>0.621443</td>\n",
       "      <td>-0.024602</td>\n",
       "      <td>0.394682</td>\n",
       "      <td>0.201067</td>\n",
       "      <td>0.313931</td>\n",
       "      <td>0.302903</td>\n",
       "      <td>0.168746</td>\n",
       "      <td>0.228959</td>\n",
       "      <td>-0.003927</td>\n",
       "      <td>0.812817</td>\n",
       "      <td>0.868592</td>\n",
       "      <td>0.234215</td>\n",
       "      <td>0.894171</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.695904</td>\n",
       "      <td>-0.241638</td>\n",
       "      <td>-0.024160</td>\n",
       "      <td>-0.004331</td>\n",
       "      <td>-0.027622</td>\n",
       "      <td>-0.020517</td>\n",
       "      <td>-0.050838</td>\n",
       "      <td>-0.001105</td>\n",
       "      <td>-0.008172</td>\n",
       "      <td>0.282124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_IQR_Spindle</th>\n",
       "      <td>0.292084</td>\n",
       "      <td>0.535481</td>\n",
       "      <td>0.020059</td>\n",
       "      <td>0.387453</td>\n",
       "      <td>0.078144</td>\n",
       "      <td>0.393201</td>\n",
       "      <td>0.216677</td>\n",
       "      <td>0.138141</td>\n",
       "      <td>0.144664</td>\n",
       "      <td>-0.028404</td>\n",
       "      <td>0.727781</td>\n",
       "      <td>0.901156</td>\n",
       "      <td>-0.050757</td>\n",
       "      <td>0.804379</td>\n",
       "      <td>0.695904</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.317828</td>\n",
       "      <td>0.022718</td>\n",
       "      <td>0.023447</td>\n",
       "      <td>0.017803</td>\n",
       "      <td>0.028462</td>\n",
       "      <td>-0.013437</td>\n",
       "      <td>0.009979</td>\n",
       "      <td>0.015114</td>\n",
       "      <td>0.362515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_skew_Spindle</th>\n",
       "      <td>-0.150479</td>\n",
       "      <td>-0.314127</td>\n",
       "      <td>-0.050452</td>\n",
       "      <td>-0.193610</td>\n",
       "      <td>-0.007636</td>\n",
       "      <td>-0.228759</td>\n",
       "      <td>-0.090066</td>\n",
       "      <td>-0.072530</td>\n",
       "      <td>-0.022325</td>\n",
       "      <td>0.015117</td>\n",
       "      <td>-0.377205</td>\n",
       "      <td>-0.262064</td>\n",
       "      <td>-0.108837</td>\n",
       "      <td>-0.279457</td>\n",
       "      <td>-0.241638</td>\n",
       "      <td>-0.317828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055155</td>\n",
       "      <td>-0.036812</td>\n",
       "      <td>-0.049005</td>\n",
       "      <td>-0.058380</td>\n",
       "      <td>-0.023856</td>\n",
       "      <td>-0.023837</td>\n",
       "      <td>0.019564</td>\n",
       "      <td>-0.187104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_mean_vibration</th>\n",
       "      <td>0.144910</td>\n",
       "      <td>-0.001377</td>\n",
       "      <td>0.938803</td>\n",
       "      <td>0.184598</td>\n",
       "      <td>-0.014894</td>\n",
       "      <td>0.241538</td>\n",
       "      <td>0.073134</td>\n",
       "      <td>0.053419</td>\n",
       "      <td>-0.011272</td>\n",
       "      <td>-0.034600</td>\n",
       "      <td>-0.003686</td>\n",
       "      <td>0.024305</td>\n",
       "      <td>-0.034835</td>\n",
       "      <td>0.011135</td>\n",
       "      <td>-0.024160</td>\n",
       "      <td>0.022718</td>\n",
       "      <td>-0.055155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.409031</td>\n",
       "      <td>0.949542</td>\n",
       "      <td>0.927211</td>\n",
       "      <td>0.959066</td>\n",
       "      <td>0.353124</td>\n",
       "      <td>-0.024726</td>\n",
       "      <td>-0.030139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_std_vibration</th>\n",
       "      <td>0.092484</td>\n",
       "      <td>0.003860</td>\n",
       "      <td>0.371563</td>\n",
       "      <td>0.106709</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>0.131449</td>\n",
       "      <td>0.049222</td>\n",
       "      <td>0.031489</td>\n",
       "      <td>0.002426</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.028782</td>\n",
       "      <td>-0.031469</td>\n",
       "      <td>0.017352</td>\n",
       "      <td>-0.004331</td>\n",
       "      <td>0.023447</td>\n",
       "      <td>-0.036812</td>\n",
       "      <td>0.409031</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.134430</td>\n",
       "      <td>0.707969</td>\n",
       "      <td>0.378421</td>\n",
       "      <td>0.748677</td>\n",
       "      <td>0.128871</td>\n",
       "      <td>0.023376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_min_vibration</th>\n",
       "      <td>0.127555</td>\n",
       "      <td>-0.004477</td>\n",
       "      <td>0.908012</td>\n",
       "      <td>0.166065</td>\n",
       "      <td>-0.020787</td>\n",
       "      <td>0.224069</td>\n",
       "      <td>0.061312</td>\n",
       "      <td>0.047164</td>\n",
       "      <td>-0.019036</td>\n",
       "      <td>-0.043546</td>\n",
       "      <td>-0.007394</td>\n",
       "      <td>0.017109</td>\n",
       "      <td>-0.031179</td>\n",
       "      <td>0.005019</td>\n",
       "      <td>-0.027622</td>\n",
       "      <td>0.017803</td>\n",
       "      <td>-0.049005</td>\n",
       "      <td>0.949542</td>\n",
       "      <td>0.134430</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792842</td>\n",
       "      <td>0.913936</td>\n",
       "      <td>0.119725</td>\n",
       "      <td>-0.028129</td>\n",
       "      <td>-0.033462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_max_vibration</th>\n",
       "      <td>0.148285</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.867874</td>\n",
       "      <td>0.184886</td>\n",
       "      <td>-0.013038</td>\n",
       "      <td>0.240763</td>\n",
       "      <td>0.074682</td>\n",
       "      <td>0.053228</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>-0.026378</td>\n",
       "      <td>-0.001529</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>-0.040462</td>\n",
       "      <td>0.016202</td>\n",
       "      <td>-0.020517</td>\n",
       "      <td>0.028462</td>\n",
       "      <td>-0.058380</td>\n",
       "      <td>0.927211</td>\n",
       "      <td>0.707969</td>\n",
       "      <td>0.792842</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.881990</td>\n",
       "      <td>0.520808</td>\n",
       "      <td>0.060134</td>\n",
       "      <td>-0.008914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_EME_vibration</th>\n",
       "      <td>0.059751</td>\n",
       "      <td>-0.035286</td>\n",
       "      <td>0.897019</td>\n",
       "      <td>0.076344</td>\n",
       "      <td>-0.015913</td>\n",
       "      <td>0.109682</td>\n",
       "      <td>0.024538</td>\n",
       "      <td>0.020370</td>\n",
       "      <td>-0.019103</td>\n",
       "      <td>-0.018521</td>\n",
       "      <td>-0.048700</td>\n",
       "      <td>-0.016212</td>\n",
       "      <td>-0.056224</td>\n",
       "      <td>-0.033182</td>\n",
       "      <td>-0.050838</td>\n",
       "      <td>-0.013437</td>\n",
       "      <td>-0.023856</td>\n",
       "      <td>0.959066</td>\n",
       "      <td>0.378421</td>\n",
       "      <td>0.913936</td>\n",
       "      <td>0.881990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.336511</td>\n",
       "      <td>-0.023803</td>\n",
       "      <td>-0.091285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_IQR_vibration</th>\n",
       "      <td>0.080824</td>\n",
       "      <td>0.005344</td>\n",
       "      <td>0.324826</td>\n",
       "      <td>0.090596</td>\n",
       "      <td>0.012580</td>\n",
       "      <td>0.099837</td>\n",
       "      <td>0.047534</td>\n",
       "      <td>0.027945</td>\n",
       "      <td>0.024800</td>\n",
       "      <td>0.012383</td>\n",
       "      <td>0.003257</td>\n",
       "      <td>0.015175</td>\n",
       "      <td>-0.014336</td>\n",
       "      <td>0.010900</td>\n",
       "      <td>-0.001105</td>\n",
       "      <td>0.009979</td>\n",
       "      <td>-0.023837</td>\n",
       "      <td>0.353124</td>\n",
       "      <td>0.748677</td>\n",
       "      <td>0.119725</td>\n",
       "      <td>0.520808</td>\n",
       "      <td>0.336511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>-0.014764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roll_skew_vibration</th>\n",
       "      <td>0.004756</td>\n",
       "      <td>-0.007330</td>\n",
       "      <td>-0.012851</td>\n",
       "      <td>-0.005107</td>\n",
       "      <td>0.003060</td>\n",
       "      <td>-0.008610</td>\n",
       "      <td>0.000937</td>\n",
       "      <td>-0.016413</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>0.067985</td>\n",
       "      <td>-0.021656</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>-0.042863</td>\n",
       "      <td>-0.010878</td>\n",
       "      <td>-0.008172</td>\n",
       "      <td>0.015114</td>\n",
       "      <td>0.019564</td>\n",
       "      <td>-0.024726</td>\n",
       "      <td>0.128871</td>\n",
       "      <td>-0.028129</td>\n",
       "      <td>0.060134</td>\n",
       "      <td>-0.023803</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Label_Numeric</th>\n",
       "      <td>0.332917</td>\n",
       "      <td>0.294498</td>\n",
       "      <td>-0.030853</td>\n",
       "      <td>0.418211</td>\n",
       "      <td>0.006901</td>\n",
       "      <td>0.489088</td>\n",
       "      <td>0.184882</td>\n",
       "      <td>0.115464</td>\n",
       "      <td>0.078263</td>\n",
       "      <td>-0.052913</td>\n",
       "      <td>0.398150</td>\n",
       "      <td>0.377007</td>\n",
       "      <td>0.144077</td>\n",
       "      <td>0.395365</td>\n",
       "      <td>0.282124</td>\n",
       "      <td>0.362515</td>\n",
       "      <td>-0.187104</td>\n",
       "      <td>-0.030139</td>\n",
       "      <td>0.023376</td>\n",
       "      <td>-0.033462</td>\n",
       "      <td>-0.008914</td>\n",
       "      <td>-0.091285</td>\n",
       "      <td>-0.014764</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Power   Spindle  vibration  roll_mean_Power  roll_std_Power  roll_min_Power  roll_max_Power  roll_EME_Power  roll_IQR_Power  roll_skew_Power  roll_mean_Spindle  roll_std_Spindle  roll_min_Spindle  roll_max_Spindle  roll_EME_Spindle  roll_IQR_Spindle  roll_skew_Spindle  roll_mean_vibration  roll_std_vibration  roll_min_vibration  roll_max_vibration  roll_EME_vibration  roll_IQR_vibration  roll_skew_vibration  Label_Numeric\n",
       "Power                1.000000  0.297077   0.136306         0.758871        0.416487        0.633756        0.615002        0.467822        0.330007         0.037827           0.403786          0.364195          0.190321          0.408533          0.316839          0.292084          -0.150479             0.144910            0.092484            0.127555            0.148285            0.059751            0.080824             0.004756       0.332917\n",
       "Spindle              0.297077  1.000000  -0.003516         0.382536        0.096732        0.370002        0.225524        0.137922        0.181011        -0.008789           0.758790          0.604224          0.389834          0.695199          0.621443          0.535481          -0.314127            -0.001377            0.003860           -0.004477            0.000602           -0.035286            0.005344            -0.007330       0.294498\n",
       "vibration            0.136306 -0.003516   1.000000         0.170347       -0.014697        0.224232        0.066957        0.048836       -0.012929        -0.030977          -0.005253          0.021404         -0.034147          0.008885         -0.024602          0.020059          -0.050452             0.938803            0.371563            0.908012            0.867874            0.897019            0.324826            -0.012851      -0.030853\n",
       "roll_mean_Power      0.758871  0.382536   0.170347         1.000000        0.531835        0.828167        0.794073        0.609672        0.472869         0.033640           0.526090          0.469200          0.250655          0.527540          0.394682          0.387453          -0.193610             0.184598            0.106709            0.166065            0.184886            0.076344            0.090596            -0.005107       0.418211\n",
       "roll_std_Power       0.416487  0.096732  -0.014697         0.531835        1.000000        0.019243        0.930927        0.864082        0.483590         0.169000           0.144681          0.158086          0.052256          0.173019          0.201067          0.078144          -0.007636            -0.014894            0.001871           -0.020787           -0.013038           -0.015913            0.012580             0.003060       0.006901\n",
       "roll_min_Power       0.633756  0.370002   0.224232         0.828167        0.019243        1.000000        0.379793        0.259100        0.100626        -0.020751           0.503891          0.435101          0.246138          0.488833          0.313931          0.393201          -0.228759             0.241538            0.131449            0.224069            0.240763            0.109682            0.099837            -0.008610       0.489088\n",
       "roll_max_Power       0.615002  0.225524   0.066957         0.794073        0.930927        0.379793        1.000000        0.887861        0.474198         0.152586           0.319150          0.306844          0.138396          0.340395          0.302903          0.216677          -0.090066             0.073134            0.049222            0.061312            0.074682            0.024538            0.047534             0.000937       0.184882\n",
       "roll_EME_Power       0.467822  0.137922   0.048836         0.609672        0.864082        0.259100        0.887861        1.000000        0.222218         0.037445           0.192216          0.179260          0.072814          0.196200          0.168746          0.138141          -0.072530             0.053419            0.031489            0.047164            0.053228            0.020370            0.027945            -0.016413       0.115464\n",
       "roll_IQR_Power       0.330007  0.181011  -0.012929         0.472869        0.483590        0.100626        0.474198        0.222218        1.000000         0.060439           0.244960          0.208491          0.140289          0.249621          0.228959          0.144664          -0.022325            -0.011272            0.002426           -0.019036           -0.010791           -0.019103            0.024800             0.002802       0.078263\n",
       "roll_skew_Power      0.037827 -0.008789  -0.030977         0.033640        0.169000       -0.020751        0.152586        0.037445        0.060439         1.000000          -0.010715         -0.014143          0.008709         -0.007752         -0.003927         -0.028404           0.015117            -0.034600            0.006916           -0.043546           -0.026378           -0.018521            0.012383             0.067985      -0.052913\n",
       "roll_mean_Spindle    0.403786  0.758790  -0.005253         0.526090        0.144681        0.503891        0.319150        0.192216        0.244960        -0.010715           1.000000          0.821479          0.520174          0.940448          0.812817          0.727781          -0.377205            -0.003686            0.002800           -0.007394           -0.001529           -0.048700            0.003257            -0.021656       0.398150\n",
       "roll_std_Spindle     0.364195  0.604224   0.021404         0.469200        0.158086        0.435101        0.306844        0.179260        0.208491        -0.014143           0.821479          1.000000          0.004222          0.951005          0.868592          0.901156          -0.262064             0.024305            0.028782            0.017109            0.031557           -0.016212            0.015175             0.004398       0.377007\n",
       "roll_min_Spindle     0.190321  0.389834  -0.034147         0.250655        0.052256        0.246138        0.138396        0.072814        0.140289         0.008709           0.520174          0.004222          1.000000          0.286385          0.234215         -0.050757          -0.108837            -0.034835           -0.031469           -0.031179           -0.040462           -0.056224           -0.014336            -0.042863       0.144077\n",
       "roll_max_Spindle     0.408533  0.695199   0.008885         0.527540        0.173019        0.488833        0.340395        0.196200        0.249621        -0.007752           0.940448          0.951005          0.286385          1.000000          0.894171          0.804379          -0.279457             0.011135            0.017352            0.005019            0.016202           -0.033182            0.010900            -0.010878       0.395365\n",
       "roll_EME_Spindle     0.316839  0.621443  -0.024602         0.394682        0.201067        0.313931        0.302903        0.168746        0.228959        -0.003927           0.812817          0.868592          0.234215          0.894171          1.000000          0.695904          -0.241638            -0.024160           -0.004331           -0.027622           -0.020517           -0.050838           -0.001105            -0.008172       0.282124\n",
       "roll_IQR_Spindle     0.292084  0.535481   0.020059         0.387453        0.078144        0.393201        0.216677        0.138141        0.144664        -0.028404           0.727781          0.901156         -0.050757          0.804379          0.695904          1.000000          -0.317828             0.022718            0.023447            0.017803            0.028462           -0.013437            0.009979             0.015114       0.362515\n",
       "roll_skew_Spindle   -0.150479 -0.314127  -0.050452        -0.193610       -0.007636       -0.228759       -0.090066       -0.072530       -0.022325         0.015117          -0.377205         -0.262064         -0.108837         -0.279457         -0.241638         -0.317828           1.000000            -0.055155           -0.036812           -0.049005           -0.058380           -0.023856           -0.023837             0.019564      -0.187104\n",
       "roll_mean_vibration  0.144910 -0.001377   0.938803         0.184598       -0.014894        0.241538        0.073134        0.053419       -0.011272        -0.034600          -0.003686          0.024305         -0.034835          0.011135         -0.024160          0.022718          -0.055155             1.000000            0.409031            0.949542            0.927211            0.959066            0.353124            -0.024726      -0.030139\n",
       "roll_std_vibration   0.092484  0.003860   0.371563         0.106709        0.001871        0.131449        0.049222        0.031489        0.002426         0.006916           0.002800          0.028782         -0.031469          0.017352         -0.004331          0.023447          -0.036812             0.409031            1.000000            0.134430            0.707969            0.378421            0.748677             0.128871       0.023376\n",
       "roll_min_vibration   0.127555 -0.004477   0.908012         0.166065       -0.020787        0.224069        0.061312        0.047164       -0.019036        -0.043546          -0.007394          0.017109         -0.031179          0.005019         -0.027622          0.017803          -0.049005             0.949542            0.134430            1.000000            0.792842            0.913936            0.119725            -0.028129      -0.033462\n",
       "roll_max_vibration   0.148285  0.000602   0.867874         0.184886       -0.013038        0.240763        0.074682        0.053228       -0.010791        -0.026378          -0.001529          0.031557         -0.040462          0.016202         -0.020517          0.028462          -0.058380             0.927211            0.707969            0.792842            1.000000            0.881990            0.520808             0.060134      -0.008914\n",
       "roll_EME_vibration   0.059751 -0.035286   0.897019         0.076344       -0.015913        0.109682        0.024538        0.020370       -0.019103        -0.018521          -0.048700         -0.016212         -0.056224         -0.033182         -0.050838         -0.013437          -0.023856             0.959066            0.378421            0.913936            0.881990            1.000000            0.336511            -0.023803      -0.091285\n",
       "roll_IQR_vibration   0.080824  0.005344   0.324826         0.090596        0.012580        0.099837        0.047534        0.027945        0.024800         0.012383           0.003257          0.015175         -0.014336          0.010900         -0.001105          0.009979          -0.023837             0.353124            0.748677            0.119725            0.520808            0.336511            1.000000             0.000349      -0.014764\n",
       "roll_skew_vibration  0.004756 -0.007330  -0.012851        -0.005107        0.003060       -0.008610        0.000937       -0.016413        0.002802         0.067985          -0.021656          0.004398         -0.042863         -0.010878         -0.008172          0.015114           0.019564            -0.024726            0.128871           -0.028129            0.060134           -0.023803            0.000349             1.000000       0.018025\n",
       "Label_Numeric        0.332917  0.294498  -0.030853         0.418211        0.006901        0.489088        0.184882        0.115464        0.078263        -0.052913           0.398150          0.377007          0.144077          0.395365          0.282124          0.362515          -0.187104            -0.030139            0.023376           -0.033462           -0.008914           -0.091285           -0.014764             0.018025       1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "02d3cd3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.181\n",
      "nan\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "r,p = stats.pearsonr(df.Spindle,df.roll_IQR_Power)\n",
    "print(round(r,4))\n",
    "print(round(p,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f3f7cd7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_ml, X_test, y_train_ml, y_test = train_test_split(X_test, Y_test, test_size = 0.2, random_state = 0, stratify = Y_test)\n",
    "V_train, end_test_ml, vl_train, test_label_ml = train_test_split(X_test, y_test, test_size = 0.5, random_state = 0, stratify = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e643b346",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train Features (20900, 24)\n",
      "Shape of Train Labels (20900,)\n",
      "Shape of validation Features (2613, 24)\n",
      "Shape of validation Labels (2613,)\n",
      "Shape of Test Features (2613, 24)\n",
      "Shape of Test Labels (2613,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of Train Features\",X_train_ml.shape)\n",
    "print(\"Shape of Train Labels\",y_train_ml.shape)\n",
    "\n",
    "print(\"Shape of validation Features\",V_train.shape)\n",
    "print(\"Shape of validation Labels\",vl_train.shape)\n",
    "\n",
    "print(\"Shape of Test Features\",end_test_ml.shape)\n",
    "print(\"Shape of Test Labels\",test_label_ml.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "925f49d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, accuracy_score, confusion_matrix\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c0ee8b",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3e9fa6d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RandomForestClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m RF \u001b[38;5;241m=\u001b[39m \u001b[43mRandomForestClassifier\u001b[49m(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m , min_samples_split \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m6\u001b[39m, class_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbalanced\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m RF\u001b[38;5;241m.\u001b[39mfit(X_train_ml,y_train_ml)\n\u001b[0;32m      3\u001b[0m random \u001b[38;5;241m=\u001b[39m RF\u001b[38;5;241m.\u001b[39mpredict(end_test_ml)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'RandomForestClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "RF = RandomForestClassifier(n_estimators=4 , min_samples_split = 6, class_weight='balanced')\n",
    "RF.fit(X_train_ml,y_train_ml)\n",
    "random = RF.predict(end_test_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5dbaf817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.92      0.91      1632\n",
      "           1       0.86      0.84      0.85       981\n",
      "\n",
      "    accuracy                           0.89      2613\n",
      "   macro avg       0.88      0.88      0.88      2613\n",
      "weighted avg       0.89      0.89      0.89      2613\n",
      "\n",
      "0.8901645618063528\n",
      "[[1502  130]\n",
      " [ 157  824]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_label_ml, random))\n",
    "print(accuracy_score(test_label_ml, random))\n",
    "cf_matrix = confusion_matrix(test_label_ml, random)\n",
    "print(confusion_matrix(test_label_ml, random))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d96a2f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT8AAAEWCAYAAAAQBZBVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAh00lEQVR4nO3de7xVdZ3/8df7gCLeuIgichHMKzqKSIhajmmjYE44M6koKZr+qMZ0ypxG0yJvk800WeZtKB01De8mJYJmmVqpICqhmOCFuIZcxAuYIp/fH+t7YHM55+x1ztln77P3++ljPc7a3/Xda33WAT5+v+u71ncpIjAzqzV15Q7AzKwcnPzMrCY5+ZlZTXLyM7Oa5ORnZjXJyc/MapKTX5WR1FnSLyWtlHR3C/YzWtLDrRlbOUh6SNKYcsdhlcfJr0wknSJpmqR3JS1K/0g/0Qq7/hzQE9ghIk5o7k4i4vaIOLoV4tmApCMkhaT7Nyo/IJU/VuR+viPptqbqRcSIiLilmeFaFXPyKwNJ5wE/BP6TLFH1A64DRrbC7ncFXomINa2wr1J5EzhE0g4FZWOAV1rrAMr477c1LCK8tOECdAHeBU5opE4nsuS4MC0/BDqlbUcA84GvA0uARcAZadslwAfAh+kYZwLfAW4r2Hd/IICO6fPpwGvAO8DrwOiC8icLvncoMBVYmX4eWrDtMeAy4PdpPw8DPRo4t/r4bwDOTmUdgAXAt4HHCur+CJgHvA08C3wylQ/f6DxfKIjjihTHamD3VHZW2n49cG/B/r8HPAqo3H8vvLT94v8ztr1DgK2A+xupcxEwDBgEHAAMBS4u2L4zWRLtTZbgrpXULSLGkbUm74yIbSPixsYCkbQNcDUwIiK2I0twz2+mXnfgwVR3B+AHwIMbtdxOAc4AdgK2BM5v7NjArcBpaf0YYCZZoi80lex30B34OXC3pK0iYvJG53lAwXdOBcYC2wFzN9rf14G/k3S6pE+S/e7GRISf8axBTn5tbwdgaTTeLR0NXBoRSyLiTbIW3akF2z9M2z+MiElkrZ+9mhnPWmA/SZ0jYlFEvLiZOp8BZkfEzyJiTURMAF4G/rGgzv9FxCsRsRq4iyxpNSgi/gB0l7QXWRK8dTN1bouIZemY/0PWIm7qPG+OiBfTdz7caH+ryH6PPwBuA86JiPlN7M+qlJNf21sG9JDUsZE6u7Bhq2VuKlu3j42S5ypg27yBRMR7wEnAl4BFkh6UtHcR8dTH1Lvg8+JmxPMz4CvAp9hMS1jS+ZJmpZHrt8hauz2a2Oe8xjZGxNNk3XyRJWmrUU5+be+PwN+A4xups5Bs4KJePzbtEhbrPWDrgs87F26MiCkR8Q9AL7LW3E+KiKc+pgXNjKnez4B/BSalVtk6qVv6DeBEoFtEdCW73qj60BvYZ6NdWElnk7UgF6b9W41y8mtjEbGS7ML+tZKOl7S1pC0kjZD0X6naBOBiSTtK6pHqN3lbRwOeBw6X1E9SF+DC+g2Sekoama79/Y2s+7x2M/uYBOyZbs/pKOkkYCDwq2bGBEBEvA78Pdk1zo1tB6whGxnuKOnbwPYF2/8K9M8zoitpT+By4PNk3d9vSBrUvOitvXPyK4N0/eo8skGMN8m6al8BfpGqXA5MA2YAfwKmp7LmHOsR4M60r2fZMGHVpTgWAsvJEtGXN7OPZcBxZAMGy8haTMdFxNLmxLTRvp+MiM21aqcAk8luf5kLvM+GXdr6G7iXSZre1HHSZYbbgO9FxAsRMRv4JvAzSZ1acg7WPskDXWZWi9zyM7Oa5ORnZjXJyc/MapKTn5nVpMZutG1z6tg5tOV25Q7Dcjhwn37lDsFymDv3DZYuXaqmazasw/a7RqxZXVTdWP3mlIgY3pLjlUplJb8tt6PTXieWOwzL4fdPX1PuECyHww4e0uJ9xJr36bT3qKLqvv/cj5t6IqdsKir5mVk7IEAtajxWBCc/M8uvCqZKdPIzs/zc8jOz2iOo61DuIFrMyc/M8hHu9ppZLZK7vWZWo9zyM7Oa5JafmdUeueVnZjVIeLTXzGpRdbT82v8ZmFnbq1NxSxMk3SRpiaSZm9n2dUmR3mODMldLmiNphqTBBXXHSJqdljFFnUKO0zUzW3+fXzFL024GNpn1RVJf4GjgLwXFI4A90jIWuD7V7Q6MAw4GhgLjJHVr6sBOfmaWn1Tc0oSIeJzs5Vkbu4rsRVmFLxkaCdwamaeArpJ6AccAj0TE8ohYATzCZhLqxnzNz8xyKu3jbZJGAgsi4gVtmEB7s+Eb/OansobKG+XkZ2b5FT/g0UPStILP4yNifIO7lbYme6Xo0S2IrihOfmaWT5Fd2mRpROSZQfVjwACgvtXXB5guaSiwAOhbULdPKlsAHLFR+WNNHcjX/Mwsv9Yb8NhARPwpInaKiP4R0Z+sCzs4IhYDE4HT0qjvMGBlRCwie8H90ZK6pYGOo1NZo9zyM7P8WunxNkkTyFptPSTNB8ZFxI0NVJ8EHAvMAVYBZwBExHJJlwFTU71LI2JzgygbcPIzs5xa7ybniDi5ie39C9YDOLuBejcBN+U5tpOfmeXjx9vMrDZVx+NtTn5mlp+ntDKzmuSWn5nVJLf8zKzmyNf8zKxGqc7Jz8xqjAC522tmNUdpaeec/MwsJ7nlZ2a1ycnPzGpSnQc8zKzm+JqfmdUi+ZqfmdUqJz8zq0lOfmZWk5z8zKz2CFTn5GdmNcYDHmZWs5z8zKw2tf/c5+RnZjnJLT8zq1HVkPza/wN6ZtamhKirqytqaXJf0k2SlkiaWVD235JeljRD0v2SuhZsu1DSHEl/lnRMQfnwVDZH0gXFnIeTn5nlpyKXpt0MDN+o7BFgv4jYH3gFuBBA0kBgFLBv+s51kjpI6gBcC4wABgInp7qNcvIzs3zSNb9ilqZExOPA8o3KHo6INenjU0CftD4SuCMi/hYRrwNzgKFpmRMRr0XEB8AdqW6jnPzMLLccya+HpGkFy9ich/oC8FBa7w3MK9g2P5U1VN4oD3iYWW45BjyWRsSQZh7jImANcHtzvt8UJz8zy63Uj7dJOh04DjgqIiIVLwD6FlTrk8popLxB7vY2ww3jRjP30e8y7e5vriu76IvH8uqUy3nqjgt46o4LOOYT66+3nv+Fo5n5wDheuP9bfPqQfQDo07Mrk8efy/R7L+LZey7i7JOPaOvTqFlfPOsL9NtlJw4atN+6skvGfYuPH7g/Bx80iONGHM3ChQsBiAjO++q57Lv37nz8wP15bvr0coVdMYrt8jb3dhhJw4FvAJ+NiFUFmyYCoyR1kjQA2AN4BpgK7CFpgKQtyQZFJjZ1nJImv+YMP7cHP/vlU4w8+9pNyn98228ZNupKho26kilPvgTA3rvtzAnHDGbw567gs2dfx48uPJG6OrHmo7Vc8IP7GPwvV/D3p32fL550OHvvtnNbn0pNOnXM6Tzwq8kblH3t6//O1Odm8PSzzzPi2OP47uWXAjBl8kO8Omc2M2fN5prrx3PuV75cjpArTmslP0kTgD8Ce0maL+lM4BpgO+ARSc9LugEgIl4E7gJeAiYDZ0fER2lw5CvAFGAWcFeq26iSdXsLhp//gewC5FRJEyPipVIds638fvqr9OvVvai6xx2xP3dPmc4HH65h7sJlvDpvKR/frz9Pz3idxUvfBuDdVX/j5dcXs8uOXXn5tcWlDN2AT3zycOa+8cYGZdtvv/269VWr3lv3D/dXEx/glM+fhiQOHjaMlSvfYtGiRfTq1astQ644rXWTc0ScvJniGxupfwVwxWbKJwGT8hy7lC2/Zg0/t2dfGnU4z9x5ITeMG03X7ToD0HvHLsxfvGJdnQVLVrDLTl02+F6/Xt0ZtFcfps58oy3DtY2M+9ZF7D6gL3dMuJ1vfSdr+S1cuIA+fdZfTurduw8LFzR5Oan6td59fmVTyuRX1PCzpLH1w+CxZnUJwymtn9z9BAP/8TscPOpKFi99myvP++eivrdN5y2Z8P2z+Pfv38s7771f4iitMZdcdgVzXp/HqJNHc8N115Q7nIpWymt+baXsAx4RMT4ihkTEEHXsXO5wmm3J8ndYuzaICG667/cM2W9XABa8uZI+O3dbV6/3Tt1YuGQlAB071jHh+/+POx+axgO/eaEscdumTjp5NL+4/14AdtmlN/Pnr/9/+IIF89mld5O3kFU1CerqVNRSyUqZ/Boblq46O/dYf81o5JEH8NKriwB48LEZnHDMYLbcoiO77rIDu/fbcV339oZxo/nz64u5+rbflCNkKzBn9ux167+a+AB77rU3AJ/5x8/y89tuJSJ4+qmn2H77LjV/vQ9KO9rbVkp5n9+64WeypDcKOKWEx2szt3z3dD550B706LotcyZfxmU3TOLwg/Zg/736EBHMXbSccy6fAMCs1xZz78PP8dy9F7Hmo7V89cq7WLs2OHTQbow+7mD+9MoCnrojGwgfd83EdaPEVjqnff5knvjdYyxdupSP9e/Dt759CZMnT2L2K3+mTnX023VXrr72BgCGjziWKQ9NYt+9d2frzlvzvz/9vzJHXxkqPK8VRevvHyzBzqVjgR8CHYCb0khNg+q23ik67XViyeKx1rdiqq+NtSeHHTyEZ5+d1qLUtdXOe8auY35cVN1X/mv4s819wqPUSvqER3OGn82swqk6Wn5+vM3MchFU/GBGMZz8zCw3Jz8zqz3u9ppZLRLV8Q4PJz8zy6ny7+ErhpOfmeVWBbnPyc/McpIHPMysBvman5nVrCrIfU5+ZpafW35mVpOqIPc5+ZlZTnLLz8xqkKj8iUqL4eRnZrlVQcPPyc/M8nO318xqT5VMbFD2FxiZWftSf5NzK720/CZJSyTNLCjrLukRSbPTz26pXJKuljRH0gxJgwu+MybVny1pTDHn4eRnZrm14guMbgaGb1R2AfBoROwBPJo+A4wA9kjLWOD6FEt3YBxwMNn7wsfVJ8zGOPmZWW6t9erKiHgcWL5R8UjglrR+C3B8QfmtkXkK6CqpF3AM8EhELI+IFcAjbJpQN+FrfmaWT75rfj0kTSv4PD4ixjfxnZ4RsSitLwZ6pvXewLyCevNTWUPljXLyM7NclG8+v6UteXtbRISkkrxi0t1eM8tNKm5ppr+m7izp55JUvgDoW1CvTyprqLxRTn5mlludVNTSTBOB+hHbMcADBeWnpVHfYcDK1D2eAhwtqVsa6Dg6lTXK3V4zy0WtOJmppAnAEWTXBueTjdpeCdwl6UxgLnBiqj4JOBaYA6wCzgCIiOWSLgOmpnqXRsTGgyibcPIzs9xa69HeiDi5gU1HbaZuAGc3sJ+bgJvyHNvJz8xyq+rH2yT9GGhwlCUizi1JRGZW8aog9zXa8pvWyDYzq1Eiu92lvWsw+UXELYWfJW0dEatKH5KZVboqmM6v6VtdJB0i6SXg5fT5AEnXlTwyM6tMKu7Rtkqf8LSY+/x+SPbs3DKAiHgBOLyEMZlZBRMlv8+vTRQ12hsR8zYa3fmoNOGYWXtQ4XmtKMUkv3mSDgVC0hbAvwGzShuWmVWyarjVpZhu75fIbizsDSwEBtHAjYZmVv2Kfa630vNjky2/iFgKjG6DWMysnehQ6ZmtCMWM9u4m6ZeS3kzTTT8gabe2CM7MKlMrzuRcNsV0e38O3AX0AnYB7gYmlDIoM6tc2WhvcUslKyb5bR0RP4uINWm5Ddiq1IGZWYUqstVX6S2/xp7t7Z5WH5J0AXAH2bO+J5FNLWNmNarC81pRGhvweJYs2dWf5hcLtgVwYamCMrPKVumtumI09mzvgLYMxMzaBwEdKv2CXhGKesJD0n7AQAqu9UXEraUKyswqW/tPfUUkP0njyKaZHkh2rW8E8CTg5GdWgyQq/rndYhQz2vs5simlF0fEGcABQJeSRmVmFa0mnvAAVkfEWklrJG1P9hq5vk19ycyqV1UPeBSYJqkr8BOyEeB3gT+WMigzq2xVkPuKerb3X9PqDZImA9tHxIzShmVmlUpSdY/2Shrc2LaImF6akMys0rVWt1fS14CzyO4d/hPZu3h7kT1UsQNZb/PUiPhAUieygdaDyCZXPiki3mjusRtr+f1PI9sCOLK5B23IoH368fgfrm7t3VoJnffAS+UOwXKY99b7rbKfYkZKmyKpN3AuMDAiVku6CxhF9mLyqyLiDkk3AGcC16efKyJid0mjgO+RPXHWLI3d5Pyp5u7UzKqXaNUBj45AZ0kfAlsDi8gaVqek7bcA3yFLfiPTOsA9wDWSlF5mnltrJHAzqzGtMatLRCwAvg/8hSzprSTr5r4VEWtStflkEymTfs5L312T6u/Q7HNo7hfNrDZJ2eNtxSxAD0nTCpax6/ejbmStuQFk0+VtAwxvq/Mo6vE2M7NCOQZ7l0bEkAa2fRp4PSLeBJB0H3AY0FVSx9S66wMsSPUXkN1jPF9SR7KHLZY17wyKm8lZkj4v6dvpcz9JQ5t7QDNr/1rpCY+/AMMkba3sIuJRwEvAb8meLAMYAzyQ1iemz6Ttv2nu9T4ortt7HXAIcHL6/A5wbXMPaGbtW2u9tzciniYbuJhOdptLHTAe+A/gPElzyK7p3Zi+ciOwQyo/D7igJedRTLf34IgYLOm5FPAKSVu25KBm1r611mBBRIwDxm1U/BqwSe8yIt4HTmilQxeV/D6U1IHs3j4k7Qisba0AzKz9qYnH24CrgfuBnSRdQdbXvrikUZlZxar6x9vqRcTtkp4luxgp4PiImFXyyMysYlVB7itqMtN+wCrgl4VlEfGXUgZmZpWpfsCjvSum2/sg619ktBXZDYl/BvYtYVxmVsGqIPcV1e39u8LPabaXf22guplVu3bwQvJi5H7CIyKmSzq4FMGYWfugKniFUTHX/M4r+FgHDAYWliwiM6toAjpWwawAxbT8titYX0N2DfDe0oRjZu1B1b/DI93cvF1EnN9G8ZhZhctGe8sdRcs1No19x4hYI+mwtgzIzCpcO3gtZTEaa/k9Q3Z973lJE4G7gffqN0bEfSWOzcwqVK3c57cV2ZxZR7L+fr8AnPzMapCADlU+4LFTGumdyfqkV6/Zc2iZWXsn6qr8VpcOwLaw2bN08jOrUdkLjModRcs1lvwWRcSlbRaJmbUPNfCERxWcnpmVQrUPeBzVZlGYWbtR9d3eiFjeloGYWftRE5OZmpkVEtXxwm8nPzPLRzXwbK+Z2ea0/9Tn5GdmOVXLNPbV0HU3szamIpcm9yN1lXSPpJclzZJ0iKTukh6RNDv97JbqStLVkuZImpFmlW82Jz8zy0nU1RW3FOFHwOSI2Bs4AJgFXAA8GhF7AI+mzwAjgD3SMha4viVn4eRnZrnUj/YWszS6H6kLcDhwI0BEfBARbwEjgVtStVuA49P6SODWyDwFdJXUq7nn4eRnZrlJKmoBekiaVrCMLdjNAOBN4P8kPSfpp5K2AXpGxKJUZzHQM633BuYVfH9+KmsWD3iYWW45hjuWRsSQBrZ1JJsz9JyIeFrSj1jfxQUgIkJSSSZSccvPzPJRrpZfY+YD8yPi6fT5HrJk+Nf67mz6uSRtXwD0Lfh+n1TWLE5+ZpaLgA5SUUtjImIxME/SXqnoKOAlYCIwJpWNAR5I6xOB09Ko7zBgZUH3ODd3e80st1a8y+8c4HZJWwKvAWeQNcruknQmMBc4MdWdBBwLzAFWpbrN5uRnZrm11j3OEfE8sLlrgpvMKhURAZzdOkd28jOznLJbXdr/Ex5OfmaWWxU83ebkZ2Z5CbnlZ2a1pn60t71z8jOzfORur5nVKCc/M6tJvuZnZjUnm8y03FG0nJOfmeVWDTM5O/mZWW7V0O31xAYt9OWxZzKg784MHbz/urL/vOwS9tytL4cOHcyhQwczZfIkAO6ccPu6skOHDmb7zh2Z8cLzZYq8th25e3cu/ofduPjTu3HG0N50rBOnf7w3447+GBd/ejc+f1CvTbp2u3bbih//0z4c2Hu78gRdIeq7vcUslaxkyU/STZKWSJpZqmNUgtGnjuH+iZM2KT/7nK/yh2em84dnpnPM8GMBOOnk0evKfnLTLfTvP4D9DxjUxhFbl606csTu3fneo69z+a9fo04wpO/2TJ23kksefpXLf/0aW3So47AB3dZ9R8Dx+/Vk1pJ3yxd4xVDR/1WyUrb8bgaGl3D/FeETnzycbt265/7e3Xfewb+ccFIJIrJidJDYooOoE2zZoY6Vq9fw4uL1iW3u8tV067z+qtARu3fnuQVv8877H5Uj3MqS7vMrZqlkJUt+EfE4sLxU+69046+/lmFDBvHlsWeyYsWKTbbfd89dnHDSqDJEZivfX8OvZy/j8mP35Luf2ZPVH65l1pL31m2vEwzt14UXF2dlXbbqyKBdtuOJ1zb9c6xVrfX2tnIq+zU/SWPr5/df+uab5Q6nVZw19kvMmDWbPzwznZ137sU3/+P8DbZPfeZpOm+9NQP33a9MEda2zlvUsX+v7fj2Q7O58MFX6NRRDO3bZd32UQf2Ys7SVby6bBUAJxywM/fPXEJJ5lJvh1prMtNyK/tob0SMB8YDDD5oSFX8/dqpZ89166d/4SxO+OfPbrD93rvv5HMnutVXLnvvtA3L3vuAdz/IurDPL3iH3XbozDPzVnLsPj3YrlMHxk9fP0Fwv25bcebQ7D0523TqyH47b8vagBcWvlOW+CtCZee1opQ9+VWjxYsWsXOv7I16v5z4Cwbuu++6bWvXruW+e+9myq9/V67wat6KVWvov0NntuggPvwo2GunbfjLitUc2r8rA3tuy48en7tBK+/bk+esWz/1oF2Yufid2k58VMetLk5+LXTGqafwxBO/Y9nSpez1sX588+JxPPn475gx4wUk0W/XXbn6mhvW1f/9E4/Tu09fBuy2Wxmjrm1vrFjNc/Pf4cKjdmPt2mDeW+/z5OtvcdXIvVm+6kPO/9QAAJ5f8DYPvby0zNFWpgrv0RZF2czQJdixNAE4AugB/BUYFxE3NvadwQcNicf/8ExJ4rHS+MavXi53CJbDPd84kSWvzmxR6trn7w6MWx94rKi6Qz/W9dlGXl1ZViVr+UXEyaXat5mVWRW0/NztNbNcJD/ba2Y1qv2nPic/M2uOKsh+Zb/J2czam9Z9tldSB0nPSfpV+jxA0tOS5ki6M73QHEmd0uc5aXv/lpyFk5+Z5dbKz/b+GzCr4PP3gKsiYndgBXBmKj8TWJHKr0r1ms3Jz8xyEa2X/CT1AT4D/DR9FnAkcE+qcgtwfFofmT6Tth+V6jeLk5+Z5Zaj29uj/tn9tIzdaFc/BL4BrE2fdwDeiog16fN8oHda7w3MA0jbV6b6zeIBDzPLLUd7a2lDNzlLOg5YEhHPSjqidSIrnpOfmeXWSoO9hwGflXQssBWwPfAjoKukjql11wdYkOovAPoC8yV1BLoAy5p7cHd7zSyfYifzayJDRsSFEdEnIvoDo4DfRMRo4LfA51K1McADaX1i+kza/ptowfO5Tn5mlluJp7H/D+A8SXPIrunVzwlwI7BDKj8PuKAl5+Bur5nlUor39kbEY8Bjaf01YOhm6rwPnNBax3TyM7P8quAJDyc/M8vNk5maWU2qgkldnPzMLL8qyH1OfmbWDFWQ/Zz8zCwXT2ZqZjWr/ac+Jz8za44qyH5OfmaWU4ue3qgYTn5mllsVXPJz8jOzfOonM23vnPzMLDd3e82sJrnlZ2Y1qQpyn5OfmeWU781sFcvJz8yaof1nPyc/M8ulFJOZloOTn5nl5m6vmdUk3+piZrWp/ec+Jz8zy68Kcp+Tn5nlI9/qYma1SlWQ/fzScjPLTUUuje5D6ivpt5JekvSipH9L5d0lPSJpdvrZLZVL0tWS5kiaIWlwS87Byc/Mcqvv+ja1NGEN8PWIGAgMA86WNBC4AHg0IvYAHk2fAUYAe6RlLHB9S87Byc/MclLR/zUmIhZFxPS0/g4wC+gNjARuSdVuAY5P6yOBWyPzFNBVUq/mnoWv+ZlZLjnn8+shaVrB5/ERMX6TfUr9gQOBp4GeEbEobVoM9EzrvYF5BV+bn8oW0QxOfmaWW47ktzQihjS+L20L3At8NSLeLhxMiYiQFM2NszHu9ppZbq3R7QWQtAVZ4rs9Iu5LxX+t786mn0tS+QKgb8HX+6SyZnHyM7N8ihzsaKp1qKyJdyMwKyJ+ULBpIjAmrY8BHigoPy2N+g4DVhZ0j3Nzt9fMcinmNpYiHQacCvxJ0vOp7JvAlcBdks4E5gInpm2TgGOBOcAq4IyWHNzJz8zya4XsFxFPNrKnozZTP4CzW37kjJOfmeXmWV3MrCZ5MlMzq01OfmZWi9ztNbOak/MJj4qlbAClMkh6k2xou9r0AJaWOwjLpVr/zHaNiB1bsgNJk8l+P8VYGhHDW3K8Uqmo5FetJE1r6hEfqyz+M6t+fsLDzGqSk5+Z1SQnv7axyRQ+VvH8Z1blfM3PzGqSW35mVpOc/MysJjn5lZCk4ZL+nN42dUHT37Byk3STpCWSZpY7FistJ78SkdQBuJbsjVMDgZPTm6msst0MVORNuda6nPxKZygwJyJei4gPgDvI3j5lFSwiHgeWlzsOKz0nv9Jp6E1TZlYBnPzMrCY5+ZVOq75pysxal5Nf6UwF9pA0QNKWwCiyt0+ZWQVw8iuRiFgDfAWYAswC7oqIF8sblTVF0gTgj8BekuanN4hZFfLjbWZWk9zyM7Oa5ORnZjXJyc/MapKTn5nVJCc/M6tJTn7tiKSPJD0vaaakuyVt3YJ93Szpc2n9p41NuiDpCEmHNuMYb0ja5C1fDZVvVOfdnMf6jqTz88ZotcvJr31ZHRGDImI/4APgS4UbJTXrPcwRcVZEvNRIlSOA3MnPrJI5+bVfTwC7p1bZE5ImAi9J6iDpvyVNlTRD0hcBlLkmzS/4a2Cn+h1JekzSkLQ+XNJ0SS9IelRSf7Ik+7XU6vykpB0l3ZuOMVXSYem7O0h6WNKLkn5K9n7rRkn6haRn03fGbrTtqlT+qKQdU9nHJE1O33lC0t6t8tu0mtOsloKVV2rhjQAmp6LBwH4R8XpKICsj4uOSOgG/l/QwcCCwF9ncgj2Bl4CbNtrvjsBPgMPTvrpHxHJJNwDvRsT3U72fA1dFxJOS+pE9xbIPMA54MiIulfQZoJinI76QjtEZmCrp3ohYBmwDTIuIr0n6dtr3V8heLPSliJgt6WDgOuDIZvwarcY5+bUvnSU9n9afAG4k644+ExGvp/Kjgf3rr+cBXYA9gMOBCRHxEbBQ0m82s/9hwOP1+4qIhua1+zQwUFrXsNte0rbpGP+cvvugpBVFnNO5kv4prfdNsS4D1gJ3pvLbgPvSMQ4F7i44dqcijmG2CSe/9mV1RAwqLEhJ4L3CIuCciJiyUb1jWzGOOmBYRLy/mViKJukIskR6SESskvQYsFUD1SMd962NfwdmzeFrftVnCvBlSVsASNpT0jbA48BJ6ZpgL+BTm/nuU8Dhkgak73ZP5e8A2xXUexg4p/6DpEFp9XHglFQ2AujWRKxdgBUp8e1N1vKsVwfUt15PIetOvw28LumEdAxJOqCJY5htlpNf9fkp2fW86eklPP9L1sK/H5idtt1KNnPJBiLiTWAsWRfzBdZ3O38J/FP9gAdwLjAkDai8xPpR50vIkueLZN3fvzQR62Sgo6RZwJVkybfee8DQdA5HApem8tHAmSm+F/GrAayZPKuLmdUkt/zMrCY5+ZlZTXLyM7Oa5ORnZjXJyc/MapKTn5nVJCc/M6tJ/x+5/B3J+K2M8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(test_label_ml, random)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea3c408",
   "metadata": {},
   "source": [
    "# MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b9c64307",
   "metadata": {},
   "outputs": [],
   "source": [
    "MLP = MLPClassifier(max_iter=200,\n",
    "                    learning_rate='constant',\n",
    "                    power_t=0.5, solver='lbfgs', alpha=0.0001, random_state=100)\n",
    "MLP.fit(X_train_ml,y_train_ml)\n",
    "random_MLP = MLP.predict(end_test_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9def6632",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.89      0.90      1632\n",
      "           1       0.83      0.85      0.84       981\n",
      "\n",
      "    accuracy                           0.88      2613\n",
      "   macro avg       0.87      0.87      0.87      2613\n",
      "weighted avg       0.88      0.88      0.88      2613\n",
      "\n",
      "0.8756218905472637\n",
      "[[1459  173]\n",
      " [ 152  829]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_label_ml, random_MLP))\n",
    "print(accuracy_score(test_label_ml, random_MLP))\n",
    "cf_matrix = confusion_matrix(test_label_ml, random_MLP)\n",
    "print(confusion_matrix(test_label_ml, random_MLP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "74e46600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT8AAAEWCAYAAAAQBZBVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAibUlEQVR4nO3deZwU1b3+8c8zDKui7KiAgooLmqgEcfcazTVgvMHkatyiqHjJYpYbk1+CZiFqFrO6xBivUSLGxD0qRqMSE+MSRUFRATdcAVHZVRbZvr8/qgabYZauoXu6p/t5+6rXVJ06fer0AF/PqVPnlCICM7NqU1PqCpiZlYKDn5lVJQc/M6tKDn5mVpUc/MysKjn4mVlVcvCrMJI6S7pT0jJJN29GOSdLuq+QdSsFSX+TNLrU9bDy4+BXIpJOkjRV0vuS5qf/SA8uQNHHAn2BnhFxXEsLiYg/RcSRBajPRiQdJikk3VYvfa80/YE8y/mhpOuayxcRIyNiYguraxXMwa8EJJ0NXAz8hCRQbQ9cDowqQPE7AC9GxNoClFUsC4ADJPXMSRsNvFioCyjhv9/WuIjw1oobsDXwPnBcE3k6kgTHN9PtYqBjeu4wYC7wTeAdYD5wenruPGA1sCa9xhjgh8B1OWUPBAKoTY9PA14B3gNeBU7OSX8453MHAk8Ay9KfB+acewC4AHgkLec+oFcj362u/lcAZ6Vp7YB5wA+AB3LyXgLMAd4FpgGHpOkj6n3Pp3Pq8eO0HiuBndO0M9PzvwNuzSn/Z8D9gEr998Jb62/+P2PrOwDoBNzWRJ7vAvsDewN7AcOB7+Wc34YkiPYjCXC/ldQ9IsaTtCZvjIgtI+LqpioiaQvgUmBkRHQlCXDTG8jXA7grzdsT+DVwV72W20nA6UAfoAPwraauDVwLnJrufxKYQRLocz1B8jvoAfwZuFlSp4i4p9733CvnM6cAY4GuwOv1yvsm8BFJp0k6hOR3NzoiPMezCjn4tb6ewMJoult6MnB+RLwTEQtIWnSn5Jxfk55fExF3k7R+dm1hfdYDe0rqHBHzI2JmA3k+BbwUEX+MiLURcT3wPPBfOXn+EBEvRsRK4CaSoNWoiPg30EPSriRB8NoG8lwXEYvSa/6KpEXc3Pe8JiJmpp9ZU6+8FSS/x18D1wFfjYi5zZRnFcrBr/UtAnpJqm0iz3Zs3Gp5PU3bUEa94LkC2DJrRSJiOXA88EVgvqS7JO2WR33q6tQv5/itFtTnj8BXgI/TQEtY0rckPZeOXC8lae32aqbMOU2djIgpJN18kQRpq1IOfq3vUeAD4Jgm8rxJMnBRZ3s27RLmaznQJed4m9yTEXFvRPwnsC1Ja+73edSnrk7zWlinOn8EvgzcnbbKNki7pd8GPgd0j4huJPcbVVf1Rspssgsr6SySFuSbaflWpRz8WllELCO5sf9bScdI6iKpvaSRkn6eZrse+J6k3pJ6pfmbfayjEdOBQyVtL2lr4Jy6E5L6ShqV3vv7gKT7vL6BMu4Gdkkfz6mVdDwwBPhrC+sEQES8CvwHyT3O+roCa0lGhmsl/QDYKuf828DALCO6knYBfgR8nqT7+21Je7es9tbWOfiVQHr/6mySQYwFJF21rwC3p1l+BEwFngGeBZ5M01pyrcnAjWlZ09g4YNWk9XgTWEwSiL7UQBmLgKNJBgwWkbSYjo6IhS2pU72yH46Ihlq19wL3kDz+8jqwio27tHUPcC+S9GRz10lvM1wH/Cwino6Il4BzgT9K6rg538HaJnmgy8yqkVt+ZlaVHPzMrCo5+JlZVXLwM7Oq1NSDtq1OtZ1DHbqWuhqWwd67b1/qKlgGb7z+GgsXLlTzORvXbqsdItauzCtvrFxwb0SM2JzrFUt5Bb8OXem46+dKXQ3L4MF/X1rqKlgGhx44fLPLiLWr6LjbCXnlXfXUb5qbkVMyZRX8zKwNEKDNajyWBQc/M8uuApZKdPAzs+zc8jOz6iOoaVfqSmw2Bz8zy0ZURLe37X8DM2tlSrq9+WzNlSRNkPSOpBkNnPtm+lKrXumxJF0qabakZyQNzck7WtJL6ZbX2/oc/MwsO9XktzXvGpJ3smxcvDQAOBJ4Iyd5JDA43caSvJOl7jUL44H9SF75MF5S9+Yu7OBnZtkVqOUXEQ+SLKdW30UkS6flLjs1Crg2Eo8B3SRtS/IOmMkRsTgilgCTaSCg1ud7fmaWkbLc8+slaWrO8ZURcWWTpUujgHkR8bQ2DqD92HhNx7lpWmPpTXLwM7NsRJbR3oURMSzvoqUuJIvMHtmCmmXibq+ZZaRC3vOrbydgEPC0pNeA/sCTkrYheWfMgJy8/dO0xtKb5OBnZtnVKL8to4h4NiL6RMTAiBhI0oUdGhFvAZOAU9NR3/2BZRExn+SVB0dK6p4OdByZpjXJ3V4zy6aAz/lJuh44jOTe4FxgfERc3Uj2u4GjgNkkr0c9HSAiFku6gOQl95C807qhQZSNOPiZWXYFmt4WESc2c35gzn4AZzWSbwIwIcu1HfzMLCNPbzOzalUB09sc/MwsmzwfYC53Dn5mlp1bfmZWldzyM7Pqk2l6W9ly8DOzbLJNbytbDn5mlpFbfmZWrXzPz8yqklt+ZlaV3PIzs6oj3/MzsyqlGgc/M6syAuRur5lVHaVbG+fgZ2YZyS0/M6tODn5mVpVqPOBhZlXH9/zMrBrJ9/zMrFo5+JlZVXLwM7Oq5OBnZtVHoJq2H/za/ni1mbWqugGPfLZmy5ImSHpH0oyctF9Iel7SM5Juk9Qt59w5kmZLekHSJ3PSR6RpsyWNy+d7OPiZWWaFCn7ANcCIemmTgT0j4qPAi8A56TWHACcAe6SfuVxSO0ntgN8CI4EhwIlp3iY5+JlZdspza0ZEPAgsrpd2X0SsTQ8fA/qn+6OAGyLig4h4FZgNDE+32RHxSkSsBm5I8zbJwc/MslGmll8vSVNztrEZr3YG8Ld0vx8wJ+fc3DStsfQmecDDzDLLMNq7MCKGtfAa3wXWAn9qyeeb4+BnZpkIFX1ur6TTgKOBIyIi0uR5wICcbP3TNJpIb5S7vWaWXYHu+TVYtDQC+Dbw6YhYkXNqEnCCpI6SBgGDgceBJ4DBkgZJ6kAyKDKpueu45Wdm2ahwDzlLuh44jOTe4FxgPMnobkdgcnqdxyLiixExU9JNwCyS7vBZEbEuLecrwL1AO2BCRMxs7toOfmaWWaGCX0Sc2EDy1U3k/zHw4wbS7wbuznJtBz8zy8zT28ysKnl6W5W6YvzJvH7/T5l687mbnPv6KYez8qnL6NltCwAO+dhg3nrwFzx2wzgeu2Ec54z98GH2s048jKk3n8u0W77LV046rLWqX/W+NHYMgwZsw/ChH92QNvrzJ3Dg8KEcOHwoe+yyIwcOHwrA1Cce35B+wL77MOmO20pV7bKR7zN+5d46LGrLLx21uYTkJuRVEXFhMa/XWv5452NcceO/uOqCUzdK79+3G0fsvztvzN/ogXUeeepl/vvrV2yUNmSnbTn9swdyyCm/YPWadUz67Ze5+6EZvDJnYdHrX+1OPmU0X/jSWYwdc9qGtInX3bBh/5zvfIutt9oagCF77MmD/36c2tpa3po/nwOG78NRn/ovamuru9NU7oEtH0Vr+bV0vl1b8MiTL7N42YpN0n/+rf/mu5fczoePJTVut0Hb8MSM11i5ag3r1q3noWmzOebwvYtQW6vv4EMOpXv3Hg2eiwhuu+Vmjj3+BAC6dOmyIdCtWrWqIv7RF0IltPyK2e1t0Xy7turowz7Cm+8s5dkXN322cr+PDmLKjeO4/bIvsfuO2wAw8+U3OWifnemx9RZ07tSeEQfvQf9turd2ta2eRx5+iD59+7LzzoM3pD3x+BT23ecj7D9sLy7+zeVV3+oDivqcX2sp5p9iQ/Pt9qufKZ3rl8z3a79lEatTPJ07tefbZ3ySo7982Sbnpj8/h12P+j7LV67mkwcP4aaLxvKRUefzwqtv86trJnPn5WexYtVqnn5hLuvWrS9B7S3XLTfdwLGfO2GjtH2H78cTTz3L888/xxfPPJ0jPzmSTp06laiG5aHcW3X5KPmAR0RcGRHDImKYajuXujotsmP/3uzQryeP33gOz991Hv36dOPRP3+Hvj278t7yVSxfuRqAex+eRfvadhsGQybe/igHnfxz/nPMxSx9dwUvvf5OKb9G1Vu7di2T7riN/z72cw2e32233dliiy2ZNXNGg+erhQQ1NcprK2fFbPk1NQ+vosyc/SY7HHHOhuPn7zqPg07+OYuWLqdvz668veg9AIbtsQM1EouWLgegd/ctWbDkfQZs051Rh+/Ff5z6q5LU3xL//Mff2WWX3ejXv/+GtNdefZX+AwZQW1vLG6+/zosvPs/2OwwsXSXLQvnfz8tHMYPfhvl2JEHvBOCkIl6v1Uz86Wkc8rHB9Oq2JbPvuYALrribibc/2mDez3xiH/7nuENYu24dq1at4dRz/rDh3PW/PJMe3bZgzdp1/O+FN7Hs/ZWt9RWq2umnnMRDD/2LRQsXsutO23Pu98Yz+vQx3HLTjRx3/PEb5X303w/z61/+nPbt21NTU8OvL7mMXr16lajm5aMCYh/KZ2SyxYVLRwEX8+F8u02mpeSq6dInOu7acJfDytOCxy4tdRUsg0MPHM6T06ZuVujqtM0uscPo3+SV98Wfj5jW0iWtiq2ow1YtmW9nZmVOldHy85i9mWUiKPvBjHw4+JlZZg5+ZlZ93O01s2okKuMhZwc/M8vIz/mZWZWqgNjn4GdmGckDHmZWhXzPz8yqVgXEPgc/M8vOLT8zq0oVEPsc/MwsowK+tLyUHPzMLBNR/guV5qPkKzmbWdsj5bc1X44mSHpH0oyctB6SJkt6Kf3ZPU2XpEslzZb0jKShOZ8ZneZ/SdLofL6Dg5+ZZVbAt7ddA4yolzYOuD8iBgP3p8eQvAlycLqNBX6X1qUHMJ7kHUHDgfF1AbMpDn5mlk2erb58Yl9EPAgsrpc8CpiY7k8EjslJvzYSjwHdJG0LfBKYHBGLI2IJMJlNA+omfM/PzDLJ+JBzL0lTc46vjIgrm/lM34iYn+6/BfRN9xt6I2S/JtKb5OBnZpllCH4LN2cZ+4gISUV514a7vWaWWZFfXfl22p0l/Vn3TtfG3gjZojdFOviZWTYFvOfXiElA3YjtaOCOnPRT01Hf/YFlaff4XuBISd3TgY4j07QmudtrZpmogOv5SboeOIzk3uBcklHbC4GbJI0BXgfqXul4N3AUMBtYAZwOEBGLJV1A8rpcgPMjov4gyiYc/Mwss0JN8IiIExs5dUQDeQM4q5FyJgATslzbwc/MMqvx9DYzqzbyYqZmVq0qIPY5+JlZdhW9qouk3wCNPlwYEV8rSo3MrOxVQOxrsuU3tYlzZlalRPK4S1vXaPCLiIm5x5K6RMSK4lfJzMpdJdzza3aGh6QDJM0Cnk+P95J0edFrZmblSflNbSv3EeF8prddTLJkzCKAiHgaOLSIdTKzMiaS5/zy2cpZXqO9ETGn3ujOuuJUx8zagjKPa3nJJ/jNkXQgEJLaA18HnitutcysnFXCoy75dHu/SDKfrh/wJrA3jcyvM7PKl++KLuUeH5tt+UXEQuDkVqiLmbUR7co9suUhn9HeHSXdKWlB+palOyTt2BqVM7PyVMAXGJVMPt3ePwM3AdsC2wE3A9cXs1JmVr6S0d78tnKWT/DrEhF/jIi16XYd0KnYFTOzMpVnq6/cW35Nze3tke7+TdI44AaSub7Hk6yoamZVqszjWl6aGvCYRhLs6r7mF3LOBXBOsSplZuWt3Ft1+Whqbu+g1qyImbUNAtqV+w29POQ1w0PSnsAQcu71RcS1xaqUmZW3th/68gh+ksaTvF1pCMm9vpHAw4CDn1kVkirjHR75jPYeS/Impbci4nRgL2DrotbKzMpaVczwAFZGxHpJayVtRfL29AHNfcjMKldFD3jkmCqpG/B7khHg94FHi1kpMytvFRD78prb++V09wpJ9wBbRcQzxa2WmZUrSQUb7ZX0DeBMksfnngVOJ5lNdgPQk6TBdUpErJbUkWSs4WMk64seHxGvtfTajd7zkzS0/gb0AGrTfTOrUoWY4SGpH/A1YFhE7Am0A04AfgZcFBE7A0uAMelHxgBL0vSL0nwt1lTL71dNnAvg8M25cEP22X17HplyWaGLtSL62m0zSl0Fy2DO0pUFKSefkdI81QKdJa0BugDzSWLLSen5icAPgd8Bo9J9gFuAyyQpIhp9y2RzF25QRHy8JQWaWWUTmQY8eknKfRPklRFxJUBEzJP0S+ANYCVwH0k3d2lErE3zzyVZS5T055z0s2slLSPpGi9syffwS8vNLLMMt/wWRsSwhk5I6k7SmhsELCVZMWpEAaqXFwc/M8tEKtj0tk8Ar0bEgqRc/QU4COgmqTZt/fUH5qX555E8ZjdXUi3J88aLWnrxAnbdzaxaFGg9vzeA/SV1UdKPPgKYBfyTZHIFwGjgjnR/UnpMev4fLb3fB/mt5CxJn5f0g/R4e0nDW3pBM2v7CjHDIyKmkAxcPEnymEsNcCXwHeBsSbNJ7uldnX7kaqBnmn42MG5zvkM+3d7LgfUkIzDnA+8BtwL7bs6FzaxtqntvbyFExHhgfL3kV4BNGlgRsQo4riAXJr/gt19EDJX0VFqBJZI6FKoCZtb2VML9snyC3xpJ7Uie7UNSb5KWoJlVqaqY3gZcCtwG9JH0Y5Ibjd8raq3MrGwVcnpbKeUzt/dPkqaRjMQIOCYinit6zcysbFVA7MtrMdPtgRXAnblpEfFGMStmZuWpkAMepZRPt/cuPnyRUSeSp7FfAPYoYr3MrIxVQOzLq9v7kdzjdEWXLzeS3cwqXRt4IXk+Mk9vi4gnJe1XjMqYWdugCniFUT73/M7OOawBhgJvFq1GZlbWBNRWwIN++bT8uubsryW5B3hrcapjZm1Bxb/DI324uWtEfKuV6mNmZS4Z7S11LTZfo8GvbkkZSQe1ZoXMrMy1gddS5qOplt/jJPf3pkuaRLLQ4PK6kxHxlyLXzczKVLU859eJZMHAw/nweb8AHPzMqpCAdhU+4NEnHemdwYdBr06LFxA0s7ZO1FT4oy7tgC2hwW/p4GdWpZIXGJW6FpuvqeA3PyLOb7WamFnbUAUzPCrg65lZMVT6gMcRrVYLM2szKr7bGxGLW7MiZtZ2VMVipmZmuUT1vMPDzOxDqoK5vWZmDWn7oc/Bz8wyqpRl7Cuh625mrUx5bs2WI3WTdIuk5yU9J+kAST0kTZb0Uvqze5pXki6VNFvSM+mq8i3m4GdmGYmamvy2PFwC3BMRuwF7Ac8B44D7I2IwcH96DDASGJxuY4Hfbc63cPAzs0zqRnvz2ZosR9oaOBS4GiAiVkfEUmAUMDHNNhE4Jt0fBVwbiceAbpK2ben3cPAzs8wk5bUBvSRNzdnG5hQzCFgA/EHSU5KukrQF0Dci5qd53gL6pvv9gDk5n5+bprWIBzzMLLMMwx0LI2JYI+dqSdYM/WpETJF0CR92cQGIiJBUlIVU3PIzs2yUqeXXlLnA3IiYkh7fQhIM367rzqY/30nPzwMG5Hy+f5rWIg5+ZpaJgHZSXltTIuItYI6kXdOkI4BZwCRgdJo2Grgj3Z8EnJqO+u4PLMvpHmfmbq+ZZVbAp/y+CvxJUgfgFeB0kkbZTZLGAK8Dn0vz3g0cBcwGVqR5W8zBz8wyK9QzzhExHWjonuAmq0pFRABnFebKDn5mllHyqEvbn+Hh4GdmmVXA7DYHPzPLSsgtPzOrNnWjvW2dg5+ZZSN3e82sSjn4mVlV8j0/M6s6yWKmpa7F5nPwM7PMKmElZwc/M8vM3V7jC2eewd/u/iu9+/Rh2vQZAPzo/B8y4erf07tXbwDO+9FPGDHyKO7/+2S+f+44Vq9eTYcOHfjJz37BYR8/vIS1r16fGNyTgwd1J4B5y1ZxzRPzGD2sHzv06My69cFri1dy3bR5rAvo0r6G0fv2p/cWHVizfj0Tn5jHm+9+UOqvUDKV0u0t2qoukiZIekfSjGJdoxycMvo07vjrPZukf/Xr32DKtOlMmTadESOPAqBnz17ccvudTJ3+LL+fMJEzTjultatrQLdOtRw+uCc//vvLnHffbGok9h2wNVPeWMoP7nmJ8+6bTft24uBBPQAYuXtv5ixdyfmTZ/OHx+dy/N4tXjy4Qijv/8pZMZe0ugYYUcTyy8LBhxxKjx498sq79z77sN122wEwZI89WLVyJR98UL0tiFKqkWjfroYaQYd2Ytmqtcx46/0N519bvJLuXZKO0XZbdeL5d5YD8NZ7q+m1RQe6dmxXknqXhfQ5v3y2cla04BcRDwKLi1V+ubvi8svYd5+P8oUzz2DJkiWbnL/tL7ey9z5D6dixYwlqV92WrlrLfS8s5MKjd+EX/7UbK9esZ9bbHwa+doL9d+i2IRjOWbqKof22AmBg98706NKe7p3bl6Tu5aJQb28rpZIvZippbN36/gsWLih1dQrif77wJWa98DJTpk1nm223Zdz/++ZG52fNnMn3zv0Ol13+fyWqYXXr0r6Gvft15dy7XuTbdz5Px9oa9tt+6w3nTxq6HS8uWM7shSsAuOf5BXRu347v/+dOHD64J3OWrmR9URZWbxsKtZhpqZU8+EXElRExLCKG1Q0QtHV9+/alXbt21NTUcMaY/2Hq1Mc3nJs7dy7HH/cZrppwLTvutFMJa1m9du+7JQuXr+H91etYF/DkvHfZqWcXAI4e0puuHWu5+em3NuRftXY9E6fO44LJLzPh8bls2bGWhctXl6r65aECmn4lD36VaP78D1fWvuP22xiyx54ALF26lM9++lNc8OMLOfCgg0pVvaq3eMUaduzRmQ7tkn+du/XZgvnvfcDBg7qzR9+u/P6xOeQ27Dq3r9nQijl4UHdeWrCcVWvXl6Dm5aMSBjz8qMtmOvXzJ/LQvx5g4cKF7DSwP9//wXk8+K8HeObp6Uhih4ED+U3avb3i8st4+eXZ/PRH5/PTH50PwJ1/u48+ffqU8itUnVcXr2Ta3Hf53id2Zl0Ec5au4qFXlvCbzwxh8Yo1jDtiRwCenPsudz23gG27duT04f0J4M1lq7h2aovfmVMxyrxHmxclK0MXoWDpeuAwoBfwNjA+Iq5u6jMf+9iweGTK1KLUx4rja7dV9JNMFee2ccez4OWZmxW6dv/IPnHtHQ/klXf4Tt2mNfHqypIqWssvIk4sVtlmVmIV0PJzt9fMMpE8t9fMqlTbD30OfmbWEhUQ/Rz8zCyj8n+MJR9+zs/MMivk3F5J7SQ9Jemv6fEgSVMkzZZ0o6QOaXrH9Hh2en7g5nwHBz8zy0QUfGGDrwPP5Rz/DLgoInYGlgBj0vQxwJI0/aI0X4s5+JlZZoWa4SGpP/Ap4Kr0WMDhwC1plonAMen+qPSY9PwRaf4WcfAzs8wK2PK7GPg2UDdfsCewNCLWpsdzgX7pfj9gDkB6flmav0Uc/MwsswzrGvSqW7Up3cZuKEM6GngnIqa1bu0THu01s2yyrdiysInpbQcBn5Z0FNAJ2Aq4BOgmqTZt3fUH6iZTzwMGAHMl1QJbA4ta9B1wy8/MWqAQ9/wi4pyI6B8RA4ETgH9ExMnAP4Fj02yjgTvS/UnpMen5f8RmLE7g4GdmmdS9wCifrYW+A5wtaTbJPb26BVGuBnqm6WcD4zbne7jba2bZFfgZ54h4AHgg3X8FGN5AnlXAcYW6poOfmWVWCTM8HPzMLLMKWNTFwc/MsquA2OfgZ2YtUAHRz8HPzDLxYqZmVrXafuhz8DOzlqiA6OfgZ2YZVcZipg5+ZpZZBdzyc/Azs2zqFjNt6xz8zCwzd3vNrCq55WdmVakCYp+Dn5lllO3lRGXLwc/MWqDtRz8HPzPLpG4x07bOwc/MMnO318yqkh91MbPq1PZjn4OfmWVXAbHPwc/MspEfdTGzaqUKiH4OfmaWWdsPfQ5+ZtYCFdDwc/Azs6wqYzHTmlJXwMzalrr1/PLZmixHGiDpn5JmSZop6etpeg9JkyW9lP7snqZL0qWSZkt6RtLQzfkeDn5mllkhgh+wFvhmRAwB9gfOkjQEGAfcHxGDgfvTY4CRwOB0Gwv8bnO+g4OfmWWmPP9rSkTMj4gn0/33gOeAfsAoYGKabSJwTLo/Crg2Eo8B3SRt29Lv4Ht+ZpZNtuf8ekmamnN8ZURcuUmR0kBgH2AK0Dci5qen3gL6pvv9gDk5H5ubps2nBRz8zCwTkelRl4URMazJ8qQtgVuB/42Id3OfIYyIkBQtq2nT3O01s+yU59ZcMVJ7ksD3p4j4S5r8dl13Nv35Tpo+DxiQ8/H+aVqLOPiZWWaFuOenpIl3NfBcRPw659QkYHS6Pxq4Iyf91HTUd39gWU73ODN3e80sswItZnoQcArwrKTpadq5wIXATZLGAK8Dn0vP3Q0cBcwGVgCnb87FHfzMLLsCBL+IeLiJko5oIH8AZ23+lRMOfmaWWSXM8HDwM7NM6mZ4tHVKWpLlQdICkj5+pekFLCx1JSyTSv0z2yEiem9OAZLuIfn95GNhRIzYnOsVS1kFv0olaWpzzzpZefGfWeXzoy5mVpUc/MysKjn4tY5N5jJa2fOfWYXzPT8zq0pu+ZlZVXLwM7Oq5OBXRJJGSHohXXZ7XPOfsFKTNEHSO5JmlLouVlwOfkUiqR3wW5Klt4cAJ6ZLdFt5uwYoy4dyrbAc/IpnODA7Il6JiNXADSTLcFsZi4gHgcWlrocVn4Nf8TS25LaZlQEHPzOrSg5+xVPQJbfNrLAc/IrnCWCwpEGSOgAnkCzDbWZlwMGvSCJiLfAV4F6S95HeFBEzS1sra46k64FHgV0lzU2XUrcK5OltZlaV3PIzs6rk4GdmVcnBz8yqkoOfmVUlBz8zq0oOfm2IpHWSpkuaIelmSV02o6xrJB2b7l/V1KILkg6TdGALrvGapE3e8tVYer0872e81g8lfStrHa16Ofi1LSsjYu+I2BNYDXwx96SkFr2HOSLOjIhZTWQ5DMgc/MzKmYNf2/UQsHPaKntI0iRglqR2kn4h6QlJz0j6AoASl6XrC/4d6FNXkKQHJA1L90dIelLS05LulzSQJMh+I211HiKpt6Rb02s8Iemg9LM9Jd0naaakq0jeb90kSbdLmpZ+Zmy9cxel6fdL6p2m7STpnvQzD0narSC/Tas6LWopWGmlLbyRwD1p0lBgz4h4NQ0gyyJiX0kdgUck3QfsA+xKsrZgX2AWMKFeub2B3wOHpmX1iIjFkq4A3o+IX6b5/gxcFBEPS9qeZBbL7sB44OGIOF/Sp4B8ZkeckV6jM/CEpFsjYhGwBTA1Ir4h6Qdp2V8hebHQFyPiJUn7AZcDh7fg12hVzsGvbeksaXq6/xBwNUl39PGIeDVNPxL4aN39PGBrYDBwKHB9RKwD3pT0jwbK3x94sK6siGhsXbtPAEOkDQ27rSRtmV7js+ln75K0JI/v9DVJn0n3B6R1XQSsB25M068D/pJe40Dg5pxrd8zjGmabcPBrW1ZGxN65CWkQWJ6bBHw1Iu6tl++oAtajBtg/IlY1UJe8STqMJJAeEBErJD0AdGoke6TXXVr/d2DWEr7nV3nuBb4kqT2ApF0kbQE8CByf3hPcFvh4A599DDhU0qD0sz3S9PeArjn57gO+Wncgae9090HgpDRtJNC9mbpuDSxJA99uJC3POjVAXev1JJLu9LvAq5KOS68hSXs1cw2zBjn4VZ6rSO7nPZm+hOf/SFr4twEvpeeuJVm5ZCMRsQAYS9LFfJoPu513Ap+pG/AAvgYMSwdUZvHhqPN5JMFzJkn3941m6noPUCvpOeBCkuBbZzkwPP0OhwPnp+knA2PS+s3ErwawFvKqLmZWldzyM7Oq5OBnZlXJwc/MqpKDn5lVJQc/M6tKDn5mVpUc/MysKv1/XYNC3QnmbXIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(test_label_ml, random_MLP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434b10e6",
   "metadata": {},
   "source": [
    "# XGB Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "51c6f30a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgbo = XGBClassifier(learning_rate=0.1 , max_depth=5 ,base_score=0.3)\n",
    "xgbo.fit(X_train_ml,y_train_ml)\n",
    "random_xgbo = xgbo.predict(end_test_ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "244b0669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.91      0.92      1632\n",
      "           1       0.85      0.89      0.87       981\n",
      "\n",
      "    accuracy                           0.90      2613\n",
      "   macro avg       0.89      0.90      0.89      2613\n",
      "weighted avg       0.90      0.90      0.90      2613\n",
      "\n",
      "0.8993494068120934\n",
      "[[1477  155]\n",
      " [ 108  873]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_label_ml, random_xgbo))\n",
    "print(accuracy_score(test_label_ml, random_xgbo))\n",
    "cf_matrix = confusion_matrix(test_label_ml, random_xgbo)\n",
    "print(confusion_matrix(test_label_ml, random_xgbo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "af0d7bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(test_label_ml, random_xgbo)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2818274f",
   "metadata": {},
   "source": [
    "# ROC Machine Learning and Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "759fe588",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_RF = RF.fit(X_train_ml,y_train_ml).predict_proba(end_test_ml)[:,1]\n",
    "y_pred_XGB = xgbo.fit(X_train_ml,y_train_ml).predict_proba(end_test_ml)[:,1]\n",
    "y_pred_MLP = MLP.fit(X_train_ml,y_train_ml).predict_proba(end_test_ml)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "364fa448",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "y_pred_keras = model_cnn.predict(end_test).ravel()\n",
    "fpr_keras, tpr_keras, thresholds_keras = roc_curve(test_label, y_pred_keras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5f1c8f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LS\n",
    "y_pred_rf = model_lstm.predict(end_test).ravel()\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(test_label, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9f9ce164",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #LS\n",
    "y_pred_hb = model_hybrid.predict(end_test).ravel()\n",
    "fpr_hb, tpr_hb, thresholds_hb = roc_curve(test_label, y_pred_hb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d56c4655",
   "metadata": {},
   "outputs": [],
   "source": [
    "#machine learning\n",
    "fpr_keras_ml, tpr_keras_ml, thresholds_keras_ml = roc_curve(test_label_ml, y_pred_RF)\n",
    "fpr_xgb, tpr_xgb, thresholds_keras = roc_curve(test_label_ml, y_pred_XGB)\n",
    "fpr_mlp, tpr_mlp, thresholds_keras = roc_curve(test_label_ml, y_pred_MLP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "4b59d4f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import auc\n",
    "auc_rf_ml = auc(fpr_keras_ml, tpr_keras_ml)\n",
    "auc_xgb = auc(fpr_xgb, tpr_xgb)\n",
    "auc_mlp = auc(fpr_mlp, tpr_mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "cac9e543",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import auc\n",
    "\n",
    "\n",
    "auc_keras = auc(fpr_keras, tpr_keras)\n",
    "auc_rf = auc(fpr_rf, tpr_rf)\n",
    "auc_hb = auc(fpr_hb, tpr_hb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f35405c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "507f08af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure(figsize=(10, 10))\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_keras_ml, tpr_keras_ml, label='RF (area = {:.4f})'.format(auc_rf_ml))\n",
    "plt.plot(fpr_xgb, tpr_xgb, label='XGB (area = {:.4f})'.format(auc_xgb))\n",
    "plt.plot(fpr_mlp, tpr_mlp, label='MLP (area = {:.4f})'.format(auc_mlp))\n",
    "plt.plot(fpr_rf, tpr_rf, label='LSTM (area = {:.4f})'.format(auc_rf))\n",
    "plt.plot(fpr_hb, tpr_hb, label='hybrid (area = {:.4f})'.format(auc_hb))\n",
    "plt.plot(fpr_keras, tpr_keras, label='CNN (area = {:.4f})'.format(auc_keras))\n",
    "\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f01bcc57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaaab5b8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}