{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries \n",
    "import pandas as pd\n",
    "import mne\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from scipy import stats\n",
    "from scipy.stats.contingency import expected_freq\n",
    "from scipy.stats import chi2_contingency\n",
    "import os \n",
    "import zipfile\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Import Data \n",
    "new_eeg_ndar = pd.read_csv(\"new_eeg_ndar.csv\")\n",
    "new_eeg_ndar = new_eeg_ndar.drop(['Unnamed: 0'], axis = 1)\n",
    "explr_data = new_eeg_ndar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "new_col = []\n",
    "for i in range(len(explr_data['Phenotype/diagnosis for the subject'])):\n",
    "    if explr_data['Phenotype/diagnosis for the subject'][i] == 'Case':\n",
    "        new_col.append(1)\n",
    "    else:\n",
    "        new_col.append(0)\n",
    "explr_data['y_var'] = new_col\n",
    "\n",
    "plt.hist(x = explr_data['y_var'], bins=[-.5,.5,1.5], ec=\"k\")\n",
    "plt.xticks((0,1), labels=['Controls', 'Cases'])\n",
    "plt.title('Histogram for BSNIP Dataset Subject Mental Illness Status')\n",
    "plt.xlabel('Subject Mental Illness Status')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig('BSNIP_Count.png')\n",
    "\n",
    "#Race\n",
    "explr_data['Race of study subject'] = explr_data['Race of study subject'].map({'More than one race': 'Multiple', 'White': 'White', 'Black or African American': 'Black', 'Asian': 'Asian', 'Unknown or not reported': 'Unknown', 'American Indian/Alaska Native': 'Native'})\n",
    "count_race = Counter(explr_data['Race of study subject'])\n",
    "df = pd.DataFrame.from_dict(count_race, orient='index')\n",
    "df.plot(kind='bar', legend=False, title = \"Histogram for Subject Race in BSNIP Dataset\", ylabel = 'Count')\n",
    "plt.savefig('BSNIP_race.png')\n",
    "\n",
    "#Age In Years\n",
    "explr_data['Age_Years'] = explr_data['Age in months at the time of the interview/test/sampling/imaging._x'].astype('float')/12\n",
    "plt.hist(explr_data['Age_Years'])\n",
    "plt.title(\"Histogram for Subject Age in BSNIP Dataset\")\n",
    "plt.xlabel('Age in Years')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig('BSNIP_age.png')\n",
    "\n",
    "#Sex at Birth \n",
    "count_gender = Counter(explr_data['Sex of subject at birth_x'])\n",
    "df = pd.DataFrame.from_dict(count_gender, orient='index')\n",
    "df.plot(kind='bar', legend=False, rot = 1, title = 'Histogram for Subject Sex at Birth in BSNIP dataset', xlabel = 'Sex at Birth', ylabel = 'Count' )\n",
    "plt.savefig('BSNIP_gender.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.602203588978631, pvalue=0.0003566737131968837)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean and std dev for Age \n",
    "explr_data['Age_Years'] = explr_data['Age in months at the time of the interview/test/sampling/imaging._x']/12\n",
    "explr_data.groupby('Phenotype/diagnosis for the subject').mean()['Age_Years']\n",
    "explr_data.groupby('Phenotype/diagnosis for the subject').std()['Age_Years']\n",
    "\n",
    "# Welch's t.test\n",
    "Case_age = explr_data[explr_data['Phenotype/diagnosis for the subject'] == 'Case']['Age_Years']\n",
    "Control_age = explr_data[explr_data['Phenotype/diagnosis for the subject'] == 'Control']['Age_Years']\n",
    "stats.ttest_ind(Case_age, Control_age, equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.00146974340333,\n",
       " 0.004673939654435343,\n",
       " 1,\n",
       " array([[164.45242718, 162.54757282],\n",
       "        [ 94.54757282,  93.45242718]]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count for Sex by case/control \n",
    "Sex_crosstab = pd.crosstab(explr_data['Phenotype/diagnosis for the subject'],\n",
    "                            explr_data['Sex of subject at birth_x'], \n",
    "                               margins = False)\n",
    "# chi Sq. test \n",
    "chi2_contingency(Sex_crosstab, correction = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "explr_data['Race of study subject'] = explr_data['Race of study subject'].map({'More than one race': 'Other', 'White': 'White', 'Black or African American': 'Black', 'Asian': 'Other', 'Unknown or not reported': 'Other', 'American Indian/Alaska Native': 'Other'})\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Race of study subject</th>\n",
       "      <th>Other</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenotype/diagnosis for the subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <td>8</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>7</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Race of study subject                Other  White\n",
       "Phenotype/diagnosis for the subject              \n",
       "Case                                     8    192\n",
       "Control                                  7    117"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count for Race \n",
    "Race_crosstab = pd.crosstab(explr_data['Phenotype/diagnosis for the subject'],\n",
    "                            explr_data['Race of study subject'], \n",
    "                               margins = False)\n",
    "Race_crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.46920638897588485,\n",
       " 0.4933524934269964,\n",
       " 1,\n",
       " array([[  9.25925926, 190.74074074],\n",
       "        [  5.74074074, 118.25925926]]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chi Sq. test \n",
    "chi2_contingency(Race_crosstab, correction = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Data\n",
    "X1 = pd.read_csv('X1.csv').drop(['Y', 'Unnamed: 0'], axis=1)\n",
    "X2 = pd.read_csv('X2.csv').drop(['Y', 'Unnamed: 0'], axis=1)\n",
    "Y = pd.DataFrame(pd.read_csv('X1.csv')['Y'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>p300_m_amp_avg</th>\n",
       "      <th>p300_m_amp_var</th>\n",
       "      <th>n200_m_amp_avg</th>\n",
       "      <th>n200_m_amp_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000119</td>\n",
       "      <td>9.282724e-07</td>\n",
       "      <td>-0.000067</td>\n",
       "      <td>8.292068e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.001020</td>\n",
       "      <td>1.565223e-05</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>1.465661e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.000077</td>\n",
       "      <td>2.373843e-14</td>\n",
       "      <td>-0.012584</td>\n",
       "      <td>5.664196e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.020132</td>\n",
       "      <td>3.440068e-04</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>3.269596e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      p300_m_amp_avg  p300_m_amp_var  n200_m_amp_avg  n200_m_amp_var\n",
       "mean        0.000119    9.282724e-07       -0.000067    8.292068e-07\n",
       "std         0.001020    1.565223e-05        0.000613    1.465661e-05\n",
       "min        -0.000077    2.373843e-14       -0.012584    5.664196e-14\n",
       "max         0.020132    3.440068e-04        0.000021    3.269596e-04"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.describe().loc[['mean','std','min', 'max']]\n",
    "X2.describe().loc[['mean','std','min', 'max']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test Split \n",
    "x1_train, x1_test, y1_train, y1_test = train_test_split(X1, Y, test_size=0.33, random_state = 42)\n",
    "x2_train, x2_test, y2_train, y2_test = train_test_split(X2, Y, test_size=0.33, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1_train_constant = sm.add_constant(x1_train)\n",
    "X1_test_constant = sm.add_constant(x1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.631687\n",
      "         Iterations: 35\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>Y</td>        <th>  No. Observations:  </th>  <td>   345</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   336</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 02 Apr 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.05801</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>18:13:21</td>     <th>  Log-Likelihood:    </th> <td> -217.93</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>False</td>      <th>  LL-Null:           </th> <td> -231.35</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>0.0007522</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>   -2.6540</td> <td>    1.809</td> <td>   -1.467</td> <td> 0.142</td> <td>   -6.199</td> <td>    0.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p300_amp_avg</th> <td> -853.0921</td> <td> 2374.772</td> <td>   -0.359</td> <td> 0.719</td> <td>-5507.561</td> <td> 3801.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p300_amp_var</th> <td>-1597.6444</td> <td> 4.09e+05</td> <td>   -0.004</td> <td> 0.997</td> <td>-8.04e+05</td> <td> 8.01e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p300_lat_avg</th> <td>   -2.3770</td> <td>    4.227</td> <td>   -0.562</td> <td> 0.574</td> <td>  -10.661</td> <td>    5.907</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p300_lat_var</th> <td>  339.9954</td> <td>  136.471</td> <td>    2.491</td> <td> 0.013</td> <td>   72.517</td> <td>  607.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n200_amp_avg</th> <td>-1876.0645</td> <td> 3138.879</td> <td>   -0.598</td> <td> 0.550</td> <td>-8028.153</td> <td> 4276.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n200_amp_var</th> <td>-3398.9531</td> <td> 3.83e+05</td> <td>   -0.009</td> <td> 0.993</td> <td>-7.53e+05</td> <td> 7.46e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n200_lat_avg</th> <td>   16.5093</td> <td>    4.667</td> <td>    3.537</td> <td> 0.000</td> <td>    7.362</td> <td>   25.657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n200_lat_var</th> <td> -326.6794</td> <td>  152.126</td> <td>   -2.147</td> <td> 0.032</td> <td> -624.840</td> <td>  -28.519</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                      Y   No. Observations:                  345\n",
       "Model:                          Logit   Df Residuals:                      336\n",
       "Method:                           MLE   Df Model:                            8\n",
       "Date:                Sun, 02 Apr 2023   Pseudo R-squ.:                 0.05801\n",
       "Time:                        18:13:21   Log-Likelihood:                -217.93\n",
       "converged:                      False   LL-Null:                       -231.35\n",
       "Covariance Type:            nonrobust   LLR p-value:                 0.0007522\n",
       "================================================================================\n",
       "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const           -2.6540      1.809     -1.467      0.142      -6.199       0.891\n",
       "p300_amp_avg  -853.0921   2374.772     -0.359      0.719   -5507.561    3801.376\n",
       "p300_amp_var -1597.6444   4.09e+05     -0.004      0.997   -8.04e+05    8.01e+05\n",
       "p300_lat_avg    -2.3770      4.227     -0.562      0.574     -10.661       5.907\n",
       "p300_lat_var   339.9954    136.471      2.491      0.013      72.517     607.474\n",
       "n200_amp_avg -1876.0645   3138.879     -0.598      0.550   -8028.153    4276.024\n",
       "n200_amp_var -3398.9531   3.83e+05     -0.009      0.993   -7.53e+05    7.46e+05\n",
       "n200_lat_avg    16.5093      4.667      3.537      0.000       7.362      25.657\n",
       "n200_lat_var  -326.6794    152.126     -2.147      0.032    -624.840     -28.519\n",
       "================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = sm.Logit(y1_train, X1_train_constant).fit()\n",
    "lin_reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold for Cook Distance = 0.011594202898550725\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# check Assumptions1 Linearity of features with logit(p) \n",
    "\n",
    "# Visualize predictor continuous variable vs logit values (Age)\n",
    "\n",
    "predicted = lin_reg.predict(X1_train_constant)\n",
    "log_odds = np.log(predicted / (1 - predicted))\n",
    "\n",
    "#plt.scatter(x = x1_train['p300_amp_avg'].values, y = log_odds) \n",
    "#plt.title('P300 Amp Avg vs. Log Odds')\n",
    "#plt.xlabel ('P300 Amp Avg' )\n",
    "#plt.savefig('lr_fs1_1.png')\n",
    "plt.scatter(x = x1_train['p300_lat_var'].values, y = log_odds);\n",
    "plt.title('P300 Lat Var vs. Log Odds')\n",
    "plt.xlabel ('P300 Lat Var' )\n",
    "plt.savefig('lr_fs1_2.png')\n",
    "\n",
    "####################################################################\n",
    "\n",
    "# Assumption 2: multi collinearity \n",
    "corrMatrix = x1_train.corr()\n",
    "heatmap = sns.heatmap(corrMatrix, annot=True, cmap=\"RdYlGn\")\n",
    "fig = heatmap.get_figure()\n",
    "fig.savefig(\"lr_fs1_3.png\")\n",
    "#################################################################\n",
    "\n",
    "# Assumption 3: Independence of observations/residuals \n",
    "\n",
    "# Setup plot \n",
    "\n",
    "fig = plt.figure(figsize=(8,5))\n",
    "ax = fig.add_subplot(111, title=\"Residual Series Plot\",\n",
    "                     xlabel=\"Index Number\", \n",
    "                     ylabel=\"Deviance Residuals\")\n",
    "\n",
    "# Generate residual series plot using standardized deviance residuals\n",
    "plt.scatter(X1_train_constant.index.tolist(), lin_reg.resid_dev)\n",
    "\n",
    "# Draw horizontal line at y=0\n",
    "plt.axhline(y = 0, ls=\"--\", color='red')\n",
    "plt.savefig('lr_fs1_4.png')\n",
    "############################################################################\n",
    "# Assumption 4: No Influential Outliers \n",
    "from scipy import stats\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "from statsmodels.genmod import families\n",
    "import statsmodels.stats.tests.test_influence\n",
    "\n",
    "lin_reg = GLM(y1_train,X1_train_constant,family=families.Binomial()).fit() \n",
    "# had to use GLM as LogitResults for Logit did not have .get_influence()\n",
    "\n",
    "# Get influence measures\n",
    "influence = lin_reg.get_influence(observed=False)\n",
    "\n",
    "# Obtain summary df of influence measures\n",
    "summ_df = influence.summary_frame()\n",
    "\n",
    "# Filter summary df to Cook distance\n",
    "diagnosis_df = summ_df.loc[:,['cooks_d']]\n",
    "\n",
    "# Append absolute standardized residual values\n",
    "diagnosis_df['std_resid'] = stats.zscore(lin_reg.resid_pearson)\n",
    "diagnosis_df['std_resid'] = diagnosis_df.loc[:,'std_resid'].apply(lambda x: np.abs(x))\n",
    "\n",
    "# Sort by Cook's Distance\n",
    "diagnosis_df.sort_values(\"cooks_d\", ascending=False)\n",
    "diagnosis_df.head()\n",
    "\n",
    "# Set Cook's distance threshold\n",
    "cook_threshold = 4 / len(X1_train_constant)\n",
    "print(f\"Threshold for Cook Distance = {cook_threshold}\")\n",
    "\n",
    "# Plot influence measures (Cook's distance)\n",
    "fig = influence.plot_index(y_var=\"cooks\", threshold=cook_threshold)\n",
    "plt.axhline(y = cook_threshold, ls=\"--\", color='red')\n",
    "plt.tight_layout(pad=2)\n",
    "plt.savefig('lr_fs1_5.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6941176470588235\n",
      "[[  0  52]\n",
      " [  0 118]]\n",
      "Test Accuracy Score 0.6941176470588235\n",
      "precision =  0.6941176470588235\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.8194444444444444\n"
     ]
    },
    {
     "data": {
      "image/png": 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4JiLJpEsIvnP3XQDuvt3M3o2YCECQA9gvYbk9QWujRAXA9DAR2AcYYmbFEXIbUgnqOCYiyaRLCH5oZkvDzwZ0DpcNcHc/rIJjFwJdzKwT8DFwJjAicQd371Ty2cweBWYpEai6dG/+6jgmIonSJQT/UdkTu3uxmV1O0BqoHjDF3Zeb2ahw+8TKnlsqpjd/EclEukHnqjTQnLvPBmaXW5c0AXD386tyLSlLb/4iElXUnsVSC6nyV0SqQ9SexVILlRQBlaciIBHJROQcgZk1Bjq4+6oY45EkVPkrInGKlCMws58AhcCccLmHmc2MMS5JoDd/EYlT1BzBWIIhI14GcPdCM+sYT0iSjN78RSQuUesIit19c6yRiIhIjYiaI1hmZiOAembWBbgSeD2+sEREJFui5giuIJiv+FvgjwTDUV8dU0wiIpJFUXMEB7v7DcANcQYjIiLZFzVHcI+ZvWNmt5nZIbFGJCIiWRUpIXD3/kA/YAMwyczeNrMb4wxMRESyI3LPYnf/zN0nAKMI+hTcFFdQIiKSPVE7lP2HmY01s2UEU1S+TjC/gIiI5LiolcV/AKYBA929/OQyIiKSwyIlBO5+dNyBiIhIzagwITCzp939DDN7m7LzDUeZoUxERHJAuhzBVeHvk+MOREREaka6Gco+DT9e6u6jE7eZ2V3A6N2PksrSRDMiUhOiNh/9cZJ1g6szENFw0yJSM9LVEfwncClwgJktTdjUDPhHnIHlKw03LSLZlq6O4I/A34A7gDEJ67e4+6bYohIRkaxJlxC4u681s8vKbzCzlkoMRERyX5QcwcnAYoLmo5awzYEDYopLRESyJF2roZPD352yE07+SNZCSK2DRKQmRB1r6DgzaxJ+PsfM7jGzDvGGVrclayGk1kEiUhOijjX0ENDdzLoDvwQeAZ4A+sYVWD5QCyERqQ0ymbzegaHA79z9dwRNSEVEJMdFzRFsMbNfAecCfcysHtAgvrBERCRbouYIhhNMXP//3P0zoB1wd2xRiYhI1kSdqvIzYCrQ3MxOBra7++OxRiYiIlkRqWjIzM4gyAG8TNCX4D4zu97d/5zmuEHA74B6wGR3v7Pc9rP598B1W4H/dPe3MrqDWk4DyYlIbRe1juAG4Eh3Xw9gZq2Bl4CUCUFYj/AAwYB1RcBCM5vp7isSdlsD9HX3L8xsMDAJ6JX5bdReJc1Eyz/01VRURGqLqAnBHiWJQGgj6YuVjgJWu/sHAGY2naDVUWlC4O6vJ+z/BnV0HmQ1ExWR2ixqQjDHzF4gmLcYgsrj2WmOaQd8lLBcRMVv+xcSDHC3GzO7BLgEoEMH9WMTEalOUecsvt7MTgV6E9QRTHL3GWkOsyTrPMk6zKw/QULQO8X1JxEUG1FQUJD0HCIiUjnp5iPoAowDOgNvA9e5++41n8kVAfslLLcHPklyjcOAycBgd98Y8dwiIlJN0pXzTwFmAacRjEB6XwbnXgh0MbNOZtYQOBOYmbhDOF7Rs8C57v5uBucWEZFqkq5oqJm7Pxx+XmVmb0Y9sbsXm9nlwAsEzUenuPtyMxsVbp8I3AS0Ah40MwiGsijI9CZERKTy0iUEjcysJ/8u72+cuOzuFSYM7j6bcpXKYQJQ8vki4KJMgxYRkeqTLiH4FLgnYfmzhGUHBsQRlIiIZE+6iWn6ZyuQXKcexCKSq6IOOidpJJtoBtSDWERqv6gdyiSU7s1fPYhFJNcoR5AhvfmLSF0TdfRRA84GDnD3W8P2/z9w9wWxRldL6c1fROqSqDmCB4FjgLPC5S0EI4uKiEiOi1pH0MvdDzezJQDhsNENY4xLRESyJGqOYEc4v4BD6XwEu2KLSkREsiZqQjABmAG0MbP/Bl4DfhNbVCIikjVRh6GeamaLgR8RDC9xiruvjDUyERHJiqithjoA24DnE9e5+7q4AhMRkeyIWln8V4L6AQMaAZ2AVcAhMcUlIiJZErVoqFvispkdDvw8lohqCY0dJCL5olI9i8Php4+s5lhqFfUgFpF8EbWO4NqExT2Aw4ENsURUi6gHsYjkg6h1BM0SPhcT1Bk8U/3hiIhItqVNCMKOZE3d/fosxCMiIllWYR2BmdV3950ERUEiIlIHpcsRLCBIBArNbCbwJ+Drko3u/myMsYmISBZErSNoCWwkmKO4pD+BAzmfEKiZqIjku3QJQZuwxdAy/p0AlPDYoopBqgf+P9dsAqBXp5Zl1quZaG7YsWMHRUVFbN++vaZDEakVGjVqRPv27WnQoEHkY9IlBPWAppRNAErkVEJQ0i+g/Ft+r04tGdqjHSN6daihyKQqioqKaNasGR07diSYP0kkf7k7GzdupKioiE6dOkU+Ll1C8Km731q10GoP9Quoe7Zv365EQCRkZrRq1YoNGzLr5pWuZ7H+d0mtp0RA5N8q8/8hXULwo8qFIiIiuaLChMDdN2UrEJFc1bRp0yqfY9GiRVx55ZUpt69du5Y//vGPkfcH6NixI926deOwww6jb9++fPjhh1WOs7pMnDiRxx9/vFrO9emnn3LyySeXWXfVVVfRrl07du3690SKY8eOZdy4cWX269ixI59//jkAn332GWeeeSadO3ema9euDBkyhHfffbdKsX377bcMHz6cAw88kF69erF27dqk+/Xr14+DDz6YHj160KNHD9avXw/AunXr6N+/Pz179uSwww5j9uzZAGzYsIFBgwZVKbZElRp0TkSqV0FBARMmTEi5vXxCkG7/EnPnzmXp0qX069eP22+/vcpxunuZh2tljRo1ipEjR1b5PAD33HMPF198cenyrl27mDFjBvvttx/z5s2LdA53Z9iwYfTr14/333+fFStW8Jvf/IZ//etfVYrtkUceoUWLFqxevZprrrmG0aNHp9x36tSpFBYWUlhYSJs2bQC4/fbbOeOMM1iyZAnTp0/n0ksvBaB169a0bduWf/zjH1WKr0TUfgQitd4tzy9nxSe7jxhbFV333Yubf5L5tBuFhYWMGjWKbdu20blzZ6ZMmUKLFi1YuHAhF154IU2aNKF379787W9/Y9myZbz88suMGzeOWbNm8corr3DVVVcBQXnvvHnzGDNmDCtXrqRHjx6cd9559OzZs3T/rVu3csUVV7Bo0SLMjJtvvpnTTjutTDzHHHNMacKxYcMGRo0axbp1wbxS48eP57jjjmPDhg2MGDGCjRs3cuSRRzJnzhwWL17M1q1bGTx4MP3792f+/Pk899xzPP300zz99NN8++23DBs2jFtuuYWvv/6aM844g6KiInbu3Mmvf/1rhg8fzpgxY5g5cyb169dn4MCBjBs3jrFjx9K0aVOuu+66lN9Vv3796NWrF3PnzuXLL7/kkUceoU+fPrt9188880yZRG7u3LkceuihDB8+nGnTptGvX7+0f6+5c+fSoEEDRo0aVbquR48emf7Zd/OXv/yFsWPHAnD66adz+eWX4+6Ry/HNjK++Cv5Nb968mX333bd02ymnnMLUqVM57rjjqhyncgQiMRg5ciR33XUXS5cupVu3btxyyy0AXHDBBUycOJH58+dTr169pMeOGzeOBx54gMLCQl599VUaN27MnXfeSZ8+fSgsLOSaa64ps/9tt91G8+bNefvtt1m6dCkDBgzY7Zxz5szhlFNOAYJik2uuuYaFCxfyzDPPcNFFFwFwyy23MGDAAN58802GDRtWmlAArFq1ipEjR7JkyRJWrVrFe++9x4IFCygsLGTx4sXMmzePOXPmsO+++/LWW2+xbNkyBg0axKZNm5gxYwbLly9n6dKl3HjjjZG/K4Di4mIWLFjA+PHjy6wvsWbNGlq0aMGee+5Zum7atGmcddZZDBs2jFmzZrFjx45Uf6ZSy5Yt44gjjki7H0CfPn1Ki3ASf1566aXd9v3444/Zb7/9AKhfvz7Nmzdn48aNSc97wQUX0KNHD2677Tbcg9b5Y8eO5cknn6R9+/YMGTKE++67r3T/goICXn311Ugxp6McgdQZlXlzj8PmzZv58ssv6du3LwDnnXceP/vZz/jyyy/ZsmULxx57LAAjRoxg1qxZux1/3HHHce2113L22Wdz6qmn0r59+wqv99JLLzF9+vTS5RYtWpR+7t+/P//6179o06ZN6VvzSy+9xIoVK0r3+eqrr9iyZQuvvfYaM2bMAGDQoEFlzrP//vtz9NFHA/Diiy/y4osv0rNnTwC2bt3Ke++9R58+fbjuuusYPXo0J598Mn369KG4uJhGjRpx0UUXcdJJJ+1Wlp/quypx6qmnAnDEEUckLV//9NNPad26denyd999x+zZs7n33ntp1qwZvXr14sUXX+Skk05K+RaeaSubTB6+JQ/0dNebOnUq7dq1Y8uWLZx22mk88cQTjBw5kmnTpnH++efzi1/8gvnz53PuueeybNky9thjD9q0acMnn3ySUeypxJojMLNBZrbKzFab2Zgk283MJoTbl4Yzn4nUSckeCsmMGTOGyZMn880333D00UfzzjvvpD1vqofZ3Llz+fDDDznkkEO46aabgKAMff78+aXl0R9//DHNmjWrML4mTZqUud6vfvWr0uNXr17NhRdeyEEHHcTixYvp1q0bv/rVr7j11lupX78+CxYs4LTTTuO5557LuIKz5E2/Xr16FBcX77a9cePGZXqVz5kzh82bN9OtWzc6duzIa6+9xrRp0wBo1aoVX3zxRZnjt2zZwt57780hhxzC4sWLI8WUSY6gffv2fPTRR0CQu9m8eTMtW7bcbb927YJRDJo1a8aIESNYsGABENQxnHHGGUBQvLd9+/bSyu3t27fTuHHjSDGnE1tCEA5f/QAwGOgKnGVmXcvtNhjoEv5cAjwUVzwi2dK8eXNatGhR+ub4xBNP0LdvX1q0aEGzZs144403AMq8xSd6//336datG6NHj6agoIB33nmHZs2asWXLlqT7Dxw4kPvvv790ufzDrnHjxowfP57HH3+cTZs27bZ/YWEhAL179+bpp58Ggrf+8ucpceKJJzJlyhS2bt0KBMUf69ev55NPPuF73/se55xzDtdddx1vvvkmW7duZfPmzQwZMoTx48eXXivddxXVQQcdVCanMG3aNCZPnszatWtZu3Yta9as4cUXX2Tbtm0cf/zxzJw5s/R7fPbZZ+nevTv16tVjwIABfPvttzz88MOl51q4cCGvvPLKbtd89dVXSxPBxJ8TTjhht31/+tOf8thjjwHw5z//mQEDBuyWaBcXF5c+3Hfs2MGsWbM49NBDAejQoQN///vfAVi5ciXbt28vzQG9++67pftVVZxFQ0cBq939AwAzmw4MBVYk7DMUeNyDV5E3zGxvM2vr7p/GGJdItdq2bVuZ4ptrr72Wxx57rLQC9IADDuAPf/gDELzhXXzxxTRp0oR+/frRvHnz3c43fvx45s6dS7169ejatSuDBw9mjz32oH79+nTv3p3zzz+/tFgG4MYbb+Syyy7j0EMPpV69etx8882lRSol2rZty1lnncUDDzzAhAkTuOyyyzjssMMoLi7m+OOPZ+LEidx8882cddZZPPXUU/Tt25e2bdvSrFmz0gd+iYEDB7Jy5UqOOSbopd+0aVOefPJJVq9ezfXXX88ee+xBgwYNeOihh9iyZQtDhw5l+/btuDv33nvvbveb6ruKokmTJnTu3JnVq1ez77778sILL/D73/++zPbevXvz/PPPM3z4cC6//HJ69+6NmdGmTRsmT54MBMU1M2bM4Oqrr+bOO++kUaNGdOzYkfHjx0eOJZkLL7yQc889lwMPPJCWLVuWSfx79OhBYWEh3377LSeeeCI7duxg586dnHDCCaWtoH77299y8cUXc++992JmPProo6UJydy5cznppJOqFF8pd4/lBzgdmJywfC5wf7l9ZgG9E5b/DhQkOdclwCJgUYcOHbwyxs5c5mNnLqvUsVJ7rVixoqZDyMiWLVtKP99xxx1+5ZVX1mA0ZW3fvt137Njh7u6vv/66d+/evWYDiujZZ5/1G264oabDyLo+ffr4pk2bkm5L9v8CWOQpntdx5giiDFQXaTA7d58ETAIoKCio1GB3taUiUfLbX//6V+644w6Ki4vZf//9efTRR2s6pFLr1q3jjDPOYNeuXTRs2LBMMUltNmzYsJQtceqqDRs2cO2115ap0K+KOBOCImC/hOX2QPkq7ij7iNQZw4cPZ/jw4TUdRlJdunRhyZIlNR1GpZQ0gc0XrVu3Lm0OXB3ibDW0EOhiZp3MrCFwJjCz3D4zgZFh66Gjgc2u+gHJkEdsjSOSDyrz/yG2HIG7F5vZ5cALBPMaTHH35WY2Ktw+EZgNDAFWA9uAC+KKR+qmRo0asXHjRlq1aqVRSCXveTgfQaNGjTI6znLtbaqgoMAXLVpU02FILaEZykTKSjVDmZktdveCZMeoZ7HktAYNGmQ0E5OI7E5jDYmI5DklBCIieU4JgYhInsu5ymIz2wBUdqqlfYDPqzGcXKB7zg+65/xQlXve391bJ9uQcwlBVZjZolS15nWV7jk/6J7zQ1z3rKIhEZE8p4RARCTP5VtCMKmmA6gBuuf8oHvOD7Hcc17VEYiIyO7yLUcgIiLlKCEQEclzdTIhMLNBZrbKzFab2Zgk283MJoTbl5rZ4TURZ3WKcM9nh/e61MxeN7PuNRFndUp3zwn7HWlmO83s9GzGF4co92xm/cys0MyWm9nuk+7mmAj/tpub2fNm9lZ4zzk9irGZTTGz9Wa2LMX26n9+pZq6LFd/CIa8fh84AGgIvAV0LbfPEOBvBDOkHQ38s6bjzsI9Hwu0CD8Pzod7Ttjv/wiGPD+9puPOwt95b4J5wTuEy21qOu4s3PN/AXeFn1sDm4CGNR17Fe75eOBwYFmK7dX+/KqLOYKjgNXu/oG7fwdMB4aW22co8LgH3gD2NrO22Q60GqW9Z3d/3d2/CBffIJgNLpdF+TsDXAE8A6zPZnAxiXLPI4Bn3X0dgLvn+n1HuWcHmlkwIUVTgoSgOLthVh93n0dwD6lU+/OrLiYE7YCPEpaLwnWZ7pNLMr2fCwneKHJZ2ns2s3bAMGBiFuOKU5S/80FACzN72cwWm9nIrEUXjyj3fD/wHwTT3L4NXOXuu7ITXo2o9udXXZyPINk0VeXbyEbZJ5dEvh8z60+QEPSONaL4Rbnn8cBod99ZR2Yvi3LP9YEjgB8BjYH5ZvaGu78bd3AxiXLPJwKFwACgM/C/Zvaqu38Vc2w1pdqfX3UxISgC9ktYbk/wppDpPrkk0v2Y2WHAZGCwu2/MUmxxiXLPBcD0MBHYBxhiZsXu/lxWIqx+Uf9tf+7uXwNfm9k8oDuQqwlBlHu+ALjTgwL01Wa2BvghsCA7IWZdtT+/6mLR0EKgi5l1MrOGwJnAzHL7zARGhrXvRwOb3f3TbAdajdLes5l1AJ4Fzs3ht8NEae/Z3Tu5e0d37wj8Gbg0hxMBiPZv+y9AHzOrb2bfA3oBK7McZ3WKcs/rCHJAmNn3gYOBD7IaZXZV+/OrzuUI3L3YzC4HXiBocTDF3Zeb2ahw+0SCFiRDgNXANoI3ipwV8Z5vAloBD4ZvyMWewyM3RrznOiXKPbv7SjObAywFdgGT3T1pM8RcEPHvfBvwqJm9TVBsMtrdc3Z4ajObBvQD9jGzIuBmoAHE9/zSEBMiInmuLhYNiYhIBpQQiIjkOSUEIiJ5TgmBiEieU0IgIpLnlBDkgXDkzcKEn44V7Lu1Gq73qJmtCa/1ppkdU4lzTDazruHn/yq37fWqxhiep+R7WRaOXrl3mv17mNmQSlynrZnNCj/3M7PNZrbEzFaa2c2VON9PS0bhNLNTSr6ncPlWMzsh03Mmucajlma01nAYi8hNkMN7nxVhv6Sjb5rZODMbEPV6Ep0Sgvzwjbv3SPhZm4VrXu/uPYAxwO8zPdjdL3L3FeHif5XbdmzVwwP+/b0cSjDI12Vp9u9B0H47U9cCDycsv+ruPQl6Pp9jZkdkcjJ3n+nud4aLpwBdE7bd5O4vVSLG2uRRYFCS9fcR/HuSaqaEIA+ZWVMz+3v4tv62me02amf4Fjsv4Y25T7h+oJnND4/9k5k1TXO5ecCB4bHXhudaZmZXh+uamNlfLRhLfpmZDQ/Xv2xmBWZ2J9A4jGNquG1r+PupxDf08C32NDOrZ2Z3m9lCC8Zr/3mEr2U+4cBdZnaUBXM2LAl/Hxz2ar0VGB7GMjyMfUp4nSXJvsfQacCc8ivDYSAWA53D3MYbYbwzzKxFGMuVZrYiXD89XHe+md1vZscCPwXuDmPqXPImb2aDzezphO+mn5k9H37O6G9oZjeF97jMzCaZlRm46ZzwO1pmZkeF+0f9XpJKNfqmu38ItDKzH2RyPokgW2Ns66fmfoCdBINyFQIzCHqU7xVu24egh2JJ58Kt4e9fADeEn+sBzcJ95wFNwvWjgZuSXO9RwrH/gZ8B/yQYCO1toAnBUMHLgZ4ED8mHE45tHv5+GShIjClhn5IYhwGPhZ8bEozI2Bi4BLgxXL8nsAjolCTOrQn39ydgULi8F1A//HwC8Ez4+Xzg/oTjfwOcE37em2A8nyblrtEJWJyw3A+YFX5uBawFDiHoCdw3XH8rMD78/AmwZ8k1yseR+F0nLod/43UJf6uHgHMq+TdsmbD+CeAnCX+jh8PPxxOOn5/qeyl37wUEvZ5T/ZvtSJLx+AlyVqfV9P+puvZT54aYkKS+8aCYBgAzawD8xsyOJxiGoB3wfeCzhGMWAlPCfZ9z90Iz60tQDPGP8KWwIcGbdDJ3m9mNwAaC0U5/BMzw4C0YM3sW6EPwpjzOzO4ieEi8msF9/Q2YYGZ7EhQlzHP3b8xsIHBYQhl3c6ALsKbc8Y3NrJDgobMY+N+E/R8zsy4Eozo2SHH9gcBPzey6cLkR0IGyY/u0Db+DRH3MbAnBd38nwSBie7t7yWxijxEkTBAkEFPN7DnguRRx7MaDoRnmAD8xsz8DJwG/BDL5G5bob2a/BL4HtCRIxJ8Pt00LrzfPzPayoJ4l1feSGN8i4KKo95NgPbBvJY6TCighyE9nE8zkdIS77zCztQT/WUuF/7GPJ3iAPGFmdwNfAP/r7mdFuMb17v7nkgVLUYHp7u+GZeRDgDvM7EV3vzXKTbj7djN7mWAY4uGEDyWC8WaucPcX0pziG3fvYWbNgVkEdQQTCMaumevuwyyoWH85xfFG8Ha6qqJrUO67JagjOLn0JMH1UzmJ4G37p8CvzeyQCvYt7ymCe9oELHT3LWGxTtS/IWbWCHiQIHf2kZmNpez9lB+jxknxvVgwIFxVNSL4TqUaqY4gPzUH1oeJQH9g//I7mNn+4T4PA48QTJ33BnCcmZWU+X/PzA6KeM15wCnhMU0IinVeNbN9gW3u/iQwLrxOeTvCnEky0wkG3epDMDAZ4e//LDnGzA4Kr5mUu28GrgSuC49pDnwcbj4/YdctBEVkJV4ArigpMzeznklO/y5BjiOl8PpfWFgPA5wLvGJmewD7uftcgrf5vQmK1RKVjynRywTf58UEiQJk/jcseeh/HtYllG9JVFKn05tgFMzNRPteKusgIGcH0autlBDkp6lAgZktIsgdvJNkn35AYViEcRrwO3ffQPBgnGZmSwkeKj+MckF3f5Og3HkBQZ3BZHdfAnQDFoRFNDcAtyc5fBKw1MLK4nJeJHhjfsmDqQwhmHNhBfCmBU0Qf0+a3G8Yy1sEwxz/D0Hu5B8E9Qcl5gJdSyqLCXIODcLYloXL5c/7NfB+yYO3AucRFKctJWiddGt47SctGFVzCXCvu39Z7rjpwPVhpWznctfeSZDTGRz+JtO/YXi9hwnqd54jKDJM9IUFzXknEhQBQoTvxYKGAJOTXdOC0TfnAwebWZGZXRiub0DQ8GBRqnilcjT6qEjMzGwYQTHcjTUdSy4Lv8fD3f3XNR1LXaM6ApGYufsMM2tV03HUAfWB39Z0EHWRcgQiInlOdQQiInlOCYGISJ5TQiAikueUEIiI5DklBCIiee7/A6ikPlmxP0DkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression().fit(X1_train_constant, y1_train)\n",
    "y1_pred = lr.predict(X1_test_constant)\n",
    "\n",
    "#accuracy\n",
    "print('Test Accuracy Score', lr.score(X1_test_constant, y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(lr, X1_test_constant, y1_test)\n",
    "plt.savefig('rocauc/lr_1.png')\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', lr.score(X1_test_constant, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.664319\n",
      "         Iterations: 35\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      Y   No. Observations:                  345\n",
      "Model:                          Logit   Df Residuals:                      340\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Sun, 02 Apr 2023   Pseudo R-squ.:                0.009352\n",
      "Time:                        18:13:41   Log-Likelihood:                -229.19\n",
      "converged:                      False   LL-Null:                       -231.35\n",
      "Covariance Type:            nonrobust   LLR p-value:                    0.3635\n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "const              0.4068      0.140      2.912      0.004       0.133       0.681\n",
      "p300_m_amp_avg -1019.9872   2490.479     -0.410      0.682   -5901.236    3861.261\n",
      "p300_m_amp_var  4315.5970   4.43e+05      0.010      0.992   -8.64e+05    8.73e+05\n",
      "n200_m_amp_avg -2510.5487   4875.865     -0.515      0.607   -1.21e+04    7045.971\n",
      "n200_m_amp_var  1516.1851   5.98e+05      0.003      0.998   -1.17e+06    1.17e+06\n",
      "==================================================================================\n"
     ]
    }
   ],
   "source": [
    "X2_train_constant = sm.add_constant(x2_train)\n",
    "X2_test_constant = sm.add_constant(x2_test)\n",
    "lin_reg = sm.Logit(y2_train,X2_train_constant).fit()\n",
    "print(lin_reg.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold for Cook Distance = 0.011594202898550725\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# check Assumptions1 Linearity of features with logit(p) \n",
    "predicted = lin_reg.predict(X2_train_constant)\n",
    "\n",
    "# Get log odds values\n",
    "log_odds = np.log(predicted / (1 - predicted))\n",
    "\n",
    "# Visualize predictor continuous variable vs logit values (Age)\n",
    "#plt.scatter(x = x2_train['p300_m_amp_avg'].values, y = log_odds);\n",
    "#plt.title(\"P300 Mean Amp Avg Vs Log Odds\")\n",
    "#plt.xlabel(\"P300 Mean Amp Avg\")\n",
    "#plt.savefig('lr_fs2_1.png')\n",
    "\n",
    "plt.scatter(x = x2_train['p300_m_amp_var'].values, y = log_odds);\n",
    "plt.title(\"P300 Mean Amp Var Vs Log Odds\")\n",
    "plt.xlabel(\"P300 Mean Amp Var\")\n",
    "plt.savefig('lr_fs2_2.png')\n",
    "\n",
    "####################################################################\n",
    "\n",
    "# Assumption 2: multi collinearity \n",
    "corrMatrix = x2_train.corr()\n",
    "plt.subplots(figsize=(9, 5))\n",
    "heatmap = sns.heatmap(corrMatrix, annot=True, cmap=\"RdYlGn\")\n",
    "fig = heatmap.get_figure()\n",
    "fig.savefig(\"lr_fs2_3.png\")\n",
    "\n",
    "#################################################################\n",
    "\n",
    "# Assumption 3: Independence of observations/residuals \n",
    "\n",
    "# Setup plot \n",
    "\n",
    "fig = plt.figure(figsize=(8,5))\n",
    "ax = fig.add_subplot(111, title=\"Residual Series Plot\",\n",
    "                     xlabel=\"Index Number\", \n",
    "                     ylabel=\"Deviance Residuals\")\n",
    "\n",
    "# Generate residual series plot using standardized deviance residuals\n",
    "plt.scatter(X2_train_constant.index.tolist(), \n",
    "        stats.zscore(lin_reg.resid_dev))\n",
    "\n",
    "# Draw horizontal line at y=0\n",
    "plt.axhline(y = 0, ls=\"--\", color='red')\n",
    "\n",
    "plt.savefig('lr_fs2_4.png')\n",
    "\n",
    "############################################################################\n",
    "# Assumption 4: No Influential Outliers \n",
    "from scipy import stats\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "from statsmodels.genmod import families\n",
    "import statsmodels.stats.tests.test_influence\n",
    "\n",
    "lin_reg = GLM(y2_train,X2_train_constant,family=families.Binomial()).fit() \n",
    "# had to use GLM as LogitResults for Logit did not have .get_influence()\n",
    "\n",
    "# Get influence measures\n",
    "influence = lin_reg.get_influence(observed=False)\n",
    "\n",
    "# Obtain summary df of influence measures\n",
    "summ_df = influence.summary_frame()\n",
    "\n",
    "# Filter summary df to Cook distance\n",
    "diagnosis_df = summ_df.loc[:,['cooks_d']]\n",
    "\n",
    "# Append absolute standardized residual values\n",
    "diagnosis_df['std_resid'] = stats.zscore(lin_reg.resid_pearson)\n",
    "diagnosis_df['std_resid'] = diagnosis_df.loc[:,'std_resid'].apply(lambda x: np.abs(x))\n",
    "\n",
    "# Sort by Cook's Distance\n",
    "diagnosis_df.sort_values(\"cooks_d\", ascending=False)\n",
    "diagnosis_df.head()\n",
    "\n",
    "# Set Cook's distance threshold\n",
    "cook_threshold = 4 / len(X2_train_constant)\n",
    "print(f\"Threshold for Cook Distance = {cook_threshold}\")\n",
    "\n",
    "# Plot influence measures (Cook's distance)\n",
    "fig = influence.plot_index(y_var=\"cooks\", threshold=cook_threshold)\n",
    "plt.axhline(y = cook_threshold, ls=\"--\", color='red')\n",
    "fig.tight_layout(pad=2)\n",
    "plt.savefig('lr_fs2_5.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6941176470588235\n",
      "[[  0  52]\n",
      " [  0 118]]\n",
      "Test Accuracy Score 0.6941176470588235\n",
      "precision =  0.6941176470588235\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.8194444444444444\n"
     ]
    },
    {
     "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": [
    "# Logistic Regression \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression().fit(X2_train_constant, y2_train)\n",
    "y2_pred = lr.predict(X2_test_constant)\n",
    "\n",
    "#accuracy\n",
    "print('Test Accuracy Score', lr.score(X2_test_constant, y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(lr, X2_test_constant, y2_test)\n",
    "plt.savefig('rocauc/lr_2.png')\n",
    "\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', lr.score(X2_test_constant, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max ROC AUC:- 0.6233702737940026 at K = 22\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "error_rate = []\n",
    "for i in range(1,40):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x1_train,y1_train)\n",
    "    pred_i = knn.predict(x1_test)\n",
    "    #error_rate.append(np.mean(pred_i != y1_test.to_numpy() ))\n",
    "    #new error rate/ 'auc-roc'\n",
    "    error_rate.append(roc_auc_score(y1_test, pred_i))\n",
    "    \n",
    "    \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('Feature Set 1: ROC AUC vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('ROC AUC')\n",
    "print(\"Max ROC AUC:-\",max(error_rate),\"at K =\",error_rate.index(max(error_rate)))\n",
    "plt.savefig('KNN_fs1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.7\n",
      "[[20 32]\n",
      " [19 99]]\n",
      "Cross-Validation Accuracy Scores [0.59223301 0.57281553 0.6407767  0.67961165 0.62135922]\n",
      "Test Accuracy Score 0.7\n",
      "precision =  0.7557251908396947\n",
      "Sensitivity :  0.38461538461538464\n",
      "Specificity :  0.8389830508474576\n",
      "f1 score =  0.7951807228915663\n"
     ]
    },
    {
     "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": [
    "knn = KNeighborsClassifier(n_neighbors=22)\n",
    "knn.fit(x1_train, y1_train) \n",
    "y1_pred = knn.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y1_test, y1_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x1_test, y1_test)\n",
    "plt.savefig('rocauc/KNN_1.png')\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, X1, Y, cv=5, scoring = 'accuracy'))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', knn.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum ROC AUC:- 0.6134289439374185 at K = 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "error_rate = []\n",
    "for i in range(1,40):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x2_train,y2_train)\n",
    "    pred_i = knn.predict(x2_test)\n",
    "    #error_rate.append(np.mean(pred_i != y2_test.to_numpy() ))\n",
    "    error_rate.append(roc_auc_score(y2_test, pred_i))\n",
    "    \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('Feature Set 2: ROC AUC vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('ROC AUC')\n",
    "print(\"Maximum ROC AUC:-\",max(error_rate),\"at K =\",error_rate.index(max(error_rate)))\n",
    "plt.savefig('KNN_fs2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6411764705882353\n",
      "[[24 28]\n",
      " [33 85]]\n",
      "Cross-Validation Accuracy Scores [0.61165049 0.62135922 0.53398058 0.55339806 0.55339806]\n",
      "Test Accuracy Score 0.6411764705882353\n",
      "precision =  0.7522123893805309\n",
      "Sensitivity :  0.46153846153846156\n",
      "Specificity :  0.7203389830508474\n",
      "f1 score =  0.735930735930736\n"
     ]
    },
    {
     "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": [
    "knn = KNeighborsClassifier(n_neighbors=7)\n",
    "knn.fit(x2_train, y2_train) \n",
    "y2_pred = knn.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y2_test, y2_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x2_test, y2_test)\n",
    "plt.savefig('rocauc/KNN_2.png')\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, X2, Y, cv=5, scoring = 'accuracy'))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', knn.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param for GS {'max_depth': 3, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "CV score for GS 0.6145987737670158\n",
      "Train AUC ROC Score for GS:  0.6474282296650717\n",
      "Test AUC ROC Score for GS:  0.5751303780964797\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "param_grid = {\n",
    "    \"max_depth\": [3,5,10,15,20,None],\n",
    "    \"min_samples_split\": [2,5,7,10],\n",
    "    \"min_samples_leaf\": [1,2,5]\n",
    "}\n",
    "\n",
    "clf = DecisionTreeClassifier(random_state=42)\n",
    "grid_cv = GridSearchCV(clf, param_grid, scoring=\"roc_auc\", n_jobs=-1, cv=3).fit(x1_train, y1_train)\n",
    "\n",
    "print(\"Param for GS\", grid_cv.best_params_)\n",
    "print(\"CV score for GS\", grid_cv.best_score_)\n",
    "print(\"Train AUC ROC Score for GS: \", roc_auc_score(y1_train, grid_cv.predict(x1_train)))\n",
    "print(\"Test AUC ROC Score for GS: \", roc_auc_score(y1_test, grid_cv.predict(x1_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5882352941176471\n",
      "[[23 29]\n",
      " [41 77]]\n",
      "Cross-Validation Accuracy Scores [0.65048544 0.59223301 0.54368932 0.61165049 0.63106796]\n",
      "Test Accuracy Score 0.5882352941176471\n",
      "precision =  0.7264150943396226\n",
      "Sensitivity :  0.4423076923076923\n",
      "Specificity :  0.652542372881356\n",
      "f1 score =  0.6875\n"
     ]
    },
    {
     "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": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier(criterion = 'gini', max_depth = 5, min_samples_leaf =  1, min_samples_split = 7)\n",
    "dtc = dtc.fit(x1_train, y1_train) \n",
    "y1_pred = dtc.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', dtc.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(dtc, x1_test, y1_test)\n",
    "plt.savefig('rocauc/DT_1.png')\n",
    "\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(dtc, X1, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', dtc.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)\n",
    "tree.plot_tree(dtc,filled = True);\n",
    "fig.savefig('DT_FS1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param for GS {'max_depth': 3, 'min_samples_leaf': 5, 'min_samples_split': 2}\n",
      "CV score for GS 0.5796240285654274\n",
      "Train AUC ROC Score for GS:  0.6061075147762454\n",
      "Test AUC ROC Score for GS:  0.5920795306388527\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "param_grid = {\n",
    "    \"max_depth\": [3,5,10,15,20,None],\n",
    "    \"min_samples_split\": [2,5,7,10],\n",
    "    \"min_samples_leaf\": [1,2,5]\n",
    "}\n",
    "\n",
    "clf = DecisionTreeClassifier(random_state=42)\n",
    "grid_cv = GridSearchCV(clf, param_grid, scoring=\"roc_auc\", n_jobs=-1, cv=3).fit(x2_train, y2_train)\n",
    "\n",
    "print(\"Param for GS\", grid_cv.best_params_)\n",
    "print(\"CV score for GS\", grid_cv.best_score_)\n",
    "print(\"Train AUC ROC Score for GS: \", roc_auc_score(y2_train, grid_cv.predict(x2_train)))\n",
    "print(\"Test AUC ROC Score for GS: \", roc_auc_score(y2_test, grid_cv.predict(x2_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6352941176470588\n",
      "[[25 27]\n",
      " [35 83]]\n",
      "Cross-Validation Accuracy Scores [0.63106796 0.6407767  0.57281553 0.63106796 0.63106796]\n",
      "Test Accuracy Score 0.6352941176470588\n",
      "precision =  0.7545454545454545\n",
      "Sensitivity :  0.4807692307692308\n",
      "Specificity :  0.7033898305084746\n",
      "f1 score =  0.7280701754385965\n"
     ]
    },
    {
     "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": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier(criterion = 'gini', max_depth = 3, min_samples_leaf =  2, min_samples_split = 7)\n",
    "dtc = dtc.fit(x2_train, y2_train) \n",
    "y2_pred = dtc.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', dtc.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(dtc, x2_test, y2_test)\n",
    "plt.savefig('rocauc/DT_2.png')\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(dtc, X2, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', dtc.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)\n",
    "tree.plot_tree(dtc,filled = True);\n",
    "fig.savefig('DT_FS2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param for GS {'max_depth': 2, 'max_features': 6, 'max_leaf_nodes': 2, 'min_samples_leaf': 20, 'n_estimators': 100}\n",
      "CV score for GS 0.628217061241636\n",
      "Train AUC ROC Score for GS:  0.5747607655502392\n",
      "Test AUC ROC Score for GS:  0.5399282920469362\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [2,3,5,10,20],\n",
    "    'min_samples_leaf': [5,10,20,50,100,200],\n",
    "    'n_estimators': [10,25,30,50,100,200], \n",
    "    'max_leaf_nodes': [2,5,7,10],\n",
    "    'max_features': [2,4,6,8]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier(criterion = 'gini', random_state=42)\n",
    "grid_cv = GridSearchCV(rf, param_grid, scoring=\"roc_auc\", n_jobs=-1, cv=3).fit(x1_train, y1_train)\n",
    "\n",
    "print(\"Param for GS\", grid_cv.best_params_)\n",
    "print(\"CV score for GS\", grid_cv.best_score_)\n",
    "print(\"Train AUC ROC Score for GS: \", roc_auc_score(y1_train, grid_cv.predict(x1_train)))\n",
    "print(\"Test AUC ROC Score for GS: \", roc_auc_score(y1_test, grid_cv.predict(x1_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6647058823529411\n",
      "[[19 33]\n",
      " [24 94]]\n",
      "Cross-Validation Accuracy Scores [0.62135922 0.66019417 0.62135922 0.66019417 0.57281553]\n",
      "Test Accuracy Score 0.6647058823529411\n",
      "precision =  0.7401574803149606\n",
      "Sensitivity :  0.36538461538461536\n",
      "Specificity :  0.7966101694915254\n",
      "f1 score =  0.7673469387755102\n"
     ]
    },
    {
     "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": [
    "rfc = RandomForestClassifier(max_depth =  10, max_features= 4, max_leaf_nodes= 10, min_samples_leaf =  5, n_estimators = 25)\n",
    "rfc = rfc.fit(x1_train, y1_train) \n",
    "y1_pred = rfc.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x1_test, y1_test)\n",
    "plt.savefig('rocauc/RF_1.png')\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(rfc, X1, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', rfc.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param for GS {'max_depth': 5, 'max_features': 2, 'max_leaf_nodes': 10, 'min_samples_leaf': 5, 'n_estimators': 10}\n",
      "CV score for GS 0.5668074934237506\n",
      "Train AUC ROC Score for GS:  0.5283035462989023\n",
      "Test AUC ROC Score for GS:  0.5246088657105606\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [2,3,5,10,20],\n",
    "    'min_samples_leaf': [5,10,20,50,100,200],\n",
    "    'n_estimators': [10,25,30,50,100,200], \n",
    "    'max_leaf_nodes': [2,5,7,10],\n",
    "    'max_features': [2,4,6,8]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier(criterion = 'gini', random_state=42)\n",
    "grid_cv = GridSearchCV(rf, param_grid, scoring=\"roc_auc\", n_jobs=-1, cv=3).fit(x2_train, y2_train)\n",
    "\n",
    "print(\"Param for GS\", grid_cv.best_params_)\n",
    "print(\"CV score for GS\", grid_cv.best_score_)\n",
    "print(\"Train AUC ROC Score for GS: \", roc_auc_score(y2_train, grid_cv.predict(x2_train)))\n",
    "print(\"Test AUC ROC Score for GS: \", roc_auc_score(y2_test, grid_cv.predict(x2_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6941176470588235\n",
      "[[  0  52]\n",
      " [  0 118]]\n",
      "Cross-Validation Accuracy Scores [0.63106796 0.63106796 0.63106796 0.6407767  0.6407767 ]\n",
      "Test Accuracy Score 0.6941176470588235\n",
      "precision =  0.6941176470588235\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.8194444444444444\n"
     ]
    },
    {
     "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": [
    "rfc = RandomForestClassifier(max_depth =  2, max_features= 2, max_leaf_nodes= 2, min_samples_leaf =  10, n_estimators = 50)\n",
    "rfc = rfc.fit(x2_train, y2_train) \n",
    "y2_pred = rfc.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x2_test, y2_test)\n",
    "plt.savefig('rocauc/RF_2.png')\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(rfc, X2, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', rfc.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param for GS {'C': 0.01, 'degree': 1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "CV score for GS 0.6209563817125253\n",
      "Train AUC ROC Score for GS:  0.5\n",
      "Test AUC ROC Score for GS:  0.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "param_grid = {\n",
    "    'C':[0.01,0.1,1,10],\n",
    "    'kernel' : [\"linear\",\"poly\",\"rbf\",\"sigmoid\"],\n",
    "    'degree' : [1,3,5,7],\n",
    "    'gamma' : [0.01,1]\n",
    "}\n",
    "\n",
    "\n",
    "svc = svm.SVC(random_state=42)\n",
    "grid_cv = GridSearchCV(svc, param_grid, scoring=\"roc_auc\", n_jobs=-1, cv=3).fit(x1_train, y1_train)\n",
    "\n",
    "print(\"Param for GS\", grid_cv.best_params_)\n",
    "print(\"CV score for GS\", grid_cv.best_score_)\n",
    "print(\"Train AUC ROC Score for GS: \", roc_auc_score(y1_train, grid_cv.predict(x1_train)))\n",
    "print(\"Test AUC ROC Score for GS: \", roc_auc_score(y1_test, grid_cv.predict(x1_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6941176470588235\n",
      "[[  0  52]\n",
      " [  0 118]]\n",
      "Cross-Validation Accuracy Scores [0.63106796 0.63106796 0.63106796 0.6407767  0.6407767 ]\n",
      "Test Accuracy Score 0.6941176470588235\n",
      "precision =  0.6941176470588235\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.8194444444444444\n"
     ]
    },
    {
     "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": [
    "# SVM \n",
    "from sklearn import svm\n",
    "clf = svm.SVC(C= 10, degree = 1, gamma = 1, kernel= 'rbf') \n",
    "clf.fit(x1_train, y1_train)\n",
    "y1_pred = clf.predict(x1_test)\n",
    "\n",
    "#accuracy\n",
    "print('Test Accuracy Score', clf.score(x1_test, y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(clf, x1_test, y1_test)\n",
    "plt.savefig('rocauc/SVM_1.png')\n",
    "\n",
    "#cross valiation  \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(clf, X1, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', clf.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param for GS {'C': 0.01, 'degree': 1, 'gamma': 0.01, 'kernel': 'poly'}\n",
      "CV score for GS 0.5245876716576149\n",
      "Train AUC ROC Score for GS:  0.5\n",
      "Test AUC ROC Score for GS:  0.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "param_grid = {\n",
    "    'C':[0.01,0.1,1,10],\n",
    "    'kernel' : [\"linear\",\"poly\",\"rbf\",\"sigmoid\"],\n",
    "    'degree' : [1,3,5,7],\n",
    "    'gamma' : [0.01,1]\n",
    "}\n",
    "\n",
    "\n",
    "svc = svm.SVC(random_state=42)\n",
    "grid_cv = GridSearchCV(svc, param_grid, scoring=\"roc_auc\", n_jobs=-1, cv=3).fit(x2_train, y2_train)\n",
    "\n",
    "print(\"Param for GS\", grid_cv.best_params_)\n",
    "print(\"CV score for GS\", grid_cv.best_score_)\n",
    "print(\"Train AUC ROC Score for GS: \", roc_auc_score(y2_train, grid_cv.predict(x2_train)))\n",
    "print(\"Test AUC ROC Score for GS: \", roc_auc_score(y2_test, grid_cv.predict(x2_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6941176470588235\n",
      "[[  0  52]\n",
      " [  0 118]]\n",
      "Cross-Validation Accuracy Scores [0.63106796 0.63106796 0.63106796 0.6407767  0.6407767 ]\n",
      "Test Accuracy Score 0.6941176470588235\n",
      "precision =  0.6941176470588235\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.8194444444444444\n"
     ]
    },
    {
     "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": [
    "# SVM \n",
    "from sklearn import svm\n",
    "clf = svm.SVC(C= 0.1, degree = 1, gamma = 0.01, kernel= 'rbf') \n",
    "clf.fit(x2_train, y2_train)\n",
    "y2_pred = clf.predict(x2_test)\n",
    "\n",
    "#accuracy\n",
    "print('Test Accuracy Score', clf.score(x2_test, y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(clf, x2_test, y2_test)\n",
    "plt.savefig('rocauc/SVM_2.png')\n",
    "\n",
    "#cross valiation  \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(clf, X2, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', clf.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample = RandomOverSampler(sampling_strategy='minority')\n",
    "# fit and apply the transform\n",
    "# split once and then segregate into x1 and x2\n",
    "x1_train_over, y1_train_over = oversample.fit_resample(x1_train, y1_train)\n",
    "x2_train_over, y2_train_over = oversample.fit_resample(x2_train, y2_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param for GS {'max_depth': 5, 'max_features': 6, 'max_leaf_nodes': 10, 'min_samples_leaf': 5, 'n_estimators': 50}\n",
      "CV score for GS 0.6582352361234348\n",
      "Train AUC ROC Score for GS:  0.7727272727272728\n",
      "Test AUC ROC Score for GS:  0.6158735332464146\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [2,3,5,10,20],\n",
    "    'min_samples_leaf': [5,10,20,50,100,200],\n",
    "    'n_estimators': [10,25,30,50,100,200], \n",
    "    'max_leaf_nodes': [2,5,7,10],\n",
    "    'max_features': [2,4,6,8]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier(criterion = 'gini', random_state=42)\n",
    "grid_cv = GridSearchCV(rf, param_grid, scoring=\"roc_auc\", n_jobs=-1, cv=3).fit(x1_train_over, y1_train_over)\n",
    "\n",
    "print(\"Param for GS\", grid_cv.best_params_)\n",
    "print(\"CV score for GS\", grid_cv.best_score_)\n",
    "print(\"Train AUC ROC Score for GS: \", roc_auc_score(y1_train_over, grid_cv.predict(x1_train_over)))\n",
    "print(\"Test AUC ROC Score for GS: \", roc_auc_score(y1_test, grid_cv.predict(x1_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6235294117647059\n",
      "[[30 22]\n",
      " [42 76]]\n",
      "Test Accuracy Score 0.6235294117647059\n",
      "precision =  0.7755102040816326\n",
      "Sensitivity :  0.5769230769230769\n",
      "Specificity :  0.6440677966101694\n",
      "f1 score =  0.7037037037037036\n"
     ]
    },
    {
     "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": [
    "#rfc = RandomForestClassifier(max_depth =  10, max_features= 4, max_leaf_nodes= 10, min_samples_leaf =  5, n_estimators = 200)\n",
    "rfc = rfc.fit(x1_train_over, y1_train_over) \n",
    "y1_pred = rfc.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x1_test, y1_test)\n",
    "plt.savefig('rocauc/rf_FS1_OS.png')\n",
    "\n",
    "#cross valiation \n",
    "#print('Cross-Validation Accuracy Scores', cross_val_score(rfc, X1, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', rfc.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param for GS {'max_depth': 10, 'max_features': 4, 'max_leaf_nodes': 10, 'min_samples_leaf': 5, 'n_estimators': 100}\n",
      "CV score for GS 0.6157497781721384\n",
      "Train AUC ROC Score for GS:  0.7200956937799042\n",
      "Test AUC ROC Score for GS:  0.588820078226858\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [2,3,5,10,20],\n",
    "    'min_samples_leaf': [5,10,20,50,100,200],\n",
    "    'n_estimators': [10,25,30,50,100,200], \n",
    "    'max_leaf_nodes': [2,5,7,10],\n",
    "    'max_features': [2,4,6,8]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier(criterion = 'gini', random_state=42)\n",
    "grid_cv = GridSearchCV(rf, param_grid, scoring=\"roc_auc\", n_jobs=-1, cv=3).fit(x2_train_over, y2_train_over)\n",
    "\n",
    "print(\"Param for GS\", grid_cv.best_params_)\n",
    "print(\"CV score for GS\", grid_cv.best_score_)\n",
    "print(\"Train AUC ROC Score for GS: \", roc_auc_score(y2_train_over, grid_cv.predict(x2_train_over)))\n",
    "print(\"Test AUC ROC Score for GS: \", roc_auc_score(y2_test, grid_cv.predict(x2_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5235294117647059\n",
      "[[36 16]\n",
      " [65 53]]\n",
      "Cross-Validation Accuracy Scores [0.59223301 0.6407767  0.59223301 0.61165049 0.59223301]\n",
      "Test Accuracy Score 0.5235294117647059\n",
      "precision =  0.7681159420289855\n",
      "Sensitivity :  0.6923076923076923\n",
      "Specificity :  0.4491525423728814\n",
      "f1 score =  0.5668449197860963\n"
     ]
    },
    {
     "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": [
    "rfc = RandomForestClassifier(max_depth =  5, max_features= 4, max_leaf_nodes= 10, min_samples_leaf =  5, n_estimators = 200)\n",
    "rfc = rfc.fit(x2_train_over, y2_train_over) \n",
    "y2_pred = rfc.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x2_test, y2_test)\n",
    "plt.savefig('rocauc/rf_FS2_OS.png')\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(rfc, X2, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', rfc.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max ROC AUC:- 0.6269556714471969 at K = 27\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "error_rate = []\n",
    "for i in range(1,40):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x1_train_over,y1_train_over)\n",
    "    pred_i = knn.predict(x1_test)\n",
    "    #error_rate.append(np.mean(pred_i != y1_test.to_numpy() ))\n",
    "    #new error rate/ 'auc-roc'\n",
    "    error_rate.append(roc_auc_score(y1_test, pred_i))\n",
    "    \n",
    "    \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('Feature Set 1: ROC AUC vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('ROC AUC')\n",
    "print(\"Max ROC AUC:-\",max(error_rate),\"at K =\",error_rate.index(max(error_rate)))\n",
    "plt.savefig('KNN_fs1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5941176470588235\n",
      "[[37 15]\n",
      " [54 64]]\n",
      "Cross-Validation Accuracy Scores [0.61165049 0.59223301 0.63106796 0.65048544 0.62135922]\n",
      "Test Accuracy Score 0.5941176470588235\n",
      "precision =  0.810126582278481\n",
      "Sensitivity :  0.7115384615384616\n",
      "Specificity :  0.5423728813559322\n",
      "f1 score =  0.649746192893401\n"
     ]
    },
    {
     "data": {
      "image/png": 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NWzsxuP25VIywJqCm/PM3QtlI9+NAVc3Mu0xEbKQME9K2pR7jk8Xb+Hz5Do5m+ujRtA5/ub49l9g4wCbMeK0jWATkH8i+oHnGlGmqysJNqYxbtJVZ61OIEGFQ+3O5+6I4GwfYhC1/dQQNgIZAVRG5EKd7CYAaQFSAYzOmxKSf8PHVyp2MW5TEppQ0YqpV4uFLm3N791jOqWEPtya8+XsiuBIYDjQCXssz/yjwxwDFZEyJ2Z6azieLk/jMLf5p37Amr93ckcEdzqVyRasANgb81xHkvFF8g6p+WUoxGXNWVJVFm1P5aGESs9bvJUKEge3PZXivODrH1rLyf2Py8Vc0NExVxwNxIvJo/uWq+loBmxkTFOknfExetZNxC5P4OSWNutGVeKjfBdzevTENalrxjzGF8Vc0lDOCRrUz2bmIDADeBCKAsar6cgHr9AXeACKB/ap6yZkcy4SvHQfc4p9lOziS6aNdwxr8/San+McGgTfGP39FQ++5v58v7o7dEc3eAfoDycAyEZmqqol51qmFMx7yAFXdLiL1i3scE55UlcWbU/loURIz1+2lgggD2zXg7ovi6BxrHcAZUxxe+xr6K/AikAFMBzoC/+cWGxWmG7BJVbe4+5gEDOHUzuqGAl+p6nYAVU0p9hmYsJJxIssp/lm0lY1706gTXYkH+17A7T1iObem9f9jzJnw+h7BFar6hIhch3N3fxMwGygqETQEduSZTga651unBRApInOA6sCbqvpJ/h2JyEicN5yJjY3Nv9iEgR0H0hm/ZBuTlu3gcMZJ2p5Xg7/d2IGrO55nxT/GnCWviSDS/T0ImKiqBzw8ehe0ghZw/C44Q2BWBRaLyBJV3XjKRqpjgDEA8fHx+fdhyilVZfGWVD5elMT3iXsREQa0bcDwi+KIb2zFP8aUFK+J4GsRWY9TNPSAiNQDMv1skwycn2e6EbCrgHX2q+ox4JiIzMMpdtqICVsZJ7KYstpp/bNh71FqR0Vy3yXNGNajMedZ98/GlDivI5Q96Y5JcERVs0TkGE55f1GWAc1FpAmwE7gVp04gr/8Ab4tIRaASTtHR68U5AVN+JB9M59Ml25i01Cn+aX1uDf56YweuseIfYwLKa2VxJHAH0Md9HJ8LjC5qG1X1ichDwAyc5qMfqupaEbnPXT5aVdeJyHRgDZCN08Q04YzPxoQcVeWHrQcYtzCJ7xL3ICJc2fYchvdqYsM/GlNKRNV/kbuIjMWpJ/jYnXUHkKWqIwIYW4Hi4+N1+fLlpX1YU8IyT2bxn9U7+WhhEuv3HKVWVCS3dYtlWI/GNvqXMQEgIitUNb6gZV7rCLqqasc80/8TkR/PPjQTbnYeyuDTxduYtGw7h9JP0qpBdV65oT1DOjW04h9jgsRrIsgSkWaquhlARJoCWYELy5QnqsrSrQcYtyiJGWv3AHBl2wbc1SuO7k3qWPGPMUHmNRE8DswWkS04zUIbA3cHLCpTLmSezGLq6l18tCiJdbuPUCsqkpF9mjGsRyyNalsv5saUFX4TgdtU9DDOm8L1cRLBelU9HuDYTIjadSjDbf2znYNu8c/L1zvFPzb2rzFlj7/eR0cALwGbgSbASFWdWhqBmdCiqixLOsi4RVuZsXYvqkr/Nk7rnx5NrfjHmLLM3xPB/wFtVXWfWy8wAbBEYE7zwYKtvPjNOmpWjWTExU24o0djK/4xJkT4SwQnVHUfgKpuEZHKpRCTCUGfL99Bp/Nr8a9fdyeqkteqJ2NMWeDvf2wjEXmrsGlVfSQwYZlQsmVfGhv3pvHMVW0sCRgTgvz9r3083/SKQAViQteMtXsBuKLtOUGOxBhzJryMWWxMkWas3UP7hjWtTsCYEFWhqIUiMkZE2hWyLFpE7hGR2wMTmgkFew5nsnrHIa60pwFjQpa/oqF3gWdEpD2QAOwDqgDNgRrAhzgtiUyY+i7xlzeFjTGhyV/R0GrgZhGpBsQD5+KMSbBOVTcEPjxT1s1Yu4em9aK5oH61YIdijDlDXscjSAPmBDYUE2oOpZ9gyZYDjOzT1F4YMyaEFVlHYExRZq1LIStbrVjImBBnicCcselr93BuzSp0aFgz2KEYY85CsRKBiEQHKhATWtJP+Ji3cR9XtDmHChWsWMiYUOYpEYhILxFJBNa50x1F5N2ARmbKtHkb93Hcl23FQsaUA16fCF4HrgRSAVT1R6BPoIIyZd/0hD3UioqkW5M6wQ7FGHOWPBcNqeqOfLNshLIwdcKXzaz1KVze+hwqRlg1kzGhzmsPYTtEpBegIlIJeAS3mMiEnyVbUjma6bNiIWPKCa+3c/cBDwINgWSgE/BAgGIyZdz0tXuIqhTBxc1jgh2KMaYEeH0iaKmqp/QpJCIXAQtLPiRTlmVnK98n7qVvy3pUibRhJ40pD7w+EfzD4zxTzs39eR/7jh63YiFjyhF/Yxb3BHoB9UTk0TyLagB2OxhGsrOVMfO38PfvNtCwVlUubVU/2CEZY0qIv6KhSkA1d73qeeYfAW4MVFCmbNlzOJNHP1/Nos2pDGrfgJeua0/1KpHBDssYU0L89T46F5grIuNUdVspxWTKkOkJe3jyqzUcP5nNX2/owE3xjayDOWPKGa+Vxeki8jegLc54BACo6qUBicoEXfoJHy9MS2Ti0h20b1iTN2/tRNN61tW0MeWR10QwAfgMuAqnKeldOIPUmHIoYedhHpm0iq37j3HfJc14tH8LKlW0F8eMKa+8JoK6qvqBiPwmT3HR3EAGZkpfdrYydsEW/jZjA3WjKzNhRHd6NbN3BYwp77wmgpPu790iMhjYBTQKTEgmGPYeyeSxz39kwab9DGjbgL9c357a0ZWCHZYxphR4TQQvikhN4DGc9wdqAP/nbyMRGQC8idPUdKyqvlzIel2BJcAtqvpvjzGZEvLd2j38/ss1ZJ7M5i/Xt+fWrudbhbAxYcTrUJXT3I+HgX6Q+2ZxoUQkAngH6I/TLcUyEZmqqokFrPcKMKN4oZuzlXEiixe+SeRfP2ynXcMavHnrhTSzCmFjwo6/F8oigJtx+hiarqoJInIV8EegKnBhEZt3Azap6hZ3X5OAIUBivvUeBr4Eup7RGZgzkrDzML+ZtIrN+45xb5+mPHZFS6sQNiZM+Xsi+AA4H1gKvCUi24CewJOqOsXPtg2BvF1XJwPd864gIg2B64BLKSIRiMhIYCRAbGysn8OaomRnKx8u3Mor09dTJ7oSE0Z056ILrELYmHDmLxHEAx1UNVtEqgD7gQtUdY+HfRdUyKz5pt8Afq+qWUWVSavqGGAMQHx8fP59GI9SjmTy2Bc/Mv/n/VzR5hxeuaGDVQgbY/wmghOqmg2gqpkistFjEgDnCeD8PNONcFob5RUPTHKTQAwwSER8Hp42TDHNTNzLE1+uIf2Ejz9f146h3WKtQtgYA/hPBK1EZI37WYBm7rQAqqodith2GdBcRJoAO4FbgaF5V1DVJjmfRWQcMM2SQMnKOJHFn79NZPyS7bQ5twZv3daJC+pX97+hMSZs+EsErc90x6rqE5GHcFoDRQAfqupaEbnPXT76TPdtvEncdYRHJq1iU0oav764Cb+7siWVK1qnscaYU/nrdO6sOppT1W+Bb/PNKzABqOrwszmW+UVOhfBfp2+gVlQkn/6qGxc3rxfssIwxZZTXF8pMiEg77uM3E1e5g8vX55UbOlC3WuVgh2WMKcMsEZQjuw5lcM+4Zfycksbz17Tlzp6NrULYGOOX50QgIlWBWFXdEMB4zBlK2HmYe8YtI/1EFh8N70qfFlYUZIzxxtOrpCJyNbAamO5OdxKRqQGMyxTDzMS93DR6MZERFfjy/l6WBIwxxeK1T4HncLqMOASgqquBuEAEZIrno4Vb+fWny2l+TjUmP9CLlg2saagxpni8Fg35VPWwlTeXHb6sbF6YlsjHi7dxRZtzeOPWTkRVsiofY0zxeb1yJIjIUCBCRJoDjwCLAheWKcqx4z4enriK/61P4dcXN+HJga2JqGBJ2hhzZrwWDT2MM17xceBfON1R/1+AYjJF2H04g5tGL2buxn28eG07nhrcxpKAMeaseH0iaKmqTwFPBTIYU7SEnYf51cfLOHY8iw/uiqdvy/rBDskYUw54fSJ4TUTWi8gLItI2oBGZAs1at5eb31tMBRG+uK+nJQFjTInxlAhUtR/QF9gHjBGRn0TkT4EMzPzi40VJ/PqT5TStF82UBy+i9bk1gh2SMaYc8TwklaruUdW3gPtw3il4JlBBGUdWtvL812t5dupaLm11Dp/f25NzalQJdljGmHLGUx2BiLQGbgFuBFKBSTgD2ZsAOXbcx28mrWLmuhTuuagJTw22lkHGmMDwWln8ETARuEJV8w8uY0rY3iOZ3DNuGet2H2HUkLbc2TMu2CEZY8oxT4lAVXsEOhDjSNx1hF99vIwjGSf54K6u9GtllcLGmMAqMhGIyOeqerOI/MSp4w17GaHMFNPsDSk8NGEl1atE8vl9PWl7Xs1gh2SMCQP+ngh+4/6+KtCBhLtPl2zj2f8k0KpBDT4c3pUGNa1S2BhTOopsNaSqu92PD6jqtrw/wAOBD6/8y8pWXpyWyNNTEujbsj5f3NfTkoAxplR5bT7av4B5A0sykHCUfsLHfeNXMHbBVob3iuP9O+OJrmwdxxljSpe/OoL7ce78m4rImjyLqgMLAxlYeZdyJJNffbyctbsO8+zVbbj7oibBDskYE6b83X7+C/gv8BfgyTzzj6rqgYBFVc6t33OEez5axqGMk7x/ZzyXtT4n2CEZY8KYv0SgqpokIg/mXyAidSwZFN/cjft4cMJKoitH8Pm9PWnX0FoGGWOCy8sTwVXACpzmo3lfbVWgaYDiKpcm/LCNZ/6zlhbnVOfD4fGcW7NqsEMyxpiiE4GqXuX+tgLss5Cdrbw8fT1j5m2hX8t6/GNoZ6pZpbAxpozwOnj9RSIS7X4eJiKviUhsYEMrHzJOZPHAhJWMmbeFO3o05v074y0JGGPKFK/NR/8JpItIR+AJYBvwacCiKid2H87g1jGLmZG4h6evasOoIW2pGOG5w1djjCkVxRm8XkVkCPCmqn4gIncFMrBQt3hzKg9PXEnGiSzeG9aFK9o2CHZIxhhTIK+J4KiI/AG4A7hYRCKAyMCFFbpUlQ8WbOUv/11P47pRTBrZgwvqVw92WMYYUyivieAWYChwj6rucesH/ha4sELTseM+fv/lGqat2c2Vbc/h1Zs6Ur2K5UtjTNnmtRvqPSIyAegqIlcBS1X1k8CGFlq27j/GvZ8uZ1NKGk8MaMn9lzRDxAaSMcaUfV5bDd0MLAVuAm4GfhCRGz1sN0BENojIJhF5soDlt4vIGvdnkVsZHXJmJu7lmn8sYN/R43x8Tzce6HuBJQFjTMjwWjT0FNBVVVMARKQeMBP4d2EbuPUI7+B0WJcMLBORqaqamGe1rcAlqnpQRAYCY4DuxT+N4MjKVt6cuZG3/reJdg1rMHpYFxrVjgp2WMYYUyxeE0GFnCTgSsX/00Q3YJOqbgEQkUnAECA3EajqojzrLwEaeYwn6A6ln+A3k1Yzd+M+burSiBeubUeVyIhgh2WMMcXmNRFMF5EZOOMWg1N5/K2fbRoCO/JMJ1P03f6vcDq4O42IjARGAsTGBv89trW7DnPf+BXsOZzJn69rx9BusVYUZIwJWV4rix8XkeuB3jj9DY1R1cl+NivoyqgFzENE+uEkgt6FHH8MTrER8fHxBe6jtExelcyTX/5E7ahKfHZvTzrH1g5mOMYYc9b8jUfQHHgVaAb8BPxOVXd63HcycH6e6UbArgKO0QEYCwxU1VSP+y51J3zZvPTtOsYtSqJ7kzq8PbQz9apXDnZYxhhz1vyV838ITANuwOmB9B/F2PcyoLmINBGRSsCtwNS8K7jvI3wF3KGqG4ux71KVciSToe8vYdyiJEb0bsL4Ed0tCRhjyg1/RUPVVfV99/MGEVnpdceq6hORh4AZQATwoaquFZH73OWjgWeAusC7bhm7T1Xji3sSgbQ86QD3T1hJWqaPf9x2IVd3PC/YIRljTInylwiqiMiF/FLeXzXvtKoWmRhU9VvyVSq7CSDn8whgRHGDLg2qyseLknjxm3U0ql2V8b/qTssG1lWEMab88ZcIdgOv5Znek2dagUsDEVSwZZzI4o+Tf2Lyqp1c3ro+f7+5EzWrWlcRxpjyyd/ANP1KK5CyYntqOveOX8H6PUd4tH8LHup3ARUqWNNQY0z5ZSOk5DF7Qwr/N2k1AB8O70q/lvWDG5AxxpQCSwQ4Q0m+PXsTr8/cSKsGNXhvWBdi61pXEcaY8BD2ieBo5kl++9lqZq5L4boLG/LSde2pWsm6ijDGhA9PiUCctp23A01VdZTb/r+Bqi4NaHSl4P15W/jf+hSev6Ytd/ZsbF1FGGPCjtcBdN8FegK3udNHcXoWDXkLN6fS8fxa3NUrzpKAMSYseU0E3VX1QSATQFUPApUCFlUpST/h48cdh+jRtG6wQzHGmKDxmghOuuMLKOSOR5AdsKhKyYptB/FlqyUCY0xY85oI3gImA/VF5M/AAuClgEVVSpZsSSWighDf2HoQNcaEL6/dUE8QkRXAZTjdS1yrqusCGlkp+GHLAdo3rEl05bBvPGWMCWNexyyOBdKBr3F6ED3mzgtZ6Sd8/Jhs9QPGGOP1VvgbnPoBAaoATYANQNsAxRVwK7cd4mSW0qNpnWCHYowxQeW1aKh93mkR6QzcG5CISklu/UCcJQJjTHjzWll8Crf76a4lHEupWrIllfYNa1LN6geMMWHO65vFj+aZrAB0BvYFJKJSkFM/8KveTYMdijHGBJ3X2+G8I7L4cOoMviz5cEqH1Q8YY8wv/CYC90Wyaqr6eCnEUyp+2Gr1A8YYk6PIOgIRqaiqWThFQeWG1Q8YY8wv/F0Jl+IkgdUiMhX4AjiWs1BVvwpgbAGRcSKL1TsOcU/vJsEOxRhjygSvt8R1gFScMYpz3idQIOQSwcrtB936AXuRzBhjwH8iqO+2GErglwSQQwMWVQBZ/0Kh4eTJkyQnJ5OZmRnsUIwJKVWqVKFRo0ZERkZ63sZfIogAqnFqAsgRsomgXcOaVK/i/UsypS85OZnq1asTF2fjRBjjlaqSmppKcnIyTZp4L/72lwh2q+qoswut7LD6gdCRmZlpScCYYhIR6taty759xXvNy9+bxeXqf6HVD4QWSwLGFN+Z/L/xlwguO7NQyqYfrH7AGGNOU2QiUNUDpRVIaViy5YDVDxjPqlWrlvv522+/pXnz5mzfvp3nnnuOqKgoUlJSCly3MIMGDeLQoUNFrtO3b1+WL19+2vxx48bx0EMPeQ++GF599VVatWpFu3bt6NixI5988kmRsZyJ5cuX88gjjwBw/PhxLr/8cjp16sRnn33GiBEjSExMPKv9v/HGG7lxA/h8PmJiYvjDH/5wynpxcXHs378/d3rOnDlcddVVudP//e9/iY+Pp3Xr1rRq1Yrf/e53ZxUXwIoVK2jfvj0XXHABjzzyCKoFV6+uWbOGnj170rZtW9q3b39aQ4lrrrmGdu3a5U6//fbbfPTRR2cdH5xhp3OhKKd+wLqVMMU1a9YsHn74YaZPn05srDMMR0xMDH//+9+LtZ9vv/2WWrVqBSDCoqkq2dkFjyw7evRovv/+e5YuXUpCQgLz5s0r9EJ1NuLj43nrrbcAWLVqFSdPnmT16tXccsstjB07ljZt2njeV1ZW1inTPp+PDz/8kKFDh+bO++6772jZsiWff/655/NJSEjgoYceYvz48axbt46EhASaNj37/sjuv/9+xowZw88//8zPP//M9OnTT1vH5/MxbNgwRo8ezdq1a5kzZ84prX6++uqr02427rnnntzv9GyFTSJYteMgJ7Ky6dHE6gdCzfNfr+WW9xaX6M/zX6/1dOz58+fz61//mm+++YZmzZrlzr/nnnv47LPPOHDg9Ifm8ePH061bNzp16sS9996be+HKezf6wgsv0KpVK/r3789tt93Gq6++mrv9F198Qbdu3WjRogXz58/Pnb9jxw4GDBhAy5Ytef7553Pnv/baa7Rr14527drxxhtvAJCUlETr1q154IEH6Ny5Mzt27GD48OG0a9eO9u3b8/rrrwPw0ksv8e6771KjRg0AatasyV133XXaOd1///3Ex8fTtm1bnn322dz5Tz75JG3atKFDhw65d89ffPFF7tNFnz59gF/uvFNSUhg2bBirV6+mU6dObN68+ZQnj++++46ePXvSuXNnbrrpJtLS0nK/u1GjRtG7d2+++OKLU2L73//+R+fOnalY8Ze2LxMnTuQ3v/kNsbGxLFmypIC/7On++te/8tRTT9GqVSsAKlasyAMPPOBp28Ls3r2bI0eO0LNnT0SEO++8kylTppy23nfffUeHDh3o2LEjAHXr1iUiIgKAtLQ0XnvtNf70pz+dsk1UVBRxcXEsXbr0rGIE7y+UhbyDx04C0LB21SBHYkLF8ePHGTJkCHPmzMm9OOSoVq0a99xzD2+++eYpF+V169bx2WefsXDhQiIjI3nggQeYMGECd955Z+46y5cv58svv2TVqlX4fD46d+5Mly5dcpf7fD6WLl3Kt99+y/PPP8/MmTMBcu/ao6Ki6Nq1K4MHD0ZE+Oijj/jhhx9QVbp3784ll1xC7dq12bBhAx999BHvvvsuK1asYOfOnSQkJABw6NAhjh49ytGjR09JcIX585//TJ06dcjKyuKyyy5jzZo1NGrUiMmTJ7N+/XpEJLfYa9SoUcyYMYOGDRueVhRWv359xo4dy6uvvsq0adNOWbZ//35efPFFZs6cSXR0NK+88gqvvfYazzzzDOC0j1+wYMFpsS1cuPCU7y8jI4NZs2bx3nvvcejQISZOnEjPnj39nmNCQgKPPfaY3/Vmz57Nb3/729PmR0VFsWjRolPm7dy5k0aNGuVON2rUiJ07d5627caNGxERrrzySvbt28ett97KE088AcDTTz/NY489RlRU1GnbxcfHM3/+fLp16+Y37qKETSIwoevZq4MzEF5kZCS9evXigw8+4M033zxt+SOPPEKnTp1OuXjMmjWLFStW0LWrM1xHRkYG9evXP2W7BQsWMGTIEKpWdW5Krr766lOWX3/99QB06dKFpKSk3Pn9+/enbt26uessWLAAEeG6664jOjo6d/78+fO55ppraNy4MT169ACgadOmbNmyhYcffpjBgwdzxRVXkJaW5rmFyeeff86YMWPw+Xzs3r2bxMRE2rRpQ5UqVRgxYgSDBw/OLWu/6KKLGD58ODfffHPuuXixZMkSEhMTueiiiwA4ceLEKRfwW265pcDtdu/eTevWrXOnp02bRr9+/YiKiuKGG27ghRde4PXXXyciIqLA8y1uK5t+/fqxevVqT+sWVCxV0PF8Ph8LFixg2bJlREVFcdlll9GlSxfq1q3Lpk2beP3110/5t5Cjfv36rF+/vljxFySgRUMiMkBENojIJhF5soDlIiJvucvXuCOfGVMmVKhQgc8//5xly5bx0ksvnba8Vq1aDB06lHfffTd3nqpy1113sXr1alavXs2GDRt47rnnTtnOX5l15cqVAYiIiMDn8+XOz38BEZEi95WTHABq167Njz/+SN++fXnnnXcYMWIENWrUIDo6mi1bthQZz9atW3n11VeZNWsWa9asYfDgwWRmZlKxYkWWLl3KDTfcwJQpUxgwYADg1Du8+OKL7Nixg06dOpGamlrk/nOoKv3798/97hITE/nggw8KPJ+8qlatekrF6sSJE5k5cyZxcXF06dKF1NRUZs+eDThFLgcPHsxd98CBA8TExADQtm1bVqxY4TfO2bNn06lTp9N+evXqddq6jRo1Ijk5OXc6OTmZ8847r8D1LrnkEmJiYoiKimLQoEGsXLmSxYsXs2LFCuLi4ujduzcbN26kb9++udtlZmbm3lCcjYAlArf76neAgUAb4DYRyV8jNBBo7v6MBP4ZqHiMORNRUVFMmzaNCRMmnHJRyvHoo4/y3nvv5V6wL7vsMv7973/ntig6cOAA27ZtO2Wb3r178/XXX5OZmUlaWhrffPONp1i+//57Dhw4QEZGBlOmTOGiiy6iT58+TJkyhfT0dI4dO8bkyZO5+OKLT9t2//79ZGdn594hr1y5EoA//OEPPPjggxw5cgSAI0eOMGbMmFO2PXLkCNHR0dSsWZO9e/fy3//+F3DKrg8fPsygQYN44403cu+SN2/eTPfu3Rk1ahQxMTHs2LHD0/n16NGDhQsXsmnTJgDS09PZuHGj3+1at26du82RI0dYsGAB27dvJykpiaSkJN555x0mTpwIOC2hPv30U8CpdB4/fjz9+vUD4PHHH+ell17KPWZ2djavvfbaacfLeSLI/5O/WAjg3HPPpXr16ixZsgRV5ZNPPmHIkCGnrXfllVeyZs0a0tPT8fl8zJ07lzZt2nD//feza9cukpKSWLBgAS1atGDOnDm5223cuPGUlkRnKpBFQ92ATaq6BUBEJgFDgLztxIYAn6hzW7NERGqJyLmqujuAcRlTLHXq1GH69On06dMn9+4xR0xMDNddd11u5WubNm148cUXueKKK8jOziYyMpJ33nmHxo0b527TtWtXrrnmGjp27Ejjxo2Jj4+nZs2afuPo3bs3d9xxB5s2bWLo0KHEx8cDMHz48Nwy4hEjRnDhhReeVoywc+dO7r777tzWQ3/5y18ApxI4LS2Nrl27EhkZSWRk5Gnl5B07duTCCy+kbdu2NG3aNLfo5ujRowwZMoTMzExUNfc7ePzxx/n5559RVS677DI6duzI3Llz/Z5fvXr1GDduHLfddhvHjx8H4MUXX6RFixZFbjdw4EDuuOMOwGldc+mll+Y+VQEMGTKEJ554guPHj/P0009z//3307FjR1SVAQMGMGzYMAA6dOjAG2+8wW233UZ6ejoiwuDBg/3G7c8///lPhg8fTkZGBgMHDmTgwIEATJ06leXLlzNq1Chq167No48+SteuXRERBg0a5OnYCxcuPKXy/oypakB+gBuBsXmm7wDezrfONKB3nulZQHwB+xoJLAeWx8bG6plYnnRA7x+/XHceTD+j7U3pSkxMDHYIAXX06FFVVT127Jh26dJFV6xYEeSIQtu1116rGzduDHYYpWrlypU6bNiwApcV9P8HWK6FXK8D+UTgpaM6T53ZqeoYYAxAfHz8GTVy7tK4Nl0ad/G/ojGlYOTIkSQmJpKZmcldd91F585WPXY2Xn75ZXbv3k3z5s2DHUqp2b9/Py+88EKJ7CuQiSAZOD/PdCNg1xmsY0y5869//SvYIZQrLVu2pGXLlsEOo1T179+/xPYVyFZDy4DmItJERCoBtwJT860zFbjTbT3UAzisVj9gXBqAN1yNKe/O5P9NwJ4IVNUnIg8BM3DGNfhQVdeKyH3u8tHAt8AgYBOQDtwdqHhMaKlSpQqpqanUrVvXeiE1xiN1xyOoUqVKsbaTULvrio+P15LqCMuUXTZCmTFnprARykRkharGF7SNvVlsyqTIyMhijbBkjDlzYdPpnDHGmIJZIjDGmDBnicAYY8JcyFUWi8g+YJvfFQsWA+z3u1b5YuccHuycw8PZnHNjVa1X0IKQSwRnQ0SWF1ZrXl7ZOYcHO+fwEKhztqIhY4wJc5YIjDEmzIVbIhjjf5Vyx845PNg5h4eAnHNY1REYY4w5Xbg9ERhjjMnHEoExxoS5cpkIRGSAiGwQkU0i8mQBy0VE3nKXrxGRkB8VxMM53+6e6xoRWSQiHYMRZ0nyd8551usqIlkicmNpxhcIXs5ZRPqKyGoRWSsi/seILOM8/NuuKSJfi8iP7jmHdC/GIvKhiKSISEIhy0v++lXY0GWh+oPT5fVmoClQCfgRaJNvnUHAf3FGSOsB/BDsuEvhnHsBtd3PA8PhnPOs9z+cLs9vDHbcpfB3roUzLnisO10/2HGXwjn/EXjF/VwPOABUCnbsZ3HOfYDOQEIhy0v8+lUenwi6AZtUdYuqngAmAUPyrTME+EQdS4BaInJuaQdagvyes6ouUtWD7uQSnNHgQpmXvzPAw8CXQEppBhcgXs55KPCVqm4HUNVQP28v56xAdXEGrqiGkwh8pRtmyVHVeTjnUJgSv36Vx0TQENiRZzrZnVfcdUJJcc/nVzh3FKHM7zmLSEPgOmB0KcYVSF7+zi2A2iIyR0RWiMidpRZdYHg557eB1jjD3P4E/EZVs0snvKAo8etXeRyPoKDhrPK3kfWyTijxfD4i0g8nEfQOaESB5+Wc3wB+r6pZ5WSUMy/nXBHoAlwGVAUWi8gSVd0Y6OACxMs5XwmsBi4FmgHfi8h8VT0S4NiCpcSvX+UxESQD5+eZboRzp1DcdUKJp/MRkQ7AWGCgqqaWUmyB4uWc44FJbhKIAQaJiE9Vp5RKhCXP67/t/ap6DDgmIvOAjkCoJgIv53w38LI6BeibRGQr0ApYWjohlroSv36Vx6KhZUBzEWkiIpWAW4Gp+daZCtzp1r73AA6r6u7SDrQE+T1nEYkFvgLuCOG7w7z8nrOqNlHVOFWNA/4NPBDCSQC8/dv+D3CxiFQUkSigO7CulOMsSV7OeTvOExAicg7QEthSqlGWrhK/fpW7JwJV9YnIQ8AMnBYHH6rqWhG5z10+GqcFySBgE5COc0cRsjye8zNAXeBd9w7ZpyHcc6PHcy5XvJyzqq4TkenAGiAbGKuqBTZDDAUe/84vAONE5CecYpPfq2rIdk8tIhOBvkCMiCQDzwKRELjrl3UxYYwxYa48Fg0ZY4wpBksExhgT5iwRGGNMmLNEYIwxYc4SgTHGhDlLBGHA7XlzdZ6fuCLWTSuB440Tka3usVaKSM8z2MdYEWnjfv5jvmWLzjZGdz8530uC23tlLT/rdxKRQWdwnHNFZJr7ua+IHBaRVSKyTkSePYP9XZPTC6eIXJvzPbnTo0Tk8uLus4BjjBM/vbW63Vh4boLsnvs0D+sV2PumiLwqIpd6PZ7xzhJBeMhQ1U55fpJK4ZiPq2on4EngveJurKojVDXRnfxjvmW9zj484JfvpR1OJ18P+lm/E0777eJ6FHg/z/R8Vb0Q583nYSLSpTg7U9WpqvqyO3kt0CbPsmdUdeYZxFiWjAMGFDD/Hzj/nkwJs0QQhkSkmojMcu/WfxKR03rtdO9i5+W5Y77YnX+FiCx2t/1CRKr5Odw84AJ320fdfSWIyP+586JF5Btx+pJPEJFb3PlzRCReRF4GqrpxTHCXpbm/P8t7h+7exd4gIhEi8jcRWSZOf+33evhaFuN23CUi3cQZs2GV+7ul+1brKOAWN5Zb3Ng/dI+zqqDv0XUDMD3/TLcbiBVAM/dpY4kb72QRqe3G8oiIJLrzJ7nzhovI2yLSC7gG+JsbU7OcO3kRGSgin+f5bvqKyNfu52L9DUXkGfccE0RkjMgpHTcNc7+jBBHp5q7v9XspUGG9b6rqNqCuiDQozv6MB6XVx7b9BO8HyMLplGs1MBnnjfIa7rIYnDcUc14uTHN/PwY85X6OAKq7684Dot35vweeKeB443D7/gduAn7A6QjtJyAap6vgtcCFOBfJ9/NsW9P9PQeIzxtTnnVyYrwO+Nj9XAmnR8aqwEjgT+78ysByoEkBcablOb8vgAHudA2govv5cuBL9/Nw4O08278EDHM/18Lpzyc63zGaACvyTPcFprmf6wJJQFucN4EvceePAt5wP+8CKuccI38ceb/rvNPu33h7nr/VP4FhZ/g3rJNn/qfA1Xn+Ru+7n/vg9p9f2PeS79zjcd56LuzfbBwF9MeP82R1Q7D/T5W3n3LXxYQpUIY6xTQAiEgk8JKI9MHphqAhcA6wJ882y4AP3XWnqOpqEbkEpxhioXtTWAnnTrogfxORPwH7cHo7vQyYrM5dMCLyFXAxzp3yqyLyCs5FYn4xzuu/wFsiUhmnKGGeqmaIyBVAhzxl3DWB5sDWfNtXFZHVOBedFcD3edb/WESa4/TqGFnI8a8ArhGR37nTVYBYTu3b51z3O8jrYhFZhfPdv4zTiVgtVc0ZTexjnMQEToKYICJTgCmFxHEadbpmmA5cLSL/BgYDTwDF+Rvm6CciTwBRQB2cJP61u2yie7x5IlJDnHqWwr6XvPEtB0Z4PZ88UoDzzmA7UwRLBOHpdpyRnLqo6kkRScL5z5rL/Y/dB+cC8qmI/A04CHyvqrd5OMbjqvrvnAkppAJTVTe6ZeSDgL+IyHeqOsrLSahqpojMwemG+BbcixJOfzMPq+oMP7vIUNVOIlITmIZTR/AWTt81s1X1OnEq1ucUsr3g3J1uKOoY5PtuceoIrsrdiXP8wgzGudu+BnhaRNoWsW5+n+Gc0wFgmaoedYt1vP4NEZEqwLs4T2c7ROQ5Tj2f/H3UKIV8L+J0CHe2quB8p6YEWR1BeKoJpLhJoB/QOP8KItLYXed94AOcofOWABeJSE6Zf5SItPB4zHnAte420TjFOvNF5DwgXVXHA6+6x8nvpPtkUpBJOJ1uXYzTMRnu7/tzthGRFu4xC6Sqh4FHgN+529QEdrqLh+dZ9ShOEVmOGcDDOWXmInJhAbvfiPPEUSj3+AfFrYcB7gDmikgF4HxVnY1zN18Lp1gtr/wx5TUH5/v8NU5SgOL/DXMu+vvduoT8LYly6nR64/SCeRhv38uZagGEbCd6ZZUlgvA0AYgXkeU4TwfrC1inL7DaLcK4AXhTVffhXBgnisganItKKy8HVNWVOOXOS3HqDMaq6iqgPbDULaJ5CnixgM3HAGvErSzO5zucO+aZ6gxlCM6YC4nASnGaIL6Hn6dfN5Yfcbo5/ivO08lCnPqDHLOBNjmVxThPDpFubAnudP79HgM251x4i3AXTnHaGpzWSaPcY48Xp1fNVcDrqnoo33aTgMfdStlm+Y6dhfOkM9D9TXH/hu7x3sep35mCU2SY10FxmvOOxikCBA/fizgNAcYWdExxet9cDLQUkWQR+ZU7PxKn4cHywuI1Z8Z6HzUmwETkOpxiuD8FO5ZQ5n6PnVX16WDHUt5YHYExAaaqk0WkbrDjKAcqAn8PdhDlkT0RGGNMmLM6AmOMCXOWCIwxJsxZIjDGmDBnicAYY8KcJQJjjAlz/w9m7AlncPP2XwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=28)\n",
    "knn.fit(x1_train_over, y1_train_over) \n",
    "y1_pred = knn.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y1_test, y1_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x1_test, y1_test)\n",
    "plt.savefig('rocauc/knn_FS1_OS.png')\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, X1, Y, cv=5, scoring = 'accuracy'))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', knn.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum ROC AUC:- 0.6191329856584094 at K = 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "error_rate = []\n",
    "for i in range(1,40):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x2_train_over,y2_train_over)\n",
    "    pred_i = knn.predict(x2_test)\n",
    "    #error_rate.append(np.mean(pred_i != y2_test.to_numpy() ))\n",
    "    error_rate.append(roc_auc_score(y2_test, pred_i))\n",
    "    \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('Feature Set 2: ROC AUC vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('ROC AUC')\n",
    "print(\"Maximum ROC AUC:-\",max(error_rate),\"at K =\",error_rate.index(max(error_rate)))\n",
    "plt.savefig('KNN_fs2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5470588235294118\n",
      "[[32 20]\n",
      " [57 61]]\n",
      "Cross-Validation Accuracy Scores [0.51456311 0.66019417 0.58252427 0.63106796 0.60194175]\n",
      "Test Accuracy Score 0.5470588235294118\n",
      "precision =  0.7530864197530864\n",
      "Sensitivity :  0.6153846153846154\n",
      "Specificity :  0.5169491525423728\n",
      "f1 score =  0.6130653266331658\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jqem8OGs9ny7bQcs6Vfjg1q6c27B6oMMyxhNfI5S1UdUNOOMTN3H7GMpmI5QZAzHb9zN66mp27E/mjl7NeKCfdRFhShZfTwSjgVHkPVKZjVBmgtqJ9AzemLOZsfN/o0GNinx+x/l0b14r0GEZU2C+Rigb5X4cqKopOZeJiI2RZ4LW+t2HuX9KLBv+OMKNXRvzxOXtqFLeenU3JZPXf7lLgNwD2ec1z5hSLTNTGbdwK6/8sJHqFcvxwa1R9GlrXUSYks1XHUE9oCFQUUTOw+leAqAaUMnPsRlTrBw9kc7oKbH8ELeHgefW44WrOhBW2TrhNSWfryeC/sAIoBHwao75R4B/+CkmY4qdrYlHGfVJDNuSjvHk5e24rWeEdRFhSg1fdQRZbxRfo6pfFlFMxhQrP2/Yw98+jyU0pAyf/LmbjRlgSh1fRUPDVfVTIEJERuderqqv5rGZMaWCqvL23C288uMm2tWvxns3d6FRTSsRNaWPr6Khyu7vM3o/XkQGAG8AIcB4VX0pj3V6A68DoUCSql58JscypjAdPZHOg1NXM3vdH1x1XkP+dXUHezfAlFq+iobec38/U9AduyOavQ30BRKAFSIyQ1XjcqxTA2c85AGqukNE6hT0OMYUtm1Jxxg1MZqtScd4YnBb/nxhM6sPMKWap05QROQ/IlJNREJF5CcRSRKR4T426wZsUdWtqpoKfA4MzbXOMOArVd0BoKp7C3oCxhSmuRv3csVbi0g6eoJPbu/GyF7NLQmYUs9rb1j9VPUwcDnO3X0r4CEf2zQEduaYTnDn5dQKqCki80QkRkRuyWtHIjJKRKJFJDoxMdFjyMZ4l1UfcPuEFTSuWYkZ91xIj3OsUtgEB68vlGWNqj0ImKyq+z3cJeW1guZx/C44Q2BWBJaKyDJV3XTSRqrjgHEAUVFRufdhzFk5diKdB6et5ru1f3BFZAP+fU1HKpaz+gATPLwmgm9EZANwHLhbRGoDKT62SQAa55huBPyexzpJqnoMOCYiC4BIYBPGFIH4pGP85ZMYNu89wuOD2jKyl9UHmODjqWhIVR8FLgCiVDUNOMap5f25rQBaikgzESkH3AjMyLXOf4FeIlJWRCoB3YH1BTkBY87UPLc+YM+RFCbe3p07LrL6ABOcvA5MEwrcDFzk/keZD4w93Taqmi4i9wDf4zQf/VBV14nIne7ysaq6XkRmA2uATJwmpmvP+GyM8UBVeXf+b7z8/Uba1KvGuJu70DjM3g8wwUtUfRe5i8h4nHqCj91ZNwMZqjrSj7HlKSoqSqOjo4v6sKaUOHYinYe/WMO3v+5mSGQD/n1NByqVs15DTeknIjGqGpXXMq//A7qqamSO6Z9FZPXZh2ZM0dmxL5lRn0Szac8R/jGoDXdY01BjAO+JIENEWqjqbwAi0hzI8F9YxhSuBZsSuXfyKgA+vr0bvVrWDnBExhQfXhPBQ8BcEdmK0yy0KXCb36IyppCoKu8t2Mp/Zm+gVd2qjLs5iia1rD7AmJx8JgK3qeghnDeF6+Akgg2qesLPsRlzVpJTnfqAmWt2M7hjfV6+tqPVBxiTB1+9j44EXgR+A5oBo1Q1dxNQY4qdnPUBjw5sw1+saagx+fJ1e/R3oL2qJrr1ApM49V0AY4qVhZud+oDMTOWj27pxcSurDzDmdHwlglRVTQRQ1a0iUr4IYjLmjKgq7y/cykvfbaBlnaqMu6ULTWtV9r2hMUHOVyJoJCJv5jetqvf5JyxjCuZ4agaPfLmGGat/Z1CHerx8bSSVy1t9gDFe+PqfkruH0Rh/BWLMmdq5P5lRn8Sw4Y/DPDygNXdd3MLqA4wpAC9jFhtTbC3anMQ9k1c69QEjutK7tY1tZExB+Wo1NA54M6/+f0SkMnADcEJVJ/kpPmPydOBYKq/P2cQny7ZzTp0qjLs5iohwqw8w5kz4Khp6B3hSRDoAa4FEoALQEqgGfIjTksiYIpGWkcmny7bz+pzNHElJ40/dm/LIwDZUsfoAY86Yr6KhWOB6EakCRAH1ccYkWK+qG/0fnjH/M3fjXp6fGcdvicfo1TKcJwa3o3W9qoEOy5gSz9NtlKoeBeb5NxRj8rZl7xGem7me+ZsSaRZemQ9ujeLSNnWsQtiYQmLP06bYOpicyutzNvPJsu1UKhfCE4PbcssFEZQr63WobWOMF5YITLGTlpHJpGXbec2tBxjWvQn3X9aKWlXsfUZj/KFAiUBEKrvjCxvjF/M27uX5b9ezZe9Rep5Ti39e3o429aoFOixjSjWvQ1X2AMYDVYAmIhIJ/EVV7/ZncCZ4bNl7lBe+jWPuxkQialXi/VuiuKyt1QMYUxS8PhG8BvTH7XBOVVeLyEV+i8oEjUPJabz+0yY+WbqdiqEhPD6oLbf0aEr5siGBDs2YoOG5aEhVd+a6O7MRyswZS8/I5LPlO3j1x00cPp7Gjd2aMLpvK8KtHsCYIuc1Eex0i4dURMoB9wHr/ReWKc0WbErkuZlxbN57lAua1+LJIe1oW9/qAYwJFK+J4E7gDaAhkAD8AFj9gCmQ3xKP8uK36/lpw16a1qrEezd3oV+7ulYPYEyAeU0ErVX1TzlniEhPYHHhh2RKm0PJabz582Y+XhJPhdAQHhvYhhE9I6wewJhiwmsi+D+gs4d5xmRLz8hkslsPcPB4Gjd2bczovq2pXdXqAYwpTnz1PnoB0AOoLSKjcyyqBtjtnMnXos1JPDczjo17jtC9WRhPDmlH+wbVAx2WMSYPvp4IyuG8O1AWyNm712HgWn8FZUqubUnHeOHbOOas30vjsIqMHd6Z/u3rWT2AMcWYr95H5wPzRWSCqm4vophMCZRwIJkJi+P5eGk85ULK8MiANtzWM4IKofbgaExx57WOIFlEXgba44xHAICqXuqXqEyxpqps35fM8m37WbZtH79s3c+ug8cRgeu7NOaB/q2oU7WC7x0ZY4oFr4lgEjAFuBynKemtOIPUmCCgqvyWeIxf3Iv+8m37+eNwCgBhlcvRLSKMkb2acVGr2rSoXSXA0RpjCsprIqilqh+IyN9yFBfN92dgJnAyM5VNe4/wy9b9/LJtH8u37SfpaCoAtauWp3uzMLo3r8X5zcI4p04VK/83poTzmgjS3N+7RWQw8DvQyD8hmaKWkams332YZVudi/7y+P0cTHb+5A2qV6BXy9rZF/+IWpXswm9MKeM1ETwvItWBB3DeH6gG/N3XRiIyAOeN5BBgvKq+lM96XYFlwA2q+oXHmMwZSsvIZO2uQyzftp9ftu1nRfx+jqSkA9AkrBJ929alW7Mwzm9ei0Y1K9qF35hSzutQlTPdj4eASyD7zeJ8iUgI8DbQF6dbihUiMkNV4/JY79/A9wUL3XiVmp7JmoSD/LJtP8u27iNm+wGSU50+A5vXrszlHRu4d/xh1K9eMcDRGmOKmq8XykKA63H6GJqtqmtF5HLgH0BF4LzTbN4N2KKqW919fQ4MBeJyrXcv8CXQ9YzOwJwiJS2DVTsOZlfurtxxgBPpmQC0rluVazo3onvzMLo1C7PWPcYYn08EHwCNgeXAmyKyHbgAeFRVp/vYtiGwM8d0AtA95woi0hC4CriU0yQCERkFjAJo0qSJj8MGn+TUdGK2H8hu0RO78yCpGZmIQNt61RjWvQndm9WiW7MwwiqXC3S4xphixlciiAI6qmqmiFQAkoBzVPUPD/vOq2BZc02/DjyiqhmnK4dW1XHAOICoqKjc+wg6R1LSiI4/wC/bnFY9vyYcIj1TCSkjnNugGiN6RtC9WRhREWFUrxga6HCNMcWcr0SQqqqZAKqaIiKbPCYBcJ4AGueYboTT2iinKOBzNwmEA4NEJN3D00ZQSUnLYOHmJH7Zuo9ftu1n3e+HyFQIDRE6NqrBqIua0715Lbo0rUmV8gUahtoYY3wmgjYissb9LEALd1oAVdWOp9l2BdBSRJoBu4AbgWE5V1DVZlmfRWQCMNOSwMk2/HGY+yavYtOeo5QrW4bzGtfgnktb0r1ZGJ2b1KRiOevCwRhzdnwlgrZnumNVTReRe3BaA4UAH6rqOhG5010+9kz3HQxUlQlL4vnXdxuoViGUscO70Lt1beu7xxhT6Hx1OndWHc2p6ixgVq55eSYAVR1xNscqTRKPnOChL1Yzb2MifdrU4T/XdqSWjeVrjPETK1AuZuZu2MtDX6zmSEo6zw5tz83nN7UXuowxfmWJoJhIScvgpe82MGFJPG3qVeWzO86nVd2qvjc0xpiz5DkRiEhFoImqbvRjPEFp4x9HuG/yKjbuOcJtPSN4ZEAbqwswxhSZMl5WEpEhQCww253uJCIz/BhXUFBVPl4Sz5C3FrHv2Ak+uq0rTw1pb0nAGFOkvD4RPI3TZcQ8AFWNFZEI/4QUHJKOnuDhL9bw84a99G5dm5evjbRB3Y0xAeE1EaSr6iGrtCwc8UnHuHbsUg6npPH0kHbc2iPCKoSNMQHjNRGsFZFhQIiItATuA5b4L6zSKz0jk79PiSU1PYP//rUnbetXC3RIxpgg56mOAKeH0PbACeAznO6o/+6nmEq1t+f+RuzOg7xwVQdLAsaYYsHrE0FrVX0ceNyfwZR2sTsP8ubPm7myUwOGRDYIdDjGGAN4fyJ4VUQ2iMhzItLerxGVUsmp6dw/JZa6VcvzzNBzAx2OMcZk85QIVPUSoDeQCIwTkV9F5Al/BlbavPDteuL3HWPM9ZHWNbQxpljx+kSAqv6hqm8Cd+K8U/Ckv4IqbX7esIdJv+xg5IXN6NEiPNDhGGPMSby+UNZWRJ4WkbXAWzgthhr5NbJSYt/REzz8xa+0qVeVB/u3DnQ4xhhzCq+VxR8Bk4F+qpp7cBmTD1Xl0a9+5fDxND4d2Y3yZe2NYWNM8eMpEajq+f4OpDSaGr2TH+P28PigtrSpZ01FjTHF02kTgYhMVdXrReRXTh5v2MsIZUFt+75jPPNNHBc0r8WfL2zmewNjjAkQX08Ef3N/X+7vQEqT9IxM7p8SS0gZ4ZXrIylTxrqPMMYUX6etLFbV3e7Hu1V1e84f4G7/h1cyjZ3/Gyt3HOT5K8+lQY2KgQ7HGGNOy2vz0b55zBtYmIGUFmsSDvL6nM0MiWzA0E4NAx2OMcb45KuO4C6cO//mIrImx6KqwGJ/BlYSHU/N4O9TYqldtTzP29vDxpgSwlcdwWfAd8C/gEdzzD+iqvv9FlUJ9a/v1rM18RiTRnaneiV7e9gYUzL4SgSqqvEi8tfcC0QkzJLB/8zbuJeJS7dze89m9DzH3h42xpQcXp4ILgdicJqP5mz+okBzP8VVouw/lspDX6yhVd0qPDzA3h42xpQsp00Eqnq5+9sawudDVfnHV79yMDmVj2/rZuMNG2NKHK99DfUUkcru5+Ei8qqINPFvaCXDFzEJzF73Bw/0a027Bvb2sDGm5PHafPRdIFlEIoGHge3AJ36LqoTYuT+ZZ76Jo1uzMO7oZaVkxpiSyWsiSFdVBYYCb6jqGzhNSINWRqZy/5RYBHj1+khC7O1hY0wJ5bX30SMi8hhwM9BLREKAoG4f+d6C34jefoBXr4+kUc1KgQ7HGGPOmNcnghtwBq6/XVX/ABoCL/stqmJu7a5DvPbjJgZ3qM9V59nbw8aYks3rUJV/AJOA6iJyOZCiqhP9GlkxlZLmvD0cVrkcL1x1LiJWJGSMKdm8thq6HlgOXAdcD/wiItd62G6AiGwUkS0i8mgey/8kImvcnyVuZXSx9p/ZG9my9yhjroukRqVygQ7HGGPOmtc6gseBrqq6F0BEagNzgC/y28CtR3gbp8O6BGCFiMxQ1bgcq20DLlbVAyIyEBgHdC/4aRSN9bsPM2HJNoaf34ReLWsHOhxjjCkUXusIymQlAdc+D9t2A7ao6lZVTQU+x2l1lE1Vl6jqAXdyGcV4HGRV5Zlv1lG9YigP9rO3h40xpYfXJ4LZIvI9zrjF4FQez/KxTUNgZ47pBE5/t/9nnA7uTiEio4BRAE2aBOY9tu/W/sGyrft57spzrUjIGFOqeB2z+CERuRq4EKe/oXGq+rWPzfKqRdU85iEil+AkggvzOf44nGIjoqKi8tyHP6WkZfDCt+tpU68qw7rZC9XGmNLF13gELYExQAvgV+BBVd3lcd8JQOMc042A3/M4RkdgPDBQVfd53HeRem/+VnYdPM7no863F8eMMaWOr3L+D4GZwDU4PZD+XwH2vQJoKSLNRKQccCMwI+cKbn9FXwE3q+qmAuy7yOw6eJx3529hcIf6nN+8VqDDMcaYQueraKiqqr7vft4oIiu97lhV00XkHuB7IAT4UFXXicid7vKxwJNALeAdtz1+uqpGFfQk/Olfs9ajCo8NahPoUIwxxi98JYIKInIe/yvvr5hzWlVPmxhUdRa5KpXdBJD1eSQwsqBBF5VlW/cxc81u/tanpXUjYYwptXwlgt3Aqzmm/8gxrcCl/giqOMjIVJ75Jo6GNSpy58UtAh2OMcb4ja+BaS4pqkCKm8nLd7B+92HeHtaZiuVssBljTOnl9YWyoHIoOY1XfthI92ZhDOpQL9DhGGOMX1kiyMNrczZx6HgaT1/R3jqVM8aUepYIctn4xxE+WbadYd2b0La+DT1pjCn9vPY+Ku5YxU+6001EpJt/Qyt6qsqzM9dRpXxZHuhr/QkZY4KD1yeCd4ALgJvc6SM4PYuWKt+v28PiLfsY3bcVNStbf0LGmODgtdO57qraWURWAbjdRpeqK2VKWgYvzIqjdd2q/Km79SdkjAkeXhNBmju+gEL2eASZfosqAMYv3MrO/cf5bGR3yoZY1YkxJnh4veK9CXwN1BGRF4BFwIt+i6qI7T50nLfn/saA9vXocU54oMMxxpgi5bUb6kkiEgP0wele4kpVXe/XyIrQS99tIEOVxwe3DXQoxhhT5DwlAreX0GTgm5zzVHWHvwIrKivi9/Pf2N+579JzaBxm/QkZY4KP1zqCb3HqBwSoADQDNgLt/RRXkTh2Ip2Hpq12+hPqbf0JGWOCk9eioQ45p0WkM/AXv0RUhJ7/dj3b9yfz+R3nU6mc15xojDGlyxk1j3G7n+5ayLEUqTlxe5i8fAejLmpOdxtwxhgTxLzWEYzOMVkG6Awk+iWiIpB09ASPfrWGtvWrMbpvq0CHY4wxAeW1PKRqjs/pOHUGXxZ+OP6nqjz65a8cTknnszs6Ub6sdTFtjAluPhOB+yJZFVV9qAji8bspK3YyZ/0e/nl5O1rVrep7A2OMKeVOW0cgImVVNQOnKKjEi086xrMz4+h5Ti1u6xER6HCMMaZY8PVEsBwnCcSKyAxgGnAsa6GqfuXH2ApVekYm90+NpWwZYcx1kZQpY+MMGGMMeK8jCAP24YxRnPU+gQIlJhEs3JzEqh0HeeW6SOpXrxjocIwxptjwlQjquC2G1vK/BJBF/RaVHySnZgDQoVH1AEdivEhLSyMhIYGUlJRAh2JMiVKhQgUaNWpEaGio5218JYIQoAonJ4AsJSoRmJIlISGBqlWrEhERYcOFGuORqrJv3z4SEhJo1qyZ5+18JYLdqvrs2YVmTMGlpKRYEjCmgESEWrVqkZhYsNe8fL1ZbP8LTcBYEjCm4M7k/42vRNDnzEIxxhhTUpw2Eajq/qIKxJjipkqVKtmfZ82aRcuWLdmxYwdPP/00lSpVYu/evXmum59BgwZx8ODB067Tu3dvoqOjT5k/YcIE7rnnHu/BF8CYMWNo06YN5557LpGRkUycOPG0sZyJ6Oho7rvvPgBOnDjBZZddRqdOnZgyZQojR44kLi7urPb/+uuvZ8cNkJ6eTnh4OI899thJ60VERJCUlJQ9PW/ePC6//PLs6e+++46oqCjatm1LmzZtePDBB88qLoCYmBg6dOjAOeecw3333Ydq3tWra9as4YILLqB9+/Z06NAhu6HEgAEDiIyMpH379tx5551kZDgNX9566y0++uijs44PzrDTOWOCyU8//cS9997L7NmzadLEGc86PDycV155pUD7mTVrFjVq1PBDhKenqmRm5j2y7NixY/nxxx9Zvnw5a9euZcGCBfleqM5GVFQUb775JgCrVq0iLS2N2NhYbrjhBsaPH0+7du087yvrQpglPT2dDz/8kGHDhmXP++GHH2jdujVTp071fD5r167lnnvu4dNPP2X9+vWsXbuW5s2be44rP3fddRfjxo1j8+bNbN68mdmzZ5+yTnp6OsOHD2fs2LGsW7eOefPmZbf6mTp1KqtXr2bt2rUkJiYybdo0AG6//fbs7/RsWd/Lpth75pt1xP1+uFD32a5BNZ4a4ns4jYULF3LHHXcwa9YsWrT435gVt99+OxMmTOCRRx4hLCzspG0+/fRT3nzzTVJTU+nevTvvvPMOISEhREREEB0dTXh4OM899xyTJk2icePGhIeH06VLl+y7z2nTpnH33Xdz8OBBPvjgA3r16gXAzp07GTBgANu2bWPYsGE89dRTALz66qt8+OGHAIwcOZK///3vxMfHM3DgQC655BKWLl3K9OnTeeqpp4iOjkZEuP3227n//vt58cUXmTt3LtWqVQOgevXq3Hrrrad8D3fddRcrVqzg+PHjXHvttTzzzDMAPProo8yYMYOyZcvSr18/xowZw7Rp03jmmWcICQmhevXqLFiwgHnz5jFmzBg+/PBDhg8fTmJiIp06deLLL7/kz3/+M2PGjCEqKooffviBp556ihMnTtCiRQs++ugjqlSpQkREBLfffjs//PAD99xzDzfeeGN2bD///DOdO3embNn/Xc4mT57M3/72N959912WLVvGBRdc4PNv/Z///IfHH3+cNm3aAFC2bFnuvvtun9udzu7duzl8+HD28W+55RamT5/OwIEDT1rvhx9+oGPHjkRGRgJQq9b/ekTO+tukp6eTmpqaXQdQqVIlIiIiWL58Od26dTurOC0RGJOPEydOMHToUObNm5d9cchSpUoVbr/9dt54443siyLA+vXrmTJlCosXLyY0NJS7776bSZMmccstt2SvEx0dzZdffsmqVatIT0+nc+fOdOnSJXt5eno6y5cvZ9asWTzzzDPMmTMHIPuuvVKlSnTt2pXBgwcjInz00Uf88ssvqCrdu3fn4osvpmbNmmzcuJGPPvqId955h5iYGHbt2sXatWsBOHjwIEeOHOHIkSMnJbj8vPDCC4SFhZGRkUGfPn1Ys2YNjRo14uuvv2bDhg2ISHax17PPPsv3339Pw4YNTykKq1OnDuPHj2fMmDHMnDnzpGVJSUk8//zzzJkzh8qVK/Pvf/+bV199lSeffBJw2scvWrTolNgWL1580vd3/PhxfvrpJ9577z0OHjzI5MmTPSWCtWvX8sADD/hcb+7cudx///2nzK9UqRJLliw5ad6uXbto1KhR9nSjRo3YtWvXKdtu2rQJEaF///4kJiZy44038vDDD2cv79+/P8uXL2fgwIFce+212fOjoqJYuHChJQJT+nm5c/eH0NBQevTowQcffMAbb7xxyvL77ruPTp06nXTx+Omnn4iJiaFrV2e4juPHj1OnTp2Ttlu0aBFDhw6lYkXnDfchQ4actPzqq68GoEuXLsTHx2fP79u3b/ad4tVXX82iRYsQEa666ioqV66cPX/hwoVcccUVNG3alPPPPx+A5s2bs3XrVu69914GDx5Mv379OHr0qOcWJlOnTmXcuHGkp6eze/du4uLiaNeuHRUqVGDkyJEMHjw4u6y9Z8+ejBgxguuvvz77XLxYtmwZcXFx9OzZE4DU1NSTLuA33HBDntvt3r2btm3/N974zJkzueSSS6hUqRLXXHMNzz33HK+99hohISF5nm9BW9lccsklxMbGelo3r2KpvI6Xnp7OokWLWLFiBZUqVaJPnz506dKFPn2c9jrff/89KSkp/OlPf+Lnn3+mb9++gJNYN2zYUKD48+LXOgIRGSAiG0Vki4g8msdyEZE33eVr3JHPjCkWypQpw9SpU1mxYgUvvvjiKctr1KjBsGHDeOedd7LnqSq33norsbGxxMbGsnHjRp5++umTtvNVZl2+fHkAQkJCSE9Pz56f+wIiIqfdV1ZyAKhZsyarV6+md+/evP3224wcOZJq1apRuXJltm7detp4tm3bxpgxY/jpp59Ys2YNgwcPJiUlhbJly7J8+XKuueYapk+fzoABAwCn3uH5559n586ddOrUiX379p12/1lUlb59+2Z/d3FxcXzwwQd5nk9OFStWPOkN9MmTJzNnzhwiIiLo0qUL+/btY+7cuYBT5HLgwIHsdffv3094eDgA7du3JyYmxmecc+fOpVOnTqf89OjR45R1GzVqREJCQvZ0QkICDRo0yHO9iy++mPDwcCpVqsSgQYNYuXLlSetUqFCBK664gv/+97/Z81JSUrJvKM6G3xKB233128BAoB1wk4jkrhEaCLR0f0YB7/orHmPORKVKlZg5cyaTJk066aKUZfTo0bz33nvZF+w+ffrwxRdfZLco2r9/P9u3bz9pmwsvvJBvvvmGlJQUjh49yrfffusplh9//JH9+/dz/Phxpk+fTs+ePbnooouYPn06ycnJHDt2jK+//jq7TiGnpKQkMjMzs++Qsy4yjz32GH/96185fNipgzl8+DDjxo07advDhw9TuXJlqlevzp49e/juu+8AOHr0KIcOHWLQoEG8/vrr2XfJv/32G927d+fZZ58lPDycnTt3ejq/888/n8WLF7NlyxYAkpOT2bRpk8/t2rZtm73N4cOHWbRoETt27CA+Pp74+HjefvttJk+eDDgtoT755BPAqXT+9NNPueSSSwB46KGHePHFF7OPmZmZyauvvnrK8bKeCHL/5C4WAqhfvz5Vq1Zl2bJlqCoTJ05k6NChp6zXv39/1qxZQ3JyMunp6cyfP5927dpx9OhRdu/eDThPDbNmzTqpmHLTpk2ce+65Pr8jX/xZNNQN2KKqWwFE5HNgKJCzndhQYKI6tzXLRKSGiNRX1d1+jMuYAgkLC2P27NlcdNFF2XePWcLDw7nqqqt47bXXAGjXrh3PP/88/fr1IzMzk9DQUN5++22aNm2avU3Xrl254ooriIyMpGnTpkRFRVG9uu8+sC688EJuvvlmtmzZwrBhw4iKigJgxIgR2WXEI0eO5LzzzjupSAmcsurbbrstu/XQv/71L8CpBD569Chdu3YlNDSU0NDQU8rJIyMjOe+882jfvj3NmzfPLro5cuQIQ4cOJSUlBVXN/g4eeughNm/ejKrSp08fIiMjmT9/vs/zq127NhMmTOCmm27ixIkTADz//PO0anX6UQQHDhzIzTffDMBXX33FpZdemv1UBTB06FAefvhhTpw4wT//+U/uuusuIiMjUVUGDBjA8OHDAejYsSOvv/46N910E8nJyYgIgwcP9hm3L++++y4jRozg+PHjDBw4MLuieMaMGURHR/Pss89Ss2ZNRo8eTdeuXRERBg0axODBg9mzZw9XXHEFJ06cICMjg0svvZQ777wze9+LFy/ObjRwVlTVLz/AtcD4HNM3A2/lWmcmcGGO6Z+AqDz2NQqIBqKbNGmiZyI6fr/e9Wm07jqQfEbbm6IVFxcX6BD86siRI6qqeuzYMe3SpYvGxMQEOKKS7corr9RNmzYFOowitXLlSh0+fHiey/L6/wNEaz7Xa38+EXjpqM5TZ3aqOg4YBxAVFXVGjZy7NK1Jl6ZdfK9oTBEYNWoUcXFxpKSkcOutt9K5s1WPnY2XXnqJ3bt307Jly0CHUmSSkpJ47rnnCmVf/kwECUDjHNONgN/PYB1jSp3PPvss0CGUKq1bt6Z169aBDqNIZbUcKgz+bDW0AmgpIs1EpBxwIzAj1zozgFvc1kPnA4fU6geMS/3whqsxpd2Z/L/x2xOBqqaLyD3A9zjjGnyoqutE5E53+VhgFjAI2AIkA7f5Kx5TslSoUIF9+/ZRq1Yt64XUGI/UHY+gQoUKBdpOStpdV1RUlBZWR1im+LIRyow5M/mNUCYiMaoaldc29maxKZZCQ0MLNMKSMebMWe+jxhgT5CwRGGNMkLNEYIwxQa7EVRaLSCKw3eeKeQsHknyuVbrYOQcHO+fgcDbn3FRVa+e1oMQlgrMhItH51ZqXVnbOwcHOOTj465ytaMgYY4KcJQJjjAlywZYIxvlepdSxcw4Ods7BwS/nHFR1BMYYY04VbE8ExhhjcrFEYIwxQa5UJgIRGSAiG0Vki4g8msdyEZE33eVrRKTEjwri4Zz/5J7rGhFZIiKRgYizMPk65xzrdRWRDBG5tijj8wcv5ywivUUkVkTWiYjvMSKLOQ//tquLyDcisto95xLdi7GIfCgie0VkbT7LC//6ld/QZSX1B6fL69+A5kA5YDXQLtc6g4DvcEZIOx/4JdBxF8E59wBqup8HBsM551jvZ5wuz68NdNxF8HeugTMueBN3uk6g4y6Cc/4H8G/3c21gP1Au0LGfxTlfBHQG1uazvNCvX6XxiaAbsEVVt6pqKvA5MDTXOkOBiepYBtQQkfpFHWgh8nnOqrpEVQ+4k8twRoMrybz8nQHuBb4E9hZlcH7i5ZyHAV+p6g4AVS3p5+3lnBWoKs7AFVVwEkF60YZZeFR1Ac455KfQr1+lMRE0BHbmmE5w5xV0nZKkoOfzZ5w7ipLM5zmLSEPgKmBsEcblT17+zq2AmiIyT0RiROSWIovOP7yc81tAW5xhbn8F/qaqmUUTXkAU+vWrNI5HkNdwVrnbyHpZpyTxfD4icglOIrjQrxH5n5dzfh14RFUzSskoZ17OuSzQBegDVASWisgyVd3k7+D8xMs59wdigUuBFsCPIrJQVQ/7ObZAKfTrV2lMBAlA4xzTjXDuFAq6Tkni6XxEpCMwHhioqvuKKDZ/8XLOUcDnbhIIBwaJSLqqTi+SCAuf13/bSap6DDgmIguASKCkJgIv53wb8JI6BehbRGQb0AZYXjQhFrlCv36VxqKhFUBLEWkmIuWAG4EZudaZAdzi1r6fDxxS1d1FHWgh8nnOItIE+Aq4uQTfHebk85xVtZmqRqhqBPAFcHcJTgLg7d/2f4FeIlJWRCoB3YH1RRxnYfJyzjtwnoAQkbpAa2BrkUZZtAr9+lXqnghUNV1E7gG+x2lx8KGqrhORO93lY3FakAwCtgDJOHcUJZbHc34SqAW8494hp2sJ7rnR4zmXKl7OWVXXi8hsYA2QCYxX1TybIZYEHv/OzwETRORXnGKTR1S1xHZPLSKTgd5AuIgkAE8BoeC/65d1MWGMMUGuNBYNGWOMKQBLBMYYE+QsERhjTJCzRGCMMUHOEoExxgQ5SwRBwO15MzbHT8Rp1j1aCMebICLb3GOtFJELzmAf40Wknfv5H7mWLTnbGN39ZH0va93eK2v4WL+TiAw6g+PUF5GZ7ufeInJIRFaJyHoReeoM9ndFVi+cInJl1vfkTj8rIpcVdJ95HGOC+Oit1e3GwnMTZPfcZ3pYL8/eN0VkjIhc6vV4xjtLBMHhuKp2yvETXwTHfEhVOwGPAu8VdGNVHamqce7kP3It63H24QH/+17Oxenk668+1u+E0367oEYD7+eYXqiq5+G8+TxcRLoUZGeqOkNVX3InrwTa5Vj2pKrOOYMYi5MJwIA85v8fzr8nU8gsEQQhEakiIj+5d+u/isgpvXa6d7ELctwx93Ln9xORpe6200Skio/DLQDOcbcd7e5rrYj83Z1XWUS+Facv+bUicoM7f56IRInIS0BFN45J7rKj7u8pOe/Q3bvYa0QkREReFpEV4vTX/hcPX8tS3I67RKSbOGM2rHJ/t3bfan0WuMGN5QY39g/d46zK63t0XQPMzj3T7QYiBmjhPm0sc+P9WkRqurHcJyJx7vzP3XkjROQtEekBXAG87MbUIutOXkQGisjUHN9NbxH5xv1coL+hiDzpnuNaERknclLHTcPd72itiHRz1/f6veQpv943VXU7UEtE6hVkf8aDoupj234C9wNk4HTKFQt8jfNGeTV3WTjOG4pZLxcedX8/ADzufg4BqrrrLgAqu/MfAZ7M43gTcPv+B64DfsHpCO1XoDJOV8HrgPNwLpLv59i2uvt7HhCVM6Yc62TFeBXwsfu5HE6PjBWBUcAT7vzyQDTQLI84j+Y4v2nAAHe6GlDW/XwZ8KX7eQTwVo7tXwSGu59r4PTnUznXMZoBMTmmewMz3c+1gHigPc6bwBe7858FXnc//w6UzzpG7jhyftc5p92/8Y4cf6t3geFn+DcMyzH/E2BIjr/R++7ni3D7z8/ve8l17lE4bz3n9282gjz648d5srom0P+nSttPqetiwuTpuDrFNACISCjwoohchNMNQUOgLvBHjm1WAB+6605X1VgRuRinGGKxe1NYDudOOi8vi8gTQCJOb6d9gK/VuQtGRL4CeuHcKY8RkX/jXCQWFuC8vgPeFJHyOEUJC1T1uIj0AzrmKOOuDrQEtuXavqKIxOJcdGKAH3Os/7GItMTp1TE0n+P3A64QkQfd6QpAE07u26e++x3k1EtEVuF89y/hdCJWQ1WzRhP7GCcxgZMgJonIdGB6PnGcQp2uGWYDQ0TkC2Aw8DBQkL9hlktE5GGgEhCGk8S/cZdNdo+3QESqiVPPkt/3kjO+aGCk1/PJYS/Q4Ay2M6dhiSA4/QlnJKcuqpomIvE4/1mzuf+xL8K5gHwiIi8DB4AfVfUmD8d4SFW/yJqQfCowVXWTW0Y+CPiXiPygqs96OQlVTRGReTjdEN+Ae1HC6W/mXlX93scujqtqJxGpDszEqSN4E6fvmrmqepU4Fevz8tlecO5ON57uGOT6bnHqCC7P3olz/PwMxrnbvgL4p4i0P826uU3BOaf9wApVPeIW63j9GyIiFYB3cJ7OdorI05x8Prn7qFHy+V7E6RDubFXA+U5NIbI6guBUHdjrJoFLgKa5VxCRpu467wMf4AydtwzoKSJZZf6VRKSVx2MuAK50t6mMU6yzUEQaAMmq+ikwxj1Obmnuk0lePsfpdKsXTsdkuL/vytpGRFq5x8yTqh4C7gMedLepDuxyF4/IseoRnCKyLN8D92aVmYvIeXnsfhPOE0e+3OMfELceBrgZmC8iZYDGqjoX526+Bk6xWk65Y8ppHs73eQdOUoCC/w2zLvpJbl1C7pZEWXU6F+L0gnkIb9/LmWoFlNhO9IorSwTBaRIQJSLROE8HG/JYpzcQ6xZhXAO8oaqJOBfGySKyBuei0sbLAVV1JU6583KcOoPxqroK6AAsd4toHgeez2PzccAacSuLc/kB5455jjpDGYIz5kIcsFKcJojv4ePp141lNU43x//BeTpZjFN/kGUu0C6rshjnySHUjW2tO517v8eA37IuvKdxK05x2hqc1knPusf+VJxeNVcBr6nqwVzbfQ485FbKtsh17AycJ52B7m8K+jd0j/c+Tv3OdJwiw5wOiNOcdyxOESB4+F7EaQgwPq9jitP75lKgtYgkiMif3fmhOA0PovOL15wZ633UGD8TkatwiuGeCHQsJZn7PXZW1X8GOpbSxuoIjPEzVf1aRGoFOo5SoCzwSqCDKI3sicAYY4Kc1REYY0yQs0RgjDFBzhKBMcYEOUsExhgT5CwRGGNMkPt/TBRTBqVxzeYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=37)\n",
    "knn.fit(x2_train_over, y2_train_over) \n",
    "y2_pred = knn.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y2_test, y2_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x2_test, y2_test)\n",
    "plt.savefig('rocauc/knn_FS2_OS.png')\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, X2, Y, cv=5, scoring = 'accuracy'))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', knn.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  }
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