ROC AUC demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Import the libraries*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Get the data*\n",
    "source: https://www.kaggle.com/uciml/pima-indians-diabetes-database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"cover image.jpg\" style=\"height:350px; width:600px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "5            5      116             74              0        0  25.6   \n",
       "6            3       78             50             32       88  31.0   \n",
       "7           10      115              0              0        0  35.3   \n",
       "8            2      197             70             45      543  30.5   \n",
       "9            8      125             96              0        0   0.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  \n",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,0:-1].values\n",
    "y = data.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Train-Test split*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Feature Scaling*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *SVM classifier*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model_SVC = SVC(kernel = 'rbf', random_state = 4)\n",
    "model_SVC.fit(X_train, y_train)\n",
    "\n",
    "y_pred_svm = model_SVC.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Logistic Classifier*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model_logistic = LogisticRegression()\n",
    "model_logistic.fit(X_train, y_train)\n",
    "\n",
    "y_pred_logistic = model_logistic.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Plot ROC and compare AUC*\n",
    "For more info visit: https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "logistic_fpr, logistic_tpr, threshold = roc_curve(y_test, y_pred_logistic)\n",
    "auc_logistic = auc(logistic_fpr, logistic_tpr)\n",
    "\n",
    "svm_fpr, svm_tpr, threshold = roc_curve(y_test, y_pred_svm)\n",
    "auc_svm = auc(svm_fpr, svm_tpr)\n",
    "\n",
    "plt.figure(figsize=(5, 5), dpi=100)\n",
    "plt.plot(svm_fpr, svm_tpr, linestyle='-', label='SVM (auc = %0.3f)' % auc_svm)\n",
    "plt.plot(logistic_fpr, logistic_tpr, marker='.', label='Logistic (auc = %0.3f)' % auc_logistic)\n",
    "\n",
    "plt.xlabel('False Positive Rate -->')\n",
    "plt.ylabel('True Positive Rate -->')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}