{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wMcwTFr3IfLF"
   },
   "source": [
    "** Plotting ROC AUC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pMbqUnILG4CX"
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "colab_type": "code",
    "id": "UUYqPslNHC5F",
    "outputId": "358d41d7-826f-4a39-e6dd-b66e9778f60d"
   },
   "outputs": [],
   "source": [
    "# data doesn't have headers, so let's create headers\n",
    "_headers = ['Age', 'Delivery_Nbr', 'Delivery_Time', 'Blood_Pressure', 'Heart_Problem', 'Caesarian']\n",
    "# read in cars dataset\n",
    "df = pd.read_csv('../Dataset/caesarian.csv.arff', names=_headers, index_col=None, skiprows=15)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NnYpB2dkHHNa"
   },
   "outputs": [],
   "source": [
    "# target column is 'Caesarian'\n",
    "features = df.drop(['Caesarian'], axis=1).values\n",
    "labels = df[['Caesarian']].values\n",
    "\n",
    "# split 80% for training and 20% into an evaluation set\n",
    "X_train, X_eval, y_train, y_eval = train_test_split(features, labels, test_size=0.2, random_state=0)\n",
    "\n",
    "# further split the evaluation set into validation and test sets of 10% each\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_eval, y_eval, test_size=0.5, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161
    },
    "colab_type": "code",
    "id": "A8E1Hc5RHI_5",
    "outputId": "2cc3b9cd-4a47-42b0-c79c-ec9c9b5f26d3"
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "75B0f5oBHLVh"
   },
   "outputs": [],
   "source": [
    "y_proba = model.predict_proba(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "KbtEbYtcI7cZ",
    "outputId": "3d2dc2ae-a660-4e48-b946-3b67dfb2066e"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "_auc = roc_auc_score(y_val, y_proba[:, 0])\n",
    "print(_auc)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Exercise6_13.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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