{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression with scikit-learn: heart disease data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
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       "      <th>slope</th>\n",
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       "      <th>thal</th>\n",
       "      <th>target</th>\n",
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       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>241</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>45</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
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       "      <td>1</td>\n",
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       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"https://www.physi.uni-heidelberg.de/~reygers/lectures/2021/ml/data/heart.csv\"\n",
    "df = pd.read_csv(filename)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values\n",
    "X = df[[col for col in df.columns if col!=\"target\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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=0.5, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "lr = LogisticRegression(penalty='none', fit_intercept=True, max_iter=5000, tol=1E-5)\n",
    "rf = RandomForestClassifier(max_depth=3)\n",
    "ab = AdaBoostClassifier()\n",
    "gb = GradientBoostingClassifier()\n",
    "\n",
    "classifiers = [lr, rf, ab, gb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train models\n",
    "for clf in classifiers:\n",
    "    clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare two classifiers using the ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression AUC = 0.8872549019607843\n",
      "RandomForestClassifier AUC = 0.9033613445378151\n",
      "AdaBoostClassifier AUC = 0.8300070028011205\n",
      "GradientBoostingClassifier AUC = 0.8940826330532212\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdede7e1d68>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 29852 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "for clf in classifiers:\n",
    "    \n",
    "    y_pred_prob = clf.predict_proba(X_test) # predicted probabilities\n",
    "    fpr_lr, tpr_lr, _ = roc_curve(y_test, y_pred_prob[:,1])\n",
    "    plt.plot(tpr_lr, 1-fpr_lr, label=clf.__class__.__name__)\n",
    "    auc = roc_auc_score(y_test, y_pred_prob[:,1])\n",
    "    print(clf.__class__.__name__, f'AUC = {auc}')\n",
    "\n",
    "plt.xlabel('Recall', fontsize=18)\n",
    "plt.ylabel('Precision', fontsize=18);\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus: Check if the performance differences are just statistical fluctuations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aucs = {}\n",
    "for clf in classifiers:\n",
    "    aucs[clf.__class__.__name__] = []\n",
    "\n",
    "# reshuffle the data 1000 times, train classifiers and save AUCs\n",
    "for i in range(1000):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, shuffle=True)\n",
    "    for clf in classifiers:\n",
    "        clf.fit(X_train, y_train)\n",
    "        y_pred_prob = clf.predict_proba(X_test)\n",
    "        auc = roc_auc_score(y_test, y_pred_prob[:,1])\n",
    "        aucs[clf.__class__.__name__].append(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for clf in classifiers:\n",
    "    clfname = clf.__class__.__name__\n",
    "    plt.hist(aucs[clfname], bins=40, range=(0.7, 1), \n",
    "             histtype='step', linewidth=2, label=clfname);\n",
    "plt.legend(loc='upper left')"
   ]
  }
 ],
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