{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression with scikit-learn: heart disease data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "<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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filename = \"heart.csv\"\n",
    "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": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values\n",
    "X = df[[col for col in df.columns if col!=\"target\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 427 ms, sys: 14.1 ms, total: 441 ms\n",
      "Wall time: 587 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=5000, penalty='none', tol=1e-05)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(penalty='none', fit_intercept=True, max_iter=5000, tol=1E-5)\n",
    "%time lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test predictions on test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.75      0.79        69\n",
      "           1       0.81      0.87      0.84        83\n",
      "\n",
      "    accuracy                           0.82       152\n",
      "   macro avg       0.82      0.81      0.81       152\n",
      "weighted avg       0.82      0.82      0.81       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print(classification_report(y_test, y_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare two classifiers using the ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(max_depth=3)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred_rf = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 15120 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\n",
    "\n",
    "y_pred_prob_lr = lr.predict_proba(X_test) # predicted probabilities\n",
    "fpr_lr, tpr_lr, _ = roc_curve(y_test, y_pred_prob_lr[:,1])\n",
    "\n",
    "y_pred_prob_rf = rf.predict_proba(X_test) # predicted probabilities\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_pred_prob_rf[:,1])\n",
    "\n",
    "plt.plot(tpr_lr, 1-fpr_lr, label=\"log. regression\")\n",
    "plt.plot(tpr_rf, 1-fpr_rf, label=\"random forest\")\n",
    "\n",
    "plt.xlabel('Recall', fontsize=18)\n",
    "plt.ylabel('Precision', fontsize=18);\n",
    "plt.legend(fontsize=15)\n",
    "\n",
    "plt.savefig(\"03_ml_basics_log_regr_heart_disease.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under Curve (AUC) scores: 0.81, 0.81\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "auc_lr = roc_auc_score(y_test,y_pred_lr)\n",
    "auc_rf = roc_auc_score(y_test,y_pred_rf)\n",
    "print(f\"Area under Curve (AUC) scores: {auc_lr:.2f}, {auc_rf:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check wether data preprocessing makes a difference in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.4 ms, sys: 2.89 ms, total: 25.3 ms\n",
      "Wall time: 27.6 ms\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.81      0.81        70\n",
      "           1       0.84      0.83      0.83        82\n",
      "\n",
      "    accuracy                           0.82       152\n",
      "   macro avg       0.82      0.82      0.82       152\n",
      "weighted avg       0.82      0.82      0.82       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "pipe = make_pipeline(StandardScaler(), LogisticRegression(penalty='none', fit_intercept=False, max_iter=5000, tol=1E-5))\n",
    "%time pipe.fit(X_train, y_train)\n",
    "y_pred_pipe = pipe.predict(X_test)\n",
    "print(classification_report(y_test, y_pred_pipe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.75      0.79        69\n",
      "           1       0.81      0.87      0.84        83\n",
      "\n",
      "    accuracy                           0.82       152\n",
      "   macro avg       0.82      0.81      0.81       152\n",
      "weighted avg       0.82      0.82      0.81       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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