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{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Logistic regression with scikit-learn: heart disease data set" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "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>age</th>\n", " <th>sex</th>\n", " <th>cp</th>\n", " <th>trestbps</th>\n", " <th>chol</th>\n", " <th>fbs</th>\n", " <th>restecg</th>\n", " <th>thalach</th>\n", " <th>exang</th>\n", " <th>oldpeak</th>\n", " <th>slope</th>\n", " <th>ca</th>\n", " <th>thal</th>\n", " <th>target</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>63</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>145</td>\n", " <td>233</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>150</td>\n", " <td>0</td>\n", " <td>2.3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>37</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>130</td>\n", " <td>250</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>187</td>\n", " <td>0</td>\n", " <td>3.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>41</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>130</td>\n", " <td>204</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>172</td>\n", " <td>0</td>\n", " <td>1.4</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>56</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>120</td>\n", " <td>236</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>178</td>\n", " <td>0</td>\n", " <td>0.8</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>57</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>120</td>\n", " <td>354</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>163</td>\n", " <td>1</td>\n", " <td>0.6</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>298</th>\n", " <td>57</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>140</td>\n", " <td>241</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>123</td>\n", " <td>1</td>\n", " <td>0.2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>299</th>\n", " <td>45</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>110</td>\n", " <td>264</td>\n", " <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", " </tr>\n", " <tr>\n", " <th>300</th>\n", " <td>68</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>144</td>\n", " <td>193</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>141</td>\n", " <td>0</td>\n", " <td>3.4</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>301</th>\n", " <td>57</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>130</td>\n", " <td>131</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>115</td>\n", " <td>1</td>\n", " <td>1.2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>302</th>\n", " <td>57</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>130</td>\n", " <td>236</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>174</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</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": 3, "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": 4, "metadata": {}, "outputs": [], "source": [ "y = df['target'].values\n", "X = df[[col for col in df.columns if col!=\"target\"]]" ] }, { "cell_type": "code", "execution_count": 5, "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": 6, "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": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LogisticRegression\n", "RandomForestClassifier\n", "AdaBoostClassifier\n", "GradientBoostingClassifier\n" ] } ], "source": [ "for clf in classifiers:\n", " print(clf.__class__.__name__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train models and compare ROC curves" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "### Your code here ###" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4}
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