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ML-Kurs-SS2023/notebooks/03_ml_basics_log_regr_heart_disease-preprocessing.ipynb

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2023-04-06 09:20:17 +02:00
{
"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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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</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",
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" <td>204</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <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",
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" <tr>\n",
" <th>299</th>\n",
" <td>45</td>\n",
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" <td>264</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <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>193</td>\n",
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" <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",
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" <tr>\n",
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" <td>131</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <tr>\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": 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": {
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"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": []
}
],
"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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