637 lines
22 KiB
Plaintext
637 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "initial_id",
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"metadata": {
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"jupyter": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
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"from sklearn.model_selection import KFold\n",
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"from sklearn import decomposition"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "67503952-9074-4cdb-9d7e-d9142f7c319c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"<div>\n",
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"<style scoped>\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>age</th>\n",
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" <th>trestbps</th>\n",
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" <th>chol</th>\n",
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" <th>thalach</th>\n",
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" <th>oldpeak</th>\n",
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" <th>sex_0</th>\n",
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" <th>sex_1</th>\n",
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" <th>cp_1</th>\n",
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" <th>cp_2</th>\n",
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" <th>cp_3</th>\n",
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" <th>...</th>\n",
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" <th>slope_1</th>\n",
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" <th>slope_2</th>\n",
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" <th>slope_3</th>\n",
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" <th>thal_3.0</th>\n",
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" <th>thal_6.0</th>\n",
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" <th>thal_7.0</th>\n",
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" <th>ca_0.0</th>\n",
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" <th>ca_1.0</th>\n",
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" <th>ca_2.0</th>\n",
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" <th>ca_3.0</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.708333</td>\n",
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" <td>0.244292</td>\n",
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" <th>2</th>\n",
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" </tr>\n",
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"</table>\n",
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"<p>5 rows × 28 columns</p>\n",
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],
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"text/plain": [
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" age trestbps chol thalach oldpeak sex_0 sex_1 cp_1 \\\n",
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"0 0.708333 0.481132 0.244292 0.603053 0.370968 False True True \n",
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"1 0.791667 0.622642 0.365297 0.282443 0.241935 False True False \n",
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"2 0.791667 0.245283 0.235160 0.442748 0.419355 False True False \n",
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"3 0.166667 0.339623 0.283105 0.885496 0.564516 False True False \n",
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"4 0.250000 0.339623 0.178082 0.770992 0.225806 True False False \n",
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"\n",
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" cp_2 cp_3 ... slope_1 slope_2 slope_3 thal_3.0 thal_6.0 thal_7.0 \\\n",
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"0 False False ... False False True False True False \n",
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"1 False False ... False True False True False False \n",
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"2 False False ... False True False False False True \n",
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"3 False True ... False False True True False False \n",
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"4 True False ... True False False True False False \n",
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"\n",
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" ca_0.0 ca_1.0 ca_2.0 ca_3.0 \n",
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"0 True False False False \n",
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"1 False False False True \n",
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"2 False False True False \n",
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"3 True False False False \n",
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"4 True False False False \n",
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"\n",
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"[5 rows x 28 columns]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_csv('./data/dataset_cleaned.csv')\n",
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"df.dropna(inplace=True)\n",
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"\n",
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"# extract all columns except 'goal' --> X\n",
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"X = df.loc[:, df.columns != 'goal']\n",
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"# extract only the column 'goal' --> y\n",
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"y = df.loc[:, 'goal']\n",
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"\n",
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"# add new axis to y, new shape: (n, 1)\n",
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"y = y.to_numpy()\n",
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"y = y.reshape((len(y),1))\n",
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"\n",
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"# binarize y\n",
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"y[y>0] = 1\n",
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"\n",
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"factor_columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal', 'ca']\n",
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"numeric_columns = [column for column in X.columns if column not in factor_columns]\n",
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"\n",
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"# transform factors into onehot vectors\n",
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"X = pd.get_dummies(X, columns=factor_columns)\n",
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"\n",
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"# min max scaling of numeric columns\n",
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"scaler = MinMaxScaler()\n",
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"X[numeric_columns] = scaler.fit_transform(X[numeric_columns])\n",
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"\n",
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"X.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "2bbee865-c000-43da-84d9-ce7e04874110",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_model(n_features):\n",
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" model = tf.keras.models.Sequential([\n",
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" tf.keras.layers.InputLayer(shape=(n_features,)),\n",
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" tf.keras.layers.Dense(30, activation='relu'),\n",
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" tf.keras.layers.Dense(30, activation='relu'),\n",
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" tf.keras.layers.Dense(1, activation='sigmoid')\n",
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" ], name='test')\n",
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" model.compile(optimizer=tf.keras.optimizers.Adam(), \n",
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" loss=tf.keras.losses.BinaryCrossentropy())\n",
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" return model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training 10 folds for 20 epochs\n",
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"Fold 0\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 90.000%\n",
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"Fold 1\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 80.000%\n",
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"Fold 2\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 90.000%\n",
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"Fold 3\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 90.000%\n",
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"Fold 4\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000023D0BD63C40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
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"\tAccuracy: 90.000%\n",
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"Fold 5\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000023D0D548CC0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
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"\tAccuracy: 86.667%\n",
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"Fold 6\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 80.000%\n",
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"Fold 7\n",
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"\tTrain samples:\t268\tTest samples:\t29\n",
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"\tAccuracy: 86.207%\n",
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"Fold 8\n",
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"\tTrain samples:\t268\tTest samples:\t29\n",
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"\tAccuracy: 79.310%\n",
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"Fold 9\n",
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"\tTrain samples:\t268\tTest samples:\t29\n",
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"\tAccuracy: 82.759%\n",
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"Avg accuracy 85.494%\n"
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]
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"\n",
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"use_pca = True\n",
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"# number of components extracted from the pca\n",
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"n_features = 8\n",
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"n_features = n_features if use_pca else len(X.columns)\n",
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"\n",
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"epochs = 20\n",
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"k_folds = 10\n",
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"\n",
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"# used to split the dataset into k folds\n",
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"kf = KFold(n_splits=k_folds)\n",
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"\n",
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"accuracies = []\n",
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"print(f'Training {k_folds} folds for {epochs} epochs')\n",
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"for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n",
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"\n",
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" print(f'Fold {i}')\n",
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" \n",
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" # extract train and test data from the cleaned dataset\n",
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" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
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" y_train, y_test = y[train_idx], y[test_idx]\n",
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"\n",
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" print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n",
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"\n",
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" if use_pca:\n",
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" # do pca based on the train data of the given fold to extract 'n_features'\n",
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" pca = decomposition.PCA(n_components=n_features)\n",
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" pca.fit(X_train)\n",
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" X_train = pca.transform(X_train)\n",
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"\n",
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" # train the model using the components extracted from pca\n",
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" model = get_model(n_features)\n",
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" model.fit(X_train, y_train, epochs=epochs, verbose=0)\n",
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"\n",
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" if use_pca:\n",
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" # transform test data using on the pca model trained on the train data\n",
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" X_test = pca.transform(X_test)\n",
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" \n",
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" y_pred = model.predict(X_test, verbose=0)\n",
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" y_pred = y_pred > 0.5 # threshold to binarize\n",
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"\n",
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" # calculate the accuracy of the train data for the current fold\n",
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" accuracy = sum(y_pred == y_test)[0] / len(y_pred)\n",
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" accuracies.append(accuracy)\n",
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" print(f'\\tAccuracy: {accuracy:.3%}')\n",
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"\n",
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"# calculate the average accuracy over all folds\n",
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"avg_accuracy = sum(accuracies) / len(accuracies)\n",
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"print(f'Avg accuracy {avg_accuracy:.3%}')"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 5,
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||
"id": "95215693-47c9-4202-92f5-efbc65bc32c9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training 5 folds\n",
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"Fold 0\n",
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"\tTrain samples:\t237\tTest samples:\t60\n"
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]
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},
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{
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"name": "stderr",
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||
"output_type": "stream",
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||
"text": [
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||
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
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" warnings.warn(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\tAccuracy 58.333%\n",
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"\n",
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"Fold 1\n",
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"\tTrain samples:\t237\tTest samples:\t60\n"
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]
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||
},
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||
{
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||
"name": "stderr",
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||
"output_type": "stream",
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||
"text": [
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||
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
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||
" warnings.warn(\n"
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||
]
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||
},
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||
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\tAccuracy 50.000%\n",
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"\n",
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"Fold 2\n",
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"\tTrain samples:\t238\tTest samples:\t59\n"
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]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\tAccuracy 55.932%\n",
|
||
"\n",
|
||
"Fold 3\n",
|
||
"\tTrain samples:\t238\tTest samples:\t59\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\tAccuracy 57.627%\n",
|
||
"\n",
|
||
"Fold 4\n",
|
||
"\tTrain samples:\t238\tTest samples:\t59\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\tAccuracy 52.542%\n",
|
||
"\n",
|
||
"Avg accuracy 54.887%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.cluster import KMeans\n",
|
||
"\n",
|
||
"use_pca = True\n",
|
||
"# number of components extracted from the pca\n",
|
||
"n_features = 10\n",
|
||
"\n",
|
||
"k_folds = 5\n",
|
||
"\n",
|
||
"# used to split the dataset into k folds\n",
|
||
"kf = KFold(n_splits=k_folds)\n",
|
||
"\n",
|
||
"accuracies = []\n",
|
||
"print(f'Training {k_folds} folds')\n",
|
||
"for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n",
|
||
"\n",
|
||
" print(f'Fold {i}')\n",
|
||
" \n",
|
||
" # extract train and test data from the cleaned dataset\n",
|
||
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
|
||
" y_train, y_test = y[train_idx], y[test_idx]\n",
|
||
"\n",
|
||
" print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n",
|
||
"\n",
|
||
" if use_pca:\n",
|
||
" # do pca based on the train data of the given fold to extract 'n_features'\n",
|
||
" pca = decomposition.PCA(n_components=n_features)\n",
|
||
" pca.fit(X_train)\n",
|
||
" X_train = pca.transform(X_train)\n",
|
||
"\n",
|
||
" model = KMeans(n_clusters=2, n_init=10)\n",
|
||
" model.fit(X_train)\n",
|
||
"\n",
|
||
" if use_pca:\n",
|
||
" X_test = pca.transform(X_test)\n",
|
||
" \n",
|
||
" y_pred = model.predict(X_test)\n",
|
||
"\n",
|
||
" # calculate the accuracy of the train data for the current fold\n",
|
||
" accuracy1 = sum(y_pred == y_test)[0] / len(y_pred)\n",
|
||
" accuracy2 = sum(y_pred != y_test)[0] / len(y_pred)\n",
|
||
" accuracy = max(accuracy1, accuracy2)\n",
|
||
" accuracies.append(accuracy)\n",
|
||
" print(f'\\tAccuracy {accuracy:.3%}')\n",
|
||
" print()\n",
|
||
"\n",
|
||
"# calculate the average accuracy over all folds\n",
|
||
"avg_accuracy = sum(accuracies) / len(accuracies)\n",
|
||
"print(f'Avg accuracy {avg_accuracy:.3%}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "880302e4-82c1-47b9-9fe3-cb3567511639",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Training 5 folds\n",
|
||
"Fold 0\n",
|
||
"\tTrain samples:\t237\tTest samples:\t60\n",
|
||
"\tAccuracy 85.000%\n",
|
||
"\n",
|
||
"Fold 1\n",
|
||
"\tTrain samples:\t237\tTest samples:\t60\n",
|
||
"\tAccuracy 90.000%\n",
|
||
"\n",
|
||
"Fold 2\n",
|
||
"\tTrain samples:\t238\tTest samples:\t59\n",
|
||
"\tAccuracy 84.746%\n",
|
||
"\n",
|
||
"Fold 3\n",
|
||
"\tTrain samples:\t238\tTest samples:\t59\n",
|
||
"\tAccuracy 76.271%\n",
|
||
"\n",
|
||
"Fold 4\n",
|
||
"\tTrain samples:\t238\tTest samples:\t59\n",
|
||
"\tAccuracy 77.966%\n",
|
||
"\n",
|
||
"Avg accuracy 82.797%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"\n",
|
||
"use_pca = True\n",
|
||
"# number of components extracted from the pca\n",
|
||
"n_features = 10\n",
|
||
"\n",
|
||
"k_folds = 5\n",
|
||
"\n",
|
||
"# used to split the dataset into k folds\n",
|
||
"kf = KFold(n_splits=k_folds)\n",
|
||
"\n",
|
||
"accuracies = []\n",
|
||
"print(f'Training {k_folds} folds')\n",
|
||
"for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n",
|
||
" print(f'Fold {i}')\n",
|
||
"\n",
|
||
" # extract train and test data from the cleaned dataset\n",
|
||
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
|
||
" y_train, y_test = y[train_idx], y[test_idx]\n",
|
||
" y_train, y_test = y_train[:, 0], y_test[:, 0]\n",
|
||
"\n",
|
||
" print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n",
|
||
"\n",
|
||
" if use_pca:\n",
|
||
" # do pca based on the train data of the given fold to extract 'n_features'\n",
|
||
" pca = decomposition.PCA(n_components=n_features)\n",
|
||
" pca.fit(X_train)\n",
|
||
" X_train = pca.transform(X_train)\n",
|
||
"\n",
|
||
" model = RandomForestClassifier(max_depth=2, random_state=0)\n",
|
||
" model.fit(X_train, y_train)\n",
|
||
"\n",
|
||
" if use_pca:\n",
|
||
" X_test = pca.transform(X_test)\n",
|
||
" \n",
|
||
" y_pred = model.predict(X_test)\n",
|
||
"\n",
|
||
" # calculate the accuracy of the train data for the current fold\n",
|
||
" accuracy = sum(y_pred == y_test) / len(y_pred)\n",
|
||
" accuracies.append(accuracy)\n",
|
||
" print(f'\\tAccuracy {accuracy:.3%}')\n",
|
||
" print()\n",
|
||
"\n",
|
||
"# calculate the average accuracy over all folds\n",
|
||
"avg_accuracy = sum(accuracies) / len(accuracies)\n",
|
||
"print(f'Avg accuracy {avg_accuracy:.3%}')"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"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": "3.11.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|