{ "cells": [ { "cell_type": "code", "execution_count": 8, "id": "initial_id", "metadata": { "jupyter": { "is_executing": true } }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 25, "id": "67503952-9074-4cdb-9d7e-d9142f7c319c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathalgoal
063111452331215002.330.06.00
167141602860210811.523.03.02
267141202290212912.622.07.01
337131302500018703.530.03.00
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" ], "text/plain": [ " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", "0 63 1 1 145 233 1 2 150 0 2.3 3 \n", "1 67 1 4 160 286 0 2 108 1 1.5 2 \n", "2 67 1 4 120 229 0 2 129 1 2.6 2 \n", "3 37 1 3 130 250 0 0 187 0 3.5 3 \n", "4 41 0 2 130 204 0 2 172 0 1.4 1 \n", "\n", " ca thal goal \n", "0 0.0 6.0 0 \n", "1 3.0 3.0 2 \n", "2 2.0 7.0 1 \n", "3 0.0 3.0 0 \n", "4 0.0 3.0 0 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('./data/dataset_cleaned.csv')\n", "df.dropna(inplace=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 60, "id": "8fa945ef-34d4-4e4c-a1cd-f1e1e6da79e7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0],\n", " [1],\n", " [1],\n", " [0],\n", " [0]], dtype=int64)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# extract all columns except 'goal' --> X\n", "X = df.loc[:, df.columns != 'goal']\n", "# extract only the column 'goal' --> y\n", "y = df.loc[:, 'goal']\n", "\n", "# add new axis to y, new shape: (n, 1)\n", "y = y.to_numpy()\n", "y = y.reshape((len(y),1))\n", "\n", "# binarize y\n", "y[y>0] = 1" ] }, { "cell_type": "code", "execution_count": 91, "id": "2bbee865-c000-43da-84d9-ce7e04874110", "metadata": {}, "outputs": [], "source": [ "def get_model(n_features):\n", " model = tf.keras.models.Sequential([\n", " tf.keras.layers.InputLayer(shape=(n_features,)),\n", " tf.keras.layers.Dense(30, activation='relu'),\n", " tf.keras.layers.Dense(30, activation='relu'),\n", " tf.keras.layers.Dense(30, activation='relu'),\n", " tf.keras.layers.Dense(1, activation='sigmoid')\n", " ], name='test')\n", " model.compile(optimizer=tf.keras.optimizers.Adam(), \n", " loss=tf.keras.losses.BinaryCrossentropy())\n", " return model" ] }, { "cell_type": "code", "execution_count": 97, "id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training fold 0 for 5 epochs\n", "Train samples:\t237\n", "Test samples:\t60\n", "Accuracy of fold 0: 0.6166666666666667\n", "Training fold 1 for 5 epochs\n", "Train samples:\t237\n", "Test samples:\t60\n", "Accuracy of fold 1: 0.75\n", "Training fold 2 for 5 epochs\n", "Train samples:\t238\n", "Test samples:\t59\n", "Accuracy of fold 2: 0.6949152542372882\n", "Training fold 3 for 5 epochs\n", "Train samples:\t238\n", "Test samples:\t59\n", "Accuracy of fold 3: 0.7457627118644068\n", "Training fold 4 for 5 epochs\n", "Train samples:\t238\n", "Test samples:\t59\n", "Accuracy of fold 4: 0.6610169491525424\n", "Avg accuracy 0.6936723163841808\n" ] } ], "source": [ "from sklearn.model_selection import KFold\n", "from sklearn import decomposition\n", "import tensorflow as tf\n", "\n", "# number of components extracted from the pca\n", "n_features = 5 \n", "\n", "epochs = 5\n", "\n", "# used to split the dataset into k folds\n", "kf = KFold(n_splits=5)\n", "\n", "accuracies = []\n", "for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n", " print(f'Training fold {i} for {epochs} epochs')\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'Train samples:\\t{len(X_train)}')\n", " print(f'Test samples:\\t{len(X_test)}')\n", "\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", " # train the model using the components extracted from pca\n", " model = get_model(n_features)\n", " model.fit(X_train, y_train, epochs=epochs, verbose=0)\n", "\n", " # transform test data using on the pca model trained on the train data\n", " X_test = pca.transform(X_test)\n", " y_pred = model.predict(X_test, verbose=0)\n", " y_pred = y_pred > 0.5\n", "\n", " # calculate the accuracy of the train data for the current fold\n", " accuracy = sum(y_pred == y_test)[0] / len(y_pred)\n", " accuracies.append(accuracy)\n", " print(f'Accuracy of fold {i}: {accuracy}')\n", "\n", "# calculate the average accuracy over all folds\n", "avg_accuracy = sum(accuracies) / len(accuracies)\n", "print(f'Avg accuracy {avg_accuracy}')" ] } ], "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 }