337 lines
9.7 KiB
Plaintext
337 lines
9.7 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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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"
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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": 25,
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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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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\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>sex</th>\n",
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" <th>cp</th>\n",
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" <th>trestbps</th>\n",
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" <th>chol</th>\n",
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" <th>fbs</th>\n",
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" <th>restecg</th>\n",
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" <th>thalach</th>\n",
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" <th>exang</th>\n",
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" <th>oldpeak</th>\n",
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" <th>slope</th>\n",
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" <th>ca</th>\n",
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" <th>thal</th>\n",
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" <th>goal</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>63</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>145</td>\n",
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" <td>233</td>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>150</td>\n",
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" <td>0</td>\n",
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" <td>2.3</td>\n",
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" <td>3</td>\n",
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" <td>0.0</td>\n",
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" <td>6.0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>67</td>\n",
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" <td>1</td>\n",
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" <td>4</td>\n",
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" <td>160</td>\n",
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" <td>286</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>108</td>\n",
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" <td>1</td>\n",
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" <td>1.5</td>\n",
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" <td>2</td>\n",
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" <td>3.0</td>\n",
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" <td>3.0</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>67</td>\n",
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" <td>1</td>\n",
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" <td>4</td>\n",
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" <td>120</td>\n",
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" <td>229</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>129</td>\n",
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" <td>1</td>\n",
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" <td>2.6</td>\n",
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" <td>2</td>\n",
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" <td>2.0</td>\n",
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" <td>7.0</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>37</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>130</td>\n",
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" <td>250</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>187</td>\n",
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" <td>0</td>\n",
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" <td>3.5</td>\n",
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" <td>3</td>\n",
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" <td>0.0</td>\n",
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" <td>3.0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>41</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>130</td>\n",
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" <td>204</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>172</td>\n",
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" <td>0</td>\n",
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" <td>1.4</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>3.0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n",
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"0 63 1 1 145 233 1 2 150 0 2.3 3 \n",
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"1 67 1 4 160 286 0 2 108 1 1.5 2 \n",
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"2 67 1 4 120 229 0 2 129 1 2.6 2 \n",
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"3 37 1 3 130 250 0 0 187 0 3.5 3 \n",
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"4 41 0 2 130 204 0 2 172 0 1.4 1 \n",
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"\n",
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" ca thal goal \n",
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"0 0.0 6.0 0 \n",
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"1 3.0 3.0 2 \n",
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"2 2.0 7.0 1 \n",
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"3 0.0 3.0 0 \n",
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"4 0.0 3.0 0 "
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]
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},
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"execution_count": 25,
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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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"df.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": 60,
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"id": "8fa945ef-34d4-4e4c-a1cd-f1e1e6da79e7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0],\n",
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" [1],\n",
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" [1],\n",
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" [0],\n",
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" [0]], dtype=int64)"
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]
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},
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"execution_count": 60,
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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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"# 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"
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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": 91,
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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(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": 97,
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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 fold 0 for 5 epochs\n",
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"Train samples:\t237\n",
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"Test samples:\t60\n",
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"Accuracy of fold 0: 0.6166666666666667\n",
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"Training fold 1 for 5 epochs\n",
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"Train samples:\t237\n",
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"Test samples:\t60\n",
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"Accuracy of fold 1: 0.75\n",
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"Training fold 2 for 5 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n",
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"Accuracy of fold 2: 0.6949152542372882\n",
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"Training fold 3 for 5 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n",
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"Accuracy of fold 3: 0.7457627118644068\n",
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"Training fold 4 for 5 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n",
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"Accuracy of fold 4: 0.6610169491525424\n",
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"Avg accuracy 0.6936723163841808\n"
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]
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}
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],
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"source": [
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"from sklearn.model_selection import KFold\n",
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"from sklearn import decomposition\n",
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"import tensorflow as tf\n",
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"\n",
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"# number of components extracted from the pca\n",
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"n_features = 5 \n",
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"\n",
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"epochs = 5\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=5)\n",
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"\n",
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"accuracies = []\n",
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"for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n",
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" print(f'Training fold {i} for {epochs} epochs')\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'Train samples:\\t{len(X_train)}')\n",
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" print(f'Test samples:\\t{len(X_test)}')\n",
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"\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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" # 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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" y_pred = model.predict(X_test, verbose=0)\n",
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" y_pred = y_pred > 0.5\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'Accuracy of fold {i}: {accuracy}')\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}')"
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]
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}
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],
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"metadata": {
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||
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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||
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"codemirror_mode": {
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||
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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