2024-04-14 17:30:10 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"import zipfile\n",
"with zipfile.ZipFile(\"dataset_cleaned.zip\", 'r') as zip_ref:\n",
" zip_ref.extractall(\"dataset_cleaned_extracted\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 1,
"outputs": [],
"source": [
"import pandas as pd\n",
"valid = pd.read_csv(\"dataset_cleaned_extracted/valid.csv\")\n",
"\n",
"x_columns = ['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',\n",
" 'PhysicalActivities', 'SleepHours', 'RemovedTeeth',\n",
" 'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',\n",
" 'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',\n",
" 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',\n",
" 'DifficultyConcentrating', 'DifficultyWalking',\n",
" 'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',\n",
" 'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms',\n",
" 'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver',\n",
" 'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']\n",
"y_column = 'HadHeartAttack'\n",
"\n",
"valid_x = valid[x_columns]\n",
"valid_y = valid[y_column]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [],
"source": [
"from tensorflow import keras\n",
"model = keras.models.load_model('model_v1.keras')"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
2024-04-15 18:43:56 +02:00
"1392/1392 [==============================] - 1s 645us/step\n",
2024-04-14 17:30:10 +02:00
"Poprawność na zbiorze walidacyjnym: 86.15%\n"
]
}
],
"source": [
"import numpy as np\n",
"predictions = model.predict(valid_x)[:,0]\n",
"true_answers = valid_y.to_numpy()\n",
"validation_accuracy = np.sum(np.rint(predictions) == true_answers)/len(true_answers)\n",
"print(f\"Poprawność na zbiorze walidacyjnym: {validation_accuracy:.2%}\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.08692811 0.12067404 0.31880796 0.64843357 0.15188715 0.06517262\n",
" 0.03407578 0.49311596 0.00781232 0.2089161 0.46056542 0.45341685\n",
" 0.4294767 0.25619727 0.20345858 0.2302334 0.38631877 0.36519188\n",
" 0.04014764 0.23888215 0.27519897 0.08928084 0.05204074 0.42043713\n",
" 0.19055638 0.29787344 0.23068897 0.88435644 0.03139259 0.95048493\n",
" 0.2457671 0.5858893 0.02678488 0.06240147 0.52132165 0.01431455\n",
" 0.02444405 0.07804424 0.11274771 0.12714393 0.35450152 0.01294624\n",
" 0.190797 0.07512036 0.48486376 0.06140704 0.9019506 0.08810509\n",
" 0.61831665 0.15642735 0.03310075 0.04532438 0.10763614 0.4277772\n",
" 0.20325996 0.8980398 0.7491019 0.38502344 0.03970775 0.0401529\n",
" 0.03046079 0.10123587 0.04993626 0.05702 0.18049946 0.1223311\n",
" 0.731555 0.40104443 0.18443953 0.1265702 0.07467585 0.03895461\n",
" 0.35271063 0.38039213 0.4450048 0.03670818 0.05534125 0.91664517\n",
" 0.413391 0.12545326 0.11306539 0.4350903 0.48778924 0.40804324\n",
" 0.33885244 0.21948677 0.01242744 0.02531701 0.6693964 0.15393472\n",
" 0.9307252 0.09181138 0.05571133 0.1261858 0.02687709 0.27069062\n",
" 0.22613294 0.20686075 0.47390068 0.40349996]\n"
]
}
],
"source": [
"print(predictions[:100])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
" 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0.]\n"
]
}
],
"source": [
"print(np.rint(predictions)[:100])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0.]\n"
]
}
],
"source": [
"print(true_answers[:100])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 7,
"outputs": [],
"source": [
"np.savetxt(\"predictions.txt\",predictions)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [],
"source": [
"np.savetxt(\"predictions_two_digits.txt\",predictions, fmt='%1.2f')"
],
"metadata": {
"collapsed": false
}
2024-04-15 18:43:56 +02:00
},
{
"cell_type": "code",
"execution_count": 9,
"outputs": [
{
"data": {
"text/plain": " Unnamed: 0 State Male GeneralHealth PhysicalHealthDays \\\n7 135450 Kentucky 1.0 0.50 0.0 \n25 321301 Rhode Island 1.0 0.00 1.0 \n29 402512 Washington 1.0 0.25 0.0 \n44 128060 Kansas 1.0 0.50 0.0 \n69 130420 Kansas 1.0 0.75 0.0 \n\n MentalHealthDays LastCheckupTime \\\n7 0.0 Within past year (anytime less than 12 months ... \n25 1.0 Within past year (anytime less than 12 months ... \n29 0.1 Within past year (anytime less than 12 months ... \n44 0.0 Within past year (anytime less than 12 months ... \n69 0.0 5 or more years ago \n\n PhysicalActivities SleepHours RemovedTeeth ... HeightInMeters \\\n7 1.0 0.260870 1.000000 ... 0.613793 \n25 1.0 0.260870 0.000000 ... 0.634483 \n29 1.0 0.347826 0.333333 ... 0.510345 \n44 0.0 0.260870 0.333333 ... 0.455172 \n69 1.0 0.217391 0.333333 ... 0.544828 \n\n WeightInKilograms BMI AlcoholDrinkers HIVTesting FluVaxLast12 \\\n7 0.164353 0.095584 1.0 0.0 0.0 \n25 0.193760 0.116415 1.0 0.0 0.0 \n29 0.380616 0.389716 1.0 0.0 1.0 \n44 0.084789 0.203190 1.0 0.0 1.0 \n69 0.190289 0.153196 1.0 0.0 0.0 \n\n PneumoVaxEver TetanusLast10Tdap HighRiskLastYear CovidPos \n7 0.0 0.0 0.0 0.0 \n25 0.0 0.0 0.0 0.0 \n29 1.0 0.0 1.0 0.0 \n44 1.0 0.0 0.0 0.0 \n69 0.0 0.0 0.0 0.0 \n\n[5 rows x 41 columns]",
"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>Unnamed: 0</th>\n <th>State</th>\n <th>Male</th>\n <th>GeneralHealth</th>\n <th>PhysicalHealthDays</th>\n <th>MentalHealthDays</th>\n <th>LastCheckupTime</th>\n <th>PhysicalActivities</th>\n <th>SleepHours</th>\n <th>RemovedTeeth</th>\n <th>...</th>\n <th>HeightInMeters</th>\n <th>WeightInKilograms</th>\n <th>BMI</th>\n <th>AlcoholDrinkers</th>\n <th>HIVTesting</th>\n <th>FluVaxLast12</th>\n <th>PneumoVaxEver</th>\n <th>TetanusLast10Tdap</th>\n <th>HighRiskLastYear</th>\n <th>CovidPos</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>7</th>\n <td>135450</td>\n <td>Kentucky</td>\n <td>1.0</td>\n <td>0.50</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>Within past year (anytime less than 12 months ...</td>\n <td>1.0</td>\n <td>0.260870</td>\n <td>1.000000</td>\n <td>...</td>\n <td>0.613793</td>\n <td>0.164353</td>\n <td>0.095584</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>25</th>\n <td>321301</td>\n <td>Rhode Island</td>\n <td>1.0</td>\n <td>0.00</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>Within past year (anytime less than 12 months ...</td>\n <td>1.0</td>\n <td>0.260870</td>\n <td>0.000000</td>\n <td>...</td>\n <td>0.634483</td>\n <td>0.193760</td>\n <td>0.116415</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>29</th>\n <td>402512</td>\n <td>Washington</td>\n <td>1.0</td>\n <td>0.25</td>\n <td>0.0</td>\n <td>0.1</td>\n <td>Within past year (anytime less than 12 months ...</td>\n <td>1.0</td>\n <td>0.347826</td>\n <td>0.333333</td>\n <td>...</td>\n <td>0.510345</td>\n <td>0.380616</td>\n <td>0.389716</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>44</th>\n <td>128060</td>\n <td>Kansas</td>\n <td>1.0</td>\n <td>0.50</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>Within past year (anytime less than 12 months ...</td>\n <td>0.0</td>\n <td>0.260870</td>\n <td>0.333333</td>\n <td>...</td>\n <td>0.455172</td>\n <td>0.084789</td>\n <td>0.203190</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>69</th>\n <td>130420</td>\n <td>Kansas</td>\n <td>1.0</td>\n <td>0.75</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>5 or more years ago</td>\n <td>1.0</td>\n <td>0.217391</td>\n <td>0.333333</td>\n <td>...</td>\n <td>0.544828</td>\n <td>0.190289</td>\n <td>0.153196</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows ×
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validate_heart_disease_true = valid.loc[valid[y_column]==1]\n",
"validate_heart_disease_true.head()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 10,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"78/78 [==============================] - 0s 490us/step\n"
]
},
{
"data": {
"text/plain": "array([0.49311596, 0.29787344, 0.95048493, ..., 0.5605181 , 0.08343226,\n 0.4648933 ], dtype=float32)"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validate_heart_disease_true_x = validate_heart_disease_true[x_columns]\n",
"predictions = model.predict(validate_heart_disease_true_x)[:,0]\n",
"predictions"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"### Z osób które miały choroby serca w zbiorze walidacyjnym 70% zostało poprawnie zaklasyfikowanych jako 1, pomimo iż klasa ta stanowi bardzo mały odsetek całego zbioru"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 11,
"outputs": [
{
"data": {
"text/plain": "0.701733172108021"
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(np.rint(predictions) == np.ones_like(predictions))/len(predictions)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 15,
"outputs": [
{
"data": {
"text/plain": "<AxesSubplot:ylabel='count'>"
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": "<Figure size 640x480 with 1 Axes>",
"image/png": "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
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"valid[y_column].value_counts().plot(kind=\"pie\")"
],
"metadata": {
"collapsed": false
}
2024-04-14 17:30:10 +02:00
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
