{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import zipfile\n",
    "with zipfile.ZipFile(\"dataset_cleaned.zip\", 'r') as zip_ref:\n",
    "    zip_ref.extractall(\"dataset_cleaned_extracted\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# W pobranym zbiorze danych jest kilka podzbiorów więc celowo otwieram ten z NaN, żeby manualnie go oczyścić dla praktyki\n",
    "train = pd.read_csv(\"dataset_cleaned_extracted/train.csv\")\n",
    "test = pd.read_csv(\"dataset_cleaned_extracted/test.csv\")\n",
    "valid = pd.read_csv(\"dataset_cleaned_extracted/valid.csv\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',\n",
      "       'PhysicalActivities', 'SleepHours', 'RemovedTeeth', 'HadHeartAttack',\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",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "num_columns = train.select_dtypes(['float64']).columns\n",
    "print(num_columns)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "36"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_columns)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'PhysicalActivities', 'SleepHours', 'RemovedTeeth', 'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD', 'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis', 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty', 'DifficultyConcentrating', 'DifficultyWalking', 'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus', 'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver', 'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']\n"
     ]
    }
   ],
   "source": [
    "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",
    "print(x_columns)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "35"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_columns)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "y_column = 'HadHeartAttack'"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "train_x = train[x_columns]\n",
    "train_y = train[y_column]\n",
    "\n",
    "test_x = test[x_columns]\n",
    "test_y = test[y_column]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 676617 entries, 0 to 676616\n",
      "Data columns (total 41 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   Unnamed: 0                 676617 non-null  int64  \n",
      " 1   State                      676617 non-null  object \n",
      " 2   Male                       676617 non-null  float64\n",
      " 3   GeneralHealth              676617 non-null  float64\n",
      " 4   PhysicalHealthDays         676617 non-null  float64\n",
      " 5   MentalHealthDays           676617 non-null  float64\n",
      " 6   LastCheckupTime            676617 non-null  object \n",
      " 7   PhysicalActivities         676617 non-null  float64\n",
      " 8   SleepHours                 676617 non-null  float64\n",
      " 9   RemovedTeeth               676617 non-null  float64\n",
      " 10  HadHeartAttack             676617 non-null  float64\n",
      " 11  HadAngina                  676617 non-null  float64\n",
      " 12  HadStroke                  676617 non-null  float64\n",
      " 13  HadAsthma                  676617 non-null  float64\n",
      " 14  HadSkinCancer              676617 non-null  float64\n",
      " 15  HadCOPD                    676617 non-null  float64\n",
      " 16  HadDepressiveDisorder      676617 non-null  float64\n",
      " 17  HadKidneyDisease           676617 non-null  float64\n",
      " 18  HadArthritis               676617 non-null  float64\n",
      " 19  HadDiabetes                676617 non-null  float64\n",
      " 20  DeafOrHardOfHearing        676617 non-null  float64\n",
      " 21  BlindOrVisionDifficulty    676617 non-null  float64\n",
      " 22  DifficultyConcentrating    676617 non-null  float64\n",
      " 23  DifficultyWalking          676617 non-null  float64\n",
      " 24  DifficultyDressingBathing  676617 non-null  float64\n",
      " 25  DifficultyErrands          676617 non-null  float64\n",
      " 26  SmokerStatus               676617 non-null  float64\n",
      " 27  ECigaretteUsage            676617 non-null  float64\n",
      " 28  ChestScan                  676617 non-null  float64\n",
      " 29  RaceEthnicityCategory      676617 non-null  object \n",
      " 30  AgeCategory                676617 non-null  object \n",
      " 31  HeightInMeters             676617 non-null  float64\n",
      " 32  WeightInKilograms          676617 non-null  float64\n",
      " 33  BMI                        676617 non-null  float64\n",
      " 34  AlcoholDrinkers            676617 non-null  float64\n",
      " 35  HIVTesting                 676617 non-null  float64\n",
      " 36  FluVaxLast12               676617 non-null  float64\n",
      " 37  PneumoVaxEver              676617 non-null  float64\n",
      " 38  TetanusLast10Tdap          676617 non-null  float64\n",
      " 39  HighRiskLastYear           676617 non-null  float64\n",
      " 40  CovidPos                   676617 non-null  float64\n",
      "dtypes: float64(36), int64(1), object(4)\n",
      "memory usage: 211.6+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Definiowanie modelu"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras import layers\n",
    "from keras.optimizers import Adam\n",
    "def create_model():\n",
    "    inputs = keras.Input(shape=(35,))\n",
    "    dense1 = layers.Dense(64, activation=\"relu\")(inputs)\n",
    "    dropout1 = layers.Dropout(0.2)(dense1)\n",
    "    dense2 = layers.Dense(32, activation=\"relu\")(dropout1)\n",
    "    dropout2 = layers.Dropout(0.2)(dense2)\n",
    "    output = layers.Dense(1, activation=\"sigmoid\")(dropout2)\n",
    "    model = keras.Model(inputs=inputs, outputs=output)\n",
    "\n",
    "    model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "model = create_model()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 35)]              0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 64)                2304      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,417\n",
      "Trainable params: 4,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Trenowanie modelu"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "callback = keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience=3, restore_best_weights=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "21145/21145 [==============================] - 21s 963us/step - loss: 0.4343 - accuracy: 0.7991 - val_loss: 0.3911 - val_accuracy: 0.8412\n",
      "Epoch 2/1000\n",
      "21145/21145 [==============================] - 20s 957us/step - loss: 0.4262 - accuracy: 0.8043 - val_loss: 0.3980 - val_accuracy: 0.8347\n",
      "Epoch 3/1000\n",
      "21145/21145 [==============================] - 20s 959us/step - loss: 0.4227 - accuracy: 0.8057 - val_loss: 0.3904 - val_accuracy: 0.8396\n",
      "Epoch 4/1000\n",
      "21145/21145 [==============================] - 20s 950us/step - loss: 0.4202 - accuracy: 0.8073 - val_loss: 0.4032 - val_accuracy: 0.8285\n",
      "Epoch 5/1000\n",
      "21145/21145 [==============================] - 20s 962us/step - loss: 0.4184 - accuracy: 0.8083 - val_loss: 0.3639 - val_accuracy: 0.8613\n",
      "Epoch 6/1000\n",
      "21145/21145 [==============================] - 20s 965us/step - loss: 0.4172 - accuracy: 0.8086 - val_loss: 0.3897 - val_accuracy: 0.8328\n",
      "Epoch 7/1000\n",
      "21145/21145 [==============================] - 20s 954us/step - loss: 0.4155 - accuracy: 0.8094 - val_loss: 0.4143 - val_accuracy: 0.8272\n",
      "Epoch 8/1000\n",
      "21145/21145 [==============================] - 21s 970us/step - loss: 0.4145 - accuracy: 0.8102 - val_loss: 0.4026 - val_accuracy: 0.8323\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=1000, callbacks=[callback])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Zapisywanie modelu"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "model.save(\"model_v1.keras\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Testowanie na zbiorze walidacyjnym"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "valid_x = valid[x_columns]\n",
    "valid_y = valid[y_column]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1392/1392 [==============================] - 1s 569us/step\n",
      "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": 37,
   "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": 38,
   "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": 39,
   "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
   }
  }
 ],
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