kerasANN.ipynb

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 "cells": [
  {
   "source": [
    "# Neural Network Approach\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# Import libraries & Load Dataset"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import f1_score, roc_auc_score, confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./Dataset/processed_train.csv\")\n",
    "x = df.copy()\n",
    "x = x.drop(columns='stroke')\n",
    "y = df['stroke']\n",
    "smote = SMOTE()\n",
    "x_smote, y_smote = smote.fit_resample(x, y)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_smote, y_smote, test_size= 0.2, random_state= 42)"
   ]
  },
  {
   "source": [
    "## NN Classifier"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the keras model\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=25, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# compile the keras model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense (Dense)                (None, 12)                312       \n_________________________________________________________________\ndense_1 (Dense)              (None, 8)                 104       \n_________________________________________________________________\ndense_2 (Dense)              (None, 1)                 9         \n=================================================================\nTotal params: 425\nTrainable params: 425\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "source": [
    "### Training"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/150\n",
      "190/190 [==============================] - 0s 757us/step - loss: 0.4944 - accuracy: 0.7906\n",
      "Epoch 2/150\n",
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      "Epoch 3/150\n",
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      "Epoch 4/150\n",
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      "Epoch 5/150\n",
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      "Epoch 6/150\n",
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      "Epoch 7/150\n",
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      "Epoch 8/150\n",
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      "Epoch 9/150\n",
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      "Epoch 10/150\n",
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      "Epoch 11/150\n",
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      "Epoch 12/150\n",
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      "Epoch 13/150\n",
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      "Epoch 14/150\n",
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      "Epoch 15/150\n",
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      "Epoch 16/150\n",
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      "Epoch 17/150\n",
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      "Epoch 18/150\n",
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      "Epoch 143/150\n",
      "190/190 [==============================] - 0s 753us/step - loss: 0.0321 - accuracy: 0.9903\n",
      "Epoch 144/150\n",
      "190/190 [==============================] - 0s 770us/step - loss: 0.0317 - accuracy: 0.9889\n",
      "Epoch 145/150\n",
      "190/190 [==============================] - 0s 753us/step - loss: 0.0316 - accuracy: 0.9893\n",
      "Epoch 146/150\n",
      "190/190 [==============================] - 0s 737us/step - loss: 0.0317 - accuracy: 0.9896\n",
      "Epoch 147/150\n",
      "190/190 [==============================] - 0s 758us/step - loss: 0.0307 - accuracy: 0.9898\n",
      "Epoch 148/150\n",
      "190/190 [==============================] - 0s 742us/step - loss: 0.0321 - accuracy: 0.9893\n",
      "Epoch 149/150\n",
      "190/190 [==============================] - 0s 753us/step - loss: 0.0336 - accuracy: 0.9881\n",
      "Epoch 150/150\n",
      "190/190 [==============================] - 0s 784us/step - loss: 0.0311 - accuracy: 0.9893\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x28d3c49fdc0>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=150, batch_size=32)"
   ]
  },
  {
   "source": [
    "### Prediction"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:From <ipython-input-7-41a58b04bb02>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\nInstructions for updating:\nPlease use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [0],\n",
       "       [0],\n",
       "       ...,\n",
       "       [0],\n",
       "       [1],\n",
       "       [0]])"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "y_pred = model.predict_classes(x_test)\n",
    "y_pred"
   ]
  },
  {
   "source": [],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9792085848423878"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "f1_score(y_test,y_pred)"
   ]
  },
  {
   "source": [
    "## Generate Submission"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_submission(model, test_data, model_name = \"\"): \n",
    "    ''' \n",
    "    Generate submission based on given model and test set \n",
    "    param: \n",
    "        model: model must have predict method \n",
    "        test_data: dataframe \n",
    "        model_name  \n",
    "    return: \n",
    "        None\n",
    "    '''  \n",
    "    test_data.drop(columns= 'Unnamed: 0', inplace=True)\n",
    "    x = test_data.copy()\n",
    "    x.drop(columns='id', inplace=True)\n",
    "    id_test = test_data['id']\n",
    "    y_pred = model.predict_classes(x)\n",
    "    # y_pred = pd.DataFrame(y_pred)\n",
    "    y_pred = y_pred.reshape(-1)\n",
    "    data_test_submit = {'id': id_test, 'stroke': y_pred}\n",
    "    df_test_submit = pd.DataFrame(data= data_test_submit)\n",
    "    df_test_submit.to_csv('./Submission/submission_' + model_name + '.csv', index= False)\n",
    "\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv(\"./Dataset/processed_test.csv\")\n",
    "generate_submission(model, test_df, 'ANN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,\n",
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       "       0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "y_pred.reshape(-1)"
   ]
  }
 ]
}