LR_SVM_DT_RF.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ad85659be5a11535346af29387e2efab900ea666"
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true
      },
      "cell_type": "code",
      "source": "creditCardData = pd.read_csv('../input/creditcard.csv')",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.preprocessing import StandardScaler\ncreditCardData['normAmount'] = StandardScaler().fit_transform(creditCardData['Amount'].values.reshape(-1, 1))\ncreditCardData = creditCardData.drop(['Time','Amount'],axis=1)\ncreditCardData.head()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "         V1        V2        V3     ...           V28  Class  normAmount\n0 -1.359807 -0.072781  2.536347     ...     -0.021053      0    0.244964\n1  1.191857  0.266151  0.166480     ...      0.014724      0   -0.342475\n2 -1.358354 -1.340163  1.773209     ...     -0.059752      0    1.160686\n3 -0.966272 -0.185226  1.792993     ...      0.061458      0    0.140534\n4 -1.158233  0.877737  1.548718     ...      0.215153      0   -0.073403\n\n[5 rows x 30 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>V1</th>\n      <th>V2</th>\n      <th>V3</th>\n      <th>V4</th>\n      <th>V5</th>\n      <th>V6</th>\n      <th>V7</th>\n      <th>V8</th>\n      <th>V9</th>\n      <th>V10</th>\n      <th>V11</th>\n      <th>V12</th>\n      <th>V13</th>\n      <th>V14</th>\n      <th>V15</th>\n      <th>V16</th>\n      <th>V17</th>\n      <th>V18</th>\n      <th>V19</th>\n      <th>V20</th>\n      <th>V21</th>\n      <th>V22</th>\n      <th>V23</th>\n      <th>V24</th>\n      <th>V25</th>\n      <th>V26</th>\n      <th>V27</th>\n      <th>V28</th>\n      <th>Class</th>\n      <th>normAmount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-1.359807</td>\n      <td>-0.072781</td>\n      <td>2.536347</td>\n      <td>1.378155</td>\n      <td>-0.338321</td>\n      <td>0.462388</td>\n      <td>0.239599</td>\n      <td>0.098698</td>\n      <td>0.363787</td>\n      <td>0.090794</td>\n      <td>-0.551600</td>\n      <td>-0.617801</td>\n      <td>-0.991390</td>\n      <td>-0.311169</td>\n      <td>1.468177</td>\n      <td>-0.470401</td>\n      <td>0.207971</td>\n      <td>0.025791</td>\n      <td>0.403993</td>\n      <td>0.251412</td>\n      <td>-0.018307</td>\n      <td>0.277838</td>\n      <td>-0.110474</td>\n      <td>0.066928</td>\n      <td>0.128539</td>\n      <td>-0.189115</td>\n      <td>0.133558</td>\n      <td>-0.021053</td>\n      <td>0</td>\n      <td>0.244964</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.191857</td>\n      <td>0.266151</td>\n      <td>0.166480</td>\n      <td>0.448154</td>\n      <td>0.060018</td>\n      <td>-0.082361</td>\n      <td>-0.078803</td>\n      <td>0.085102</td>\n      <td>-0.255425</td>\n      <td>-0.166974</td>\n      <td>1.612727</td>\n      <td>1.065235</td>\n      <td>0.489095</td>\n      <td>-0.143772</td>\n      <td>0.635558</td>\n      <td>0.463917</td>\n      <td>-0.114805</td>\n      <td>-0.183361</td>\n      <td>-0.145783</td>\n      <td>-0.069083</td>\n      <td>-0.225775</td>\n      <td>-0.638672</td>\n      <td>0.101288</td>\n      <td>-0.339846</td>\n      <td>0.167170</td>\n      <td>0.125895</td>\n      <td>-0.008983</td>\n      <td>0.014724</td>\n      <td>0</td>\n      <td>-0.342475</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.358354</td>\n      <td>-1.340163</td>\n      <td>1.773209</td>\n      <td>0.379780</td>\n      <td>-0.503198</td>\n      <td>1.800499</td>\n      <td>0.791461</td>\n      <td>0.247676</td>\n      <td>-1.514654</td>\n      <td>0.207643</td>\n      <td>0.624501</td>\n      <td>0.066084</td>\n      <td>0.717293</td>\n      <td>-0.165946</td>\n      <td>2.345865</td>\n      <td>-2.890083</td>\n      <td>1.109969</td>\n      <td>-0.121359</td>\n      <td>-2.261857</td>\n      <td>0.524980</td>\n      <td>0.247998</td>\n      <td>0.771679</td>\n      <td>0.909412</td>\n      <td>-0.689281</td>\n      <td>-0.327642</td>\n      <td>-0.139097</td>\n      <td>-0.055353</td>\n      <td>-0.059752</td>\n      <td>0</td>\n      <td>1.160686</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.966272</td>\n      <td>-0.185226</td>\n      <td>1.792993</td>\n      <td>-0.863291</td>\n      <td>-0.010309</td>\n      <td>1.247203</td>\n      <td>0.237609</td>\n      <td>0.377436</td>\n      <td>-1.387024</td>\n      <td>-0.054952</td>\n      <td>-0.226487</td>\n      <td>0.178228</td>\n      <td>0.507757</td>\n      <td>-0.287924</td>\n      <td>-0.631418</td>\n      <td>-1.059647</td>\n      <td>-0.684093</td>\n      <td>1.965775</td>\n      <td>-1.232622</td>\n      <td>-0.208038</td>\n      <td>-0.108300</td>\n      <td>0.005274</td>\n      <td>-0.190321</td>\n      <td>-1.175575</td>\n      <td>0.647376</td>\n      <td>-0.221929</td>\n      <td>0.062723</td>\n      <td>0.061458</td>\n      <td>0</td>\n      <td>0.140534</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-1.158233</td>\n      <td>0.877737</td>\n      <td>1.548718</td>\n      <td>0.403034</td>\n      <td>-0.407193</td>\n      <td>0.095921</td>\n      <td>0.592941</td>\n      <td>-0.270533</td>\n      <td>0.817739</td>\n      <td>0.753074</td>\n      <td>-0.822843</td>\n      <td>0.538196</td>\n      <td>1.345852</td>\n      <td>-1.119670</td>\n      <td>0.175121</td>\n      <td>-0.451449</td>\n      <td>-0.237033</td>\n      <td>-0.038195</td>\n      <td>0.803487</td>\n      <td>0.408542</td>\n      <td>-0.009431</td>\n      <td>0.798278</td>\n      <td>-0.137458</td>\n      <td>0.141267</td>\n      <td>-0.206010</td>\n      <td>0.502292</td>\n      <td>0.219422</td>\n      <td>0.215153</td>\n      <td>0</td>\n      <td>-0.073403</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b6bf0299de0c7019337d6a69104adbf848937204"
      },
      "cell_type": "code",
      "source": "X = creditCardData.iloc[:, creditCardData.columns != 'Class']\ny = creditCardData.iloc[:, creditCardData.columns == 'Class']",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a199ec45cfd5fa96193b2d91afbfc80a0ebe126e"
      },
      "cell_type": "code",
      "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "67c516f993c4798287b8d4b0d7927238d2f19ee2"
      },
      "cell_type": "code",
      "source": "print(np.shape(X_train))\nprint(np.shape(y_train))\nprint(np.shape(X_test))\nprint(np.shape(y_test))",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "(199364, 29)\n(199364, 1)\n(85443, 29)\n(85443, 1)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "53cb2828a6c8b18739c07b739a85ec9e42303cbe"
      },
      "cell_type": "code",
      "source": "from keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.utils import to_categorical",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Using TensorFlow backend.\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "aa05bf074a85ed7cd805cd7a1230100c4feccaa3"
      },
      "cell_type": "code",
      "source": "NN = Sequential()\nNN.add(Dense(units=16,kernel_initializer='uniform',activation='relu',input_dim=29))\nNN.add(Dropout(rate=0.1))\nNN.add(Dense(units=16,kernel_initializer='uniform',activation='relu',input_dim=29))\nNN.add(Dropout(rate=0.1))\nNN.add(Dense(units=1,kernel_initializer='uniform',activation='sigmoid'))\nNN.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b0abc73054006e7eabe9398c2b9da18fd81e3638"
      },
      "cell_type": "code",
      "source": "from keras.callbacks import ReduceLROnPlateau, EarlyStopping",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d1ff46b6af8a53f730c651512d96a16d28e98edb"
      },
      "cell_type": "code",
      "source": "history = NN.fit(X_train, y_train, batch_size=20, epochs=100, callbacks=[ReduceLROnPlateau(patience=3, verbose=1, min_lr=1e-6), EarlyStopping(patience=5, verbose=1)])",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Epoch 1/100\n199364/199364 [==============================] - 14s 69us/step - loss: 0.0118 - acc: 0.9985\nEpoch 2/100\n  2340/199364 [..............................] - ETA: 13s - loss: 0.0104 - acc: 0.9983  ",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/callbacks.py:1106: RuntimeWarning: Reduce LR on plateau conditioned on metric `val_loss` which is not available. Available metrics are: loss,acc,lr\n  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning\n/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/callbacks.py:569: RuntimeWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,acc,lr\n  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "199364/199364 [==============================] - 13s 65us/step - loss: 0.0036 - acc: 0.9993\nEpoch 3/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0035 - acc: 0.9993\nEpoch 4/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0032 - acc: 0.9994\nEpoch 5/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0030 - acc: 0.9993\nEpoch 6/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0030 - acc: 0.9994\nEpoch 7/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0031 - acc: 0.9994\nEpoch 8/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0031 - acc: 0.9994\nEpoch 9/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0028 - acc: 0.9994\nEpoch 10/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0030 - acc: 0.9994\nEpoch 11/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0028 - acc: 0.9994\nEpoch 12/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0030 - acc: 0.9994\nEpoch 13/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0028 - acc: 0.9994\nEpoch 14/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0029 - acc: 0.9993\nEpoch 15/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0027 - acc: 0.9994\nEpoch 16/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0027 - acc: 0.9994\nEpoch 17/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0029 - acc: 0.9994\nEpoch 18/100\n199364/199364 [==============================] - 14s 72us/step - loss: 0.0028 - acc: 0.9994\nEpoch 19/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0028 - acc: 0.9994\nEpoch 20/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0027 - acc: 0.9994\nEpoch 21/100\n199364/199364 [==============================] - 13s 64us/step - loss: 0.0028 - acc: 0.9994\nEpoch 22/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0027 - acc: 0.9994\nEpoch 23/100\n199364/199364 [==============================] - 15s 74us/step - loss: 0.0028 - acc: 0.9994\nEpoch 24/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0027 - acc: 0.9994\nEpoch 25/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0026 - acc: 0.9995\nEpoch 26/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0026 - acc: 0.9995\nEpoch 27/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0028 - acc: 0.9994\nEpoch 28/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0026 - acc: 0.9994\nEpoch 29/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0026 - acc: 0.9994\nEpoch 30/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0027 - acc: 0.9994\nEpoch 31/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0026 - acc: 0.9994\nEpoch 32/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0026 - acc: 0.9994\nEpoch 33/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0026 - acc: 0.9995\nEpoch 34/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0027 - acc: 0.9994\nEpoch 35/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0026 - acc: 0.9994\nEpoch 36/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0026 - acc: 0.9994\nEpoch 37/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0025 - acc: 0.9995\nEpoch 38/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0026 - acc: 0.9994\nEpoch 39/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9994\nEpoch 40/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0027 - acc: 0.9995\nEpoch 41/100\n199364/199364 [==============================] - 14s 70us/step - loss: 0.0025 - acc: 0.9994\nEpoch 42/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0026 - acc: 0.9994\nEpoch 43/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0025 - acc: 0.9994\nEpoch 44/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0026 - acc: 0.9994\nEpoch 45/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0026 - acc: 0.9994\nEpoch 46/100\n199364/199364 [==============================] - 14s 71us/step - loss: 0.0025 - acc: 0.9994\nEpoch 47/100\n199364/199364 [==============================] - 14s 71us/step - loss: 0.0026 - acc: 0.9995\nEpoch 48/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9994\nEpoch 49/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0025 - acc: 0.9994\nEpoch 50/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0025 - acc: 0.9994\nEpoch 51/100\n199364/199364 [==============================] - 13s 64us/step - loss: 0.0025 - acc: 0.9994\nEpoch 52/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0025 - acc: 0.9995\nEpoch 53/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0025 - acc: 0.9995\nEpoch 54/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0026 - acc: 0.9994\nEpoch 55/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0025 - acc: 0.9994\nEpoch 56/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9994\nEpoch 57/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9994\nEpoch 58/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9995\nEpoch 59/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9995\nEpoch 60/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0025 - acc: 0.9994\nEpoch 61/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0025 - acc: 0.9994\nEpoch 62/100\n199364/199364 [==============================] - 13s 64us/step - loss: 0.0023 - acc: 0.9994\nEpoch 63/100\n199364/199364 [==============================] - 13s 64us/step - loss: 0.0025 - acc: 0.9994 0s - loss: 0.0025 - \nEpoch 64/100\n199364/199364 [==============================] - 14s 71us/step - loss: 0.0024 - acc: 0.9994\nEpoch 65/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0024 - acc: 0.9994\nEpoch 66/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0022 - acc: 0.9995\nEpoch 67/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0025 - acc: 0.9994\nEpoch 68/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9994\nEpoch 69/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0025 - acc: 0.9995\nEpoch 70/100\n199364/199364 [==============================] - 14s 73us/step - loss: 0.0023 - acc: 0.9994 0s - loss: 0.002\nEpoch 71/100\n199364/199364 [==============================] - 14s 68us/step - loss: 0.0022 - acc: 0.9994\nEpoch 72/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0024 - acc: 0.9994\nEpoch 73/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0023 - acc: 0.9995\nEpoch 74/100\n199364/199364 [==============================] - 13s 64us/step - loss: 0.0023 - acc: 0.9995\nEpoch 75/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9995\nEpoch 76/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0023 - acc: 0.9994\nEpoch 77/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0024 - acc: 0.9994\nEpoch 78/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0024 - acc: 0.9994\nEpoch 79/100\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "199364/199364 [==============================] - 13s 64us/step - loss: 0.0024 - acc: 0.9995\nEpoch 80/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0024 - acc: 0.9995\nEpoch 81/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0023 - acc: 0.9995\nEpoch 82/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0023 - acc: 0.9994\nEpoch 83/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9994\nEpoch 84/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0024 - acc: 0.9994\nEpoch 85/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0023 - acc: 0.9995\nEpoch 86/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0024 - acc: 0.9994\nEpoch 87/100\n199364/199364 [==============================] - 14s 71us/step - loss: 0.0023 - acc: 0.9994\nEpoch 88/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0022 - acc: 0.9995\nEpoch 89/100\n199364/199364 [==============================] - 13s 64us/step - loss: 0.0023 - acc: 0.9994\nEpoch 90/100\n199364/199364 [==============================] - 13s 64us/step - loss: 0.0022 - acc: 0.9995\nEpoch 91/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0023 - acc: 0.9995\nEpoch 92/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0023 - acc: 0.9995\nEpoch 93/100\n199364/199364 [==============================] - 13s 64us/step - loss: 0.0022 - acc: 0.9994\nEpoch 94/100\n199364/199364 [==============================] - 15s 74us/step - loss: 0.0023 - acc: 0.9995\nEpoch 95/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0023 - acc: 0.9995\nEpoch 96/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0023 - acc: 0.9995\nEpoch 97/100\n199364/199364 [==============================] - 13s 65us/step - loss: 0.0022 - acc: 0.9995\nEpoch 98/100\n199364/199364 [==============================] - 13s 66us/step - loss: 0.0022 - acc: 0.9995\nEpoch 99/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0023 - acc: 0.9995\nEpoch 100/100\n199364/199364 [==============================] - 13s 67us/step - loss: 0.0023 - acc: 0.9994\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e82bed7059dfcb016a7327f1e4ff90b712181ef8"
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport seaborn as sns",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0566ecb52e1549ec5f4d39df00abc8ed3fda1a89"
      },
      "cell_type": "code",
      "source": "num_epochs = len(history.history[\"loss\"])\nfig, axarr = plt.subplots(1, 2, figsize=(24, 8))\naxarr[0].set_xlabel(\"Number of Epochs\")\naxarr[0].set_ylabel(\"Loss\")\nsns.lineplot(x=range(1, num_epochs+1), y=history.history[\"loss\"], label=\"Train\", ax=axarr[0])\naxarr[1].set_xlabel(\"Number of Epochs\")\naxarr[1].set_ylabel(\"Accuracy\")\nsns.lineplot(x=range(1, num_epochs+1), y=history.history[\"acc\"], label=\"Train\", ax=axarr[1])",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fc9e8661b70>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1728x576 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "943e8e0ef544a961ecbb3f10c6b7c61ea3d196ba"
      },
      "cell_type": "code",
      "source": "import itertools\nfrom sklearn.metrics import confusion_matrix, classification_report, matthews_corrcoef, cohen_kappa_score, accuracy_score, average_precision_score, roc_auc_score,auc,precision_recall_curve,roc_curve",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3109d101c6c09edec276ad27f6fdc7aed1a538f4"
      },
      "cell_type": "code",
      "source": "def plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, cm[i, j],\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "31951251b15665cf965b20c8b4a32af228d9a076"
      },
      "cell_type": "code",
      "source": "def classification_eval(estimator, X_test, y_test):\n    y_pred = estimator.predict(X_test)\n    dec = np.int64(np.ceil(np.log10(len(y_test))))\n    \n    def plot_roc():\n        fpr, tpr, t = roc_curve(y_test, y_test)\n        plt.plot(fpr, tpr, label = 'ROC curve', linewidth = 2)\n        plt.plot([0,1],[0,1], 'k--', linewidth = 2)\n        plt.xlim([0.0,0.001])\n        plt.ylim([0.0,1.05])\n        plt.xlabel('False Positive Rate')\n        plt.ylabel('True Positive Rate')\n        plt.title('ROC Curve (AUC=%0.2f)'% auc(fpr,tpr))\n        plt.show();\n    \n    def plot_precision_recall():\n        precision, recall, thresholds = precision_recall_curve(y_test, y_test)\n        plt.step(recall, precision, color = 'b', alpha = 0.2,where = 'post')\n        plt.fill_between(recall, precision, step ='post', alpha = 0.2,color = 'b')\n        plt.plot(recall, precision, linewidth=2)\n        plt.xlim([0.0,1])\n        plt.ylim([0.0,1.05])\n        plt.xlabel('Recall')\n        plt.ylabel('Precision')\n        plt.title('Precision Recall Curve')\n        plt.show();\n\n    print('CONFUSION MATRIX')\n    cfm=confusion_matrix(y_test, y_test)\n    plot_confusion_matrix(cfm, classes = range(2)) \n    plt.show()\n    print('CLASSIFICATION REPORT')\n    print(classification_report(y_test, y_test, digits=dec))\n    print('SCALAR METRICS')\n    format_str = '%%13s = %%.%if' % dec\n    print(format_str % ('MCC', matthews_corrcoef(y_test, y_test)))\n    print(format_str % (\"Cohen's kappa\", cohen_kappa_score(y_test, y_test)))\n    print(format_str % ('Accuracy', accuracy_score(y_test, y_test)))\n    plot_roc()\n    plot_precision_recall()",
      "execution_count": 31,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e378d13f38e6d7b277505003e02e715a991940d9"
      },
      "cell_type": "code",
      "source": "classification_eval(NN, X_test, y_test)",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": "CONFUSION MATRIX\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "CLASSIFICATION REPORT\n              precision    recall  f1-score   support\n\n           0    1.00000   1.00000   1.00000     85307\n           1    1.00000   1.00000   1.00000       136\n\n   micro avg    1.00000   1.00000   1.00000     85443\n   macro avg    1.00000   1.00000   1.00000     85443\nweighted avg    1.00000   1.00000   1.00000     85443\n\nSCALAR METRICS\n          MCC = 1.00000\nCohen's kappa = 1.00000\n     Accuracy = 1.00000\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.6",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}