NN_with_SMOTE.ipynb

{
  "cells": [
    {
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7e69790f73f23542934a002e0452de9b8e46a688"
      },
      "cell_type": "code",
      "source": "creditCardData = pd.read_csv('../input/creditcard.csv')",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d0d1d0cbf511883e094dddb7794c2291021ad363"
      },
      "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": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = creditCardData.iloc[:, creditCardData.columns != 'Class']\ny = creditCardData.iloc[:, creditCardData.columns == 'Class']",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "9d927ba79a0a953e8ef57541cb7529b5ac695bf6"
      },
      "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": "1441aa1c3b3555ddf3e736d30bb25d530ce2f666"
      },
      "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": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "13fc852f8bc1521ec7ec8f19568e2d462d7cd868"
      },
      "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": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e01488f9fc896291df10ad85c49211fb6b310887"
      },
      "cell_type": "code",
      "source": "def classification_eval(estimator, X_test, y_test):\n    y_pred = estimator.predict(X_test)\n    y_score = np.argmax(y_pred, axis=1)\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_score)\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_score)\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_score)\n    plot_confusion_matrix(cfm, classes = range(2)) \n    plt.show()\n    print('CLASSIFICATION REPORT')\n    print(classification_report(y_test, y_score, digits=dec))\n    print('SCALAR METRICS')\n    format_str = '%%13s = %%.%if' % dec\n    print(format_str % ('Accuracy', accuracy_score(y_test, y_score)))\n    plot_roc()\n    plot_precision_recall()",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "029ce4a183b52d422ec51244188504dfae480d67"
      },
      "cell_type": "code",
      "source": "from imblearn.over_sampling import SMOTE\nfrom keras.models import Sequential\nfrom keras.layers import Dense",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Using TensorFlow backend.\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "72b2cfa8a1476f70ec549608819b23826cd92850"
      },
      "cell_type": "code",
      "source": "sm = SMOTE(ratio='minority', random_state=42)\nXsm_train, ysm_train = sm.fit_sample(X_train, y_train)",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "be698c78aa7712158f1d2e33edbfe1c4c879d641"
      },
      "cell_type": "code",
      "source": "n_inputs = Xsm_train.shape[1]\n\noversample_model = Sequential([\n    Dense(n_inputs,input_shape=(n_inputs, ), activation='relu'),\n    Dense(16, activation='relu'),\n    Dense(16,activation='relu'),\n    Dense(2, activation='softmax')\n])\noversample_model.compile(optimizer = 'adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "953881b3674f3127bccfcdb98e37d4f268d9296a"
      },
      "cell_type": "code",
      "source": "oversample_model.fit(Xsm_train, ysm_train, validation_split=0.2, batch_size=300, epochs=20, shuffle=True, verbose=2)",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Train on 318416 samples, validate on 79604 samples\nEpoch 1/20\n - 2s - loss: 0.0690 - acc: 0.9767 - val_loss: 0.0194 - val_acc: 0.9963\nEpoch 2/20\n - 2s - loss: 0.0106 - acc: 0.9977 - val_loss: 0.0055 - val_acc: 0.9994\nEpoch 3/20\n - 2s - loss: 0.0064 - acc: 0.9989 - val_loss: 0.0041 - val_acc: 0.9998\nEpoch 4/20\n - 2s - loss: 0.0049 - acc: 0.9991 - val_loss: 0.0071 - val_acc: 0.9978\nEpoch 5/20\n - 2s - loss: 0.0043 - acc: 0.9993 - val_loss: 0.0019 - val_acc: 0.9995\nEpoch 6/20\n - 2s - loss: 0.0038 - acc: 0.9994 - val_loss: 3.6442e-04 - val_acc: 1.0000\nEpoch 7/20\n - 2s - loss: 0.0034 - acc: 0.9995 - val_loss: 0.0023 - val_acc: 0.9996\nEpoch 8/20\n - 2s - loss: 0.0036 - acc: 0.9994 - val_loss: 8.4868e-04 - val_acc: 1.0000\nEpoch 9/20\n - 2s - loss: 0.0032 - acc: 0.9995 - val_loss: 7.2288e-04 - val_acc: 1.0000\nEpoch 10/20\n - 2s - loss: 0.0029 - acc: 0.9996 - val_loss: 3.3630e-05 - val_acc: 1.0000\nEpoch 11/20\n - 2s - loss: 0.0031 - acc: 0.9996 - val_loss: 1.9104e-04 - val_acc: 1.0000\nEpoch 12/20\n - 2s - loss: 0.0034 - acc: 0.9995 - val_loss: 0.0019 - val_acc: 0.9991\nEpoch 13/20\n - 2s - loss: 0.0030 - acc: 0.9996 - val_loss: 6.6483e-04 - val_acc: 0.9998\nEpoch 14/20\n - 2s - loss: 0.0028 - acc: 0.9997 - val_loss: 4.1208e-04 - val_acc: 1.0000\nEpoch 15/20\n - 2s - loss: 0.0028 - acc: 0.9996 - val_loss: 3.3142e-04 - val_acc: 1.0000\nEpoch 16/20\n - 2s - loss: 0.0029 - acc: 0.9996 - val_loss: 0.0029 - val_acc: 0.9991\nEpoch 17/20\n - 2s - loss: 0.0026 - acc: 0.9997 - val_loss: 8.7125e-05 - val_acc: 1.0000\nEpoch 18/20\n - 2s - loss: 0.0028 - acc: 0.9996 - val_loss: 0.0013 - val_acc: 0.9999\nEpoch 19/20\n - 2s - loss: 0.0028 - acc: 0.9996 - val_loss: 6.2026e-05 - val_acc: 1.0000\nEpoch 20/20\n - 2s - loss: 0.0024 - acc: 0.9998 - val_loss: 1.1543e-04 - val_acc: 1.0000\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "<keras.callbacks.History at 0x7fef853f24a8>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "36d355c794da8ab758414ac8e67abd6caabca0eb",
        "scrolled": true
      },
      "cell_type": "code",
      "source": "classification_eval(oversample_model, X_test, y_test)",
      "execution_count": 14,
      "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    0.99971   0.99878   0.99924     85305\n           1    0.52074   0.81884   0.63662       138\n\n   micro avg    0.99849   0.99849   0.99849     85443\n   macro avg    0.76022   0.90881   0.81793     85443\nweighted avg    0.99893   0.99849   0.99866     85443\n\nSCALAR METRICS\n     Accuracy = 0.99849\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
}