2 structured data.ipynb

{
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "951abd76-d331-4e62-9efc-6962707d0e3f",
    "_uuid": "ef0ba203819515c6a4dc9f10decd52cb7fd8c6c2",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "from subprocess import check_output\n",
    "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "e62dee04-0bdc-4136-8d3c-9efbbd6d30e4",
    "_uuid": "e8b261ed3bf92f4c7e26f6ef8c656381d7261e80",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, learning_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from xgboost import plot_importance, to_graphviz\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "507f9385-4132-4b15-a15b-ba386e5ce1d9",
    "_uuid": "74a1f29a66d01639561fdd2a9b6820db616e233c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \"\"\"\n",
    "    given a sklearn confusion matrix (cm), make a nice plot\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
    "\n",
    "    target_names: given classification classes such as [0, 1, 2]\n",
    "                  the class names, for example: ['high', 'medium', 'low']\n",
    "\n",
    "    title:        the text to display at the top of the matrix\n",
    "\n",
    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
    "\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "\n",
    "    Usage\n",
    "    -----\n",
    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
    "                                                              # sklearn.metrics.confusion_matrix\n",
    "                          normalize    = True,                # show proportions\n",
    "                          target_names = y_labels_vals,       # list of names of the classes\n",
    "                          title        = best_estimator_name) # title of graph\n",
    "\n",
    "    Citiation\n",
    "    ---------\n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    import itertools\n",
    "\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    plt.savefig('confusion.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "322444d0-bfee-44e0-9b1e-8a49050f5581",
    "_uuid": "459796f2ac540a92e84eecf7799a487e6bcee487",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('../input/PS_20174392719_1491204439457_log.csv')\n",
    "df = df.rename(columns={'oldbalanceOrg':'oldBalanceOrig', 'newbalanceOrig':'newBalanceOrig', \\\n",
    "                        'oldbalanceDest':'oldBalanceDest', 'newbalanceDest':'newBalanceDest'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "d60d77f1-9b10-4e28-9791-91aff790e330",
    "_uuid": "2210606418c582a4f0ddf163b018f32bd0bcec15"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldBalanceOrig  newBalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815        170136.0       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295         21249.0        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145           181.0            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671           181.0            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720         41554.0        29885.86   \n",
       "\n",
       "      nameDest  oldBalanceDest  newBalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0             0.0        0               0  \n",
       "1  M2044282225             0.0             0.0        0               0  \n",
       "2   C553264065             0.0             0.0        1               0  \n",
       "3    C38997010         21182.0             0.0        1               0  \n",
       "4  M1230701703             0.0             0.0        0               0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "d6b8416c-aed2-4995-9514-aa4120612ab2",
    "_uuid": "c555c4e4e0218d36f114718763bd574c4898d197"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TRANSFER', 'CASH_OUT'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.isFraud == 1].type.drop_duplicates().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "bf2306d5-b48d-441c-b217-478ede54f36d",
    "_uuid": "8b773d7747aa7fee6cf563ce2e11a4b636279314",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[(df.type == 'TRANSFER') | (df.type == 'CASH_OUT')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "e72d88af-5953-41c2-9121-9ff3779ce3db",
    "_uuid": "01ad26ddbab8ad8694d57c20559616881731e9ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2770409"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "4eb02425-627c-4fd7-9368-fa1139d6a9ba",
    "_uuid": "1738dde2daeba1f39f8e625e34f1a1b701ba412a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "445705.76"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df.isFraud == 1) & (df.type == 'TRANSFER')].amount.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "41c86e2c-be69-4c65-bea1-0dc50baf785c",
    "_uuid": "b21b6572c7d465621c464466dfa74beb9b92cff5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "486521.91000000003"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df.isFraud == 0) & (df.type == 'TRANSFER')].amount.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "02d2d83a-508d-48ba-9c6d-52e104c7f9ae",
    "_uuid": "8b925d89ed3a57de62b217beeb8271bd658bf028",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Fraud_Heuristic'] = np.where(((df['type'] == 'TRANSFER') & \n",
    "                                  (df['amount'] > 200000)),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "40c869bd-e7a7-46ed-be93-c830f91d67f3",
    "_uuid": "d0c38aec031ea0a7db6166b442f8459dfb17b1aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "409110"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fraud_Heuristic'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "9c5cbc9f-fabc-4e13-bbe2-d746a8843870",
    "_uuid": "eac3355b75135db37e92b38f32cf31bd42807d6b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "ef3fc933-ec5e-4d29-b591-e46b29427895",
    "_uuid": "29dd79d48db0a9a15cba1e004e85434536920d7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013131315551742895"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_pred=df['Fraud_Heuristic'],y_true=df['isFraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "2b65ae69-b0bf-46f1-8136-9a830cf4e925",
    "_uuid": "b8b81e8cf76e619f9764d2e2e90fee73e03911f5",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "bbfcb1f4-947b-47fe-a4cf-66418054babf",
    "_uuid": "27d9d487195374de27f878ca8418816852e2ec0a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_pred=df['Fraud_Heuristic'],y_true=df['isFraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "90f6de0d-1127-4f9c-a537-8eaab117f514",
    "_uuid": "2c1c67161bb26996050859c23e2479228459baa0"
   },
   "outputs": [
    {
     "data": {
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0bakOmZmZtRTv4JhPnjkLL0nqExGvNHtvzMzMWoj3Wciv1mBBUoeImANsA/xY\n0rvATLLPNyKiTwv10czMzFpRuczCS0AfYN8W6ouZmVmLcmIhn3LBggAi4t0W6ouZmVnLkecs5FUu\nWFhW0s9qOxgRlzdDf8zMzKyNKbfTZXugC9C1loeZmdkCTY38r87rSzdK+kzSf0rKlpY0XNI76c+l\nUrkkDZI0VtLrkvqUnHN0qv+OpKNLyvtKGpPOGZS2PGhQG+WUyyx8EhEX5LmImZnZgiZbDdHszQwG\nrgRuKSk7G3gyIi6VdHZ6fRawG9A7PbYArgG2kLQ0cB6wKdkmiaMkDY2IqanOccCLwCPAAODR+rZR\n15sol1nwSI6ZmS3U2qlxj7pExLNkOx+X2ge4OT2/me8WEuwD3BKZF4ElJfUAdgWGR8SUFCAMBwak\nY90i4oW0YeIt1a5VnzbKf05lju1U18lmZmZWb8tHxCcA6c/lUnlP5r2r87hUVq58XA3lDWmjrFqH\nISKieiRkZma2UFHj1052lzSy5PV1EXFdQ7tTQ1k0oLwhbZTVkLtOmpmZLfCaaM7C5IjYtJ7nTJTU\nIyI+SUMAn6XyccBKJfV6ARNSeb9q5c+k8l411G9IG2WVG4YwMzNbeCnblKkxjwYaClSuaDgaeKik\n/Ki0YmFLYFoaQhgG9Je0VFrV0B8Ylo5Nl7RlWgVxVLVr1aeNspxZMDMzayaShpBlBbpLGke2quFS\n4G5JxwIfAQel6o8AuwNjga+AH0A2LUDSb4GXU70LSqYKnEi24qIz2SqIR1N5vdqoi4MFMzMrrOa+\nkVREHFrLofkWEaQVDSfXcp0bgRtrKB8JrF9D+ef1baMcBwtmZlZILbTPwkLBwYKZmRWWbySVjyc4\nmpmZWVnOLJiZWUGJdt6sOBcHC2ZmVkjCwxB5eRjCzMzMynJmwczMiinnzaDMwYKZmRVYc++zsLBw\nsGBmZoXkOQv5ec6CmZmZleXMgpmZFZaHIfJxsGBmZoXlWCEfBwtmZlZIwmPxeflzMjMzs7KcWTAz\ns2ISyOMQuThYMDOzwnKokI+HIczMzKwsZxbMzKyQhJdO5uVgwczMCsuhQj4OFszMrLCcWMjHcxbM\nzMysLGcWzMysoOSlkzk5WDAzs0LyDo75OVgwM7PCcmYhHwdVZmZmVpYzC2ZmVljOK+TjYMHMzIrJ\n94bIzcMQZmZmVpYzC2ZmVkheDZGfgwUzMyssD0Pk42DBzMwKy6FCPs7AmJmZWVnOLJiZWWF5FCIf\nBwtmZlZI2QRHRwt5OFgwM7PCcmYhH89ZMDMzs7KcWTAzs4IS8jBELg4WzMyssDwMkY+DBTMzKyRP\ncMzPcxbMzMysLGcWzMysmORsguudAAAcZUlEQVRhiLwcLJiZWWE5WMjHwxBmZmZWljMLZmZWWF46\nmY+DBTMzKyQB7Rwr5OJgwczMCsuZhXw8Z8HMzMzKcmbBzMwKy6sh8nFmwRptrTVWYdONN2CLvhuz\n9Rab1lrv/vvupfMiYtTIkQAMueN2tui7cdVjsY7teG30aAD23mMAm/fZiD4brcepJ51ARUVFi7wX\nK5aPP/6YXXfegY03WIc+G63HlYP+PF+dn59xetXP6AbrrskK3Zec5/iXX37Jat/ryU9PO6Wq7JVR\no9h04w1Yb+01+NlPTyMimv29WMOokf8VhTML1iQee+JpunfvXuvx6dOnc/WVg9hs8y2qyg497HAO\nPexwAP4zZgwHHbAPG228MQC3Dbmbbt26EREcesiB3HfvPRx8yMDmfRNWOB06dODSy/7IJn36MH36\ndLbaoi877bwL66y7blWd3//xiqrnV1/5F14b/eo81zj/vF+x7Xbbz1N22ikncuU117HFlluy7167\n8/iwx9h1wG7N+2as3jzBMT9nFqxFnH/er/jZmb+gU6dONR6/+64hHHzIoVWvu3XrBsCcOXOY/e23\nyLlCawY9evRgkz59AOjatStrr70OEyaMr7X+3XcN4eCB3/2cvjJqFJ99NpGdd+5fVfbJJ58wffqX\nbPn97yOJw444ir8/9GDzvQmzFuBgwRpNEnvt1p+tNu/LDddfN9/x0a++yrhxH7P7HnvWeo1777lr\nnmABYK/dd2XlFZejS9eu7H/AgU3eb7NSH37wAaNHvzpP9mue4x9+yIcfvE+/HXYEYO7cuZz9izO4\n+NLfz1Nvwvjx9OzZq+p1z169ygYg1poaOwhRnF9i2lSwIKlC0uiSxyrN0MYqkv7T1Nctsqf++Twv\nvPwKDz78KNdecxXP/evZqmNz587lF2eezu8u+2Ot5780YgSLdV6M9dZff57yvz8yjPc//oRZs2bx\nzNNPNVv/zWbMmMGhBx/A7//4p6qsVnX33H0n++5/IO3btwfg2muuZtfddmellVaap15N8xOcGWuj\n0r0hGvMoirY2Z+HriNi4toOSOkTEnJbskNVtxRVXBGC55ZZj73334+WXX2KbbbcDsrkK/33jP/Tf\nuR8AEz/9lAP335t77x9K302zyZD33H3nPKndUp06dWLPPffm70MfYqedd2n+N2OFM3v2bA49+AAO\nOfRw9t1v/1rr3XvXnVwx6Kqq1yNefIHnn/8X1/31ambOmMG3335Lly5dOPnUnzB+/LiqeuPHjaNH\njxWb9T2YNbe2FizMR9IxwB5AJ2BxSXsDDwFLAYsA50bEQykL8XBErJ/OOxPoEhG/kdQXuBH4Cniu\nxd/EQmzmzJnMnTuXrl27MnPmTJ4Y/ji/PPfXXHPVlQCcePIpjPt0clX9/jv145Lf/aEqUJg7dy73\n33cPTzz1XTZixowZTJ8+nR49ejBnzhwee+wRtt5625Z9Y1YIEcEJPz6WtdZeh5+c/rOq8tKfX4C3\n33qLqV9MZcvvf7+qzuBbb696fuvNgxk1aiQXXnwpAF26dGXEiy+y+RZbcMdtt3Diyae2xNuxBihQ\ncqBR2lqw0FnS6PT8/YjYLz3/PrBhREyR1AHYLyK+lNQdeFHS0DquexNwakT8U9Lv66hr9fDZxIkc\ncmD21zSnYg6HDDyM/rsO4Kf/OIXvb7V1nec/969n6dmzF6uutlpV2cyZMzlwv735dtYsKuZWsH2/\nHfnx8Sc023uw4vr3889zx+23sv762dJfgPMvvJi33npznp/fu+8awkEHD8w9nDDoyms47kfH8PXX\nX9N/1928EqKNylZDOFzIQ21p/a+kGRHRpVrZMcD2EfGD9HoR4ApgO2AusBawKlnmYb7MQqo7JiJW\nTuUbAndU1qvW1nHAcQArrbxy37ff/bA53mYh7L/Pntx5z/107NixtbtiVm/++W09nRfRqIiofcOW\nJrTOBpvETQ883ahrfL/3Ui3W39bUpiY4ljGz5PnhwLJA3zS/YSJZoDCHed9P5Ro9Abkiooi4LiI2\njYhNl+2+bON7XWD3P/Sw/6G1BZZ/fs3mtaAEC6WWAD6LiNmSdgC+l8onAstJWkbSosCeABHxBTBN\n0jap3uEt3mMzM2ub1MhHQbS1OQt53A78XdJIYDTwJkAKHi4ARgDvV5YnPwBulPQVMKyF+2tmZm1U\nkfZKaIw2FSxUn6+QygYDg0teTyab8FjT+YOAQTWUjwI2Kin6TeN6amZmCwPPb8xnQRyGMDMzsxbU\npjILZmZmLcmJhXwcLJiZWXE5WsjFwYKZmRVStqDB0UIenrNgZmZmZTlYMDOzYmqhu05K+kDSmHQ3\n5ZGpbGlJwyW9k/5cKpVL0iBJYyW9LqlPyXWOTvXfkXR0SXnfdP2x6VyVa6MhHCyYmVlhteCeTDtE\nxMYlW0OfDTwZEb2BJ9NrgN2A3ulxHHANZF/8wHnAFsDmwHklX/7XpLqV5w2oo416c7BgZmbW8vYB\nbk7Pbwb2LSm/JTIvAktK6gHsCgyPiCkRMRUYDgxIx7pFxAuR3ezplmrXqqmNenOwYGZmxdX41EJ3\nSSNLHsfV0EoAj0saVXJ8+Yj4BCD9uVwq7wl8XHLuuFRWrnxcDeXl2qg3r4YwM7OCUlOshpic466T\nW0fEBEnLAcMlvVmmbk0digaUNylnFszMrLBaYoJjRExIf34GPEA252BiGkIg/flZqj4OWKnk9F7A\nhDrKe9VQTpk26s3BgpmZWTORtLikrpXPgf7Af4ChQOWKhqOBh9LzocBRaVXElsC0NIQwDOgvaak0\nsbE/MCwdmy5py7QK4qhq16qpjXrzMISZmRVSC91lennggbSasQNwR0Q8Jull4G5JxwIfAQel+o8A\nuwNjga/I7ppMREyR9Fvg5VTvgoiYkp6fSHbDxc7Ao+kBcGktbdSbgwUzMyuuZo4WIuI95r3rcWX5\n58BONZQHcHIt17oRuLGG8pHA+nnbaAgHC2ZmVlje7jkfz1kwMzOzspxZMDOzwsq7oqHoHCyYmVlh\nOVbIx8MQZmZmVpYzC2ZmVkwttHZyYeBgwczMCsurIfJxsGBmZoUkPMExL89ZMDMzs7KcWTAzs8Jy\nYiEfBwtmZlZcjhZycbBgZmaF5QmO+XjOgpmZmZXlzIKZmRWWV0Pk42DBzMwKy7FCPh6GMDMzs7Kc\nWTAzs+JyaiEXBwtmZlZI2a0hHC3k4WDBzMyKSZ7gmJfnLJiZmVlZziyYmVlhObGQj4MFMzMrLkcL\nuThYMDOzgpInOObkOQtmZmZWljMLZmZWWF4NkY+DBTMzKyThKQt5OVgwM7PicrSQi+csmJmZWVnO\nLJiZWWF5NUQ+DhbMzKywPMExHw9DmJmZWVnOLJiZWWE5sZCPgwUzMysm33UyNwcLZmZWYI4W8vCc\nBTMzMyvLmQUzMysk4WGIvBwsmJlZYTlWyMfBgpmZFZYzC/l4zoKZmZmV5cyCmZkVlrd7zsfBgpmZ\nFZdjhVw8DGFmZmZlObNgZmaF5cRCPg4WzMyskOTtnnNzsGBmZoXlCY75eM6CmZmZleXMgpmZFZcT\nC7k4WDAzs8JyrJCPgwUzMyssT3DMx3MWzMzMrCxnFszMrKDk1RA5OVgwM7NCEh6GyMvDEGZmZlaW\ngwUzMzMry8MQZmZWWB6GyMfBgpmZFZYnOObjYQgzMzMry5kFMzMrJt91MjcHC2ZmVkjC2z3n5WDB\nzMyKy9FCLp6zYGZmZmU5s2BmZoXl1RD5OFgwM7PC8gTHfBwsmJlZYTlWyMdzFszMzKwsZxbMzKy4\nnFrIxcGCmZkVlic45uNhCDMzMytLEdHafWiTJE0CPmztfizEugOTW7sTZg3gn93m9b2IWLYlGpL0\nGNnfZ2NMjogBTdGftszBgrUKSSMjYtPW7odZffln14rIwxBmZmZWloMFMzMzK8vBgrWW61q7A2YN\n5J9dKxzPWTAzM7OynFkwMzOzshwsmJmZWVkOFszMzKwsBwtmZo0kad3W7oNZc/IER2uTJCn8w2kL\nAEmLAg8AUyLiiNbuj1lzcLBgbZqkw4A1gNHA6xHxQev2yOw7ktpFxFxJSwB3A/+NiNNbu19mTc3D\nENZmSToROAn4H/AXYIfW7ZHZvCJibnq6M/AWsK+kv7Ril8yahYMFa5MkLQ1sAOwOdAbeBm6R1C6l\nfc3aBEmHAL8Frgd+Aqwi6a+t2yuzpuVgwdoESfPcVD4ippDd2e9h4PCI2CUiKoATAd/Ex9qSDsCN\nETEGeAT4GbClpGtbt1tmTcfBgrW60smMktaXtGE6NAYI4Ip0bCBwAjCxVTpqVkLSppJ6ApOA0ySt\nFBFzIuId4F/A6pJWaN1emjWNDq3dASu2ygli6flPyYKBKZJej4gTJK0K/Cgd6w4cGhFjW7HLZkjq\nBRwDfA5cClwGDJd0PNmE3O7AIRHxeat10qwJeTWEtRpJHSPi2/R8S+B04DhgJvAa8HREnCJpcbJ/\ngCdExKRW67BZCUm7AjsBM8gm4B4I9AOWBP4vIl5vvd6ZNS0HC9YqJK0F7AZcCfQgmxzWDjg2Ij6W\ntAgwEng3IvZvvZ6afUfS/sBWEXFmer0zsBfZ0NifI2KmpEUiYnZr9tOsqXnOgrWWRYGbgTWBL4AL\ngenA9pJ6pH9sNwOWl7Ri9QmQZi2hhp+7d4B+ks4HiIgnUtlA4KQU5M5p2V6aNT/PWbAWVTmZMSJe\nT8MLPyEbdvgVcA3wg1Tt6YgYB2zdit21Aqs28XYFgIgYI+lI4DpJHSLiHOAjYARwszMKtrDyMIS1\nCkk7AM8Ca5NNFKsgW6u+BdnchduBe4C53vbZWpOkM8nmJiwDXB8R10tak2xZ77vA6sBeEfFWK3bT\nrFk5WLAWJ6kLcB0wmyyTsBbwI2AWcDHQFxgbEeNbrZNWWNUyCscDh0XE9pJuAfYDfhMRf5TUmSy4\nHZuyYGYLLQcL1uLSOPAqwNlAe7IVEGsBPwUmABc4m2CtoVqgsCxZ1mACsD+wDXA52cZLl0fEBa3W\nUbMW5mDBWkwa642IuC29Xgm4APiSbOhhTWBqRHjTJWtVko4FDiBbDtmZbDLumRHxZsowrAnsGhHT\nWrGbZi3GExyt2dRwm+npwFWSZkfEXcB44DGygGFORJzRGv00k7RCRHyanm9Llkk4MiK+kvQt2dyE\nQyRNIdtV9CAHClYkXjppzaJaOrevpOUi4kHgcOASSQPTzo0BDCFt6WzW0iTtAQyVtJykpciGG/oC\n2wJExByyybiLAIcBl0XEx63VX7PW4GEIa3LVAoWTgFOBKcBg4G9k/wgPIrv3wzZA/7SfvlmLkjQA\nOAe4KCIeS2WLAacBqwF3RMQzJfUXj4iZrdFXs9bkYMGajaR9gEPIVjzsAuwJ/Be4ClghPT6PiA9a\nq49WXOk26JOB/SPiQUlrkO33cRLZrqJ7Ab2BByJieOv11Kz1eRjCmoWk5cj2T1gjImZFxMPAUGA9\n4Ayy/RNGOVCw1pJug74X8Ot0p9NrgdciYma6WdmDwDhg97RM0qywnFmwJlFt6KFDRMyRtBlwEfBy\n2ukOSfsB2wPnR8TU1uuxWSYNRTwC/DIiLq38+U3HvgdMT4GFWWE5WLAmVXKL3knAvcBywCnAexHx\n61TH477WpkjahezOkVtExDTfDMpsXh6GsEaR1K7k+Q+AI8juIHkOsAcwCvgzsLGkc1PVr1q6n2bl\npDkJpwMvSVragYLZvLzPgjWYpG2ANSW9HhEjgfXJJodtSXZjnb9GxGxJY4DzgM8g25WptfpsVpuI\neFRSR+AJSZtmRf5ZNQMHC9ZAaZz3ErL9Ebql4g+BPwEVEdE/1TuHbAhiSKt01KweIuIhSU+mPUDM\nLHGwYPUmaXvgSuDwiBhRcqgb2dbN16a16rsDBwGHtnwvzRomIma0dh/M2hoHC9YQmwB/KQ0UJF1M\ntrvdHLI78f0UWJRsy9z/tUovzcysSThYsNxKlkeuDkwrKd8NWJnspju3AR+T7dAoLzkzM1vweTWE\n5VYy2etBYAtJfdLrJ4AfpkmOtwKzImKqAwUzs4WDgwVriBeB54GBkjaPiNkR8a2kQ8nmKbzQut0z\nM7Om5E2ZrEEk9QSOBXYEXgW+JhuG2Dci/tuafTMzs6blYMEaLO2X34fsJlHjgWd890gzs4WPgwUz\nMzMry3MWzMzMrCwHC2ZmZlaWgwUzMzMry8GCmZmZleVgwczMzMpysGBmZmZlOVgwayRJFZJGS/qP\npHvSHTcbeq1+kh5Oz/eWdHaZuktKOqkBbfxG0pl5y6vVGSzpwHq0tYqk/9S3j2bWtjhYMGu8ryNi\n44hYH/gWOKH0oDL1/n8tIoZGxKVlqiwJ1DtYMDOrLwcLZk3rX8Aa6Tfq/0m6GngFWElSf0kvSHol\nZSC6AEgaIOlNSc8B+1deSNIxkq5Mz5eX9ICk19JjK+BSYPWU1fh9qvdzSS9Lel3S+SXXOkfSW5Ke\nANaq601I+nG6zmuS7quWLdlZ0r8kvS1pz1S/vaTfl7R9fGM/SDNrOxwsmDURSR2A3YAxqWgt4JaI\n2ASYCZwL7BwRfYCRwM8kdQKuB/YCtgVWqOXyg4B/RsRGZFtsvwGcDbybsho/l9Qf6A1sDmwM9JW0\nnaS+wEBgE7JgZLMcb+f+iNgstfc/svuAVFoF2B7YA/hreg/HAtMiYrN0/R9LWjVHO2a2AOjQ2h0w\nWwh0ljQ6Pf8XcAOwIvBhRLyYyrcE1gWelwTQkezunGsD71feU0PSbcBxNbSxI3AUQERUANMkLVWt\nTv/0eDW97kIWPHQFHoiIr1IbQ3O8p/UlXUg21NEFGFZy7O6ImAu8I+m99B76AxuWzGdYIrX9do62\nzKyNc7Bg1nhfR8TGpQUpIJhZWgQMj4hDq9XbGGiqG7QIuCQirq3Wxk8b0MZgsjuIvibpGKBfybHq\n14rU9qkRURpUIGmVerZrZm2QhyHMWsaLwNaS1gCQtJikNYE3gVUlrZ7qHVrL+U8CJ6Zz20vqBkwn\nyxpUGgb8sGQuRE9JywHPAvtJ6iypK9mQR126Ap9IWgQ4vNqxgyS1S31eDXgrtX1iqo+kNSUtnqMd\nM1sAOLNg1gIiYlL6DX2IpEVT8bkR8bak44B/SJoMPAesX8MlfgJcJ+lYoAI4MSJekPR8Wpr4aJq3\nsA7wQspszACOiIhXJN0FjAY+JBsqqcuvgBGp/hjmDUreAv4JLA+cEBHfSPob2VyGV5Q1PgnYN9+n\nY2ZtnW9RbWZmZmV5GMLMzMzKcrBgZmZmZTlYMGskSYtKukvSWEkjalsBIOl0SW+kbaGHpP0JKrdQ\nfj9trjQ6rZBA0tppE6dZpdswS+ok6aW0YdIbpZsvNcF7+Zukdet5Totv6Szp/9Ln/ZakXWupc0qq\nE5K613B8M2VbdR9YUnZZ+kz/J2mQMl1L/m5GS5os6U/N+f7M2hpPcLSFkqQOETGnhZo7FpgaEWtI\nGgj8DjikWn96AqcB60bE15LuJtsoaXCq8vOIuLfadaekc6pPFJwF7BgRM9Lqg+ckPVqyp0ODRcSP\nGnuN5paCmYHAemT7WTwhac20/0Sp54GHgWdquEZ7sr+nYSVlWwFbAxumoueA7SPiGbJNrirrjQLu\nb6K3Y7ZAcGbBWpSkByWNSr+9HVdSPkDZNsivSXoylXWRdJOkMcq2ED4glc8oOe9ASYPT88GSLpf0\nNPA7SZtL+rekV9Ofa6V67SX9oeS6p0raSdIDJdfdRVLeL4R9gJvT83uBndKKgOo6kG3g1AFYDJhQ\n7qIR8VlEvAzMrlYeEVH5GSySHpH6fYGkvatfS9lNom6W9LikDyTtn36LHiPpsZIlj89I2jR9RoNT\nFmSMpNPT8TUkPZH+nl7Rd0s+K9tZRdlW0K+kx1apvIekZ/XdDbe2ra2NHPYB7oyIWRHxPjCWbNfK\n6p/fqxHxQS3XOBW4D/is9BSgE9mGWYumz3VitffXG1iOfCtKzBYazixYS/thREyR1Bl4WdJ9ZEHr\n9cB2EfG+pKVT3V+RbSG8AYDm37GwJmuSbalcoWwvgu0iYo6knYGLgQPIdkhcFdgkHVsamApcJWnZ\niJgE/AC4KbV7FzXfT+HyiLgF6Al8DJCuNw1YBphcWTEixkv6A/AR8DXweEQ8XnKtiyT9mmw/hbMj\nYla5N5l+Mx4FrAFcFREjUju/LnPa6sAOZDtJvgAcEBG/SEHSHsCDJXU3Bnqmm2MhaclUfjtwaUQ8\noGwYpR3Zl2elz4Bd0nLK3sAQYFPgMGBYRFyU+r5YbW1I+jnz7+0A8GxEnEb2eZdmUcalslxSlmc/\nsl0xq7a+TktRnwY+Idtk6sqI+F+10w8F7govI7OCcbBgLe00Sful5yuRbQm8LNkXwfsAETElHd+Z\nLN1MKp+a4/r3lKSjlwBuTl9aQfabYuV1/1o5TFHZnqRbgSMk3QR8n++2V55nSKEGNWUR5vkySYHO\nPmRByhfAPZKOiIjbgP8DPiX7jfY64CzggnINpve4cfqCfUDS+hFR17yBRyNitqQxQHvgsVQ+hmyP\nhFLvAatJ+gvwD+BxZRs69YyIB1IfvknvrfS8RYArlc27qCAL3gBeBm5MGYwHI2K0sq2i52kjXff3\nwO/LvI86P+86/Ak4KwWU31002zBrHaBXKhouabuIeLbk3IHAkfVoy2yh4GEIazGS+pF9UX8/3aDo\nVbK0r6j5H/vaykvLOlU7VrrF8m+Bp9NvrnuV1K3tujcBR5D99nhPZTChbPLi6BoeR6XzxpEFPpU3\nk1qCbL5BqZ3J7gExKSJmk415bwUQEZ+koYVZqQ/zpdRrExFfkI3JD8hRfVY6Zy4wu+S347lU+8Uh\nBWYbpWufDPyNmr+kqzudLHW/EVlGoWO63rPAdsB44FZJR9XSRuWdM2v6vAelNqo+76QXdQzpVLMp\ncKekD4ADgasl7UuWbXgxImakYZ5Hye7pQerXRkCHiBhVj7bMFgoOFqwlLUE2EfArSWvz3T/ELwDb\nK92lsGQY4nHglMqTS4YhJkpaR1I7sn/gy7U3Pj0/pqT8ceCE9MVe1V5ETCD70jmX7yYeEhGHpDs7\nVn/ckqoMBY5Ozw8EnqohTf0RsKWybZ4F7ER2N0ck9Uh/imwyY9kMgaRlS1L2nckCkTfT60tKMjcN\npmz1QLuIuI9sOKhPRHwJjEtfrJWrQBarduoSwCcpIDmSLIOBpO8Bn0XE9WQ32upTUxuQZRZq+bxP\nS20MBQam9lcly069lPe9RcSqEbFKRKxCNsfkpIh4kOzvaHtJHVIGZHvS31FyKNmwilnhOFiwlvQY\n0EHS62S/9b8I2VbIZPMI7pf0GnBXqn8hsFSaAPca2Xg7ZLdmfhh4imx8uTaXAZdIep70pZX8jeyL\n4fV03cNKjt0OfBwR/63H+7oBWEbSWOBnqX9IWlHSI+k9jiD7YnqFLO3fjmzIAeD2NDQwBuie3jeS\nVpA0Ll3zXEnj0jyMHsDT6XN8mewGVQ+na21ANqTRWD2BZ5TdTXMw2VAJZAHAaantfzP/LbWvBo6W\n9CLZEERlpqcfMFrSq2TzRv5cpo2yIuIN4G7gv2Q/UydXDj1JekTSiun5aenz60X2d/23Oi59L/Au\n2d/Da8BrEfH3kuMH42DBCsrbPZuVkHQl8GpE3NDafWkIScMiosZ9B8zMGsrBglmibP38TLLZ/GVX\nI5iZFYmDBTMzMyvLcxbMzMysLAcLZmZmVpaDBTMzMyvLwYKZmZmV5WDBzMzMynKwYGZmZmX9P269\nL3nIaV4FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6748edd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm,['Genuine','Fraud'], normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "1edc03e0-9f12-4865-80dc-e306ba034cba",
    "_uuid": "4c624a6ce3b51651bb8af42ccf2f3337d4b4400a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2770409, 12)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "2995d88e-9b19-44d2-8cba-070e631670a5",
    "_uuid": "5e0089ef984e7cc1a243d5e1bdb7fbdba7118ed9",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['hour'] = df['step'] % 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "fa219d76-491e-4a06-87af-d2b88ff11b76",
    "_uuid": "7667ba25cfd6e3897d364280677730a019721088",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frauds = []\n",
    "genuine = []\n",
    "for i in range(24):\n",
    "    f = len(df[(df['hour'] == i) & (df['isFraud'] == 1)])\n",
    "    g = len(df[(df['hour'] == i) & (df['isFraud'] == 0)])\n",
    "    frauds.append(f)\n",
    "    genuine.append(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "70bb126f-c580-4e91-86f2-d453b5d76e28",
    "_uuid": "15a9ac9db0f7eef6368b9908651d854831bd2ee6",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "83181d16-aa2e-4a84-b508-9af79d0c7ef7",
    "_uuid": "d0c2b8fa5df5a7bb73cc93baa03857dc92fc65bb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mg0Ih39mA7bktMQxqFwWb2O0AEysiInJaGw/pYDRJuNXGxixcyl/jitT4YHyz/xwMRpPc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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6a50d8208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "gen = ax.plot(genuine/np.sum(genuine), label='Genuine')\n",
    "fr = ax.plot(frauds/np.sum(frauds),dashes=[5, 2], label='Fraud')\n",
    "#frgen = ax.plot(np.devide(frauds,genuine),dashes=[1, 1], label='Fraud vs Genuine')\n",
    "plt.xticks(np.arange(24))\n",
    "legend = ax.legend(loc='upper center', shadow=True)\n",
    "fig.savefig('time.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "6d9d6bac-b87e-44f6-93e4-284006ce7e28",
    "_uuid": "255c70f81e70efb04d55173c4608cc274dd51169"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Gyd2G30mJD4cgMDwREVH/OA1PAJCVlYWsrKxury1btszx+D//8z+l7Yocahra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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6a5d0ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "#gen = ax.plot(genuine/np.sum(genuine), label='Genuine')\n",
    "#fr = ax.plot(frauds/np.sum(frauds),dashes=[5, 2], label='Fraud')\n",
    "frgen = ax.plot(np.divide(frauds,np.add(genuine,frauds)), label='Share of fraud')\n",
    "plt.xticks(np.arange(24))\n",
    "legend = ax.legend(loc='upper center', shadow=True)\n",
    "fig.savefig('time_comp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "005948a1-71e8-43ea-a1c8-936c00d062af",
    "_uuid": "56f8b08a6399e018d41764e3a9a89efd562d1272",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfFraudTransfer = df[(df.isFraud == 1) & (df.type == 'TRANSFER')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "7d7d87c4-d59b-4bd9-a1eb-bba40913b16e",
    "_uuid": "4bc316821f7d2e9b4b397d57e5e99a6a6bdc7055",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfFraudCashOut = df[(df.isFraud == 1) & (df.type == 'CASH_OUT')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "089ace6e-714f-4ca7-a2c2-3480f1d9ebc0",
    "_uuid": "f639e94a4b1dd4bcd9f4a98456ab46f4acd61fae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFraudTransfer.nameDest.isin(dfFraudCashOut.nameOrig).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "a590b020-7457-4064-a61d-e512d6850a99",
    "_uuid": "d428fd42ecb27e3ecee4b307601e94bd2ba46c04",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfNotFraud = df[(df.isFraud == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "04e3c116-0915-427c-9ca3-b990c0c3521c",
    "_uuid": "f6ce4079e3d2a4e7590511397b01a01971c0ab65",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfFraud = df[(df.isFraud == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "0458bb71-a274-4ee4-8666-e9f9988fbf44",
    "_uuid": "9df88bb3f8867534b7f18448c61eddf0d285d532"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>Fraud_Heuristic</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1030443</th>\n",
       "      <td>65</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1282971.57</td>\n",
       "      <td>C1175896731</td>\n",
       "      <td>1282971.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1714931087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6039814</th>\n",
       "      <td>486</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>214793.32</td>\n",
       "      <td>C2140495649</td>\n",
       "      <td>214793.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C423543548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362556</th>\n",
       "      <td>738</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>814689.88</td>\n",
       "      <td>C2029041842</td>\n",
       "      <td>814689.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1023330867</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      type      amount     nameOrig  oldBalanceOrig  \\\n",
       "1030443    65  TRANSFER  1282971.57  C1175896731      1282971.57   \n",
       "6039814   486  TRANSFER   214793.32  C2140495649       214793.32   \n",
       "6362556   738  TRANSFER   814689.88  C2029041842       814689.88   \n",
       "\n",
       "         newBalanceOrig     nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "1030443             0.0  C1714931087             0.0             0.0        1   \n",
       "6039814             0.0   C423543548             0.0             0.0        1   \n",
       "6362556             0.0  C1023330867             0.0             0.0        1   \n",
       "\n",
       "         isFlaggedFraud  Fraud_Heuristic  hour  \n",
       "1030443               0                1    17  \n",
       "6039814               0                1     6  \n",
       "6362556               0                1    18  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFraudTransfer.loc[dfFraudTransfer.nameDest.isin(\n",
    "    dfNotFraud.loc[dfNotFraud.type == 'CASH_OUT'].nameOrig.drop_duplicates())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "bbb9c8bc-7948-4b73-ba13-bf709f86f42b",
    "_uuid": "025b7fa927483c2dd15564f31a1183a984e19ef6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4955558261293072"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfFraud[(dfFraud.oldBalanceDest == 0) & (dfFraud.newBalanceDest == 0) & (dfFraud.amount)]) / (1.0 * len(dfFraud))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "ae83f4ca-a69d-4b75-8156-8a720800e5ca",
    "_uuid": "7aa242515f5fc3e325f0b71e46cf5f62668ff2e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0006176245277308345"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfNotFraud[(dfNotFraud.oldBalanceDest == 0) & (dfNotFraud.newBalanceDest == 0) & (dfNotFraud.amount)]) / (1.0 * len(dfNotFraud))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "3ebe8e5f-7b68-4d5a-bf00-24dd7ae8377e",
    "_uuid": "9d3ba781327ccd3accb99b7ee48266dfd836f997",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfOdd = df[(df.oldBalanceDest == 0) & \n",
    "           (df.newBalanceDest == 0) & \n",
    "           (df.amount)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "e042c19c-9d13-4620-a06b-5254aa329018",
    "_uuid": "8dfc571f4f34c9853df73c31c41b836068fb7e84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7046398891966759"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfOdd[(dfOdd.isFraud == 1)]) / len(dfOdd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "2bab655e-20c1-4a0c-8a4f-5033f344e1cd",
    "_uuid": "21a6504216ac83792781291c4dd7fc3080820ece"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8966412742382271"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfOdd[(dfOdd.oldBalanceOrig <= dfOdd.amount)]) / len(dfOdd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "9516d4cc-02e1-4639-96ed-3a8d39edf7b2",
    "_uuid": "ed92818f12f764643cbc673b64b8aea112bb02e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9636363636363636"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfOdd[(dfOdd.oldBalanceOrig <= dfOdd.amount) & (dfOdd.isFraud == 1)]) / len(dfOdd[(dfOdd.isFraud == 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "b194d4b1-a08b-4a31-a96a-6c63587bfe32",
    "_uuid": "74fddda994f8cc23162fdbe2c1acdf3ecac22439"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['step', 'type', 'amount', 'nameOrig', 'oldBalanceOrig',\n",
       "       'newBalanceOrig', 'nameDest', 'oldBalanceDest', 'newBalanceDest',\n",
       "       'isFraud', 'isFlaggedFraud', 'Fraud_Heuristic', 'hour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfOdd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "4863d8dc-7898-4c76-8466-fdf02acecd97",
    "_uuid": "6577199c45acee7d024d7ef684784c2ea2360287"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>Fraud_Heuristic</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>2806.00</td>\n",
       "      <td>C1420196421</td>\n",
       "      <td>2806.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C972765878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>20128.00</td>\n",
       "      <td>C137533655</td>\n",
       "      <td>20128.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1848415041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1277212.77</td>\n",
       "      <td>C1334405552</td>\n",
       "      <td>1277212.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C431687661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>35063.63</td>\n",
       "      <td>C1364127192</td>\n",
       "      <td>35063.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1136419747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>271161.74</td>\n",
       "      <td>C1658487789</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1219161283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>25071.46</td>\n",
       "      <td>C669700766</td>\n",
       "      <td>25071.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1384210339</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>235238.66</td>\n",
       "      <td>C1872047468</td>\n",
       "      <td>235238.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C116289363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3059</th>\n",
       "      <td>2</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1096187.24</td>\n",
       "      <td>C1093223281</td>\n",
       "      <td>1096187.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C2063275841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3162</th>\n",
       "      <td>2</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>963532.14</td>\n",
       "      <td>C1440057381</td>\n",
       "      <td>963532.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C268086000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3271</th>\n",
       "      <td>2</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>14949.84</td>\n",
       "      <td>C140702728</td>\n",
       "      <td>14949.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C634210724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>2</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>18627.02</td>\n",
       "      <td>C1375503918</td>\n",
       "      <td>18627.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C234430897</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4103</th>\n",
       "      <td>3</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>10539.37</td>\n",
       "      <td>C1134864869</td>\n",
       "      <td>10539.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C118648358</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4260</th>\n",
       "      <td>3</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>22877.00</td>\n",
       "      <td>C1247938090</td>\n",
       "      <td>22877.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1002031672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4440</th>\n",
       "      <td>4</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>C7162498</td>\n",
       "      <td>12930418.44</td>\n",
       "      <td>2930418.44</td>\n",
       "      <td>C945327594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4442</th>\n",
       "      <td>4</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>2930418.44</td>\n",
       "      <td>C2047521920</td>\n",
       "      <td>2930418.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C449261773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4667</th>\n",
       "      <td>4</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>169941.73</td>\n",
       "      <td>C540962910</td>\n",
       "      <td>169941.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C2127862399</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4693</th>\n",
       "      <td>4</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>13707.11</td>\n",
       "      <td>C17222024</td>\n",
       "      <td>13707.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C410033330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4775</th>\n",
       "      <td>4</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>86070.17</td>\n",
       "      <td>C1844941220</td>\n",
       "      <td>86070.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1191544932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4857</th>\n",
       "      <td>5</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>120074.73</td>\n",
       "      <td>C1409933277</td>\n",
       "      <td>120074.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C162114152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      step      type       amount     nameOrig  oldBalanceOrig  \\\n",
       "2        1  TRANSFER       181.00  C1305486145          181.00   \n",
       "251      1  TRANSFER      2806.00  C1420196421         2806.00   \n",
       "680      1  TRANSFER     20128.00   C137533655        20128.00   \n",
       "969      1  TRANSFER   1277212.77  C1334405552      1277212.77   \n",
       "1115     1  TRANSFER     35063.63  C1364127192        35063.63   \n",
       "1248     1  TRANSFER    271161.74  C1658487789            0.00   \n",
       "1869     1  TRANSFER     25071.46   C669700766        25071.46   \n",
       "2301     1  TRANSFER    235238.66  C1872047468       235238.66   \n",
       "3059     2  TRANSFER   1096187.24  C1093223281      1096187.24   \n",
       "3162     2  TRANSFER    963532.14  C1440057381       963532.14   \n",
       "3271     2  TRANSFER     14949.84   C140702728        14949.84   \n",
       "3683     2  TRANSFER     18627.02  C1375503918        18627.02   \n",
       "4103     3  TRANSFER     10539.37  C1134864869        10539.37   \n",
       "4260     3  TRANSFER     22877.00  C1247938090        22877.00   \n",
       "4440     4  TRANSFER  10000000.00     C7162498     12930418.44   \n",
       "4442     4  TRANSFER   2930418.44  C2047521920      2930418.44   \n",
       "4667     4  TRANSFER    169941.73   C540962910       169941.73   \n",
       "4693     4  TRANSFER     13707.11    C17222024        13707.11   \n",
       "4775     4  TRANSFER     86070.17  C1844941220        86070.17   \n",
       "4857     5  TRANSFER    120074.73  C1409933277       120074.73   \n",
       "\n",
       "      newBalanceOrig     nameDest  oldBalanceDest  newBalanceDest  isFraud  \\\n",
       "2               0.00   C553264065             0.0             0.0        1   \n",
       "251             0.00   C972765878             0.0             0.0        1   \n",
       "680             0.00  C1848415041             0.0             0.0        1   \n",
       "969             0.00   C431687661             0.0             0.0        1   \n",
       "1115            0.00  C1136419747             0.0             0.0        1   \n",
       "1248            0.00  C1219161283             0.0             0.0        0   \n",
       "1869            0.00  C1384210339             0.0             0.0        1   \n",
       "2301            0.00   C116289363             0.0             0.0        1   \n",
       "3059            0.00  C2063275841             0.0             0.0        1   \n",
       "3162            0.00   C268086000             0.0             0.0        1   \n",
       "3271            0.00   C634210724             0.0             0.0        1   \n",
       "3683            0.00   C234430897             0.0             0.0        1   \n",
       "4103            0.00   C118648358             0.0             0.0        1   \n",
       "4260            0.00  C1002031672             0.0             0.0        1   \n",
       "4440      2930418.44   C945327594             0.0             0.0        1   \n",
       "4442            0.00   C449261773             0.0             0.0        1   \n",
       "4667            0.00  C2127862399             0.0             0.0        1   \n",
       "4693            0.00   C410033330             0.0             0.0        1   \n",
       "4775            0.00  C1191544932             0.0             0.0        1   \n",
       "4857            0.00   C162114152             0.0             0.0        1   \n",
       "\n",
       "      isFlaggedFraud  Fraud_Heuristic  hour  \n",
       "2                  0                0     1  \n",
       "251                0                0     1  \n",
       "680                0                0     1  \n",
       "969                0                1     1  \n",
       "1115               0                0     1  \n",
       "1248               0                1     1  \n",
       "1869               0                0     1  \n",
       "2301               0                1     1  \n",
       "3059               0                1     2  \n",
       "3162               0                1     2  \n",
       "3271               0                0     2  \n",
       "3683               0                0     2  \n",
       "4103               0                0     3  \n",
       "4260               0                0     3  \n",
       "4440               0                1     4  \n",
       "4442               0                1     4  \n",
       "4667               0                0     4  \n",
       "4693               0                0     4  \n",
       "4775               0                0     4  \n",
       "4857               0                0     5  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfOdd.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "641a0bb1-3c5d-4b61-8e6c-2e141c9e47c6",
    "_uuid": "498f706544e28e36527b3c0f5bf87fcc4cd57ffd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldBalanceOrig</th>\n",
       "      <th>newBalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldBalanceDest</th>\n",
       "      <th>newBalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>Fraud_Heuristic</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>229133.94</td>\n",
       "      <td>C905080434</td>\n",
       "      <td>15325.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C476402209</td>\n",
       "      <td>5083.0</td>\n",
       "      <td>51513.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>215310.30</td>\n",
       "      <td>C1670993182</td>\n",
       "      <td>705.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1100439041</td>\n",
       "      <td>22425.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>311685.89</td>\n",
       "      <td>C1984094095</td>\n",
       "      <td>10835.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C932583850</td>\n",
       "      <td>6267.0</td>\n",
       "      <td>2719172.89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    step      type     amount     nameOrig  oldBalanceOrig  newBalanceOrig  \\\n",
       "2      1  TRANSFER     181.00  C1305486145           181.0             0.0   \n",
       "3      1  CASH_OUT     181.00   C840083671           181.0             0.0   \n",
       "15     1  CASH_OUT  229133.94   C905080434         15325.0             0.0   \n",
       "19     1  TRANSFER  215310.30  C1670993182           705.0             0.0   \n",
       "24     1  TRANSFER  311685.89  C1984094095         10835.0             0.0   \n",
       "\n",
       "       nameDest  oldBalanceDest  newBalanceDest  isFraud  isFlaggedFraud  \\\n",
       "2    C553264065             0.0            0.00        1               0   \n",
       "3     C38997010         21182.0            0.00        1               0   \n",
       "15   C476402209          5083.0        51513.44        0               0   \n",
       "19  C1100439041         22425.0            0.00        0               0   \n",
       "24   C932583850          6267.0      2719172.89        0               0   \n",
       "\n",
       "    Fraud_Heuristic  hour  \n",
       "2                 0     1  \n",
       "3                 0     1  \n",
       "15                0     1  \n",
       "19                1     1  \n",
       "24                1     1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "8a101e7c-5718-4771-b249-ad132736047c",
    "_uuid": "985c1701b5310b06af3efd8d79f166f39430e92a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['type'] = 'type_' + df['type'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "75660ab4-72cf-41fc-81ec-eda3c22b88dc",
    "_uuid": "e3a1a9536b05cb59983294378b6cc6b9a77aef41",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get dummies\n",
    "dummies = pd.get_dummies(df['type'])\n",
    "\n",
    "# Add dummies to df\n",
    "df = pd.concat([df,dummies],axis=1)\n",
    "\n",
    "#remove original column\n",
    "del df['type']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e9ab536e56b65cf1666b50117215e8f6353c7b39"
   },
   "source": [
    "Predictive modeling with Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "a0d4089e3e2f71a17d3a593b2e7844b37991f64c"
   },
   "outputs": [],
   "source": [
    "df = df.drop(['nameOrig','nameDest','Fraud_Heuristic'], axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "d1812b07d21bce9bd2c41457fc56477f212c8271"
   },
   "outputs": [],
   "source": [
    "df['isNight'] = np.where((2 <= df['hour']) & (df['hour'] <= 6), 1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "82eb362adb24ee29462d40ef129b32e5242c59cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35705263157894734"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['isNight'] == 1].isFraud.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "cabfd1f8d9462ec5c91b43407e27f5a526d3c5a1"
   },
   "outputs": [
    {
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       "      <th>19</th>\n",
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       "      <td>705.0</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    step     amount  oldBalanceOrig  newBalanceOrig  oldBalanceDest  \\\n",
       "2      1     181.00           181.0             0.0             0.0   \n",
       "3      1     181.00           181.0             0.0         21182.0   \n",
       "15     1  229133.94         15325.0             0.0          5083.0   \n",
       "19     1  215310.30           705.0             0.0         22425.0   \n",
       "24     1  311685.89         10835.0             0.0          6267.0   \n",
       "\n",
       "    newBalanceDest  isFraud  isFlaggedFraud  hour  type_CASH_OUT  \\\n",
       "2             0.00        1               0     1              0   \n",
       "3             0.00        1               0     1              1   \n",
       "15        51513.44        0               0     1              1   \n",
       "19            0.00        0               0     1              0   \n",
       "24      2719172.89        0               0     1              0   \n",
       "\n",
       "    type_TRANSFER  isNight  \n",
       "2               1        0  \n",
       "3               0        0  \n",
       "15              0        0  \n",
       "19              1        0  \n",
       "24              1        0  "
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "dabf1aa28dedf7ea4c7160cb2be23624761790f8"
   },
   "outputs": [],
   "source": [
    "df = df.drop(['step','hour'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "00d02bafd6177e1aaf1cba1cb4221191343c28ad"
   },
   "outputs": [
    {
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       "      <td>215310.30</td>\n",
       "      <td>705.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22425.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>311685.89</td>\n",
       "      <td>10835.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6267.0</td>\n",
       "      <td>2719172.89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       amount  oldBalanceOrig  newBalanceOrig  oldBalanceDest  newBalanceDest  \\\n",
       "2      181.00           181.0             0.0             0.0            0.00   \n",
       "3      181.00           181.0             0.0         21182.0            0.00   \n",
       "15  229133.94         15325.0             0.0          5083.0        51513.44   \n",
       "19  215310.30           705.0             0.0         22425.0            0.00   \n",
       "24  311685.89         10835.0             0.0          6267.0      2719172.89   \n",
       "\n",
       "    isFraud  isFlaggedFraud  type_CASH_OUT  type_TRANSFER  isNight  \n",
       "2         1               0              0              1        0  \n",
       "3         1               0              1              0        0  \n",
       "15        0               0              1              0        0  \n",
       "19        0               0              0              1        0  \n",
       "24        0               0              0              1        0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "4bd236281848b990d5e20e4fef939504ccb4332a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['amount', 'oldBalanceOrig', 'newBalanceOrig', 'oldBalanceDest',\n",
       "       'newBalanceDest', 'isFraud', 'isFlaggedFraud', 'type_CASH_OUT',\n",
       "       'type_TRANSFER', 'isNight'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "88394bc847527331a061052d11bf0c572e0d9a2f"
   },
   "outputs": [],
   "source": [
    "y_df = df['isFraud']\n",
    "x_df = df.drop('isFraud',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "a912b77dca0e4841f32e8c4d23f00cd0e7a41ad9",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = y_df.values\n",
    "X = x_df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "1ffdb9d9c166e29eb2119a1a3184819d5dab98c6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2770409,)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_uuid": "f34bfcf76aedcac89129586dc9b54c3ae5578892"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2770409, 9)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_uuid": "001a56dcb34fe4e80cf01b2a648b0ce56eb3a115",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_uuid": "0a03972c44d1c634392b4360232b69d6a8a57e37",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.33, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_uuid": "b0b0dc266eedad2dd25a678dfd60f56ec4ee892c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, \n",
    "                                                    test_size=0.1, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "_uuid": "5b01425e7928f762f0f425eb9033076ad448a862",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE, RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_uuid": "cfd5dbebaa5f90d07b08db41544cf6e27540603d"
   },
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state=42)\n",
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "_uuid": "771ae953bb8d23930d0b22d828776cc7275adbc9",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "_uuid": "7e5274fd3cd1c79807965a56a8d67d4b44561fbb",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Log reg\n",
    "model = Sequential()\n",
    "model.add(Dense(1, input_dim=9))\n",
    "model.add(Activation('sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "_uuid": "4c661160a358175fe64df65ea708b08be41ce219"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_20 (Dense)             (None, 1)                 10        \n",
      "_________________________________________________________________\n",
      "activation_20 (Activation)   (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 10\n",
      "Trainable params: 10\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "_uuid": "18746dedd24699fb3e86c4f3ab2490502682191d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=SGD(lr=1e-5), \n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "_uuid": "547cfa8aa90682194ac861c7547d46646ae1df02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3331258 samples, validate on 185618 samples\n",
      "Epoch 1/5\n",
      "3331258/3331258 [==============================] - 21s 6us/step - loss: 0.7453 - acc: 0.4799 - val_loss: 0.4254 - val_acc: 0.9733\n",
      "Epoch 2/5\n",
      "1411840/3331258 [===========>..................] - ETA: 11s - loss: 0.7419 - acc: 0.4797"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-199-7776f19f7d34>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m           validation_data=(X_val_scale,y_val))\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m    964\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m    967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1671\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1672\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1673\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1675\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1206\u001b[0m                         \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1208\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1209\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1210\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2473\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2474\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2475\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2476\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    903\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 905\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    906\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1136\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1137\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1138\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1353\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1354\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1355\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1356\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1357\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1359\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1360\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1362\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1338\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1339\u001b[0m           return tf_session.TF_Run(session, options, feed_dict, fetch_list,\n\u001b[0;32m-> 1340\u001b[0;31m                                    target_list, status, run_metadata)\n\u001b[0m\u001b[1;32m   1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1342\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(X_train_res,y_train_res,\n",
    "          epochs=5, \n",
    "          batch_size=256, \n",
    "          validation_data=(X_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "_uuid": "9de8b8153e076e49367f6d1d557054276c0bbb89",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "_uuid": "1121ea156e06a9201d0919e138cca1c3e94ea930",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred[y_pred > 0.5] = 1\n",
    "y_pred[y_pred < 0.5] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "_uuid": "ea205eee82eff9530b84b622d28c55f9f2ae5256"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.054384286716408395"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_pred=y_pred,y_true=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "_uuid": "f4e1cdde001d61cf3defadcaac7f5db9a50a283e",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_pred=y_pred,y_true=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "_uuid": "45083bc8a3faea14315c04148b56fdfefb6078b8",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZmZkkJycza9YsYmNjgdIP9IULF/L++++zfPlyFi5c6PxQj42NJS4ujuTkZDIzM0lNTQVw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\n1P8BnIDUdgVFlXQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc60a69dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm,['Genuine','Fraud'], normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "_uuid": "771c2e9bc4ec2c505ef6e9699828b1ff58259d04",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(16,input_dim=9))\n",
    "model.add(Activation('tanh'))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "_uuid": "0755c0bc658033b6d0c03f8f2f28d3adb461502d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',optimizer=SGD(lr=1e-4), metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "_kg_hide-output": false,
    "_uuid": "308e7b1b789b8dd23325ae4c46740f7398d194d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3331258 samples, validate on 185618 samples\n",
      "Epoch 1/5\n",
      "3331258/3331258 [==============================] - 22s 7us/step - loss: 0.6064 - acc: 0.6922 - val_loss: 1.0665 - val_acc: 0.1872\n",
      "Epoch 2/5\n",
      " 729856/3331258 [=====>........................] - ETA: 17s - loss: 0.6029 - acc: 0.6968"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-205-b2b4ddf5127e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m model.fit(X_train_res,y_train_res,\n\u001b[1;32m      2\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m           validation_data=(X_val_scale,y_val))\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m    964\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m    967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1671\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1672\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1673\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1675\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1194\u001b[0m                             \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1195\u001b[0m                         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1196\u001b[0;31m                             \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1197\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1198\u001b[0m                         raise TypeError('TypeError while preparing batch. '\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/engine/training.py\u001b[0m in \u001b[0;36m_slice_arrays\u001b[0;34m(arrays, start, stop)\u001b[0m\n\u001b[1;32m    382\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m                 \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/Keras-2.1.3-py3.6.egg/keras/engine/training.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    382\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m                 \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(X_train_res,y_train_res,\n",
    "          epochs=5, batch_size=256, \n",
    "          validation_data=(X_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "_uuid": "887caba9f865017f5ac407734e6f58bf4da5ea8d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "_uuid": "ea6c7d80e31e05e7d9d47036dbd25e38ade97d8b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred[y_pred > 0.5] = 1\n",
    "y_pred[y_pred < 0.5] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "_uuid": "2ce7fa87b4c1c17dac71a8dd5af462a867843510"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001674751441885722"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_pred=y_pred,y_true=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "_uuid": "d1d96cd97a454a2a8efe343ed88a7ecf56add966",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_pred=y_pred,y_true=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "_uuid": "1f765516f1e1891f0cc258e992a3fcdd3fd83176"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ISAiBgYE0adKEkJAQtm3bRnZ2NgUFBfTu3RvDMIiJicFut59zrV/34YrmBIiI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\n/wBR32BKSc5WQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc60a8759b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm,['Genuine','Fraud'], normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6265c59cc5208a14463ab3950695a2950e5a5c0a"
   },
   "source": [
    "# Tree based methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "_uuid": "cfb5795389e6b9088b706e64edaf2ba07fa29429",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "_uuid": "d6a9e15f959757d0fb4d303a4e50d4f23454d47d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree=DecisionTreeClassifier()\n",
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "_uuid": "1aa40623ae96c0f2d97167aebc22603ae22635f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v3mzZsqWigwJQMDU1NU9Pz+fPn7dq1WrAgAHu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\n3Zj8uzF5D2Pzz4Zk/3KNc2C8YycLjRqzvhSTXTr6QCifx9k12n6Ag06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zOP0d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\nNMSiU7j/a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oK01JKv2shmMcp54rtJG65/KtYiLKYkvMwfehoJMwFENLOT/r6nybdCskXJ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\nL2+Cs09ERsmvd+IYDKp8K/LPR0lF5bzDE1sMsNUgo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WtvsfWSX1xGvqm2SgWPf90/ZnBb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HTM+L1bR0gzVXOZtHnZ4rrGasbL8m9/2dnPcuFZkxlVlxCiPmS5nbZ/vczP10n8QvrDlrH0II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\n1rtroM9+3w6dJRjriWSXROXXPksoG2mueG6mGYLA2kHa4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Q9O2Yz/jsISvx4rcpKTfW+Z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Qk9EdDC9VxPCm34oouRBcJLbS7UZ4SavhNRMxvFkXgou+H6ApReHPeqKKa5pbzXK+iFnW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owm/yzkkBN6HwCGfLNV7I0AAAKJOnj+pqe/dv5ShgIyDgkSiQQAjc2iTnOVyiebV6em2G5hi0kl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\ngg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "from subprocess import check_call\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "from IPython.display import Image as PImage\n",
    "\n",
    "#import pydotplus\n",
    "dot_data = StringIO()\n",
    "'''export_graphviz(dtree, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)'''\n",
    "with open(\"tree1.dot\", 'w') as f:\n",
    "     f = export_graphviz(dtree,\n",
    "                              out_file=f,\n",
    "                              max_depth = 3,\n",
    "                              impurity = True,\n",
    "                              feature_names = list(df.drop(['isFraud'], axis=1)),\n",
    "                              class_names = ['Genuine', 'Fraud'],\n",
    "                              rounded = True,\n",
    "                              filled= True )\n",
    "        \n",
    "#Convert .dot to .png to allow display in web notebook\n",
    "check_call(['dot','-Tpng','tree1.dot','-o','tree1.png'])\n",
    "\n",
    "# Annotating chart with PIL\n",
    "img = Image.open(\"tree1.png\")\n",
    "draw = ImageDraw.Draw(img)\n",
    "font = ImageFont.truetype('/usr/share/fonts/truetype/liberation/LiberationSerif-Bold.ttf', 26)\n",
    "img.save('sample-out.png')\n",
    "PImage(\"sample-out.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "_uuid": "ce17157402a6b8e42b2b3aa41da5cc0bea556d8d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import  RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "_uuid": "4f0ec5f0f1f325328d333378f7efb90df0861791"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10,n_jobs=-1)\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "_uuid": "c0e0b79a5b298359594f09aec6816b6a2ba3c9bc",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "_uuid": "67a1f518963b16bad41d0a43263c9e938ec8d72c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8749502190362406"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_pred=y_pred,y_true=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "_uuid": "7089b4c6b92c4b50bfacb94f49c881d28d874086"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8HdNwgIiIiEmpEiAiIqZlwcZKgJ1PD1ASICIi5qUpgiIiIuZksWBjT0DuxVIQ1BMgIiJi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\nKBEECIIgCMIQJYIAQRAEQRii/gM10yXPLhzw2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc60a5a87b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_pred=y_pred,y_true=y_test)\n",
    "plot_confusion_matrix(cm,['Genuine','Fraud'], normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "_uuid": "6d646200c6125f1b586e50ecec5a775cbd44ac90",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "_uuid": "540a36d96ad53d76a501883a4d2d115dcc9a1e42"
   },
   "outputs": [],
   "source": [
    "booster = xgb.XGBClassifier(n_jobs=-1)\n",
    "booster = booster.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "_uuid": "d2204961e8909f5b37cf14f6b80f42b9ca830981"
   },
   "outputs": [],
   "source": [
    "y_pred = booster.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "_uuid": "2537def462363071b1fa7ba7ab95115572617a52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85572959604286891"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_pred=y_pred,y_true=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "_uuid": "77bf2d6c02a829aca99b5207e23e7a0d11d9c2fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JWTfYvuOdmIYDRERETEqVABERMS0LdlYCnHx6gJIAERExL00RFBERMSeLBTt7AgouFkdQ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc60a53ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_pred=y_pred,y_true=y_test)\n",
    "plot_confusion_matrix(cm,['Genuine','Fraud'], normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "10629666-b25c-42dd-ac1f-414b973ef24e",
    "_uuid": "7294c2ceddfc6950e899ad180d730736a9273d87",
    "collapsed": true
   },
   "source": [
    "# Entity embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "fe3b6b89-f611-48a6-9a32-b5462849ca39",
    "_uuid": "d9077d24f859ae1302496d8acc5f7cb08ec4794b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reload data\n",
    "df = pd.read_csv('../input/PS_20174392719_1491204439457_log.csv')\n",
    "df = df.rename(columns={'oldbalanceOrg':'oldBalanceOrig', 'newbalanceOrig':'newBalanceOrig', \\\n",
    "                        'oldbalanceDest':'oldBalanceDest', 'newbalanceDest':'newBalanceDest'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "76f7394d-55a7-4925-bc6e-61935c71d9e3",
    "_uuid": "572c25cad9af19435385227009369e530e645a8b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "e0da3e11-3b63-4d28-b57f-05b836a42dda",
    "_uuid": "cf4ead61784891962c4f5ee201fa7d363a380f7c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop(['nameDest','nameOrig','step'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "b704f862-729e-4229-86cd-73afc1f9f0e2",
    "_uuid": "c8e3da182aae6c13cbe24736050c115910119cb1",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "608a5db9-37af-48cd-a8dd-114377c5461f",
    "_uuid": "7cc3e34985255ba699bf797719820400f4f28a6a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map_dict = {}\n",
    "for token, value in enumerate(df['type'].unique()):\n",
    "    map_dict[value] = token   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "2c423985-6322-430d-bed0-ddbf8dd35e78",
    "_uuid": "76e26412ea3642e6fe8616802d57adf70cf5d3c2",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "9712cf3f-2479-4acf-a79b-3d79a6a73c84",
    "_uuid": "f8dbb529c849539b4df1750589576b32185ae0d5",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"type\"].replace(map_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "fd5a8816-e7a4-400b-bb4d-9d096b2ec4dd",
    "_uuid": "2a7d5673047ef26d84a16a14994a79c6f6037707",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "a09a767a-db96-4610-af01-d65f0af46e47",
    "_uuid": "87ff32cc2e7f25c06abad3d1155875d373db6dfa",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "other_cols = [c for c in df.columns if ((c != 'type') and (c != 'isFraud'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "34677eca-6ffe-4a21-a3e1-568631a936ea",
    "_uuid": "4e23aa260b8e63ed567dc269ec49dfe945711583",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "other_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "ebf058a4-8f18-4db4-b2cd-49f37d611c00",
    "_uuid": "f026242afda2d3c2760f260020432cbb11a0e3ca",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Embedding, Merge, Dense, Activation, Reshape, Input, Concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "22faf983-39a0-4479-bf25-fd7886afcfe5",
    "_uuid": "e1a441c58b57a2c4e18ec16e9c14ba520e47a023",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_types = len(df['type'].unique())\n",
    "type_embedding_dim = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "79e98983-4d24-479e-83c7-a086be3b2b24",
    "_uuid": "0051a37fb914175957193d471cebff6809923b92",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs = []\n",
    "outputs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "edee8cb9-9649-4dda-8f9f-6947e970bb26",
    "_uuid": "3d5a4e76d58958186be825464444ae273bfd690e",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "type_in = Input(shape=(1,))\n",
    "type_embedding = Embedding(num_types,type_embedding_dim,input_length=1)(type_in)\n",
    "type_out = Reshape(target_shape=(type_embedding_dim,))(type_embedding)\n",
    "\n",
    "type_model = Model(type_in,type_out)\n",
    "\n",
    "inputs.append(type_in)\n",
    "outputs.append(type_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "5e5aa623-c830-47ea-a473-e1d8df3a7c68",
    "_uuid": "6e606846e3501414b222b398a0f7cb81a42f0e00",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_rest = len(other_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "85340eb9-4548-4a7f-a331-f74f1b14dc55",
    "_uuid": "fde7532f3287c0ce78cb2223d77deaa882cf688e",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rest_in = Input(shape = (num_rest,))\n",
    "rest_out = Dense(16)(rest_in)\n",
    "\n",
    "rest_model = Model(rest_in,rest_out)\n",
    "\n",
    "inputs.append(rest_in)\n",
    "outputs.append(rest_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "a8115075-a1a5-4cd8-b375-8ce5ea0aa07b",
    "_uuid": "cc7f4316380c8bc25a08a99e49fb36a32e996259",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "concatenated = Concatenate()(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "0e894cba-208a-46dc-8618-0abe0563a45a",
    "_uuid": "a05dd39222c1e711132c3ef3a433d712a790315a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = Dense(16)(concatenated)\n",
    "x = Activation('sigmoid')(x)\n",
    "x = Dense(1)(concatenated)\n",
    "model_out = Activation('sigmoid')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "a35e9953-848a-4ab9-a160-6e8e032f7963",
    "_uuid": "7e9dab3301b76be0de89f8d9e97a7f9568660c63",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "merged_model = Model(inputs, model_out)\n",
    "merged_model.compile(loss='binary_crossentropy', \n",
    "                     optimizer='adam', \n",
    "                     metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "aa9b6a4c-a33c-489b-a03c-ea7ea50f576f",
    "_uuid": "40fe68b68666ad867614c7bc34141b30e2aa2490",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "types = df['type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "77e53195-24c1-4ccb-af92-19a85148e4c1",
    "_uuid": "ed0de5bdfed505ac7850b72b93dabc77d6ac3753",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rest = df[other_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "aa1efd8d-96b0-43a2-84b9-f45e1b376d47",
    "_uuid": "bab6ad0680df5fede7ec7ad1c93496f3736a3466",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target = df['isFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "2784f1f5-1c9e-4632-b2f9-5038a3424443",
    "_uuid": "24c09ca74688aba0abbadd39277fec5d6615cab8",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "history = merged_model.fit([types.values,rest.values],target.values, \n",
    "                           epochs = 1, batch_size = 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "41a4c40f-7478-4961-afbb-0c44976d8284",
    "_uuid": "7450dc3a5e072776ad9ff26775da48789f59a5c3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "merged_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "7a574a32-5170-49bf-8cb5-85572aa5ae6d",
    "_uuid": "2803ca61422d5dea81edf44f996090d182cfa6fe",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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