Keras - 09 Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, Flatten\n",
    "from sklearn import model_selection, preprocessing, metrics, ensemble\n",
    "from mlxtend.plotting import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/data/creditcard-fraud.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data():\n",
    "    X = df.loc[:, \"V1\":\"V28\"]\n",
    "    y = df.Class\n",
    "    X_train, X_test, y_train, y_test = model_selection.train_test_split(X.values, y, test_size = 0.3, random_state = 1)\n",
    "    scaler = pre\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "X_train, X_test, y_train, y_test = get_data()\n",
    "n_x = X_train.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying convolutional layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 199364 samples, validate on 85443 samples\n",
      "Epoch 1/10\n",
      "199364/199364 [==============================] - 31s 153us/step - loss: 0.0134 - acc: 0.9988 - val_loss: 0.0042 - val_acc: 0.9994\n",
      "Epoch 2/10\n",
      "199364/199364 [==============================] - 28s 140us/step - loss: 0.0043 - acc: 0.9993 - val_loss: 0.0037 - val_acc: 0.9993\n",
      "Epoch 3/10\n",
      "199364/199364 [==============================] - 27s 136us/step - loss: 0.0044 - acc: 0.9992 - val_loss: 0.0040 - val_acc: 0.9993\n",
      "Epoch 4/10\n",
      "199364/199364 [==============================] - 27s 136us/step - loss: 0.0042 - acc: 0.9992 - val_loss: 0.0044 - val_acc: 0.9992\n",
      "Epoch 5/10\n",
      "199364/199364 [==============================] - 26s 130us/step - loss: 0.0040 - acc: 0.9993 - val_loss: 0.0047 - val_acc: 0.9994\n",
      "Epoch 6/10\n",
      "199364/199364 [==============================] - 25s 127us/step - loss: 0.0039 - acc: 0.9993 - val_loss: 0.0053 - val_acc: 0.9992\n",
      "Epoch 7/10\n",
      "199364/199364 [==============================] - 29s 143us/step - loss: 0.0042 - acc: 0.9993 - val_loss: 0.0038 - val_acc: 0.9992\n",
      "Epoch 8/10\n",
      "199364/199364 [==============================] - 29s 147us/step - loss: 0.0039 - acc: 0.9993 - val_loss: 0.0039 - val_acc: 0.9994\n",
      "Epoch 9/10\n",
      "199364/199364 [==============================] - 30s 148us/step - loss: 0.0037 - acc: 0.9994 - val_loss: 0.0039 - val_acc: 0.9993\n",
      "Epoch 10/10\n",
      "199364/199364 [==============================] - 31s 155us/step - loss: 0.0041 - acc: 0.9993 - val_loss: 0.0036 - val_acc: 0.9994\n",
      "Recall:  0.7703703703703704 \n",
      "Precision 0.832\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x130f60fd0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x11b59eb38>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130f60fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_model(n_x):\n",
    "    model = keras.Sequential()\n",
    "    model.add(Conv1D(50, kernel_size=(5,), activation=\"relu\", input_shape = (n_x,1)))\n",
    "    model.add(MaxPooling1D(pool_size=(3,)))\n",
    "    model.add(Conv1D(50, kernel_size=(5,), activation=\"relu\"))\n",
    "    model.add(MaxPooling1D(pool_size=(3,)))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(100, activation=\"relu\"))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
    "    model.compile(loss=\"binary_crossentropy\", metrics=[\"accuracy\"], optimizer=\"adam\")\n",
    "    return model\n",
    "\n",
    "model = get_model(n_x)\n",
    "model.fit(batch_size=32\n",
    "          , x=X_train.reshape(-1, n_x, 1)\n",
    "          , y=y_train\n",
    "          , epochs=10\n",
    "          , validation_data=(X_test.reshape(-1, n_x, 1), y_test))\n",
    "\n",
    "y_test_prob = model.predict(X_test.reshape(-1, n_x, 1))\n",
    "y_test_pred = np.where(y_test_prob > 0.5, 1, 0)\n",
    "print(\"Recall: \", metrics.recall_score(y_test, y_test_pred),\n",
    "      \"\\nPrecision\", metrics.precision_score(y_test, y_test_pred))\n",
    "plot_confusion_matrix(metrics.confusion_matrix(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fully connected neural network with one hidden layer with size 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 199364 samples, validate on 85443 samples\n",
      "Epoch 1/10\n",
      "199364/199364 [==============================] - 14s 69us/step - loss: 0.0078 - acc: 0.9987 - val_loss: 0.0035 - val_acc: 0.9994\n",
      "Epoch 2/10\n",
      "199364/199364 [==============================] - 14s 68us/step - loss: 0.0031 - acc: 0.9994 - val_loss: 0.0029 - val_acc: 0.9994\n",
      "Epoch 3/10\n",
      "199364/199364 [==============================] - 14s 68us/step - loss: 0.0028 - acc: 0.9994 - val_loss: 0.0031 - val_acc: 0.9995\n",
      "Epoch 4/10\n",
      "199364/199364 [==============================] - 16s 82us/step - loss: 0.0025 - acc: 0.9994 - val_loss: 0.0029 - val_acc: 0.9994\n",
      "Epoch 5/10\n",
      "199364/199364 [==============================] - 17s 84us/step - loss: 0.0024 - acc: 0.9995 - val_loss: 0.0033 - val_acc: 0.9995\n",
      "Epoch 6/10\n",
      "199364/199364 [==============================] - 14s 70us/step - loss: 0.0022 - acc: 0.9995 - val_loss: 0.0032 - val_acc: 0.9995\n",
      "Epoch 7/10\n",
      "199364/199364 [==============================] - 18s 88us/step - loss: 0.0022 - acc: 0.9995 - val_loss: 0.0032 - val_acc: 0.9994\n",
      "Epoch 8/10\n",
      "199364/199364 [==============================] - 17s 86us/step - loss: 0.0020 - acc: 0.9995 - val_loss: 0.0031 - val_acc: 0.9995\n",
      "Epoch 9/10\n",
      "199364/199364 [==============================] - 17s 83us/step - loss: 0.0018 - acc: 0.9995 - val_loss: 0.0039 - val_acc: 0.9994\n",
      "Epoch 10/10\n",
      "199364/199364 [==============================] - 15s 73us/step - loss: 0.0018 - acc: 0.9995 - val_loss: 0.0034 - val_acc: 0.9995\n",
      "Recall:  0.7333333333333333 \n",
      "Precision 0.9611650485436893\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x10f7546d8>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x11b099dd8>)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f7546d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_model(n_x):\n",
    "    model = keras.Sequential()\n",
    "    model.add(Dense(200, activation=\"relu\", input_shape = (n_x,)))\n",
    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
    "    model.compile(loss=\"binary_crossentropy\", metrics=[\"accuracy\"], optimizer=\"adam\")\n",
    "    return model\n",
    "\n",
    "model = get_model(n_x)\n",
    "model.fit(batch_size=32\n",
    "          , x=X_train\n",
    "          , y=y_train\n",
    "          , epochs=10\n",
    "          , validation_data=(X_test, y_test))\n",
    "\n",
    "y_test_prob = model.predict(X_test)\n",
    "y_test_pred = np.where(y_test_prob > 0.5, 1, 0)\n",
    "print(\"Recall: \", metrics.recall_score(y_test, y_test_pred),\n",
    "      \"\\nPrecision\", metrics.precision_score(y_test, y_test_pred))\n",
    "plot_confusion_matrix(metrics.confusion_matrix(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the result with the RandomForest model in Scikit Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall:  0.7333333333333333 \n",
      "Precision 0.9519230769230769\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x132157080>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1106025f8>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132157080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "est = ensemble.RandomForestClassifier(random_state=1)\n",
    "est.fit(X_train, y_train)\n",
    "y_train_pred = est.predict(X_train)\n",
    "y_test_pred = est.predict(X_test)\n",
    "print(\"Recall: \", metrics.recall_score(y_test, y_test_pred),\n",
    "      \"\\nPrecision\", metrics.precision_score(y_test, y_test_pred))\n",
    "plot_confusion_matrix(metrics.confusion_matrix(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}