experiments/05_FraudDetection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Experiment 05: Credit card Fraud\n",
    "\n",
    "This experiment uses the data from the Kaggle dataset [Credit Card Fraud Detection](https://www.kaggle.com/dalpozz/creditcardfraud). The dataset is made up of a number of variables which are a result of PCA transformation.\n",
    "\n",
    "The details of the machine we used and the version of the libraries can be found in [experiment 01](01_airline.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System version: 3.5.2 |Anaconda custom (64-bit)| (default, Jul  2 2016, 17:53:06) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "XGBoost version: 0.6\n",
      "LightGBM version: 0.2\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pkg_resources\n",
    "from libs.loaders import load_fraud\n",
    "from libs.timer import Timer\n",
    "from libs.utils import get_number_processors\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from xgboost import XGBClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print(\"System version: {}\".format(sys.version))\n",
    "print(\"XGBoost version: {}\".format(pkg_resources.get_distribution('xgboost').version))\n",
    "print(\"LightGBM version: {}\".format(pkg_resources.get_distribution('lightgbm').version))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "random_seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:libs.loaders:MOUNT_POINT not found in environment. Defaulting to /fileshare\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = load_fraud()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 31)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pipeline_steps = [('scale', StandardScaler())]\n",
    "continuous_pipeline = Pipeline(steps=pipeline_steps)\n",
    "featurisers = [('continuous', continuous_pipeline)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "number_processors = get_number_processors()\n",
    "print(number_processors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "xgb_clf_pipeline = Pipeline(steps=[('features', FeatureUnion(featurisers)),\n",
    "                                          ('clf', XGBClassifier(max_depth=3, \n",
    "                                                                learning_rate=0.1, \n",
    "                                                                scale_pos_weight=2,\n",
    "                                                                n_estimators=100,\n",
    "                                                                gamma=0.1,\n",
    "                                                                min_child_weight=1,\n",
    "                                                                reg_lambda=1,\n",
    "                                                                subsample=1,\n",
    "                                                                nthread=number_processors\n",
    "                                                                ))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "xgb_hist_clf_pipeline = Pipeline(steps=[('features', FeatureUnion(featurisers)),\n",
    "                                          ('clf', XGBClassifier(max_depth=0, \n",
    "                                                                learning_rate=0.1, \n",
    "                                                                scale_pos_weight=2,\n",
    "                                                                n_estimators=100,\n",
    "                                                                gamma=0.1,\n",
    "                                                                min_child_weight=1,\n",
    "                                                                reg_lambda=1,\n",
    "                                                                subsample=1,\n",
    "                                                                max_leaves=2**3,\n",
    "                                                                grow_policy='lossguide',\n",
    "                                                                tree_method='hist',\n",
    "                                                                nthread=number_processors\n",
    "                                                                ))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "lgbm_clf_pipeline = Pipeline(steps=[('features', FeatureUnion(featurisers)),\n",
    "                                          ('clf', LGBMClassifier(num_leaves=2**3, \n",
    "                                                                learning_rate=0.1, \n",
    "                                                                scale_pos_weight=2,\n",
    "                                                                n_estimators=100,\n",
    "                                                                min_split_gain=0.1,\n",
    "                                                                min_child_weight=1,\n",
    "                                                                reg_lambda=1,\n",
    "                                                                subsample=1,\n",
    "                                                                nthread=number_processors\n",
    "                                                                ))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "metrics_dict = {\n",
    "    'Accuracy': accuracy_score,\n",
    "    'Precision': precision_score,\n",
    "    'Recall': recall_score,\n",
    "    'AUC': roc_auc_score,\n",
    "    'F1': f1_score,\n",
    "}\n",
    "\n",
    "def classification_metrics(metrics, y_true, y_pred):\n",
    "    return {metric_name:metric(y_true, y_pred) for metric_name, metric in metrics.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = df[[col for col in df.columns if col.startswith('V')]].values\n",
    "y = df['Class'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=random_seed, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "results_dict = dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with Timer() as train_t:\n",
    "    xgb_clf_pipeline.fit(X_train,y_train)\n",
    "    \n",
    "with Timer() as test_t:\n",
    "    y_pred = xgb_clf_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "results_dict['xgb']={\n",
    "    'train_time': train_t.interval,\n",
    "    'test_time': test_t.interval,\n",
    "    'performance': classification_metrics(metrics_dict, \n",
    "                                          y_test, \n",
    "                                          y_pred) \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with Timer() as t_train:\n",
    "    xgb_hist_clf_pipeline.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with Timer() as t_test:\n",
    "    y_pred = xgb_hist_clf_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "results_dict['xgb_hist']={\n",
    "    'train_time': t_train.interval,\n",
    "    'test_time': t_test.interval,\n",
    "    'performance': classification_metrics(metrics_dict, \n",
    "                                          y_test, \n",
    "                                          y_pred) \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with Timer() as train_t:\n",
    "    lgbm_clf_pipeline.fit(X_train, y_train)\n",
    "    \n",
    "with Timer() as test_t:\n",
    "    y_pred = lgbm_clf_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "results_dict['lgbm']={\n",
    "    'train_time': train_t.interval,\n",
    "    'test_time': test_t.interval,\n",
    "    'performance': classification_metrics(metrics_dict, \n",
    "                                          y_test, \n",
    "                                          y_pred) \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"lgbm\": {\n",
      "        \"performance\": {\n",
      "            \"AUC\": 0.8749179318834633,\n",
      "            \"Accuracy\": 0.999403110845827,\n",
      "            \"F1\": 0.8131868131868133,\n",
      "            \"Precision\": 0.888,\n",
      "            \"Recall\": 0.75\n",
      "        },\n",
      "        \"test_time\": 0.05075380699963716,\n",
      "        \"train_time\": 0.6608378439996159\n",
      "    },\n",
      "    \"xgb\": {\n",
      "        \"performance\": {\n",
      "            \"AUC\": 0.8884197213803287,\n",
      "            \"Accuracy\": 0.9994265182636377,\n",
      "            \"F1\": 0.8243727598566308,\n",
      "            \"Precision\": 0.8778625954198473,\n",
      "            \"Recall\": 0.777027027027027\n",
      "        },\n",
      "        \"test_time\": 0.06871192899961898,\n",
      "        \"train_time\": 4.349258283999916\n",
      "    },\n",
      "    \"xgb_hist\": {\n",
      "        \"performance\": {\n",
      "            \"AUC\": 0.8715278294884368,\n",
      "            \"Accuracy\": 0.9993679997191109,\n",
      "            \"F1\": 0.8029197080291971,\n",
      "            \"Precision\": 0.873015873015873,\n",
      "            \"Recall\": 0.7432432432432432\n",
      "        },\n",
      "        \"test_time\": 0.08524090300034004,\n",
      "        \"train_time\": 2.0142575339996256\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Results\n",
    "print(json.dumps(results_dict, indent=4, sort_keys=True))"
   ]
  }
 ],
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