IEEE-CIS_Fraud_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this project is to predict the probability that an online transaction is fraudulent. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Discription"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is broken into two files identity and transaction.\n",
    "\n",
    "**Transaction**\n",
    "\n",
    "* Categorical Features:  \n",
    "ProductCD  \n",
    "card1 - card6  \n",
    "addr1, addr2  \n",
    "Pemaildomain Remaildomain  \n",
    "M1 - M9  \n",
    "\n",
    "**Identity**\n",
    "\n",
    "* Categorical Features:  \n",
    "DeviceType  \n",
    "DeviceInfo  \n",
    "id_12 - id_38  \n",
    "\n",
    "There are total 144,233 instances and 434 features. The corresponding class variable indicates fraudulent. The data is highly imbalanced. There are 92.15% instances of non-fraudulent and 7.84% instances of fraudulent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data loading and overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import collections\n",
    "import os.path\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import make_scorer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from time import time\n",
    "import datetime\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from chart_studio import tools\n",
    "import chart_studio.plotly as py\n",
    "\n",
    "from scipy import stats\n",
    "from scipy.stats import chisquare\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy.ma as ma\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from catboost import Pool, CatBoostClassifier\n",
    "import xgboost as xgb\n",
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transaction = pd.read_csv('./train_transaction.csv')\n",
    "test_transaction = pd.read_csv('./test_transaction.csv')\n",
    "\n",
    "train_identity = pd.read_csv('./train_identity.csv')\n",
    "test_identity = pd.read_csv('./test_identity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into training and test set\n",
    "train = pd.merge(train_transaction, train_identity, on = 'TransactionID', how = 'left')\n",
    "test = pd.merge(test_transaction,test_identity, on = 'TransactionID', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: (590540, 434)\n",
      "test: (506691, 433)\n"
     ]
    }
   ],
   "source": [
    "print('train:',train.shape)\n",
    "print('test:', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TransactionID', 'TransactionDT', 'TransactionAmt', 'ProductCD',\n",
       "       'card1', 'card2', 'card3', 'card4', 'card5', 'card6',\n",
       "       ...\n",
       "       'id-31', 'id-32', 'id-33', 'id-34', 'id-35', 'id-36', 'id-37', 'id-38',\n",
       "       'DeviceType', 'DeviceInfo'],\n",
       "      dtype='object', length=433)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "393\n",
      "430\n"
     ]
    }
   ],
   "source": [
    "print(list(test.columns).index(\"id-01\"))\n",
    "print(list(test.columns).index(\"id-38\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "test.columns = list(test.columns)[0:393]+['id_0'+str(i) for i in range(1,10)]+['id_'+str(i) for i in range(10,39)]+list(test.columns)[431:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_transaction, train_identity, test_transaction, test_identity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['isFraud']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_train = train['isFraud']\n",
    "X_train = train.drop(['isFraud'],axis = 1)\n",
    "X_test = test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(590540, 433)\n",
      "(506691, 433)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature list\n",
    "x_features = list(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_from_Feature_list(features, feature_list=x_features):\n",
    "    for i in features:\n",
    "        feature_list.remove(i)\n",
    "    print('# of features =', len(feature_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 432\n"
     ]
    }
   ],
   "source": [
    "# remove TransactionID from Feature list\n",
    "\n",
    "remove_from_Feature_list(['TransactionID'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_to_features_list(to_add, features_list = x_features):\n",
    "    for i in to_add:\n",
    "        features_list.append(i)\n",
    "    print('# of features =', len(x_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Email Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "emails = {'gmail': 'google', 'att.net': 'att', 'twc.com': 'spectrum', \n",
    "          'scranton.edu': 'other', 'optonline.net': 'other', 'hotmail.co.uk': 'microsoft',\n",
    "          'comcast.net': 'other', 'yahoo.com.mx': 'yahoo', 'yahoo.fr': 'yahoo',\n",
    "          'yahoo.es': 'yahoo', 'charter.net': 'spectrum', 'live.com': 'microsoft', \n",
    "          'aim.com': 'aol', 'hotmail.de': 'microsoft', 'centurylink.net': 'centurylink',\n",
    "          'gmail.com': 'google', 'me.com': 'apple', 'earthlink.net': 'other', 'gmx.de': 'other',\n",
    "          'web.de': 'other', 'cfl.rr.com': 'other', 'hotmail.com': 'microsoft', \n",
    "          'protonmail.com': 'other', 'hotmail.fr': 'microsoft', 'windstream.net': 'other', \n",
    "          'outlook.es': 'microsoft', 'yahoo.co.jp': 'yahoo', 'yahoo.de': 'yahoo','servicios-ta.com': 'other', 'netzero.net': 'other', 'suddenlink.net': 'other',\n",
    "          'roadrunner.com': 'other', 'sc.rr.com': 'other', 'live.fr': 'microsoft',\n",
    "          'verizon.net': 'yahoo', 'msn.com': 'microsoft', 'q.com': 'centurylink', \n",
    "          'prodigy.net.mx': 'att', 'frontier.com': 'yahoo', 'anonymous.com': 'other', \n",
    "          'rocketmail.com': 'yahoo', 'sbcglobal.net': 'att', 'frontiernet.net': 'yahoo', \n",
    "          'ymail.com': 'yahoo', 'outlook.com': 'microsoft', 'mail.com': 'other', \n",
    "          'bellsouth.net': 'other', 'embarqmail.com': 'centurylink', 'cableone.net': 'other', \n",
    "          'hotmail.es': 'microsoft', 'mac.com': 'apple', 'yahoo.co.uk': 'yahoo', 'netzero.com': 'other', \n",
    "          'yahoo.com': 'yahoo', 'live.com.mx': 'microsoft', 'ptd.net': 'other', 'cox.net': 'other',\n",
    "          'aol.com': 'aol', 'juno.com': 'other', 'icloud.com': 'apple'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in ['P_emaildomain', 'R_emaildomain']:\n",
    "    X_train[c + '_bin'] = train[c].map(emails)\n",
    "    X_test[c + '_bin'] = test[c].map(emails)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 430\n"
     ]
    }
   ],
   "source": [
    "remove_from_Feature_list(['P_emaildomain', 'R_emaildomain'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 432\n"
     ]
    }
   ],
   "source": [
    "add_to_features_list(['P_emaildomain_bin','R_emaildomain_bin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "google         228851\n",
       "yahoo          109149\n",
       "microsoft       59477\n",
       "other           52868\n",
       "aol             28604\n",
       "apple            8225\n",
       "att              7210\n",
       "spectrum         1046\n",
       "centurylink       654\n",
       "Name: P_emaildomain_bin, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['P_emaildomain_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove id columns with nan > 87%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 417\n"
     ]
    }
   ],
   "source": [
    "remove_from_Feature_list(['id_10', 'id_09', 'id_33', 'id_04', 'id_03', 'id_18', 'id_23', 'id_22',\n",
    "       'id_27', 'id_26', 'id_21', 'id_07', 'id_08', 'id_25', 'id_24'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimension Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA：V1 -  V339"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V330</th>\n",
       "      <th>V331</th>\n",
       "      <th>V332</th>\n",
       "      <th>V333</th>\n",
       "      <th>V334</th>\n",
       "      <th>V335</th>\n",
       "      <th>V336</th>\n",
       "      <th>V337</th>\n",
       "      <th>V338</th>\n",
       "      <th>V339</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 339 columns</p>\n",
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      "text/plain": [
       "    V1   V2   V3   V4   V5   V6   V7   V8   V9  V10  ...  V330  V331  V332  \\\n",
       "0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  ...   NaN   NaN   NaN   \n",
       "1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   NaN   NaN   NaN   \n",
       "2  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  ...   NaN   NaN   NaN   \n",
       "3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   NaN   NaN   NaN   \n",
       "4  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   0.0   0.0   0.0   \n",
       "\n",
       "   V333  V334  V335  V336  V337  V338  V339  \n",
       "0   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 339 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vcols = ['V'+str(x) for x in range(1,340)]\n",
    "train[Vcols].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train2 = train[Vcols].sample(frac=0.2)\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(train2[Vcols].corr(), cmap = 'RdBu_r',annot=False, center=0.0)\n",
    "plt.title('V1-V339 All')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the first 100 Vs correlated with each other while the last 239 Vs correlated with each other.\n",
    "\n",
    "Notice that V columns appear to be highly redundant and correlated. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similar NAN structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1      47.293494\n",
       "V2      47.293494\n",
       "V3      47.293494\n",
       "V4      47.293494\n",
       "V5      47.293494\n",
       "          ...    \n",
       "V335    86.054967\n",
       "V336    86.054967\n",
       "V337    86.054967\n",
       "V338    86.054967\n",
       "V339    86.054967\n",
       "Length: 339, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*train[['V'+str(i) for i in range(1,340)]].isna().sum()/train.shape[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We can find groups of V columns that shared similar NAN structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "nan_df = train.isna()\n",
    "nan_groups = {} # dictionary containing colunms with the same sum of nan\n",
    "for c in Vcols:\n",
    "    # sum of nan of each columns\n",
    "    col_sum = nan_df[c].sum()\n",
    "    if col_sum in nan_groups.keys():\n",
    "        nan_groups[col_sum].append(c)\n",
    "    else:\n",
    "        nan_groups[col_sum] = [c]\n",
    "del nan_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 15 groups.\n"
     ]
    }
   ],
   "source": [
    "print('There are ' + str(len(list(nan_groups.keys())))+ ' groups.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAN count 279287\n",
      "['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11']\n",
      "NAN count 76073\n",
      "['V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 'V30', 'V31', 'V32', 'V33', 'V34']\n",
      "NAN count 168969\n",
      "['V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52']\n",
      "NAN count 77096\n",
      "['V53', 'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V65', 'V66', 'V67', 'V68', 'V69', 'V70', 'V71', 'V72', 'V73', 'V74']\n",
      "NAN count 89164\n",
      "['V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94']\n",
      "NAN count 314\n",
      "['V95', 'V96', 'V97', 'V98', 'V99', 'V100', 'V101', 'V102', 'V103', 'V104', 'V105', 'V106', 'V107', 'V108', 'V109', 'V110', 'V111', 'V112', 'V113', 'V114', 'V115', 'V116', 'V117', 'V118', 'V119', 'V120', 'V121', 'V122', 'V123', 'V124', 'V125', 'V126', 'V127', 'V128', 'V129', 'V130', 'V131', 'V132', 'V133', 'V134', 'V135', 'V136', 'V137']\n",
      "NAN count 508595\n",
      "['V138', 'V139', 'V140', 'V141', 'V142', 'V146', 'V147', 'V148', 'V149', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V161', 'V162', 'V163']\n",
      "NAN count 508589\n",
      "['V143', 'V144', 'V145', 'V150', 'V151', 'V152', 'V159', 'V160', 'V164', 'V165', 'V166']\n",
      "NAN count 450909\n",
      "['V167', 'V168', 'V172', 'V173', 'V176', 'V177', 'V178', 'V179', 'V181', 'V182', 'V183', 'V186', 'V187', 'V190', 'V191', 'V192', 'V193', 'V196', 'V199', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216']\n",
      "NAN count 450721\n",
      "['V169', 'V170', 'V171', 'V174', 'V175', 'V180', 'V184', 'V185', 'V188', 'V189', 'V194', 'V195', 'V197', 'V198', 'V200', 'V201', 'V208', 'V209', 'V210']\n",
      "NAN count 460110\n",
      "['V217', 'V218', 'V219', 'V223', 'V224', 'V225', 'V226', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V235', 'V236', 'V237', 'V240', 'V241', 'V242', 'V243', 'V244', 'V246', 'V247', 'V248', 'V249', 'V252', 'V253', 'V254', 'V257', 'V258', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V269', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278']\n",
      "NAN count 449124\n",
      "['V220', 'V221', 'V222', 'V227', 'V234', 'V238', 'V239', 'V245', 'V250', 'V251', 'V255', 'V256', 'V259', 'V270', 'V271', 'V272']\n",
      "NAN count 12\n",
      "['V279', 'V280', 'V284', 'V285', 'V286', 'V287', 'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V297', 'V298', 'V299', 'V302', 'V303', 'V304', 'V305', 'V306', 'V307', 'V308', 'V309', 'V310', 'V311', 'V312', 'V316', 'V317', 'V318', 'V319', 'V320', 'V321']\n",
      "NAN count 1269\n",
      "['V281', 'V282', 'V283', 'V288', 'V289', 'V296', 'V300', 'V301', 'V313', 'V314', 'V315']\n",
      "NAN count 508189\n",
      "['V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339']\n"
     ]
    }
   ],
   "source": [
    "for key,value in nan_groups.items():\n",
    "    print('NAN count',key)\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hitogram Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = list(nan_groups.keys())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1440 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at the distribution for each group\n",
    "sub_plots(nan_groups[ind]) # function to make subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1: Subset grouped by correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **group1:** ```['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11']```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a325c9990>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train[nan_groups[ind]].corr(),cmap = 'RdBu_r',annot=False,center=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>V1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032168</td>\n",
       "      <td>0.024830</td>\n",
       "      <td>0.014216</td>\n",
       "      <td>0.013619</td>\n",
       "      <td>0.032284</td>\n",
       "      <td>0.026016</td>\n",
       "      <td>0.040820</td>\n",
       "      <td>0.034009</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>0.006408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V2</th>\n",
       "      <td>0.032168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.773039</td>\n",
       "      <td>0.318554</td>\n",
       "      <td>0.305504</td>\n",
       "      <td>0.621459</td>\n",
       "      <td>0.496921</td>\n",
       "      <td>0.732730</td>\n",
       "      <td>0.618591</td>\n",
       "      <td>0.036884</td>\n",
       "      <td>0.037459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3</th>\n",
       "      <td>0.024830</td>\n",
       "      <td>0.773039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.243684</td>\n",
       "      <td>0.353767</td>\n",
       "      <td>0.472570</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.556536</td>\n",
       "      <td>0.653087</td>\n",
       "      <td>-0.005272</td>\n",
       "      <td>0.022911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V4</th>\n",
       "      <td>0.014216</td>\n",
       "      <td>0.318554</td>\n",
       "      <td>0.243684</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.915164</td>\n",
       "      <td>0.279596</td>\n",
       "      <td>0.221621</td>\n",
       "      <td>0.325873</td>\n",
       "      <td>0.273207</td>\n",
       "      <td>-0.013160</td>\n",
       "      <td>-0.016662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5</th>\n",
       "      <td>0.013619</td>\n",
       "      <td>0.305504</td>\n",
       "      <td>0.353767</td>\n",
       "      <td>0.915164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.261490</td>\n",
       "      <td>0.294447</td>\n",
       "      <td>0.308716</td>\n",
       "      <td>0.354257</td>\n",
       "      <td>-0.035405</td>\n",
       "      <td>-0.021949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V6</th>\n",
       "      <td>0.032284</td>\n",
       "      <td>0.621459</td>\n",
       "      <td>0.472570</td>\n",
       "      <td>0.279596</td>\n",
       "      <td>0.261490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.796027</td>\n",
       "      <td>0.675501</td>\n",
       "      <td>0.561332</td>\n",
       "      <td>0.040767</td>\n",
       "      <td>0.038673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V7</th>\n",
       "      <td>0.026016</td>\n",
       "      <td>0.496921</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.221621</td>\n",
       "      <td>0.294447</td>\n",
       "      <td>0.796027</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.538586</td>\n",
       "      <td>0.594988</td>\n",
       "      <td>0.014593</td>\n",
       "      <td>0.039807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V8</th>\n",
       "      <td>0.040820</td>\n",
       "      <td>0.732730</td>\n",
       "      <td>0.556536</td>\n",
       "      <td>0.325873</td>\n",
       "      <td>0.308716</td>\n",
       "      <td>0.675501</td>\n",
       "      <td>0.538586</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.835342</td>\n",
       "      <td>0.061869</td>\n",
       "      <td>0.058033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V9</th>\n",
       "      <td>0.034009</td>\n",
       "      <td>0.618591</td>\n",
       "      <td>0.653087</td>\n",
       "      <td>0.273207</td>\n",
       "      <td>0.354257</td>\n",
       "      <td>0.561332</td>\n",
       "      <td>0.594988</td>\n",
       "      <td>0.835342</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034524</td>\n",
       "      <td>0.055620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V10</th>\n",
       "      <td>0.006574</td>\n",
       "      <td>0.036884</td>\n",
       "      <td>-0.005272</td>\n",
       "      <td>-0.013160</td>\n",
       "      <td>-0.035405</td>\n",
       "      <td>0.040767</td>\n",
       "      <td>0.014593</td>\n",
       "      <td>0.061869</td>\n",
       "      <td>0.034524</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.968670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V11</th>\n",
       "      <td>0.006408</td>\n",
       "      <td>0.037459</td>\n",
       "      <td>0.022911</td>\n",
       "      <td>-0.016662</td>\n",
       "      <td>-0.021949</td>\n",
       "      <td>0.038673</td>\n",
       "      <td>0.039807</td>\n",
       "      <td>0.058033</td>\n",
       "      <td>0.055620</td>\n",
       "      <td>0.968670</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           V1        V2        V3        V4        V5        V6        V7  \\\n",
       "V1   1.000000  0.032168  0.024830  0.014216  0.013619  0.032284  0.026016   \n",
       "V2   0.032168  1.000000  0.773039  0.318554  0.305504  0.621459  0.496921   \n",
       "V3   0.024830  0.773039  1.000000  0.243684  0.353767  0.472570  0.546875   \n",
       "V4   0.014216  0.318554  0.243684  1.000000  0.915164  0.279596  0.221621   \n",
       "V5   0.013619  0.305504  0.353767  0.915164  1.000000  0.261490  0.294447   \n",
       "V6   0.032284  0.621459  0.472570  0.279596  0.261490  1.000000  0.796027   \n",
       "V7   0.026016  0.496921  0.546875  0.221621  0.294447  0.796027  1.000000   \n",
       "V8   0.040820  0.732730  0.556536  0.325873  0.308716  0.675501  0.538586   \n",
       "V9   0.034009  0.618591  0.653087  0.273207  0.354257  0.561332  0.594988   \n",
       "V10  0.006574  0.036884 -0.005272 -0.013160 -0.035405  0.040767  0.014593   \n",
       "V11  0.006408  0.037459  0.022911 -0.016662 -0.021949  0.038673  0.039807   \n",
       "\n",
       "           V8        V9       V10       V11  \n",
       "V1   0.040820  0.034009  0.006574  0.006408  \n",
       "V2   0.732730  0.618591  0.036884  0.037459  \n",
       "V3   0.556536  0.653087 -0.005272  0.022911  \n",
       "V4   0.325873  0.273207 -0.013160 -0.016662  \n",
       "V5   0.308716  0.354257 -0.035405 -0.021949  \n",
       "V6   0.675501  0.561332  0.040767  0.038673  \n",
       "V7   0.538586  0.594988  0.014593  0.039807  \n",
       "V8   1.000000  0.835342  0.061869  0.058033  \n",
       "V9   0.835342  1.000000  0.034524  0.055620  \n",
       "V10  0.061869  0.034524  1.000000  0.968670  \n",
       "V11  0.058033  0.055620  0.968670  1.000000  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_group1 = train[nan_groups[ind]].corr()\n",
    "corr_group1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further group this group by correlation ```[[1],[2,3],[4,5],[6,7],[8,9],[10,11]]```. Thus we can replace V1 - V11 with ```[1, 3, 4, 6, 8, 11]```."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methond 2: PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V330</th>\n",
       "      <th>V331</th>\n",
       "      <th>V332</th>\n",
       "      <th>V333</th>\n",
       "      <th>V334</th>\n",
       "      <th>V335</th>\n",
       "      <th>V336</th>\n",
       "      <th>V337</th>\n",
       "      <th>V338</th>\n",
       "      <th>V339</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014107</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.008599</td>\n",
       "      <td>0.006341</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>0.000968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.999945</td>\n",
       "      <td>0.130651</td>\n",
       "      <td>0.119786</td>\n",
       "      <td>0.141076</td>\n",
       "      <td>0.146165</td>\n",
       "      <td>0.116187</td>\n",
       "      <td>0.119208</td>\n",
       "      <td>0.128463</td>\n",
       "      <td>0.130191</td>\n",
       "      <td>0.115979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014107</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.008599</td>\n",
       "      <td>0.006341</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>0.000968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014107</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.008599</td>\n",
       "      <td>0.006341</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>0.000968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.999945</td>\n",
       "      <td>0.130651</td>\n",
       "      <td>0.119786</td>\n",
       "      <td>0.141076</td>\n",
       "      <td>0.146165</td>\n",
       "      <td>0.116187</td>\n",
       "      <td>0.119208</td>\n",
       "      <td>0.128463</td>\n",
       "      <td>0.130191</td>\n",
       "      <td>0.115979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014107</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.008599</td>\n",
       "      <td>0.006341</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>0.000968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.999945</td>\n",
       "      <td>0.130651</td>\n",
       "      <td>0.119786</td>\n",
       "      <td>0.141076</td>\n",
       "      <td>0.146165</td>\n",
       "      <td>0.116187</td>\n",
       "      <td>0.119208</td>\n",
       "      <td>0.128463</td>\n",
       "      <td>0.130191</td>\n",
       "      <td>0.115979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 339 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0  1.000000  0.125000  0.111111  0.166667  0.166667  0.111111  0.111111   \n",
       "1  0.999945  0.130651  0.119786  0.141076  0.146165  0.116187  0.119208   \n",
       "2  1.000000  0.125000  0.111111  0.166667  0.166667  0.111111  0.111111   \n",
       "3  0.999945  0.130651  0.119786  0.141076  0.146165  0.116187  0.119208   \n",
       "4  0.999945  0.130651  0.119786  0.141076  0.146165  0.116187  0.119208   \n",
       "\n",
       "         V8        V9       V10  ...      V330      V331      V332      V333  \\\n",
       "0  0.125000  0.125000  0.000000  ...  0.014107  0.004511  0.008599  0.006341   \n",
       "1  0.128463  0.130191  0.115979  ...  0.014107  0.004511  0.008599  0.006341   \n",
       "2  0.125000  0.125000  0.000000  ...  0.014107  0.004511  0.008599  0.006341   \n",
       "3  0.128463  0.130191  0.115979  ...  0.014107  0.004511  0.008599  0.006341   \n",
       "4  0.128463  0.130191  0.115979  ...  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "       V334      V335      V336      V337      V338      V339  \n",
       "0  0.000178  0.001073  0.000518  0.000532  0.001453  0.000968  \n",
       "1  0.000178  0.001073  0.000518  0.000532  0.001453  0.000968  \n",
       "2  0.000178  0.001073  0.000518  0.000532  0.001453  0.000968  \n",
       "3  0.000178  0.001073  0.000518  0.000532  0.001453  0.000968  \n",
       "4  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "\n",
       "[5 rows x 339 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean impute\n",
    "pca_train = pd.DataFrame(train[Vcols].fillna(train[Vcols].mean()))\n",
    "pca_test = pd.DataFrame(test[Vcols].fillna(train[Vcols].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardilize\n",
    "scaler = MinMaxScaler()\n",
    "pca_train = scaler.fit_transform(pca_train)\n",
    "pca_test =  scaler.transform(pca_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_train = pd.DataFrame(pca_train)\n",
    "pca_test = pd.DataFrame(pca_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pca_train.columns = ['V'+str(i) for i in range(1,340)]\n",
    "pca_test.columns = ['V'+str(i) for i in range(1,340)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pca \n",
    "# select the number of components such that more than 95% of variance is explained.\n",
    "pca = PCA(n_components = 0.95)\n",
    "\n",
    "# Dataframe stores pca\n",
    "reduced_train_Vs = pd.DataFrame(train['V1'])\n",
    "reduced_test_Vs = pd.DataFrame(test['V1'])\n",
    "\n",
    "for i in list(nan_groups.keys()):\n",
    "    # get each group\n",
    "    train_sub = pca_train[nan_groups[i]]\n",
    "    test_sub = pca_test[nan_groups[i]]\n",
    "    \n",
    "    # get pca for each group\n",
    "    train_compon= pca.fit_transform(train_sub)\n",
    "    # store as dataframe\n",
    "    train_compon = pd.DataFrame(train_compon)\n",
    "    # merger to pca dataframe\n",
    "    reduced_train_Vs = reduced_train_Vs.merge(train_compon,left_index=True,right_index=True)\n",
    "    \n",
    "    # get pca for test set\n",
    "    test_compon= pca.transform(test_sub)\n",
    "    # store as dataframe\n",
    "    test_compon = pd.DataFrame(test_compon)\n",
    "    # merge to pca dataframe(test)\n",
    "    reduced_test_Vs = reduced_test_Vs.merge(train_compon,left_index=True,right_index=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduced_train_Vs.to_csv('./train_reduced_V.csv')\n",
    "#reduced_test_Vs.to_csv('./test_reduced_V.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "reduced_train_Vs = pd.read_csv('./train_reduced_V.csv')\n",
    "reduced_test_Vs = pd.read_csv('./test_reduced_V.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unrelevent columns\n",
    "reduced_train_Vs = reduced_train_Vs.drop(['V1','Unnamed: 0'],axis = 1)\n",
    "reduced_test_Vs = reduced_test_Vs.drop(['V1','Unnamed: 0'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename pca components\n",
    "reduced_train_Vs.columns = ['PC'+str(i+1) for i in range(len(reduced_train_Vs.columns))]\n",
    "reduced_test_Vs.columns = ['PC'+str(i+1) for i in range(len(reduced_test_Vs.columns))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original set has 339 V columns.\n",
      "Reduced training set has 93 V columns.\n",
      "Reduced testing set has 93 V columns.\n"
     ]
    }
   ],
   "source": [
    "print('Original set has '+str(len(Vcols))+ ' V columns.')\n",
    "print('Reduced training set has '+str(reduced_train_Vs.shape[1])+ ' V columns.')\n",
    "print('Reduced testing set has '+str(reduced_train_Vs.shape[1])+ ' V columns.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_red = reduced_train_Vs.sample(frac=0.2)\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(train_red.corr(), cmap = 'RdBu_r',annot=False, center=0.0)\n",
    "plt.title('V1-V339 Reduced')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We can see that the reduced set has less internal correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 93\n"
     ]
    }
   ],
   "source": [
    "# remove V columns\n",
    "Vcols = ['V'+str(x) for x in range(1,340)]\n",
    "remove_from_Feature_list(Vcols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(590540, 528)\n",
      "(506691, 528)\n"
     ]
    }
   ],
   "source": [
    "# Concatenate PCA components\n",
    "X_train=pd.concat([X_train, reduced_train_Vs], axis=1)\n",
    "X_test = pd.concat([X_test, reduced_test_Vs], axis=1)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 186\n"
     ]
    }
   ],
   "source": [
    "add_to_features_list(list(reduced_train_Vs.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adversarial Validation\n",
    "From EDA, we found that the training set contains some features have lots of unique values that only appear in the training set or test set.```card1, addr1, dist1, dist2, id_02, id_11, id_19, id_20, id_21, id_25, id_31, id_33, DeviceInfo```   \n",
    "Thus, we konw that the traning and test set have different set of cards. We can find features that are important in differentiating cards by performing adversarial validation using data with PCA columns.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 1: set label\n",
    "X_train['is_test'] = 0\n",
    "X_test['is_test'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 78\n"
     ]
    }
   ],
   "source": [
    "features_without_PCA = x_features[:]\n",
    "remove_from_Feature_list(Vcols,features_without_PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_without_PCA += ['is_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 2: concatenate both frames and shuffle the rows\n",
    "data = pd.concat([X_train[features_without_PCA], X_test[features_without_PCA]])\n",
    "\n",
    "#replace nan with -999\n",
    "data.fillna(-999,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split X, y\n",
    "y_adv = data['is_test']\n",
    "x_adv = data.drop(['is_test'], axis = 1)\n",
    "\n",
    "x_adv_train, x_adv_test, y_adv_train, y_adv_test = train_test_split(x_adv, y_adv, \n",
    "                                                                    train_size = 0.3,random_state = seed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_without_PCA.remove('is_test')\n",
    "# standardize D cols\n",
    "for i in range(1,16):\n",
    "    x_adv_train['D'+str(i)+'n'] = x_adv_train['D'+str(i)] - x_adv_train['TransactionDT']/(24*60*60)\n",
    "    x_adv_test['D'+str(i)+'n'] = x_adv_test['D'+str(i)] -x_adv_test['TransactionDT']/(24*60*60)\n",
    "Dncols = ['D'+str(i)+'n' for i in range(1,16)]\n",
    "add_to_features_list(Dncols, features_without_PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = []\n",
    "for i in features_without_PCA:\n",
    "    if x_adv_test[i].dtype =='object' or x_adv_train[i].dtype == 'object' :\n",
    "        cat_cols.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ProductCD', 'card4', 'card6', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6',\n",
       "       'M7', 'M8', 'M9', 'id_12', 'id_15', 'id_16', 'id_28', 'id_29',\n",
       "       'id_30', 'id_31', 'id_34', 'id_35', 'id_36', 'id_37', 'id_38',\n",
       "       'DeviceType', 'DeviceInfo', 'P_emaildomain_bin',\n",
       "       'R_emaildomain_bin'], dtype='<U17')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TransactionDT', 'TransactionAmt', 'ProductCD', 'card1', 'card2',\n",
       "       'card3', 'card4', 'card5', 'card6', 'addr1', 'addr2', 'dist1',\n",
       "       'dist2', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9',\n",
       "       'C10', 'C11', 'C12', 'C13', 'C14', 'D1', 'D2', 'D3', 'D4', 'D5',\n",
       "       'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15',\n",
       "       'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'id_01',\n",
       "       'id_02', 'id_05', 'id_06', 'id_11', 'id_12', 'id_13', 'id_14',\n",
       "       'id_15', 'id_16', 'id_17', 'id_19', 'id_20', 'id_28', 'id_29',\n",
       "       'id_30', 'id_31', 'id_32', 'id_34', 'id_35', 'id_36', 'id_37',\n",
       "       'id_38', 'DeviceType', 'DeviceInfo', 'P_emaildomain_bin',\n",
       "       'R_emaildomain_bin', 'D1n', 'D2n', 'D3n', 'D4n', 'D5n', 'D6n',\n",
       "       'D7n', 'D8n', 'D9n', 'D10n', 'D11n', 'D12n', 'D13n', 'D14n',\n",
       "       'D15n'], dtype='<U17')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(features_without_PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = Pool(\n",
    "    data=x_adv_train,\n",
    "    label=y_adv_train,\n",
    "    cat_features=cat_cols,\n",
    ")\n",
    "holdout_data = Pool(\n",
    "    data=x_adv_test,\n",
    "    label=y_adv_test,\n",
    "    cat_features=cat_cols,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'iterations': 100,\n",
       " 'eval_metric': 'AUC',\n",
       " 'od_type': 'Iter',\n",
       " 'od_wait': 50,\n",
       " 'verbose': False,\n",
       " 'random_seed': 2019,\n",
       " 'ignored_features': ['D1n',\n",
       "  'D2n',\n",
       "  'D3n',\n",
       "  'D4n',\n",
       "  'D5n',\n",
       "  'D6n',\n",
       "  'D7n',\n",
       "  'D8n',\n",
       "  'D9n',\n",
       "  'D10n',\n",
       "  'D11n',\n",
       "  'D12n',\n",
       "  'D13n',\n",
       "  'D14n',\n",
       "  'D15n']}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'iterations': 100,\n",
    "    'eval_metric': 'AUC',\n",
    "    'od_type': 'Iter',\n",
    "    'od_wait': 50,\n",
    "    'verbose': False,\n",
    "    'random_seed': seed,\n",
    "    \"ignored_features\": Dncols\n",
    "}\n",
    "\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x2c2934e3dc8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_adv = CatBoostClassifier(**params)\n",
    "cat_adv.fit(train_data, eval_set=holdout_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learn': {'Logloss': 0.0023062295570092793},\n",
       " 'validation': {'Logloss': 0.00230199615766531, 'AUC': 0.999995574264964}}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_adv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c290416ec8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_score = cat_adv.get_feature_importance(prettified=True)\n",
    "feature_score[:20].sort_values('Importances').plot(\n",
    "    'Feature Id', 'Importances', kind='barh', color='skyblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "remove version  \n",
    "ignore TransactionDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove version\n",
    "\n",
    "def remove_version(train, test, feature):\n",
    "    train.loc[:, feature] = train[feature].str.replace(r'[^A-Za-z]', '', regex=True)\n",
    "    test.loc[:, feature] = test[feature].str.replace(r'[^A-Za-z]', '', regex=True)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\14807\\Anaconda3\\envs\\py37\\lib\\site-packages\\pandas\\core\\indexing.py:966: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "remove_version(x_adv_train,x_adv_test,'id_31')\n",
    "remove_version(x_adv_train,x_adv_test,'id_30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'iterations': 100,\n",
       " 'eval_metric': 'AUC',\n",
       " 'od_type': 'Iter',\n",
       " 'od_wait': 50,\n",
       " 'verbose': False,\n",
       " 'random_seed': 2019,\n",
       " 'ignored_features': ['TransactionDT',\n",
       "  'D1n',\n",
       "  'D2n',\n",
       "  'D3n',\n",
       "  'D4n',\n",
       "  'D5n',\n",
       "  'D6n',\n",
       "  'D7n',\n",
       "  'D8n',\n",
       "  'D9n',\n",
       "  'D10n',\n",
       "  'D11n',\n",
       "  'D12n',\n",
       "  'D13n',\n",
       "  'D14n',\n",
       "  'D15n']}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# update params2, ignore TransactionDT\n",
    "params2 = dict(params)\n",
    "params2.update({\"ignored_features\": ['TransactionDT']+Dncols})\n",
    "params2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x2c3af8143c8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_adv2 = CatBoostClassifier(**params2)\n",
    "cat_adv2.fit(train_data, eval_set=holdout_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learn': {'Logloss': 0.3700329796141567},\n",
       " 'validation': {'Logloss': 0.3747348031217162, 'AUC': 0.9020945922658941}}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_adv2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c3af819648>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_score2 = cat_adv2.get_feature_importance(prettified=True)\n",
    "feature_score2[:20].sort_values('Importances').plot(\n",
    "    'Feature Id', 'Importances', kind='barh', color='skyblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "add Dn cols  \n",
    "ignore TransactionDT  \n",
    "remove version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'iterations': 100,\n",
       " 'eval_metric': 'AUC',\n",
       " 'od_type': 'Iter',\n",
       " 'od_wait': 50,\n",
       " 'verbose': False,\n",
       " 'random_seed': 2019,\n",
       " 'ignored_features': ['TransactionDT']}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params3 = dict(params2)\n",
    "params3.update({\"ignored_features\": ['TransactionDT']})\n",
    "params3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x2c3aef9a348>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_adv3 = CatBoostClassifier(**params3)\n",
    "cat_adv3.fit(train_data, eval_set=holdout_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learn': {'Logloss': 0.00034802992931631544},\n",
       " 'validation': {'Logloss': 0.0004370931434627594, 'AUC': 0.9999999131102739}}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_adv3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c3aefa6708>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_score3 = cat_adv3.get_feature_importance(prettified=True)\n",
    "feature_score3[:20].sort_values('Importances').plot(\n",
    "    'Feature Id', 'Importances', kind='barh', color='skyblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ignore D, Dn cols  \n",
    "ignore TransactionDT   \n",
    "remove version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D1', 'D2', 'D3', 'D4', 'D5', 'D10', 'D11', 'D15']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc = []\n",
    "for i in ['D'+str(i) for i in range(1,16)]:\n",
    "    if i in ['D6','D7','D8','D9','D12','D14','D13']:\n",
    "        continue\n",
    "    else:\n",
    "        dc.append(i)\n",
    "dc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'iterations': 100,\n",
       " 'eval_metric': 'AUC',\n",
       " 'od_type': 'Iter',\n",
       " 'od_wait': 50,\n",
       " 'verbose': False,\n",
       " 'random_seed': 2019,\n",
       " 'ignored_features': ['TransactionDT',\n",
       "  'D1n',\n",
       "  'D2n',\n",
       "  'D3n',\n",
       "  'D4n',\n",
       "  'D5n',\n",
       "  'D6n',\n",
       "  'D7n',\n",
       "  'D8n',\n",
       "  'D9n',\n",
       "  'D10n',\n",
       "  'D11n',\n",
       "  'D12n',\n",
       "  'D13n',\n",
       "  'D14n',\n",
       "  'D15n',\n",
       "  'D1',\n",
       "  'D2',\n",
       "  'D3',\n",
       "  'D4',\n",
       "  'D5',\n",
       "  'D10',\n",
       "  'D11',\n",
       "  'D15']}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params4 = dict(params3)\n",
    "params4.update({\"ignored_features\": ['TransactionDT']+Dncols+dc})\n",
    "params4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x2c3af00ab08>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_adv4 = CatBoostClassifier(**params4)\n",
    "cat_adv4.fit(train_data, eval_set=holdout_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learn': {'Logloss': 0.4426856410660547},\n",
       " 'validation': {'Logloss': 0.44575677668664954, 'AUC': 0.8560384707304748}}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_adv4.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c3af02bc88>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2160x2160 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "feature_score4 = cat_adv4.get_feature_importance(prettified=True)\n",
    "feature_score4[:20].sort_values('Importances').plot(\n",
    "    'Feature Id', 'Importances', kind='barh', color='skyblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concolusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ignore D & Dn cols')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d1 = feature_score[:20]\n",
    "d2 = feature_score2[:20]\n",
    "d3 = feature_score3[:20]\n",
    "d4 = feature_score4[:20]\n",
    "\n",
    "plt.figure(figsize=(16,12))\n",
    "\n",
    "plt.subplot(221)\n",
    "sns.barplot(x=\"Importances\", y=\"Feature Id\", data=d1,color='skyblue')\n",
    "plt.title(\"All\",fontsize=15)\n",
    "\n",
    "plt.subplot(222)\n",
    "sns.barplot(x=\"Importances\", y=\"Feature Id\", data=d2,color='skyblue')\n",
    "plt.title(\"Ignore transactionDT & remove version\",fontsize=15)\n",
    "\n",
    "plt.subplot(223)\n",
    "sns.barplot(x=\"Importances\", y=\"Feature Id\", data=d3,color='skyblue')\n",
    "plt.title('Add Dn cols',fontsize=20)\n",
    "\n",
    "plt.subplot(224)\n",
    "sns.barplot(x=\"Importances\", y=\"Feature Id\", data=d4,color='skyblue')\n",
    "plt.title('Ignore D & Dn cols',fontsize=20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All: AUC = 0.999995574264964\n",
      "Removing TransactionDT & Version: AUC = 0.9020945922658941\n",
      "Add Normalized D columns: AUC = 0.9999999131102739\n",
      "Removing D & Dn columns: AUC = 0.8560384707304748\n"
     ]
    }
   ],
   "source": [
    "print('All: AUC =',cat_adv.best_score_['validation']['AUC'])\n",
    "print('Removing TransactionDT & Version: AUC =',cat_adv2.best_score_['validation']['AUC'])\n",
    "print('Add Normalized D columns: AUC =',cat_adv3.best_score_['validation']['AUC'])\n",
    "print('Removing D & Dn columns: AUC =',cat_adv4.best_score_['validation']['AUC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, we can see that normalized D columns are very important in differentiating training data and test data. TransactionDT and version should be removed. Most important columns to identify users are D10n, D1n, D15n, C13, D4n, card1, D2n, card2, addr1, TransactionAmt, and dist1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove software version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "id_30 and id_31 contain software version. The data is collect by time. Version is related to time, indicating that test set contains version that have not been seen in the training set. Thus, we should remove version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove version\n",
    "\n",
    "def remove_version(train, test, feature):\n",
    "    train.loc[:, feature] = train[feature].str.replace(r'[^A-Za-z]', '', regex=True)\n",
    "    test.loc[:, feature] = test[feature].str.replace(r'[^A-Za-z]', '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_version(X_train, X_test, 'id_31')\n",
    "remove_version(X_train, X_test, 'id_30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id_30 Index(['Windows', 'iOS', 'MacOSX', 'Android', 'Linux', 'Mac', 'WindowsVista',\n",
      "       'WindowsXP', 'other', 'func'],\n",
      "      dtype='object')\n",
      "id_31 Index(['chrome', 'chromeforandroid', 'mobilesafari', 'mobilesafarigeneric',\n",
      "       'iefordesktop', 'safarigeneric', 'firefox', 'edge', 'chromegeneric',\n",
      "       'samsungbrowser'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "for i in ['id_30','id_31']:\n",
    "    print(i, X_train[i].value_counts().index[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove TransactionDT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TransactionDT is timedelta from the transaction day, therefore the test set contain transactionDT that has not been seen in the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 185\n"
     ]
    }
   ],
   "source": [
    "remove_from_Feature_list(['TransactionDT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize D columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "D columns are timedelta from a given reference datetime.\n",
    "Transform the D columns into their points in the past by ```D15n = Transaction_Day - D15``` and ```Transaction_Day = TransactionDT/(24*60*60). ```\n",
    "The normalized D columns are specified time in the past that something happend.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# most important cols:['D1n','D3n','D10n','D5','D11','D15','D2','D4n']\n",
    "\n",
    "for col in range(1,16):\n",
    "    if col in [2,5,11]: \n",
    "        continue\n",
    "    else:   \n",
    "        ori_nm = 'D'+str(col)\n",
    "        nm ='D'+str(col)+'n'\n",
    "        X_train[nm] = X_train[ori_nm] - X_train['TransactionDT']/(24*3600)\n",
    "        X_test[nm] = X_test[ori_nm] - X_test['TransactionDT']/(24*3600)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 178\n"
     ]
    }
   ],
   "source": [
    "# remove cols with nan% > 87% \n",
    "remove_from_Feature_list(['D6','D7','D8','D9','D12','D14','D13'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fe_CB(col1, col2, df1 = X_train, df2 = X_test):\n",
    "    '''\n",
    "    Combine two cols as new string col\n",
    "    '''\n",
    "    nm = col1+'_'+col2\n",
    "    df1[nm] = df1[col1].astype(str)+'_'+df1[col2].astype(str)\n",
    "    df2[nm] = df2[col1].astype(str)+'_'+df2[col2].astype(str)\n",
    "    \n",
    "    print(nm,', ', end = '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency Encoding \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fe_FE(cols, df1 = X_train, df2 = X_test):\n",
    "    for col in cols:\n",
    "        freq_map1 = df1[col].value_counts(normalize = True).to_dict()\n",
    "        freq_map2 = df2[col].value_counts(normalize = True).to_dict()\n",
    "        nm = col+'_FE'\n",
    "        df1[nm] = df1[col].map(freq_map1)\n",
    "        df2[nm] = df2[col].map(freq_map2)\n",
    "        print(nm, ', ', end = '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace all frequency encoded features' values that appear less than 0.1% with a new value\n",
    "\n",
    "def replace_rare_FE(cols, val,df1 = X_train, df2 = X_test):\n",
    "    for col in cols:\n",
    "        df1[col][df1[col] < val] = -888\n",
    "        df2[col][df2[col] < val] = -888\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregations & Group Statistics \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fe_AG(uids, t_cols, aggregations, df1 = X_train, df2 = X_test):\n",
    "    '''\n",
    "    GROUP AGGREGATION mean, std\n",
    "    '''\n",
    "    for col in t_cols:\n",
    "        for i in uids:\n",
    "            for agg_tpye in aggregations:\n",
    "                new_col_name = col+'_'+i+'_'+agg_tpye\n",
    "                \n",
    "                temp_df_train = df1[[col,i]]\n",
    "                temp_df_test = df2[[col,i]]\n",
    "                    \n",
    "                #group by uid, get mean of col for each uid\n",
    "                map1 = temp_df_train.groupby(i)[col].agg(agg_tpye).to_dict()\n",
    "                map2 = temp_df_test.groupby(i)[col].agg(agg_tpye).to_dict()\n",
    "                \n",
    "                df1[new_col_name] = df1[i].map(map1).astype('float32')\n",
    "                df2[new_col_name]  = df2[i].map(map2).astype('float32')\n",
    "                \n",
    "                print(new_col_name,', ',end = '')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fe_AG2(uids, t_cols, df1 = X_train, df2 = X_test):\n",
    "    '''\n",
    "    GROUP AGGREGATION nunique\n",
    "    '''\n",
    "    for col in t_cols:\n",
    "        for i in uids:\n",
    "            temp_df1 = df1[[col,i]]\n",
    "            temp_df2 = df2[[col,i]]\n",
    "            \n",
    "            # group by uid, get # of distinct value of col for each group\n",
    "            # set uid as index, to_dict then index will be key\n",
    "            mp1 = temp_df1.groupby(i)[col].agg('nunique').to_dict()\n",
    "            mp2 = temp_df2.groupby(i)[col].agg('nunique').to_dict()\n",
    "            \n",
    "            nm = col+'_'+i+'_'+'ct'\n",
    "            \n",
    "            df1[nm] = df1[i].map(mp1).astype('float32')\n",
    "            df2[nm] = df2[i].map(mp2).astype('float32')\n",
    "            \n",
    "            print(nm,', ',end = '')\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform TransactionDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "START_DATE = datetime.datetime.strptime('2017-11-30', '%Y-%m-%d')\n",
    "X_train['DT_M'] = X_train['TransactionDT'].apply(lambda x: (START_DATE + datetime.timedelta(seconds = x)))\n",
    "X_train['DT_M'] = (X_train['DT_M'].dt.year-2017)*12 + X_train['DT_M'].dt.month \n",
    "\n",
    "X_test['DT_M'] = X_test['TransactionDT'].apply(lambda x: (START_DATE + datetime.timedelta(seconds = x)))\n",
    "X_test['DT_M'] = (X_test['DT_M'].dt.year-2017)*12 + X_test['DT_M'].dt.month "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "card1_addr1 , card1_addr1_P_emaildomain_bin , card1_addr1_D1n , "
     ]
    }
   ],
   "source": [
    "# Combine columns to create a new feature(UID) that can help model find different clients (credit cards).\n",
    "\n",
    "fe_CB('card1','addr1')\n",
    "fe_CB('card1_addr1','P_emaildomain_bin')\n",
    "fe_CB('card1_addr1','D1n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 181\n"
     ]
    }
   ],
   "source": [
    "add_to_features_list(['card1_addr1', 'card1_addr1_P_emaildomain_bin', 'card1_addr1_D1n'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Frequency Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Frequency Encoding\n",
    "# # Frequency encoding for UID fe_FE(['card1_addr1_D1n'])\n",
    "\n",
    "cat_cols = []\n",
    "for i in x_features:\n",
    "    if X_train[i].dtype=='object' or X_test[i].dtype=='object':\n",
    "        cat_cols.append(i)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols += ['addr1','addr2','card1','card2','card3','card5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProductCD_FE , card4_FE , card6_FE , M1_FE , M2_FE , M3_FE , M4_FE , M5_FE , M6_FE , M7_FE , M8_FE , M9_FE , id_12_FE , id_15_FE , id_16_FE , id_23_FE , id_27_FE , id_28_FE , id_29_FE , id_30_FE , id_31_FE , id_33_FE , id_34_FE , id_35_FE , id_36_FE , id_37_FE , id_38_FE , DeviceType_FE , DeviceInfo_FE , P_emaildomain_bin_FE , R_emaildomain_bin_FE , card1_addr1_FE , card1_addr1_P_emaildomain_bin_FE , card1_addr1_D1n_FE , addr1_FE , addr2_FE , card1_FE , card2_FE , card3_FE , card5_FE , "
     ]
    }
   ],
   "source": [
    "fe_FE(cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 221\n"
     ]
    }
   ],
   "source": [
    "dd = 'ProductCD_FE , card4_FE , card6_FE , M1_FE , M2_FE , M3_FE , M4_FE , M5_FE , M6_FE , M7_FE , M8_FE , M9_FE , id_12_FE , id_15_FE , id_16_FE , id_23_FE , id_27_FE , id_28_FE , id_29_FE , id_30_FE , id_31_FE , id_33_FE , id_34_FE , id_35_FE , id_36_FE , id_37_FE , id_38_FE , DeviceType_FE , DeviceInfo_FE , P_emaildomain_bin_FE , R_emaildomain_bin_FE , card1_addr1_FE , card1_addr1_P_emaildomain_FE , card1_addr1_D1n_FE , addr1_FE , addr2_FE , card1_FE , card2_FE , card3_FE , card5_FE'\n",
    "to_add_f = dd.split(' , ')\n",
    "add_to_features_list(to_add_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 181\n"
     ]
    }
   ],
   "source": [
    "remove_from_Feature_list(cat_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**group statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TransactionAmt_card1_mean , TransactionAmt_card1_std , TransactionAmt_card1_addr1_mean , TransactionAmt_card1_addr1_std , TransactionAmt_card1_addr1_P_emaildomain_bin_mean , TransactionAmt_card1_addr1_P_emaildomain_bin_std , D1n_card1_mean , D1n_card1_std , D1n_card1_addr1_mean , D1n_card1_addr1_std , D1n_card1_addr1_P_emaildomain_bin_mean , D1n_card1_addr1_P_emaildomain_bin_std , D11_card1_mean , D11_card1_std , D11_card1_addr1_mean , D11_card1_addr1_std , D11_card1_addr1_P_emaildomain_bin_mean , D11_card1_addr1_P_emaildomain_bin_std , TransactionAmt_card1_addr1_D1n_mean , TransactionAmt_card1_addr1_D1n_std , D4_card1_addr1_D1n_mean , D4_card1_addr1_D1n_std , D9_card1_addr1_D1n_mean , D9_card1_addr1_D1n_std , D10_card1_addr1_D1n_mean , D10_card1_addr1_D1n_std , D15_card1_addr1_D1n_mean , D15_card1_addr1_D1n_std , C1_card1_addr1_D1n_mean , C2_card1_addr1_D1n_mean , C4_card1_addr1_D1n_mean , C5_card1_addr1_D1n_mean , C6_card1_addr1_D1n_mean , C7_card1_addr1_D1n_mean , C8_card1_addr1_D1n_mean , C9_card1_addr1_D1n_mean , C10_card1_addr1_D1n_mean , C11_card1_addr1_D1n_mean , C12_card1_addr1_D1n_mean , C13_card1_addr1_D1n_mean , C14_card1_addr1_D1n_mean , M1_FE_card1_addr1_D1n_mean , M2_FE_card1_addr1_D1n_mean , M3_FE_card1_addr1_D1n_mean , M4_FE_card1_addr1_D1n_mean , M5_FE_card1_addr1_D1n_mean , M6_FE_card1_addr1_D1n_mean , M7_FE_card1_addr1_D1n_mean , M8_FE_card1_addr1_D1n_mean , M9_FE_card1_addr1_D1n_mean , P_emaildomain_bin_card1_addr1_D1n_ct , dist1_card1_addr1_D1n_ct , DT_M_card1_addr1_D1n_ct , id_02_card1_addr1_D1n_ct , C13_card1_addr1_D1n_ct , C14_card1_addr1_D1n_std , outsider15\n"
     ]
    }
   ],
   "source": [
    "fe_AG(uids = ['card1','card1_addr1','card1_addr1_P_emaildomain_bin'],\n",
    "      t_cols = ['TransactionAmt','D1n','D11'],\n",
    "      aggregations = ['mean','std'])\n",
    "\n",
    "fe_AG(uids = ['card1_addr1_D1n'],\n",
    "      t_cols = ['TransactionAmt','D4','D9','D10','D15'],\n",
    "      aggregations = ['mean','std'])\n",
    "\n",
    "fe_AG(['card1_addr1_D1n'], ['C'+str(x) for x in range(1,15) if x!=3], ['mean'])\n",
    "\n",
    "fe_AG(['card1_addr1_D1n'], ['M'+str(x)+'_FE' for x in range(1,10)], ['mean'])\n",
    "\n",
    "fe_AG(['card1_addr1_D1n'], ['C14'], ['std'])\n",
    "\n",
    "\n",
    "# card associate with too much emails, too much or too few transaction per month, different dist1, id_02, C13\n",
    "fe_AG2(['card1_addr1_D1n'], ['P_emaildomain_bin','dist1','DT_M','id_02'])\n",
    "\n",
    "fe_AG2(['card1_addr1_D1n'], ['C13'])\n",
    "           \n",
    "\n",
    "X_train['outsider15'] = (np.abs(X_train.D1-X_train.D15)>3).astype('int8')\n",
    "X_test['outsider15'] = (np.abs(X_test.D1-X_test.D15)>3).astype('int8')\n",
    "\n",
    "print('outsider15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2 = 'TransactionAmt_card1_mean , TransactionAmt_card1_std , TransactionAmt_card1_addr1_mean , TransactionAmt_card1_addr1_std , TransactionAmt_card1_addr1_P_emaildomain_mean , TransactionAmt_card1_addr1_P_emaildomain_std , D1n_card1_mean , D1n_card1_std , D1n_card1_addr1_mean , D1n_card1_addr1_std , D1n_card1_addr1_P_emaildomain_mean , D1n_card1_addr1_P_emaildomain_std , D11_card1_mean , D11_card1_std , D11_card1_addr1_mean , D11_card1_addr1_std , D11_card1_addr1_P_emaildomain_mean , D11_card1_addr1_P_emaildomain_std , TransactionAmt_card1_addr1_D1n_mean , TransactionAmt_card1_addr1_D1n_std , D4_card1_addr1_D1n_mean , D4_card1_addr1_D1n_std , D9_card1_addr1_D1n_mean , D9_card1_addr1_D1n_std , D10_card1_addr1_D1n_mean , D10_card1_addr1_D1n_std , D15_card1_addr1_D1n_mean , D15_card1_addr1_D1n_std , C1_card1_addr1_D1n_mean , C2_card1_addr1_D1n_mean , C4_card1_addr1_D1n_mean , C5_card1_addr1_D1n_mean , C6_card1_addr1_D1n_mean , C7_card1_addr1_D1n_mean , C8_card1_addr1_D1n_mean , C9_card1_addr1_D1n_mean , C10_card1_addr1_D1n_mean , C11_card1_addr1_D1n_mean , C12_card1_addr1_D1n_mean , C13_card1_addr1_D1n_mean , C14_card1_addr1_D1n_mean , M1_FE_card1_addr1_D1n_mean , M2_FE_card1_addr1_D1n_mean , M3_FE_card1_addr1_D1n_mean , M4_FE_card1_addr1_D1n_mean , M5_FE_card1_addr1_D1n_mean , M6_FE_card1_addr1_D1n_mean , M7_FE_card1_addr1_D1n_mean , M8_FE_card1_addr1_D1n_mean , M9_FE_card1_addr1_D1n_mean , P_emaildomain_bin_card1_addr1_D1n_ct , dist1_card1_addr1_D1n_ct , DT_M_card1_addr1_D1n_ct , id_02_card1_addr1_D1n_ct , C13_card1_addr1_D1n_ct , C14_card1_addr1_D1n_std , outsider15'\n",
    "to_add2= d2.split(' , ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of features = 238\n"
     ]
    }
   ],
   "source": [
    "add_to_features_list(to_add2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total 100 new features added\n"
     ]
    }
   ],
   "source": [
    "print('Total',3+len(to_add_f)+len(to_add2),'new features added')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOW USING THE FOLLOWING 238 FEATURES.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['TransactionAmt', 'dist1', 'dist2', 'C1', 'C2', 'C3', 'C4', 'C5',\n",
       "       'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13', 'C14', 'D1',\n",
       "       'D2', 'D3', 'D4', 'D5', 'D10', 'D11', 'D15', 'id_01', 'id_02',\n",
       "       'id_03', 'id_04', 'id_05', 'id_06', 'id_07', 'id_08', 'id_09',\n",
       "       'id_10', 'id_11', 'id_13', 'id_14', 'id_17', 'id_18', 'id_19',\n",
       "       'id_20', 'id_21', 'id_22', 'id_24', 'id_25', 'id_26', 'id_32',\n",
       "       'PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7', 'PC8', 'PC9',\n",
       "       'PC10', 'PC11', 'PC12', 'PC13', 'PC14', 'PC15', 'PC16', 'PC17',\n",
       "       'PC18', 'PC19', 'PC20', 'PC21', 'PC22', 'PC23', 'PC24', 'PC25',\n",
       "       'PC26', 'PC27', 'PC28', 'PC29', 'PC30', 'PC31', 'PC32', 'PC33',\n",
       "       'PC34', 'PC35', 'PC36', 'PC37', 'PC38', 'PC39', 'PC40', 'PC41',\n",
       "       'PC42', 'PC43', 'PC44', 'PC45', 'PC46', 'PC47', 'PC48', 'PC49',\n",
       "       'PC50', 'PC51', 'PC52', 'PC53', 'PC54', 'PC55', 'PC56', 'PC57',\n",
       "       'PC58', 'PC59', 'PC60', 'PC61', 'PC62', 'PC63', 'PC64', 'PC65',\n",
       "       'PC66', 'PC67', 'PC68', 'PC69', 'PC70', 'PC71', 'PC72', 'PC73',\n",
       "       'PC74', 'PC75', 'PC76', 'PC77', 'PC78', 'PC79', 'PC80', 'PC81',\n",
       "       'PC82', 'PC83', 'PC84', 'PC85', 'PC86', 'PC87', 'PC88', 'PC89',\n",
       "       'PC90', 'PC91', 'PC92', 'PC93', 'ProductCD_FE', 'card4_FE',\n",
       "       'card6_FE', 'M1_FE', 'M2_FE', 'M3_FE', 'M4_FE', 'M5_FE', 'M6_FE',\n",
       "       'M7_FE', 'M8_FE', 'M9_FE', 'id_12_FE', 'id_15_FE', 'id_16_FE',\n",
       "       'id_23_FE', 'id_27_FE', 'id_28_FE', 'id_29_FE', 'id_30_FE',\n",
       "       'id_31_FE', 'id_33_FE', 'id_34_FE', 'id_35_FE', 'id_36_FE',\n",
       "       'id_37_FE', 'id_38_FE', 'DeviceType_FE', 'DeviceInfo_FE',\n",
       "       'P_emaildomain_bin_FE', 'R_emaildomain_bin_FE', 'card1_addr1_FE',\n",
       "       'card1_addr1_P_emaildomain_FE', 'card1_addr1_D1n_FE', 'addr1_FE',\n",
       "       'addr2_FE', 'card1_FE', 'card2_FE', 'card3_FE', 'card5_FE',\n",
       "       'TransactionAmt_card1_mean', 'TransactionAmt_card1_std',\n",
       "       'TransactionAmt_card1_addr1_mean',\n",
       "       'TransactionAmt_card1_addr1_std',\n",
       "       'TransactionAmt_card1_addr1_P_emaildomain_mean',\n",
       "       'TransactionAmt_card1_addr1_P_emaildomain_std', 'D1n_card1_mean',\n",
       "       'D1n_card1_std', 'D1n_card1_addr1_mean', 'D1n_card1_addr1_std',\n",
       "       'D1n_card1_addr1_P_emaildomain_mean',\n",
       "       'D1n_card1_addr1_P_emaildomain_std', 'D11_card1_mean',\n",
       "       'D11_card1_std', 'D11_card1_addr1_mean', 'D11_card1_addr1_std',\n",
       "       'D11_card1_addr1_P_emaildomain_mean',\n",
       "       'D11_card1_addr1_P_emaildomain_std',\n",
       "       'TransactionAmt_card1_addr1_D1n_mean',\n",
       "       'TransactionAmt_card1_addr1_D1n_std', 'D4_card1_addr1_D1n_mean',\n",
       "       'D4_card1_addr1_D1n_std', 'D9_card1_addr1_D1n_mean',\n",
       "       'D9_card1_addr1_D1n_std', 'D10_card1_addr1_D1n_mean',\n",
       "       'D10_card1_addr1_D1n_std', 'D15_card1_addr1_D1n_mean',\n",
       "       'D15_card1_addr1_D1n_std', 'C1_card1_addr1_D1n_mean',\n",
       "       'C2_card1_addr1_D1n_mean', 'C4_card1_addr1_D1n_mean',\n",
       "       'C5_card1_addr1_D1n_mean', 'C6_card1_addr1_D1n_mean',\n",
       "       'C7_card1_addr1_D1n_mean', 'C8_card1_addr1_D1n_mean',\n",
       "       'C9_card1_addr1_D1n_mean', 'C10_card1_addr1_D1n_mean',\n",
       "       'C11_card1_addr1_D1n_mean', 'C12_card1_addr1_D1n_mean',\n",
       "       'C13_card1_addr1_D1n_mean', 'C14_card1_addr1_D1n_mean',\n",
       "       'M1_FE_card1_addr1_D1n_mean', 'M2_FE_card1_addr1_D1n_mean',\n",
       "       'M3_FE_card1_addr1_D1n_mean', 'M4_FE_card1_addr1_D1n_mean',\n",
       "       'M5_FE_card1_addr1_D1n_mean', 'M6_FE_card1_addr1_D1n_mean',\n",
       "       'M7_FE_card1_addr1_D1n_mean', 'M8_FE_card1_addr1_D1n_mean',\n",
       "       'M9_FE_card1_addr1_D1n_mean',\n",
       "       'P_emaildomain_bin_card1_addr1_D1n_ct', 'dist1_card1_addr1_D1n_ct',\n",
       "       'DT_M_card1_addr1_D1n_ct', 'id_02_card1_addr1_D1n_ct',\n",
       "       'C13_card1_addr1_D1n_ct', 'C14_card1_addr1_D1n_std', 'outsider15'],\n",
       "      dtype='<U45')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('NOW USING THE FOLLOWING',len(x_features),'FEATURES.')\n",
    "np.array(x_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train[x_features].to_csv('./X_train_f.csv')\n",
    "# X_test[x_features].to_csv('./X_test_f.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(590540, 239)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.read_csv('./X_train_f.csv')\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506691, 239)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = pd.read_csv('./X_test_f.csv')\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.drop('Unnamed: 0',axis = 1, inplace =True)\n",
    "X_test.drop('Unnamed: 0',axis = 1, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(590540, 238)\n",
      "(506691, 238)\n",
      "(590540,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NAN Processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.fillna(-999)\n",
    "X_test = X_test.fillna(-999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Strategy\n",
    "\n",
    "The validation strategy used in this project is time-based validation, training on the first 5 months, and predicting the last month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN 5 PREDICT 1 MONTH\n",
    "idxT = X_train.index[:417559]\n",
    "idxV = X_train.index[417559:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 2001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-aucpr:0.35131\tvalidation_0-auc:0.81678\n",
      "Multiple eval metrics have been passed: 'validation_0-auc' will be used for early stopping.\n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.\n",
      "[50]\tvalidation_0-aucpr:0.47593\tvalidation_0-auc:0.86733\n",
      "[100]\tvalidation_0-aucpr:0.50068\tvalidation_0-auc:0.88118\n",
      "[150]\tvalidation_0-aucpr:0.52888\tvalidation_0-auc:0.89685\n",
      "[200]\tvalidation_0-aucpr:0.54698\tvalidation_0-auc:0.90726\n",
      "[250]\tvalidation_0-aucpr:0.56105\tvalidation_0-auc:0.91542\n",
      "[300]\tvalidation_0-aucpr:0.57297\tvalidation_0-auc:0.92183\n",
      "[350]\tvalidation_0-aucpr:0.58040\tvalidation_0-auc:0.92485\n",
      "[400]\tvalidation_0-aucpr:0.58589\tvalidation_0-auc:0.92637\n",
      "[450]\tvalidation_0-aucpr:0.58977\tvalidation_0-auc:0.92726\n",
      "[500]\tvalidation_0-aucpr:0.59306\tvalidation_0-auc:0.92764\n",
      "[550]\tvalidation_0-aucpr:0.59534\tvalidation_0-auc:0.92764\n",
      "[600]\tvalidation_0-aucpr:0.59716\tvalidation_0-auc:0.92762\n",
      "Stopping. Best iteration:\n",
      "[511]\tvalidation_0-aucpr:0.59378\tvalidation_0-auc:0.92783\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.4,\n",
       "              eval_metric=['aucpr', 'auc'], gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.02, max_delta_step=0, max_depth=12,\n",
       "              min_child_weight=1, missing=-1, monotone_constraints='()',\n",
       "              n_estimators=2000, n_jobs=0, num_parallel_tree=1,\n",
       "              objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=1, subsample=0.8,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = xgb.XGBClassifier( \n",
    "        n_estimators=2000,\n",
    "        max_depth=12, \n",
    "        learning_rate=0.02, \n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.4, \n",
    "        missing=-999, \n",
    "        eval_metric=['aucpr','auc'])\n",
    "\n",
    "clf.fit(X_train.loc[idxT,], y_train[idxT], \n",
    "        eval_set=[(X_train.loc[idxV,],y_train[idxV])],\n",
    "        verbose=50, early_stopping_rounds=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperopt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Initialize domain space for range of values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "space={'max_depth': hp.quniform(\"max_depth\", 9, 15, 1),\n",
    "       'gamma': hp.uniform ('gamma', .01, .8),\n",
    "       'learning_rate': hp.uniform('learning_rate', 0.01, 0.1),\n",
    "       'reg_alpha' : hp.uniform('reg_alpha', 0.01, 0.4),\n",
    "       'reg_lambda' : hp.uniform('reg_lambda', 0, 1),\n",
    "       'colsample_bytree' : hp.uniform('colsample_bytree', 0.5,1),\n",
    "       'subsample': hp.choice('subsample', [.7, .8, .9]),\n",
    "       'min_child_weight' : hp.quniform('min_child_weight', 1, 10, 1),\n",
    "       'n_estimators': hp.choice('n_estimators', [5000,3000]),\n",
    "       'seed': SEED\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Define objective function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(space):\n",
    "    time1 = time()\n",
    "    \n",
    "    params = {\n",
    "        'learning_rate': \"{:.2f}\".format(space['learning_rate']),\n",
    "        'reg_lambda': \"{:.2f}\".format(space['reg_lambda']),\n",
    "        'reg_alpha': \"{:.2f}\".format(space['reg_lambda']),\n",
    "        'gamma': \"{:.3f}\".format(space['gamma']),\n",
    "        'colsample_bytree': '{:.2f}'.format(space['colsample_bytree']),\n",
    "        'max_depth': int(space['max_depth']),\n",
    "        'n_estimators': int(space['n_estimators']), \n",
    "        'subsample': \"{:.1f}\".format(space['subsample']),\n",
    "        'min_child_weight': int(space['min_child_weight'])\n",
    "    }\n",
    "    \n",
    "\n",
    "    print(\"\\n############## New Run ################\")\n",
    "    print(f'params = {params}')\n",
    "\n",
    "    clf=xgb.XGBClassifier(**params, missing = -999)\n",
    "    \n",
    "    \n",
    "    clf.fit(X_train.iloc[idxT,], y_train[idxT],\n",
    "            eval_set=[(X_train.iloc[idxV,],y_train[idxV])],\n",
    "            eval_metric=\"auc\",\n",
    "            early_stopping_rounds=100,\n",
    "            verbose=50)\n",
    "    \n",
    "    score = make_scorer(roc_auc_score, needs_proba=True)(clf, X_train.iloc[idxV,],y_train[idxV])    \n",
    "    \n",
    "    time2 = time() - time1\n",
    "    print(f\"Total Run Time:{round(time2/60,2)} mins\")\n",
    "    print (\"ROC_AUC:\", score)\n",
    "    return {'loss': -score, 'status': STATUS_OK }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Optimization algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.02', 'reg_lambda': '0.55', 'reg_alpha': '0.55', 'gamma': '0.672', 'colsample_bytree': '0.79', 'max_depth': 11, 'n_estimators': 3000, 'subsample': '0.8', 'min_child_weight': 7}\n",
      "[0]\tvalidation_0-auc:0.81088                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.85772                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.87381                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.89026                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.89935                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.90847                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.91529                                                                                         \n",
      "\n",
      "[350]\tvalidation_0-auc:0.91900                                                                                         \n",
      "\n",
      "[400]\tvalidation_0-auc:0.92109                                                                                         \n",
      "\n",
      "[450]\tvalidation_0-auc:0.92231                                                                                         \n",
      "\n",
      "[500]\tvalidation_0-auc:0.92373                                                                                         \n",
      "\n",
      "[550]\tvalidation_0-auc:0.92474                                                                                         \n",
      "\n",
      "[600]\tvalidation_0-auc:0.92538                                                                                         \n",
      "\n",
      "[650]\tvalidation_0-auc:0.92580                                                                                         \n",
      "\n",
      "[700]\tvalidation_0-auc:0.92626                                                                                         \n",
      "\n",
      "[750]\tvalidation_0-auc:0.92672                                                                                         \n",
      "\n",
      "[800]\tvalidation_0-auc:0.92681                                                                                         \n",
      "\n",
      "[850]\tvalidation_0-auc:0.92711                                                                                         \n",
      "\n",
      "[900]\tvalidation_0-auc:0.92726                                                                                         \n",
      "\n",
      "[950]\tvalidation_0-auc:0.92745                                                                                         \n",
      "\n",
      "[1000]\tvalidation_0-auc:0.92766                                                                                        \n",
      "\n",
      "[1050]\tvalidation_0-auc:0.92786                                                                                        \n",
      "\n",
      "[1100]\tvalidation_0-auc:0.92773                                                                                        \n",
      "\n",
      "[1150]\tvalidation_0-auc:0.92763                                                                                        \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[1055]\tvalidation_0-auc:0.92787\n",
      "\n",
      "\n",
      "Total Run Time:45.73 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9278685638767008                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.05', 'reg_lambda': '0.62', 'reg_alpha': '0.62', 'gamma': '0.458', 'colsample_bytree': '0.84', 'max_depth': 11, 'n_estimators': 3000, 'subsample': '0.8', 'min_child_weight': 6}\n",
      "[0]\tvalidation_0-auc:0.81686                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.88144                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.90835                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.91964                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.92182                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.92313                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.92459                                                                                         \n",
      "\n",
      "[350]\tvalidation_0-auc:0.92561                                                                                         \n",
      "\n",
      "[400]\tvalidation_0-auc:0.92563                                                                                         \n",
      "\n",
      "[450]\tvalidation_0-auc:0.92553                                                                                         \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[373]\tvalidation_0-auc:0.92579\n",
      "\n",
      "\n",
      "Total Run Time:18.92 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9257911175474718                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.01', 'reg_lambda': '0.85', 'reg_alpha': '0.85', 'gamma': '0.108', 'colsample_bytree': '0.70', 'max_depth': 14, 'n_estimators': 5000, 'subsample': '0.8', 'min_child_weight': 9}\n",
      "[0]\tvalidation_0-auc:0.81935                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.85918                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.86564                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.87169                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.88084                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.88842                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.89445                                                                                         \n",
      "\n",
      "[350]\tvalidation_0-auc:0.89953                                                                                         \n",
      "\n",
      "[400]\tvalidation_0-auc:0.90333                                                                                         \n",
      "\n",
      "[450]\tvalidation_0-auc:0.90761                                                                                         \n",
      "\n",
      "[500]\tvalidation_0-auc:0.91186                                                                                         \n",
      "\n",
      "[550]\tvalidation_0-auc:0.91518                                                                                         \n",
      "\n",
      "[600]\tvalidation_0-auc:0.91820                                                                                         \n",
      "\n",
      "[650]\tvalidation_0-auc:0.92039                                                                                         \n",
      "\n",
      "[700]\tvalidation_0-auc:0.92191                                                                                         \n",
      "\n",
      "[750]\tvalidation_0-auc:0.92305                                                                                         \n",
      "\n",
      "[800]\tvalidation_0-auc:0.92368                                                                                         \n",
      "\n",
      "[850]\tvalidation_0-auc:0.92448                                                                                         \n",
      "\n",
      "[900]\tvalidation_0-auc:0.92498                                                                                         \n",
      "\n",
      "[950]\tvalidation_0-auc:0.92547                                                                                         \n",
      "\n",
      "[1000]\tvalidation_0-auc:0.92596                                                                                        \n",
      "\n",
      "[1050]\tvalidation_0-auc:0.92615                                                                                        \n",
      "\n",
      "[1100]\tvalidation_0-auc:0.92636                                                                                        \n",
      "\n",
      "[1150]\tvalidation_0-auc:0.92664                                                                                        \n",
      "\n",
      "[1200]\tvalidation_0-auc:0.92694                                                                                        \n",
      "\n",
      "[1250]\tvalidation_0-auc:0.92712                                                                                        \n",
      "\n",
      "[1300]\tvalidation_0-auc:0.92735                                                                                        \n",
      "\n",
      "[1350]\tvalidation_0-auc:0.92752                                                                                        \n",
      "\n",
      "[1400]\tvalidation_0-auc:0.92767                                                                                        \n",
      "\n",
      "[1450]\tvalidation_0-auc:0.92767                                                                                        \n",
      "\n",
      "[1500]\tvalidation_0-auc:0.92771                                                                                        \n",
      "\n",
      "[1550]\tvalidation_0-auc:0.92786                                                                                        \n",
      "\n",
      "[1600]\tvalidation_0-auc:0.92794                                                                                        \n",
      "\n",
      "[1650]\tvalidation_0-auc:0.92788                                                                                        \n",
      "\n",
      "[1700]\tvalidation_0-auc:0.92785                                                                                        \n",
      "\n",
      "[1750]\tvalidation_0-auc:0.92782                                                                                        \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[1678]\tvalidation_0-auc:0.92795\n",
      "\n",
      "\n",
      "Total Run Time:76.5 mins                                                                                               \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9279500815306508                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.02', 'reg_lambda': '0.64', 'reg_alpha': '0.64', 'gamma': '0.087', 'colsample_bytree': '0.65', 'max_depth': 13, 'n_estimators': 5000, 'subsample': '0.9', 'min_child_weight': 10}\n",
      "[0]\tvalidation_0-auc:0.81174                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.86505                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.87994                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.89417                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.90318                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.91130                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.91743                                                                                         \n",
      "\n",
      "[350]\tvalidation_0-auc:0.92140                                                                                         \n",
      "\n",
      "[400]\tvalidation_0-auc:0.92327                                                                                         \n",
      "\n",
      "[450]\tvalidation_0-auc:0.92434                                                                                         \n",
      "\n",
      "[500]\tvalidation_0-auc:0.92509                                                                                         \n",
      "\n",
      "[550]\tvalidation_0-auc:0.92569                                                                                         \n",
      "\n",
      "[600]\tvalidation_0-auc:0.92637                                                                                         \n",
      "\n",
      "[650]\tvalidation_0-auc:0.92654                                                                                         \n",
      "\n",
      "[700]\tvalidation_0-auc:0.92693                                                                                         \n",
      "\n",
      "[750]\tvalidation_0-auc:0.92706                                                                                         \n",
      "\n",
      "[800]\tvalidation_0-auc:0.92716                                                                                         \n",
      "\n",
      "[850]\tvalidation_0-auc:0.92725                                                                                         \n",
      "\n",
      "[900]\tvalidation_0-auc:0.92713                                                                                         \n",
      "\n",
      "[950]\tvalidation_0-auc:0.92700                                                                                         \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[878]\tvalidation_0-auc:0.92728\n",
      "\n",
      "\n",
      "Total Run Time:36.54 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9272799260158628                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.03', 'reg_lambda': '0.12', 'reg_alpha': '0.12', 'gamma': '0.055', 'colsample_bytree': '0.99', 'max_depth': 14, 'n_estimators': 5000, 'subsample': '0.8', 'min_child_weight': 4}\n",
      "[0]\tvalidation_0-auc:0.82689                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.87355                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.89997                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.91288                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.92001                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.92346                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.92481                                                                                         \n",
      "\n",
      "[350]\tvalidation_0-auc:0.92544                                                                                         \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[400]\tvalidation_0-auc:0.92589                                                                                         \n",
      "\n",
      "[450]\tvalidation_0-auc:0.92582                                                                                         \n",
      "\n",
      "[500]\tvalidation_0-auc:0.92572                                                                                         \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[430]\tvalidation_0-auc:0.92619\n",
      "\n",
      "\n",
      "Total Run Time:31.85 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9261930123768262                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.06', 'reg_lambda': '0.96', 'reg_alpha': '0.96', 'gamma': '0.175', 'colsample_bytree': '0.80', 'max_depth': 14, 'n_estimators': 5000, 'subsample': '0.7', 'min_child_weight': 7}\n",
      "[0]\tvalidation_0-auc:0.82793                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.89160                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.91468                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.92217                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.92302                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.92420                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.92497                                                                                         \n",
      "\n",
      "[350]\tvalidation_0-auc:0.92497                                                                                         \n",
      "\n",
      "[400]\tvalidation_0-auc:0.92482                                                                                         \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[314]\tvalidation_0-auc:0.92509\n",
      "\n",
      "\n",
      "Total Run Time:19.71 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9250924112073513                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.08', 'reg_lambda': '0.56', 'reg_alpha': '0.56', 'gamma': '0.752', 'colsample_bytree': '0.58', 'max_depth': 10, 'n_estimators': 3000, 'subsample': '0.9', 'min_child_weight': 3}\n",
      "[0]\tvalidation_0-auc:0.79946                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.89633                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.92256                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.92451                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.92566                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.92604                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.92595                                                                                         \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[239]\tvalidation_0-auc:0.92650\n",
      "\n",
      "\n",
      "Total Run Time:9.12 mins                                                                                               \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9265019301306999                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.03', 'reg_lambda': '0.19', 'reg_alpha': '0.19', 'gamma': '0.306', 'colsample_bytree': '0.76', 'max_depth': 10, 'n_estimators': 3000, 'subsample': '0.9', 'min_child_weight': 6}\n",
      "[0]\tvalidation_0-auc:0.81619                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.86552                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.88652                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.90233                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.91304                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.91899                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.92103                                                                                         \n",
      "\n",
      "[350]\tvalidation_0-auc:0.92256                                                                                         \n",
      "\n",
      "[400]\tvalidation_0-auc:0.92336                                                                                         \n",
      "\n",
      "[450]\tvalidation_0-auc:0.92395                                                                                         \n",
      "\n",
      "[500]\tvalidation_0-auc:0.92448                                                                                         \n",
      "\n",
      "[550]\tvalidation_0-auc:0.92465                                                                                         \n",
      "\n",
      "[600]\tvalidation_0-auc:0.92491                                                                                         \n",
      "\n",
      "[650]\tvalidation_0-auc:0.92517                                                                                         \n",
      "\n",
      "[700]\tvalidation_0-auc:0.92468                                                                                         \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[640]\tvalidation_0-auc:0.92522\n",
      "\n",
      "\n",
      "Total Run Time:25.06 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9252181842525831                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.06', 'reg_lambda': '0.35', 'reg_alpha': '0.35', 'gamma': '0.087', 'colsample_bytree': '0.63', 'max_depth': 13, 'n_estimators': 5000, 'subsample': '0.9', 'min_child_weight': 2}\n",
      "[0]\tvalidation_0-auc:0.80650                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.89717                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.91987                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.92361                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.92436                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.92451                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.92410                                                                                         \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[216]\tvalidation_0-auc:0.92491\n",
      "\n",
      "\n",
      "Total Run Time:12.55 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.924908687180318                                                                                                      \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': '0.03', 'reg_lambda': '0.37', 'reg_alpha': '0.37', 'gamma': '0.774', 'colsample_bytree': '0.80', 'max_depth': 10, 'n_estimators': 3000, 'subsample': '0.9', 'min_child_weight': 3}\n",
      "[0]\tvalidation_0-auc:0.81220                                                                                           \n",
      "\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.                                                       \n",
      "\n",
      "[50]\tvalidation_0-auc:0.86247                                                                                          \n",
      "\n",
      "[100]\tvalidation_0-auc:0.88476                                                                                         \n",
      "\n",
      "[150]\tvalidation_0-auc:0.90210                                                                                         \n",
      "\n",
      "[200]\tvalidation_0-auc:0.91591                                                                                         \n",
      "\n",
      "[250]\tvalidation_0-auc:0.92162                                                                                         \n",
      "\n",
      "[300]\tvalidation_0-auc:0.92304                                                                                         \n",
      "\n",
      "[350]\tvalidation_0-auc:0.92423                                                                                         \n",
      "\n",
      "[400]\tvalidation_0-auc:0.92485                                                                                         \n",
      "\n",
      "[450]\tvalidation_0-auc:0.92526                                                                                         \n",
      "\n",
      "[500]\tvalidation_0-auc:0.92587                                                                                         \n",
      "\n",
      "[550]\tvalidation_0-auc:0.92624                                                                                         \n",
      "\n",
      "[600]\tvalidation_0-auc:0.92632                                                                                         \n",
      "\n",
      "[650]\tvalidation_0-auc:0.92636                                                                                         \n",
      "\n",
      "Stopping. Best iteration:                                                                                              \n",
      "[573]\tvalidation_0-auc:0.92649\n",
      "\n",
      "\n",
      "Total Run Time:27.27 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9264945244266417                                                                                                     \n",
      "100%|███████████████████████████████████████████| 10/10 [5:03:15<00:00, 1819.53s/trial, best loss: -0.9279500815306508]\n"
     ]
    }
   ],
   "source": [
    "trials = Trials()\n",
    "\n",
    "best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 10,\n",
    "                        trials = trials)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best parameters** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are :  \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.01,\n",
       " 'reg_lambda': 0.85,\n",
       " 'reg_alpha': 0.85,\n",
       " 'gamma': 0.108,\n",
       " 'colsample_bytree': 0.7,\n",
       " 'max_depth': 14,\n",
       " 'n_estimators': 5000,\n",
       " 'subsample': 0.8,\n",
       " 'min_child_weight': 9,\n",
       " 'missing': -999,\n",
       " 'seed': 2001}"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The best hyperparameters are : \",\"\\n\")\n",
    "best_hyperparams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.7, gamma=0.108, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.01, max_delta_step=0, max_depth=14,\n",
       "              min_child_weight=9, missing=-999, monotone_constraints='()',\n",
       "              n_estimators=5000, n_jobs=0, num_parallel_tree=1,\n",
       "              objective='binary:logistic', random_state=2001, reg_alpha=0.85,\n",
       "              reg_lambda=0.85, scale_pos_weight=1, seed=2001, subsample=0.8,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_xgb = xgb.XGBClassifier(**best_hyperparams)\n",
    "\n",
    "clf_xgb.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "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>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3663549</td>\n",
       "      <td>0.001213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3663550</td>\n",
       "      <td>0.001332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3663551</td>\n",
       "      <td>0.002203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3663552</td>\n",
       "      <td>0.000143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3663553</td>\n",
       "      <td>0.000162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID   isFraud\n",
       "0        3663549  0.001213\n",
       "1        3663550  0.001332\n",
       "2        3663551  0.002203\n",
       "3        3663552  0.000143\n",
       "4        3663553  0.000162"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds_xgb = clf_xgb.predict_proba(X_test)[:,1] \n",
    "submission_xgb = pd.DataFrame({'TransactionID':test['TransactionID'],'isFraud':y_preds_xgb})\n",
    "submission_xgb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_xgb.to_csv('./submission_xgb.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_important = clf_xgb.get_booster().get_score(importance_type = 'gain')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfip = pd.DataFrame({'score':list(feature_important.values()),\n",
    "                    'features':list(feature_important.keys())})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": false
   },
   "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>score</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>226.602109</td>\n",
       "      <td>PC54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>77.715365</td>\n",
       "      <td>C7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.065651</td>\n",
       "      <td>C7_card1_addr1_D1n_mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.489741</td>\n",
       "      <td>PC65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>51.787617</td>\n",
       "      <td>C5_card1_addr1_D1n_mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>42.730035</td>\n",
       "      <td>addr2_FE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>40.519869</td>\n",
       "      <td>C14_card1_addr1_D1n_mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>40.387751</td>\n",
       "      <td>C12_card1_addr1_D1n_mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>31.956654</td>\n",
       "      <td>id_17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>31.109089</td>\n",
       "      <td>C8_card1_addr1_D1n_mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>29.927246</td>\n",
       "      <td>id_22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>29.591783</td>\n",
       "      <td>C1_card1_addr1_D1n_mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>28.118297</td>\n",
       "      <td>PC66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.362640</td>\n",
       "      <td>PC69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.829200</td>\n",
       "      <td>C14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>21.776454</td>\n",
       "      <td>C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>20.295509</td>\n",
       "      <td>PC6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>19.786629</td>\n",
       "      <td>PC78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.672388</td>\n",
       "      <td>PC42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>17.449916</td>\n",
       "      <td>card3_FE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          score                  features\n",
       "0    226.602109                      PC54\n",
       "138   77.715365                        C7\n",
       "2     57.065651   C7_card1_addr1_D1n_mean\n",
       "1     53.489741                      PC65\n",
       "195   51.787617   C5_card1_addr1_D1n_mean\n",
       "117   42.730035                  addr2_FE\n",
       "157   40.519869  C14_card1_addr1_D1n_mean\n",
       "113   40.387751  C12_card1_addr1_D1n_mean\n",
       "38    31.956654                     id_17\n",
       "131   31.109089   C8_card1_addr1_D1n_mean\n",
       "116   29.927246                     id_22\n",
       "156   29.591783   C1_card1_addr1_D1n_mean\n",
       "66    28.118297                      PC66\n",
       "18    24.362640                      PC69\n",
       "3     23.829200                       C14\n",
       "153   21.776454                        C1\n",
       "197   20.295509                       PC6\n",
       "63    19.786629                      PC78\n",
       "5     19.672388                      PC42\n",
       "32    17.449916                  card3_FE"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=dfip.sort_values(by = 'score',ascending=False).iloc[:20]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16ed0eb0e48>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "sns.barplot(x = 'score',y = 'features', data = data,color='skyblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CatBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperopt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "space={'max_depth': hp.quniform(\"max_depth\", 8, 16, 1),\n",
    "       'learning_rate': hp.uniform('learning_rate', 0.01, 0.2),\n",
    "       'reg_lambda' : hp.uniform('reg_lambda', .1, 1),\n",
    "       'subsample': hp.choice('subsample', [.7, .8, .9]),\n",
    "       'n_estimators': hp.choice('n_estimators', [300,600])\n",
    "    }\n",
    "\n",
    "def objective(space):\n",
    "    time1 = time()\n",
    "    \n",
    "    params = {\n",
    "        'learning_rate': round(space['learning_rate'],2),\n",
    "        'reg_lambda': round(space['reg_lambda'],2),\n",
    "        'max_depth': int(space['max_depth']),\n",
    "        'n_estimators': int(space['n_estimators']), \n",
    "        'subsample': round(space['subsample'],2),\n",
    "        'early_stopping_rounds': 100,\n",
    "    }\n",
    "    \n",
    "\n",
    "    print(\"\\n############## New Run ################\")\n",
    "    print(f'params = {params}')\n",
    "\n",
    "    cbc =  CatBoostClassifier(**params,random_seed = SEED)\n",
    "    \n",
    "    \n",
    "    cbc.fit(\n",
    "        X_train.loc[idxT,], y_train[idxT], \n",
    "        eval_set=[(X_train.loc[idxV,],y_train[idxV])],\n",
    "        use_best_model = True,\n",
    "        plot = False, \n",
    "        verbose = 50\n",
    "        \n",
    "    )\n",
    "\n",
    "    \n",
    "    score = make_scorer(roc_auc_score, needs_proba=True)(cbc, X_train.iloc[idxV,],y_train[idxV])    \n",
    "    \n",
    "    time2 = time() - time1\n",
    "    \n",
    "    print(f\"Total Run Time:{round(time2/60,2)} mins\")\n",
    "    print (\"ROC_AUC:\", score)\n",
    "    return {'loss': -score, 'status': STATUS_OK }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.01, 'reg_lambda': 0.7, 'max_depth': 12, 'n_estimators': 300, 'subsample': 0.8, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.6752683\ttest: 0.6748075\tbest: 0.6748075 (0)\ttotal: 2.93s\tremaining: 14m 36s                                \n",
      "\n",
      "50:\tlearn: 0.2300615\ttest: 0.2308011\tbest: 0.2308011 (50)\ttotal: 2m 23s\tremaining: 11m 40s                             \n",
      "\n",
      "100:\tlearn: 0.1326363\ttest: 0.1369700\tbest: 0.1369700 (100)\ttotal: 4m 45s\tremaining: 9m 22s                            \n",
      "\n",
      "150:\tlearn: 0.1047896\ttest: 0.1128531\tbest: 0.1128531 (150)\ttotal: 7m 7s\tremaining: 7m 1s                              \n",
      "\n",
      "200:\tlearn: 0.0926306\ttest: 0.1040741\tbest: 0.1040741 (200)\ttotal: 9m 30s\tremaining: 4m 40s                            \n",
      "\n",
      "250:\tlearn: 0.0857162\ttest: 0.1001980\tbest: 0.1001980 (250)\ttotal: 11m 56s\tremaining: 2m 19s                           \n",
      "\n",
      "299:\tlearn: 0.0813201\ttest: 0.0980947\tbest: 0.0980947 (299)\ttotal: 14m 14s\tremaining: 0us                              \n",
      "\n",
      "bestTest = 0.09809466499                                                                                               \n",
      "\n",
      "bestIteration = 299                                                                                                    \n",
      "\n",
      "Total Run Time:14.29 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.8844590338667093                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.18, 'reg_lambda': 0.57, 'max_depth': 13, 'n_estimators': 300, 'subsample': 0.9, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.4163801\ttest: 0.4162815\tbest: 0.4162815 (0)\ttotal: 4.91s\tremaining: 24m 27s                                \n",
      "\n",
      "50:\tlearn: 0.0496630\ttest: 0.0932452\tbest: 0.0932452 (50)\ttotal: 4m 8s\tremaining: 20m 14s                              \n",
      "\n",
      "100:\tlearn: 0.0320299\ttest: 0.0929802\tbest: 0.0927031 (72)\ttotal: 8m 17s\tremaining: 16m 19s                            \n",
      "\n",
      "150:\tlearn: 0.0234658\ttest: 0.0954869\tbest: 0.0927031 (72)\ttotal: 12m 27s\tremaining: 12m 17s                           \n",
      "\n",
      "Stopped by overfitting detector  (100 iterations wait)                                                                 \n",
      "\n",
      "bestTest = 0.09270310456                                                                                               \n",
      "\n",
      "bestIteration = 72                                                                                                     \n",
      "\n",
      "Shrink model to first 73 iterations.                                                                                   \n",
      "Total Run Time:14.36 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.89664126289362                                                                                                       \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.15, 'reg_lambda': 0.86, 'max_depth': 12, 'n_estimators': 300, 'subsample': 0.9, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.4558857\ttest: 0.4552839\tbest: 0.4552839 (0)\ttotal: 2.75s\tremaining: 13m 42s                                \n",
      "\n",
      "50:\tlearn: 0.0640655\ttest: 0.0942688\tbest: 0.0942688 (50)\ttotal: 2m 29s\tremaining: 12m 7s                              \n",
      "\n",
      "100:\tlearn: 0.0455695\ttest: 0.0917691\tbest: 0.0917611 (99)\ttotal: 4m 54s\tremaining: 9m 40s                             \n",
      "\n",
      "150:\tlearn: 0.0352957\ttest: 0.0910084\tbest: 0.0908582 (144)\ttotal: 7m 20s\tremaining: 7m 15s                            \n",
      "\n",
      "200:\tlearn: 0.0291120\ttest: 0.0914169\tbest: 0.0908582 (144)\ttotal: 9m 45s\tremaining: 4m 48s                            \n",
      "\n",
      "Stopped by overfitting detector  (100 iterations wait)                                                                 \n",
      "\n",
      "bestTest = 0.09085820244                                                                                               \n",
      "\n",
      "bestIteration = 144                                                                                                    \n",
      "\n",
      "Shrink model to first 145 iterations.                                                                                  \n",
      "Total Run Time:12.01 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.902712227454046                                                                                                      \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.18, 'reg_lambda': 0.63, 'max_depth': 15, 'n_estimators': 300, 'subsample': 0.9, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.4157885\ttest: 0.4150033\tbest: 0.4150033 (0)\ttotal: 16.4s\tremaining: 1h 21m 52s                             \n",
      "\n",
      "50:\tlearn: 0.0300741\ttest: 0.0937378\tbest: 0.0935920 (42)\ttotal: 14m 2s\tremaining: 1h 8m 32s                           \n",
      "\n",
      "100:\tlearn: 0.0169548\ttest: 0.0969619\tbest: 0.0935920 (42)\ttotal: 27m 44s\tremaining: 54m 39s                           \n",
      "\n",
      "Stopped by overfitting detector  (100 iterations wait)                                                                 \n",
      "\n",
      "bestTest = 0.09359203492                                                                                               \n",
      "\n",
      "bestIteration = 42                                                                                                     \n",
      "\n",
      "Shrink model to first 43 iterations.                                                                                   \n",
      "Total Run Time:39.41 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.8980194210455619                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.1, 'reg_lambda': 0.39, 'max_depth': 13, 'n_estimators': 600, 'subsample': 0.7, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.5296473\ttest: 0.5262650\tbest: 0.5262650 (0)\ttotal: 4.45s\tremaining: 44m 26s                                \n",
      "\n",
      "50:\tlearn: 0.0620727\ttest: 0.0936635\tbest: 0.0936635 (50)\ttotal: 3m 51s\tremaining: 41m 36s                             \n",
      "\n",
      "100:\tlearn: 0.0443547\ttest: 0.0909526\tbest: 0.0908275 (92)\ttotal: 7m 40s\tremaining: 37m 55s                            \n",
      "\n",
      "150:\tlearn: 0.0346899\ttest: 0.0902835\tbest: 0.0901074 (128)\ttotal: 11m 31s\tremaining: 34m 17s                          \n",
      "\n",
      "200:\tlearn: 0.0274745\ttest: 0.0908554\tbest: 0.0900314 (159)\ttotal: 15m 22s\tremaining: 30m 30s                          \n",
      "\n",
      "250:\tlearn: 0.0234196\ttest: 0.0915945\tbest: 0.0900314 (159)\ttotal: 19m 12s\tremaining: 26m 41s                          \n",
      "\n",
      "Stopped by overfitting detector  (100 iterations wait)                                                                 \n",
      "\n",
      "bestTest = 0.09003139331                                                                                               \n",
      "\n",
      "bestIteration = 159                                                                                                    \n",
      "\n",
      "Shrink model to first 160 iterations.                                                                                  \n",
      "Total Run Time:19.96 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9072145207277087                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.2, 'reg_lambda': 0.68, 'max_depth': 11, 'n_estimators': 300, 'subsample': 0.7, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.4059954\ttest: 0.3998129\tbest: 0.3998129 (0)\ttotal: 1.59s\tremaining: 7m 54s                                 \n",
      "\n",
      "50:\tlearn: 0.0638188\ttest: 0.0952051\tbest: 0.0952051 (50)\ttotal: 1m 25s\tremaining: 6m 56s                              \n",
      "\n",
      "100:\tlearn: 0.0491756\ttest: 0.0931085\tbest: 0.0930543 (88)\ttotal: 2m 49s\tremaining: 5m 33s                             \n",
      "\n",
      "150:\tlearn: 0.0392110\ttest: 0.0928531\tbest: 0.0923084 (125)\ttotal: 4m 13s\tremaining: 4m 9s                             \n",
      "\n",
      "200:\tlearn: 0.0324101\ttest: 0.0937582\tbest: 0.0923084 (125)\ttotal: 5m 37s\tremaining: 2m 46s                            \n",
      "\n",
      "Stopped by overfitting detector  (100 iterations wait)                                                                 \n",
      "\n",
      "bestTest = 0.0923084101                                                                                                \n",
      "\n",
      "bestIteration = 125                                                                                                    \n",
      "\n",
      "Shrink model to first 126 iterations.                                                                                  \n",
      "Total Run Time:6.4 mins                                                                                                \n",
      "ROC_AUC:                                                                                                               \n",
      "0.8948217129919307                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.11, 'reg_lambda': 0.6, 'max_depth': 12, 'n_estimators': 300, 'subsample': 0.7, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.5168955\ttest: 0.5129043\tbest: 0.5129043 (0)\ttotal: 2.61s\tremaining: 13m                                    \n",
      "\n",
      "50:\tlearn: 0.0677356\ttest: 0.0942828\tbest: 0.0942828 (50)\ttotal: 2m 16s\tremaining: 11m 6s                              \n",
      "\n",
      "100:\tlearn: 0.0519062\ttest: 0.0905118\tbest: 0.0905118 (100)\ttotal: 4m 29s\tremaining: 8m 51s                            \n",
      "\n",
      "150:\tlearn: 0.0417949\ttest: 0.0892891\tbest: 0.0892819 (149)\ttotal: 6m 42s\tremaining: 6m 37s                            \n",
      "\n",
      "200:\tlearn: 0.0348451\ttest: 0.0900344\tbest: 0.0891688 (167)\ttotal: 8m 57s\tremaining: 4m 24s                            \n",
      "\n",
      "250:\tlearn: 0.0291672\ttest: 0.0905400\tbest: 0.0891688 (167)\ttotal: 11m 14s\tremaining: 2m 11s                           \n",
      "\n",
      "Stopped by overfitting detector  (100 iterations wait)                                                                 \n",
      "\n",
      "bestTest = 0.08916879829                                                                                               \n",
      "\n",
      "bestIteration = 167                                                                                                    \n",
      "\n",
      "Shrink model to first 168 iterations.                                                                                  \n",
      "Total Run Time:12.04 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9057000645747572                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.02, 'reg_lambda': 0.61, 'max_depth': 14, 'n_estimators': 300, 'subsample': 0.8, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.6574300\ttest: 0.6566134\tbest: 0.6566134 (0)\ttotal: 8.23s\tremaining: 41m 1s                                 \n",
      "\n",
      "50:\tlearn: 0.1243480\ttest: 0.1325628\tbest: 0.1325628 (50)\ttotal: 7m 43s\tremaining: 37m 41s                             \n",
      "\n",
      "100:\tlearn: 0.0816621\ttest: 0.1013706\tbest: 0.1013706 (100)\ttotal: 15m 27s\tremaining: 30m 28s                          \n",
      "\n",
      "150:\tlearn: 0.0679173\ttest: 0.0958064\tbest: 0.0958064 (150)\ttotal: 23m 25s\tremaining: 23m 6s                           \n",
      "\n",
      "200:\tlearn: 0.0602602\ttest: 0.0937282\tbest: 0.0937282 (200)\ttotal: 31m 6s\tremaining: 15m 19s                           \n",
      "\n",
      "250:\tlearn: 0.0538475\ttest: 0.0916801\tbest: 0.0916801 (250)\ttotal: 38m 29s\tremaining: 7m 30s                           \n",
      "\n",
      "299:\tlearn: 0.0484160\ttest: 0.0905284\tbest: 0.0905284 (299)\ttotal: 45m 39s\tremaining: 0us                              \n",
      "\n",
      "bestTest = 0.09052843783                                                                                               \n",
      "\n",
      "bestIteration = 299                                                                                                    \n",
      "\n",
      "Total Run Time:45.74 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9111811223151914                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.06, 'reg_lambda': 0.31, 'max_depth': 15, 'n_estimators': 600, 'subsample': 0.9, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.5861141\ttest: 0.5856668\tbest: 0.5856668 (0)\ttotal: 16.5s\tremaining: 2h 44m 23s                             \n",
      "\n",
      "50:\tlearn: 0.0596588\ttest: 0.0956633\tbest: 0.0956633 (50)\ttotal: 14m 9s\tremaining: 2h 32m 27s                          \n",
      "\n",
      "100:\tlearn: 0.0398748\ttest: 0.0919637\tbest: 0.0919136 (97)\ttotal: 28m 20s\tremaining: 2h 20m 1s                         \n",
      "\n",
      "150:\tlearn: 0.0277681\ttest: 0.0911196\tbest: 0.0908933 (133)\ttotal: 42m 39s\tremaining: 2h 6m 50s                        \n",
      "\n",
      "200:\tlearn: 0.0214702\ttest: 0.0923531\tbest: 0.0908933 (133)\ttotal: 56m 43s\tremaining: 1h 52m 36s                       \n",
      "\n",
      "Stopped by overfitting detector  (100 iterations wait)                                                                 \n",
      "\n",
      "bestTest = 0.09089333682                                                                                               \n",
      "\n",
      "bestIteration = 133                                                                                                    \n",
      "\n",
      "Shrink model to first 134 iterations.                                                                                  \n",
      "Total Run Time:65.89 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9091907792767129                                                                                                     \n",
      "                                                                                                                       \n",
      "############## New Run ################\n",
      "params = {'learning_rate': 0.06, 'reg_lambda': 0.7, 'max_depth': 15, 'n_estimators': 600, 'subsample': 0.7, 'early_stopping_rounds': 100}\n",
      "0:\tlearn: 0.5878269\ttest: 0.5865735\tbest: 0.5865735 (0)\ttotal: 16s\tremaining: 2h 40m 2s                                \n",
      "\n",
      "50:\tlearn: 0.0629294\ttest: 0.0955225\tbest: 0.0955225 (50)\ttotal: 13m 25s\tremaining: 2h 24m 32s                         \n",
      "\n",
      "100:\tlearn: 0.0414727\ttest: 0.0908301\tbest: 0.0908301 (100)\ttotal: 26m 59s\tremaining: 2h 13m 21s                       \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150:\tlearn: 0.0318714\ttest: 0.0892284\tbest: 0.0892284 (150)\ttotal: 40m 42s\tremaining: 2h 1m 2s                         \n",
      "\n",
      "200:\tlearn: 0.0254797\ttest: 0.0890685\tbest: 0.0890072 (157)\ttotal: 54m 30s\tremaining: 1h 48m 11s                       \n",
      "\n",
      "250:\tlearn: 0.0212120\ttest: 0.0896506\tbest: 0.0890072 (157)\ttotal: 1h 7m 59s\tremaining: 1h 34m 32s                     \n",
      "\n",
      "Stopped by overfitting detector  (100 iterations wait)                                                                 \n",
      "\n",
      "bestTest = 0.08900716943                                                                                               \n",
      "\n",
      "bestIteration = 157                                                                                                    \n",
      "\n",
      "Shrink model to first 158 iterations.                                                                                  \n",
      "Total Run Time:69.99 mins                                                                                              \n",
      "ROC_AUC:                                                                                                               \n",
      "0.9146246432222351                                                                                                     \n",
      "100%|███████████████████████████████████████████| 10/10 [5:00:06<00:00, 1800.62s/trial, best loss: -0.9146246432222351]\n"
     ]
    }
   ],
   "source": [
    "trials = Trials()\n",
    "\n",
    "best_hyperparams_cbc = fmin(fn = objective,\n",
    "                            space = space,\n",
    "                            algo = tpe.suggest,\n",
    "                            max_evals = 10,\n",
    "                            trials = trials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are :  \n",
      "\n",
      "{'learning_rate': 0.06, 'reg_lambda': 0.7, 'max_depth': 15, 'n_estimators': 600, 'subsample': 0.7, 'early_stopping_rounds': 100}\n"
     ]
    }
   ],
   "source": [
    "print(\"The best hyperparameters are : \",\"\\n\")\n",
    "print(best_hyperparams_cbc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x16f581e3bc8>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_cbc = CatBoostClassifier(**best_hyperparams_cbc,random_seed = SEED, verbose = False)\n",
    "clf_cbc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds_cbc = clf_cbc.predict_proba(X_test)[:,1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3663549</td>\n",
       "      <td>0.000560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3663550</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3663551</td>\n",
       "      <td>0.000169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3663552</td>\n",
       "      <td>0.001364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3663553</td>\n",
       "      <td>0.000121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID   isFraud\n",
       "0        3663549  0.000560\n",
       "1        3663550  0.000134\n",
       "2        3663551  0.000169\n",
       "3        3663552  0.001364\n",
       "4        3663553  0.000121"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_cbc = pd.DataFrame({'TransactionID':test['TransactionID'],'isFraud':y_preds_cbc})\n",
    "submission_cbc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_cbc.to_csv('./submission_cbc.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "feature_score_cbc= clf_cbc.get_feature_importance(prettified=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16ebd960508>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "sns.barplot(x = 'Importances',y = 'Feature Id', data = feature_score_cbc[:20].sort_values('Importances',ascending=False), color='skyblue')\n"
   ]
  }
 ],
 "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": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "212px",
    "width": "282px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "262px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}