ligthgbm_models/fraud-fs-fe9477-no-day-seed-88.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "import datetime\n",
    "import gc\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import random\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "sns.set()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seed_everything(seed=13):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    \n",
    "    \n",
    "def read_from_disk(path, filename):\n",
    "    with open(os.path.join(path, filename), 'rb') as handle:\n",
    "        return pickle.load(handle)\n",
    "    \n",
    "    \n",
    "def save_to_disk(obj, filename):\n",
    "    with open(filename, 'wb') as handle:\n",
    "        pickle.dump(obj, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_features_by_importance(x_train, x_val, y_val, model, importance_tresh=0.1, permut_tresh=1e-4):\n",
    "    all_features = x_train.columns\n",
    "    feature_importance = calc_feature_importance(all_features, model)\n",
    "    garbage_features = find_garbage_features(feature_importance, importance_tresh)\n",
    "    retain_features = find_features_to_retain(garbage_features, x_val, y_val, model, threshold=permut_tresh)\n",
    "    remove_features = set(garbage_features) - set(retain_features)\n",
    "    return list(set(all_features) - remove_features), list(remove_features), feature_importance\n",
    "\n",
    "\n",
    "def calc_feature_importance(features, model):\n",
    "    importance = pd.DataFrame([\n",
    "        list(features),\n",
    "        model.feature_importance(importance_type='split').tolist(),\n",
    "        model.feature_importance(importance_type='gain').tolist()\n",
    "    ]).T\n",
    "    importance.columns = ['feature', 'split', 'gain']\n",
    "    return importance\n",
    "\n",
    "\n",
    "def find_garbage_features(importance, treshold):\n",
    "    min_gain = importance['gain'].quantile(treshold)\n",
    "    min_split = importance['split'].quantile(treshold)\n",
    "    garbage_features = importance[ \n",
    "        (importance['split'] < min_split) # | (importance['gain'] < min_gain) \n",
    "    ]['feature']\n",
    "    return list(garbage_features)\n",
    "\n",
    "\n",
    "def find_features_to_retain(features, x, y, model, threshold=1e-4):\n",
    "    scores = permutation_importance(x.copy(), y, model, columns=features)\n",
    "    retain_features = [feat for feat, score in scores.items() if score < -threshold]\n",
    "    return retain_features\n",
    "\n",
    "\n",
    "def permutation_importance(x, y, model, base_score=None, columns=None): \n",
    "    diff = {}\n",
    "    if not base_score:\n",
    "        base_score = roc_auc_score(y, model.predict(x))\n",
    "    if not columns:\n",
    "        columns = x.columns\n",
    "    for col in columns:\n",
    "        value = x[col].copy()\n",
    "        x[col] = np.random.permutation(x[col].values)\n",
    "        diff[col] = roc_auc_score(y, model.predict(x)) - base_score\n",
    "        x[col] = value\n",
    "    return diff\n",
    "\n",
    "\n",
    "def plot_feature_importance(model, max_features=50):\n",
    "    f, ax = plt.subplots(1,2, figsize=[20,10])\n",
    "    lgb.plot_importance(model, max_num_features=max_features, ax=ax[0], importance_type='gain')\n",
    "    ax[0].set_title(\"Light GBM GAIN Feature Importance\")\n",
    "\n",
    "    lgb.plot_importance(model, max_num_features=max_features, ax=ax[1], importance_type='split')\n",
    "    ax[1].set_title(\"Light GBM SPLIT Feature Importance\")\n",
    "    plt.tight_layout(pad=1)\n",
    "    \n",
    "    \n",
    "def avg_feature_importance(dfs):\n",
    "    temp = pd.concat(dfs).reset_index(drop=True)\n",
    "    for col in ['split', 'gain']:\n",
    "        temp[col] = temp[col].astype(float)\n",
    "    agg = temp.groupby('feature').mean()\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DFS_DIR = '../input/fraud-messing-v31'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, y_train, df_val, y_val, df_hold, y_hold, df_test = map(partial(read_from_disk, DFS_DIR), [\n",
    "    'df_train.pkl', 'y_train.pkl', 'df_val.pkl', 'y_val.pkl', 'df_hold.pkl', 'y_hold.pkl', 'df_test.pkl'\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TransactionDT', 'TransactionAmt', 'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5', 'card6', 'addr1', 'addr2', 'dist1', 'dist2', 'P_emaildomain', 'R_emaildomain', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13', 'C14', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 'V30', 'V31', 'V32', 'V33', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52', 'V53', 'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V65', 'V66', 'V67', 'V68', 'V69', 'V70', 'V71', 'V72', 'V73', 'V74', 'V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94', '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', 'V138', 'V139', 'V140', 'V141', 'V142', 'V143', 'V144', 'V145', 'V146', 'V147', 'V148', 'V149', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V159', 'V160', 'V161', 'V162', 'V163', 'V164', 'V165', 'V166', 'V167', 'V168', 'V169', 'V170', 'V171', 'V172', 'V173', 'V174', 'V175', 'V176', 'V177', 'V178', 'V179', 'V180', 'V181', 'V182', 'V183', 'V184', 'V185', 'V186', 'V187', 'V188', 'V189', 'V190', 'V191', 'V192', 'V193', 'V194', 'V195', 'V196', 'V197', 'V198', 'V199', 'V200', 'V201', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V208', 'V209', 'V210', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216', 'V217', 'V218', 'V219', 'V220', 'V221', 'V222', 'V223', 'V224', 'V225', 'V226', 'V227', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V234', 'V235', 'V236', 'V237', 'V238', 'V239', 'V240', 'V241', 'V242', 'V243', 'V244', 'V245', 'V246', 'V247', 'V248', 'V249', 'V250', 'V251', 'V252', 'V253', 'V254', 'V255', 'V256', 'V257', 'V258', 'V259', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V269', 'V270', 'V271', 'V272', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278', 'V279', 'V280', 'V281', 'V282', 'V283', 'V284', 'V285', 'V286', 'V287', 'V288', 'V289', 'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V296', 'V297', 'V298', 'V299', 'V300', 'V301', 'V302', 'V303', 'V304', 'V305', 'V306', 'V307', 'V308', 'V309', 'V310', 'V311', 'V312', 'V313', 'V314', 'V315', 'V316', 'V317', 'V318', 'V319', 'V320', 'V321', 'V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339', 'id_01', 'id_02', 'id_03', 'id_04', 'id_05', 'id_06', 'id_07', 'id_08', 'id_09', 'id_10', 'id_11', 'id_12', 'id_13', 'id_14', 'id_15', 'id_16', 'id_17', 'id_18', 'id_19', 'id_20', 'id_21', 'id_22', 'id_23', 'id_24', 'id_25', 'id_26', 'id_27', 'id_28', 'id_29', 'id_30', 'id_31', 'id_32', 'id_33', 'id_34', 'id_35', 'id_36', 'id_37', 'id_38', 'DeviceType', 'DeviceInfo', 'Cents', 'Hour', 'DayOfWeek', 'Day', 'card1__addr1', 'card1__addr1_count', 'card1__card5', 'card1__card5_count', 'card2__dist1', 'card2__id_20', 'card5__P_emaildomain', 'uuid3_count', 'TransactionAmt_mean_by_uuid2_ProductCD', 'dist2_mean_by_uuid2_addr1', 'card1_count', 'card2_count', 'card5_count', 'addr1_count', 'C1_count', 'C13_count', 'P_emaildomain_count', 'D2_na', 'D3_na', 'D5_na', 'D6_na', 'D7_na', 'D13_na', 'D14_na', 'M4_na', 'M5_na', 'M6_na', 'M7_na', 'M8_na', 'P_emaildomain_parts', 'P_emaildomain_sfx2', 'P_emaildomain_sfx1', 'P_emaildomain_bin', 'R_emaildomain_parts', 'R_emaildomain_sfx2', 'R_emaildomain_sfx1', 'R_emaildomain_bin', 'Browser', 'OS', 'id_16_na', 'id_33_0', 'id_33_1']\n"
     ]
    }
   ],
   "source": [
    "print(list(df_train.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dfs = [df_train, df_val, df_hold, df_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding uid\n",
      "Encoding uid2\n",
      "Encoding uid3\n"
     ]
    }
   ],
   "source": [
    "def frequency(col, dfs):\n",
    "    counts = pd.concat(map(lambda df: df[col], dfs), ignore_index=True).value_counts(dropna=False)\n",
    "    return map(lambda df: df[col].map(counts), dfs)\n",
    "\n",
    "# from https://www.kaggle.com/tolgahancepel/lightgbm-single-model-and-feature-engineering\n",
    "def concat_features(data): \n",
    "    data['uid'] = data['card1'].astype(str)+'_'+data['card2'].astype(str)\n",
    "    data['uid2'] = data['uid'].astype(str)+'_'+data['card3'].astype(str)+'_'+data['card5'].astype(str)\n",
    "    data['uid3'] = data['uid2'].astype(str)+'_'+data['addr1'].astype(str)+'_'+data['addr2'].astype(str)\n",
    "    return data\n",
    "\n",
    "for df in all_dfs:\n",
    "    df = concat_features(df)\n",
    "\n",
    "\n",
    "freq_cols = ['card1','card2','card3','card5',\n",
    "          'C2','C3','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13','C14',\n",
    "          'D1','D2','D3','D4','D5','D6','D7','D8',\n",
    "          'addr1','addr2',\n",
    "          'dist1','dist2',\n",
    "          'P_emaildomain', 'R_emaildomain',\n",
    "          'DeviceInfo','DeviceType',\n",
    "          'id_30','id_33',\n",
    "          'uid','uid2','uid3',\n",
    "         ]\n",
    "for col in freq_cols:\n",
    "    for df, freq in zip(all_dfs, frequency(col, all_dfs)):\n",
    "        df[col+'_count'] = freq\n",
    "\n",
    "\n",
    "agg_cols = ['card1','card2','card3','card5', 'uid', 'uid2', 'uid3']  \n",
    "for col in agg_cols:\n",
    "    for agg_type in ['mean','std']:\n",
    "        new_col_name = col+'_TransactionAmt_'+agg_type\n",
    "        temp_df = pd.concat([df_train[[col, 'TransactionAmt']], df_val[[col, 'TransactionAmt']], df_hold[[col, 'TransactionAmt']], df_test[[col,'TransactionAmt']]])\n",
    "        #temp_df['TransactionAmt'] = temp_df['TransactionAmt'].astype(int)\n",
    "        temp_df = temp_df.groupby([col])['TransactionAmt'].agg([agg_type]).reset_index().rename(\n",
    "                                                columns={agg_type: new_col_name})\n",
    "\n",
    "        temp_df.index = list(temp_df[col])\n",
    "        temp_df = temp_df[new_col_name].to_dict()   \n",
    "        \n",
    "        for df in all_dfs:\n",
    "            df[new_col_name] = df[col].map(temp_df)\n",
    "        del temp_df\n",
    "\n",
    "\n",
    "encode_cols = ['uid', 'uid2', 'uid3']\n",
    "for col in encode_cols:\n",
    "    print('Encoding', col)\n",
    "    temp_df = pd.concat([df_train[col], df_val[col], df_hold[col], df_test[col]])\n",
    "    col_encoded = temp_df.value_counts().to_dict()\n",
    "    del temp_df\n",
    "    for df in all_dfs:\n",
    "        df[col] = df[col].map(col_encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initial_features = {\n",
    "#     'C1', 'C10', 'C11', 'C12', 'C13', 'C14', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', \n",
    "#     'Cents', 'D1', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 'D2', 'D3', 'D4', 'D5', 'D6', \n",
    "#     'D7', 'D8', 'D9', 'DayOfMonth', 'DayOfWeek', 'DeviceInfo', 'DeviceType', 'Hour', \n",
    "#     'IsMonthEnd', 'IsMonthStart', 'IsQuarterEnd', 'IsQuarterStart', 'M1', 'M2', 'M3', 'M4', \n",
    "#     'M5', 'M6', 'M7', 'M8', 'M9', 'MonthFraction', 'P_emaildomain', 'ProductCD', 'Quarter', \n",
    "#     'R_emaildomain', 'TransactionAmt', 'TransactionDT', 'V1', 'V10', 'V100', 'V101', 'V102', \n",
    "#     'V103', 'V104', 'V105', 'V106', 'V108', 'V109', 'V11', 'V110', 'V112', 'V114', 'V115', \n",
    "#     'V116', 'V12', 'V123', 'V124', 'V125', 'V126', 'V127', 'V128', 'V129', 'V13', 'V130', \n",
    "#     'V131', 'V132', 'V133', 'V134', 'V135', 'V136', 'V137', 'V139', 'V140', 'V141', 'V142', \n",
    "#     'V143', 'V144', 'V145', 'V146', 'V147', 'V148', 'V149', 'V15', 'V150', 'V151', 'V152', \n",
    "#     'V154', 'V155', 'V156', 'V157', 'V158', 'V159', 'V16', 'V160', 'V161', 'V162', 'V163', \n",
    "#     'V164', 'V165', 'V166', 'V167', 'V168', 'V169', 'V17', 'V170', 'V171', 'V172', 'V173', \n",
    "#     'V174', 'V175', 'V176', 'V177', 'V178', 'V179', 'V18', 'V180', 'V181', 'V182', 'V183', \n",
    "#     'V184', 'V185', 'V186', 'V187', 'V188', 'V189', 'V19', 'V190', 'V192', 'V193', 'V198', \n",
    "#     'V199', 'V2', 'V20', 'V200', 'V201', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', \n",
    "#     'V208', 'V209', 'V210', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216', 'V217', 'V218', \n",
    "#     'V219', 'V22', 'V220', 'V221', 'V222', 'V223', 'V224', 'V225', 'V226', 'V227', 'V228', \n",
    "#     'V229', 'V23', 'V230', 'V231', 'V232', 'V233', 'V234', 'V235', 'V236', 'V237', 'V238', \n",
    "#     'V239', 'V24', 'V242', 'V243', 'V244', 'V245', 'V246', 'V247', 'V248', 'V249', 'V25', \n",
    "#     'V250', 'V251', 'V252', 'V253', 'V254', 'V255', 'V256', 'V257', 'V258', 'V259', 'V26', \n",
    "#     'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V270', 'V271', \n",
    "#     'V272', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278', 'V279', 'V280', 'V281', 'V282', \n",
    "#     'V283', 'V284', 'V285', 'V286', 'V287', 'V288', 'V289', 'V29', 'V290', 'V291', 'V292', \n",
    "#     'V293', 'V294', 'V295', 'V296', 'V297', 'V298', 'V299', 'V3', 'V30', 'V300', 'V301', \n",
    "#     'V302', 'V303', 'V304', 'V306', 'V307', 'V308', 'V309', 'V310', 'V311', 'V312', 'V313', \n",
    "#     'V314', 'V315', 'V316', 'V317', 'V318', 'V319', 'V32', 'V320', 'V321', 'V322', 'V323', \n",
    "#     'V324', 'V326', 'V327', 'V329', 'V33', 'V331', 'V332', 'V333', 'V335', 'V336', 'V337', \n",
    "#     'V338', 'V339', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V4', 'V40', 'V42', 'V43', 'V44', \n",
    "#     'V45', 'V46', 'V47', 'V48', 'V49', 'V5', 'V50', 'V51', 'V52', 'V53', 'V54', 'V55', 'V56', \n",
    "#     'V57', 'V58', 'V59', 'V6', 'V60', 'V61', 'V62', 'V63', 'V64', 'V66', 'V67', 'V69', 'V7', \n",
    "#     'V70', 'V71', 'V72', 'V73', 'V74', 'V75', 'V76', 'V77', 'V78', 'V79', 'V8', 'V80', 'V81', \n",
    "#     'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V9', 'V90', 'V91', 'V92', 'V93', 'V94', 'V95', \n",
    "#     'V96', 'V97', 'V98', 'V99', 'addr1', 'addr2', 'card1', 'card2', 'card3', 'card4', 'card5', \n",
    "#     'card6', 'dist1', 'dist2', 'id_01', 'id_02', 'id_03', 'id_04', 'id_05', 'id_06', 'id_07', \n",
    "#     'id_08', 'id_09', 'id_10', 'id_11', 'id_12', 'id_13', 'id_14', 'id_15', 'id_16', 'id_16_na', \n",
    "#     'id_17', 'id_18', 'id_19', 'id_20', 'id_21', 'id_24', 'id_25', 'id_26', 'id_28', 'id_29', \n",
    "#     'id_30', 'id_31', 'id_32', 'id_33', 'id_33_0', 'id_33_1', 'id_34', 'id_35', 'id_36', 'id_37', \n",
    "#     'id_38'\n",
    "# }\n",
    "\n",
    "# for df in [df_train, df_val, df_hold]:   \n",
    "#     for col in df.columns:\n",
    "#         if col not in initial_features:\n",
    "#             df.drop(col, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_params():\n",
    "    \"\"\"\n",
    "    Params are from https://www.kaggle.com/kyakovlev/ieee-lgbm-with-groupkfold-cv\n",
    "    \"\"\"\n",
    "    return {\n",
    "        'objective':'binary',\n",
    "        'boosting_type':'gbdt',\n",
    "        'metric':'auc',\n",
    "        'n_jobs':-1,\n",
    "        'learning_rate':0.01,\n",
    "        'num_leaves': 2**8,\n",
    "        'max_depth':-1,\n",
    "        'tree_learner':'serial',\n",
    "        'colsample_bytree': 0.5,\n",
    "        'subsample_freq':1,\n",
    "        'subsample':0.7,\n",
    "        'max_bin':255,\n",
    "        'verbose':1,\n",
    "        'seed': 88,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rnd_int():\n",
    "    return np.random.randint(np.iinfo(np.int32).max)\n",
    "\n",
    "\n",
    "def sample(x, y, neg_frac=0.2, seed=None):\n",
    "    if not seed:\n",
    "        seed = rnd_int()\n",
    "    x_pos, y_pos = x[y==1], y[y==1]\n",
    "    x_neg, _, y_neg, _ = train_test_split(x[y==0], y[y==0], train_size=neg_frac, random_state=seed)\n",
    "    x = pd.concat([x_pos, x_neg])\n",
    "    y = pd.concat([y_pos, y_neg])\n",
    "    return shuffle(x, y, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def search(n_runs=5, neg_frac=0.2, importance_tresh=0.1, permut_tresh=1e-4):\n",
    "    f_imps = []\n",
    "    selected_features = set()\n",
    "    params = model_params()\n",
    "    \n",
    "    for run in range(n_runs):\n",
    "        lgb_train = lgb.Dataset(*sample(df_train, y_train, neg_frac))\n",
    "        lgb_eval = lgb.Dataset(df_val, y_val, reference=lgb_train)\n",
    "        \n",
    "        seed_everything(rnd_int())\n",
    "        params['seed'] = rnd_int()\n",
    "        print('Starting %d run...' % run)\n",
    "        \n",
    "        lgb_model = lgb.train(params, lgb_train, \n",
    "                              num_boost_round=3000, \n",
    "                              valid_sets=lgb_eval, \n",
    "                              early_stopping_rounds=100,\n",
    "                              verbose_eval=False)\n",
    "        \n",
    "        print('Finished %d run. Selecting features...' % run)\n",
    "        top_features, worst_features, f_imp = split_features_by_importance(df_train, df_val, y_val, lgb_model, \n",
    "                                   importance_tresh, permut_tresh)\n",
    "        f_imps.append(f_imp)\n",
    "        selected_features.update(top_features)\n",
    "        print('Consider to drop: ', sorted(worst_features))\n",
    "    return list(selected_features), avg_feature_importance(f_imps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See in https://www.kaggle.com/blackitten13/fraud-feature-selection-fe9477"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selected_features, feature_importance = search(n_runs=4, neg_frac=0.5, importance_tresh=0.05, permut_tresh=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [],
   "source": [
    "# fi = feature_importance['gain'].nlargest(60)\n",
    "# print('Best 60 features by GAIN feature importnace:\\n\\n{}'.format(fi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [],
   "source": [
    "# fi = feature_importance['gain'].nsmallest(60)\n",
    "# print('Worst 60 features by GAIN feature importnace:\\n\\n{}'.format(fi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [],
   "source": [
    "# fi = feature_importance['split'].nlargest(60)\n",
    "# print('Best 60 features by SPLIT feature importnace:\\n\\n{}'.format(fi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [],
   "source": [
    "# fi = feature_importance['split'].nsmallest(60)\n",
    "# print('Worst 60 features by SPLIT feature importnace:\\n\\n{}'.format(fi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('{} / {}'.format(len(selected_features), len(df_train.columns)))\n",
    "# print(sorted(selected_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = ['Browser', 'C1', 'C10', 'C10_count', 'C11', 'C11_count', 'C12', 'C12_count', 'C13', 'C13_count', 'C14', 'C14_count', 'C1_count', 'C2', 'C2_count', 'C3', 'C3_count', 'C4', 'C4_count', 'C5', 'C5_count', 'C6', 'C6_count', 'C7', 'C7_count', 'C8', 'C8_count', 'C9', 'C9_count', 'Cents', 'D1', 'D10', 'D11', 'D12', 'D13', 'D13_na', 'D14', 'D14_na', 'D15', 'D1_count', 'D2', 'D2_count', 'D2_na', 'D3', 'D3_count', 'D3_na', 'D4', 'D4_count', 'D5', 'D5_count', 'D5_na', 'D6', 'D6_count', 'D6_na', 'D7', 'D7_count', 'D7_na', 'D8', 'D8_count', 'D9', 'DayOfWeek', 'DeviceInfo', 'DeviceInfo_count', 'DeviceType', 'DeviceType_count', 'Hour', 'M1', 'M2', 'M3', 'M4', 'M4_na', 'M5', 'M5_na', 'M6', 'M6_na', 'M7', 'M7_na', 'M8', 'M8_na', 'M9', 'OS', 'P_emaildomain', 'P_emaildomain_bin', 'P_emaildomain_count', 'P_emaildomain_parts', 'P_emaildomain_sfx1', 'P_emaildomain_sfx2', 'ProductCD', 'R_emaildomain', 'R_emaildomain_bin', 'R_emaildomain_count', 'R_emaildomain_parts', 'R_emaildomain_sfx1', 'R_emaildomain_sfx2', 'TransactionAmt', 'TransactionDT', 'V1', 'V10', 'V100', 'V101', 'V102', 'V103', 'V104', 'V105', 'V106', 'V108', 'V109', 'V11', 'V110', 'V111', 'V112', 'V114', 'V115', 'V116', 'V12', 'V122', 'V123', 'V124', 'V125', 'V126', 'V127', 'V128', 'V129', 'V13', 'V130', 'V131', 'V132', 'V133', 'V134', 'V135', 'V136', 'V137', 'V138', 'V139', 'V140', 'V141', 'V142', 'V143', 'V144', 'V145', 'V146', 'V147', 'V148', 'V149', 'V15', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V159', 'V16', 'V160', 'V161', 'V162', 'V163', 'V164', 'V165', 'V166', 'V167', 'V168', 'V169', 'V17', 'V170', 'V171', 'V172', 'V173', 'V174', 'V175', 'V176', 'V177', 'V178', 'V179', 'V18', 'V180', 'V181', 'V182', 'V183', 'V184', 'V185', 'V186', 'V187', 'V188', 'V189', 'V19', 'V190', 'V191', 'V192', 'V193', 'V194', 'V195', 'V196', 'V197', 'V198', 'V199', 'V2', 'V20', 'V200', 'V201', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V208', 'V209', 'V21', 'V210', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216', 'V217', 'V218', 'V219', 'V22', 'V220', 'V221', 'V222', 'V223', 'V224', 'V225', 'V226', 'V227', 'V228', 'V229', 'V23', 'V230', 'V231', 'V232', 'V233', 'V234', 'V235', 'V236', 'V237', 'V238', 'V239', 'V24', 'V240', 'V242', 'V243', 'V244', 'V245', 'V246', 'V247', 'V248', 'V249', 'V25', 'V250', 'V251', 'V252', 'V253', 'V254', 'V255', 'V256', 'V257', 'V258', 'V259', 'V26', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V270', 'V271', 'V272', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278', 'V279', 'V280', 'V281', 'V282', 'V283', 'V284', 'V285', 'V286', 'V287', 'V288', 'V289', 'V29', 'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V296', 'V297', 'V298', 'V299', 'V3', 'V30', 'V300', 'V301', 'V302', 'V303', 'V304', 'V306', 'V307', 'V308', 'V309', 'V31', 'V310', 'V311', 'V312', 'V313', 'V314', 'V315', 'V316', 'V317', 'V318', 'V319', 'V32', 'V320', 'V321', 'V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V33', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V4', 'V40', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V5', 'V50', 'V51', 'V52', 'V53', 'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V6', 'V60', 'V61', 'V62', 'V63', 'V64', 'V66', 'V67', 'V69', 'V7', 'V70', 'V71', 'V72', 'V73', 'V74', 'V75', 'V76', 'V77', 'V78', 'V79', 'V8', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V9', 'V90', 'V91', 'V92', 'V93', 'V94', 'V95', 'V96', 'V97', 'V98', 'V99', 'addr1', 'addr1_count', 'addr2', 'addr2_count', 'card1', 'card1_TransactionAmt_mean', 'card1_TransactionAmt_std', 'card1__addr1', 'card1__addr1_count', 'card1__card5', 'card1__card5_count', 'card1_count', 'card2', 'card2_TransactionAmt_mean', 'card2_TransactionAmt_std', 'card2__dist1', 'card2__id_20', 'card2_count', 'card3', 'card3_TransactionAmt_mean', 'card3_TransactionAmt_std', 'card3_count', 'card4', 'card5', 'card5_TransactionAmt_mean', 'card5_TransactionAmt_std', 'card5__P_emaildomain', 'card5_count', 'card6', 'dist1', 'dist1_count', 'dist2', 'dist2_count', 'id_01', 'id_02', 'id_03', 'id_04', 'id_05', 'id_06', 'id_07', 'id_08', 'id_09', 'id_10', 'id_11', 'id_12', 'id_13', 'id_14', 'id_15', 'id_16', 'id_16_na', 'id_17', 'id_18', 'id_19', 'id_20', 'id_21', 'id_22', 'id_23', 'id_24', 'id_25', 'id_26', 'id_28', 'id_29', 'id_30', 'id_30_count', 'id_31', 'id_32', 'id_33', 'id_33_0', 'id_33_1', 'id_33_count', 'id_34', 'id_35', 'id_36', 'id_37', 'id_38', 'uid', 'uid2', 'uid2_TransactionAmt_mean', 'uid2_TransactionAmt_std', 'uid2_count', 'uid3', 'uid3_TransactionAmt_mean', 'uid3_TransactionAmt_std', 'uid3_count', 'uid_TransactionAmt_mean', 'uid_TransactionAmt_std', 'uid_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drop:  ['Day', 'TransactionAmt_mean_by_uuid2_ProductCD', 'V107', 'V113', 'V117', 'V118', 'V119', 'V120', 'V121', 'V14', 'V241', 'V269', 'V27', 'V28', 'V305', 'V41', 'V65', 'V68', 'V88', 'V89', 'dist2_mean_by_uuid2_addr1', 'id_27', 'uuid3_count']\n"
     ]
    }
   ],
   "source": [
    "drop_features = set(df_train.columns) - set(selected_features)\n",
    "print('Drop: ', sorted(list(drop_features)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test selected features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [df_train, df_val, df_hold, df_test]:\n",
    "    for col in df.columns:\n",
    "        if col not in selected_features:\n",
    "            df.drop(col, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.concat([df_train, df_val, df_hold], axis=0, ignore_index=True)\n",
    "y = pd.concat([y_train, y_val, y_hold], axis=0, ignore_index=True)\n",
    "del df_val, df_hold, y_train, y_val, y_hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_cents(df):\n",
    "    return (np.modf(df['TransactionAmt'])[0] * 1000).round(5).astype(np.uint16)\n",
    "\n",
    "train_trans = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')\n",
    "df_train['Cents'] = feature_cents(train_trans).values\n",
    "del train_trans\n",
    "\n",
    "test_trans = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')\n",
    "df_test['Cents'] = feature_cents(test_trans).values\n",
    "del test_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/ops/__init__.py:1115: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  result = method(y)\n"
     ]
    }
   ],
   "source": [
    "def feature_ProductCD_W_cents(df):\n",
    "    a = ((df['Cents'] == 0) | (df['Cents'] == 500) | (df['Cents'] == 950))\n",
    "    b = df['ProductCD'] == 'W'\n",
    "    return (a & b).astype(np.uint8)\n",
    "\n",
    "df_train['Cents_ProductCD_W'] = feature_ProductCD_W_cents(df_train)\n",
    "df_test['Cents_ProductCD_W'] = feature_ProductCD_W_cents(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TransactionDT', 'TransactionAmt', 'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5', 'card6', 'addr1', 'addr2', 'dist1', 'dist2', 'P_emaildomain', 'R_emaildomain', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13', 'C14', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V29', 'V30', 'V31', 'V32', 'V33', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52', 'V53', 'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V66', 'V67', 'V69', 'V70', 'V71', 'V72', 'V73', 'V74', 'V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V90', 'V91', 'V92', 'V93', 'V94', 'V95', 'V96', 'V97', 'V98', 'V99', 'V100', 'V101', 'V102', 'V103', 'V104', 'V105', 'V106', 'V108', 'V109', 'V110', 'V111', 'V112', 'V114', 'V115', 'V116', 'V122', 'V123', 'V124', 'V125', 'V126', 'V127', 'V128', 'V129', 'V130', 'V131', 'V132', 'V133', 'V134', 'V135', 'V136', 'V137', 'V138', 'V139', 'V140', 'V141', 'V142', 'V143', 'V144', 'V145', 'V146', 'V147', 'V148', 'V149', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V159', 'V160', 'V161', 'V162', 'V163', 'V164', 'V165', 'V166', 'V167', 'V168', 'V169', 'V170', 'V171', 'V172', 'V173', 'V174', 'V175', 'V176', 'V177', 'V178', 'V179', 'V180', 'V181', 'V182', 'V183', 'V184', 'V185', 'V186', 'V187', 'V188', 'V189', 'V190', 'V191', 'V192', 'V193', 'V194', 'V195', 'V196', 'V197', 'V198', 'V199', 'V200', 'V201', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V208', 'V209', 'V210', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216', 'V217', 'V218', 'V219', 'V220', 'V221', 'V222', 'V223', 'V224', 'V225', 'V226', 'V227', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V234', 'V235', 'V236', 'V237', 'V238', 'V239', 'V240', 'V242', 'V243', 'V244', 'V245', 'V246', 'V247', 'V248', 'V249', 'V250', 'V251', 'V252', 'V253', 'V254', 'V255', 'V256', 'V257', 'V258', 'V259', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V270', 'V271', 'V272', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278', 'V279', 'V280', 'V281', 'V282', 'V283', 'V284', 'V285', 'V286', 'V287', 'V288', 'V289', 'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V296', 'V297', 'V298', 'V299', 'V300', 'V301', 'V302', 'V303', 'V304', 'V306', 'V307', 'V308', 'V309', 'V310', 'V311', 'V312', 'V313', 'V314', 'V315', 'V316', 'V317', 'V318', 'V319', 'V320', 'V321', 'V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339', 'id_01', 'id_02', 'id_03', 'id_04', 'id_05', 'id_06', 'id_07', 'id_08', 'id_09', 'id_10', 'id_11', 'id_12', 'id_13', 'id_14', 'id_15', 'id_16', 'id_17', 'id_18', 'id_19', 'id_20', 'id_21', 'id_22', 'id_23', 'id_24', 'id_25', 'id_26', 'id_28', 'id_29', 'id_30', 'id_31', 'id_32', 'id_33', 'id_34', 'id_35', 'id_36', 'id_37', 'id_38', 'DeviceType', 'DeviceInfo', 'Cents', 'Hour', 'DayOfWeek', 'card1__addr1', 'card1__addr1_count', 'card1__card5', 'card1__card5_count', 'card2__dist1', 'card2__id_20', 'card5__P_emaildomain', 'card1_count', 'card2_count', 'card5_count', 'addr1_count', 'C1_count', 'C13_count', 'P_emaildomain_count', 'D2_na', 'D3_na', 'D5_na', 'D6_na', 'D7_na', 'D13_na', 'D14_na', 'M4_na', 'M5_na', 'M6_na', 'M7_na', 'M8_na', 'P_emaildomain_parts', 'P_emaildomain_sfx2', 'P_emaildomain_sfx1', 'P_emaildomain_bin', 'R_emaildomain_parts', 'R_emaildomain_sfx2', 'R_emaildomain_sfx1', 'R_emaildomain_bin', 'Browser', 'OS', 'id_16_na', 'id_33_0', 'id_33_1', 'uid', 'uid2', 'uid3', 'card3_count', 'C2_count', 'C3_count', 'C4_count', 'C5_count', 'C6_count', 'C7_count', 'C8_count', 'C9_count', 'C10_count', 'C11_count', 'C12_count', 'C14_count', 'D1_count', 'D2_count', 'D3_count', 'D4_count', 'D5_count', 'D6_count', 'D7_count', 'D8_count', 'addr2_count', 'dist1_count', 'dist2_count', 'R_emaildomain_count', 'DeviceInfo_count', 'DeviceType_count', 'id_30_count', 'id_33_count', 'uid_count', 'uid2_count', 'uid3_count', 'card1_TransactionAmt_mean', 'card1_TransactionAmt_std', 'card2_TransactionAmt_mean', 'card2_TransactionAmt_std', 'card3_TransactionAmt_mean', 'card3_TransactionAmt_std', 'card5_TransactionAmt_mean', 'card5_TransactionAmt_std', 'uid_TransactionAmt_mean', 'uid_TransactionAmt_std', 'uid2_TransactionAmt_mean', 'uid2_TransactionAmt_std', 'uid3_TransactionAmt_mean', 'uid3_TransactionAmt_std', 'Cents_ProductCD_W']\n"
     ]
    }
   ],
   "source": [
    "print(list(df_train.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "START_DATE = datetime.datetime.strptime('2017-11-30', '%Y-%m-%d')\n",
    "TransactionDT1 = df_train['TransactionDT'].apply(lambda x: (START_DATE + datetime.timedelta(seconds = x)))\n",
    "dt_m = TransactionDT1.dt.date.astype('str').str[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((590540, 504), (506691, 504))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.drop(['TransactionDT'], axis=1)\n",
    "df_test = df_test.drop(['TransactionDT'], axis=1)\n",
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_kg_hide-output": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(453219, 504) (453219,) (137321, 504) (137321,)\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.963519\tvalid_1's auc: 0.895709\n",
      "[400]\ttraining's auc: 0.987543\tvalid_1's auc: 0.910288\n",
      "[600]\ttraining's auc: 0.995187\tvalid_1's auc: 0.915841\n",
      "[800]\ttraining's auc: 0.997976\tvalid_1's auc: 0.918903\n",
      "[1000]\ttraining's auc: 0.999172\tvalid_1's auc: 0.920177\n",
      "[1200]\ttraining's auc: 0.999654\tvalid_1's auc: 0.920881\n",
      "[1400]\ttraining's auc: 0.99985\tvalid_1's auc: 0.921036\n",
      "[1600]\ttraining's auc: 0.999939\tvalid_1's auc: 0.921336\n",
      "[1800]\ttraining's auc: 0.999974\tvalid_1's auc: 0.921473\n",
      "[2000]\ttraining's auc: 0.999991\tvalid_1's auc: 0.92173\n",
      "[2200]\ttraining's auc: 0.999997\tvalid_1's auc: 0.921483\n",
      "[2400]\ttraining's auc: 0.999999\tvalid_1's auc: 0.921479\n",
      "Early stopping, best iteration is:\n",
      "[1983]\ttraining's auc: 0.99999\tvalid_1's auc: 0.921801\n",
      "Fold 1 | AUC: 0.921800849275633\n",
      "(497955, 504) (497955,) (92585, 504) (92585,)\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.960676\tvalid_1's auc: 0.914502\n",
      "[400]\ttraining's auc: 0.986732\tvalid_1's auc: 0.931782\n",
      "[600]\ttraining's auc: 0.995026\tvalid_1's auc: 0.938702\n",
      "[800]\ttraining's auc: 0.99795\tvalid_1's auc: 0.941644\n",
      "[1000]\ttraining's auc: 0.999116\tvalid_1's auc: 0.943451\n",
      "[1200]\ttraining's auc: 0.999621\tvalid_1's auc: 0.944146\n",
      "[1400]\ttraining's auc: 0.999836\tvalid_1's auc: 0.944755\n",
      "[1600]\ttraining's auc: 0.999931\tvalid_1's auc: 0.944895\n",
      "[1800]\ttraining's auc: 0.999971\tvalid_1's auc: 0.945285\n",
      "[2000]\ttraining's auc: 0.999989\tvalid_1's auc: 0.945235\n",
      "[2200]\ttraining's auc: 0.999996\tvalid_1's auc: 0.945187\n",
      "[2400]\ttraining's auc: 0.999999\tvalid_1's auc: 0.945144\n",
      "[2600]\ttraining's auc: 1\tvalid_1's auc: 0.945149\n",
      "Early stopping, best iteration is:\n",
      "[2285]\ttraining's auc: 0.999997\tvalid_1's auc: 0.945328\n",
      "Fold 2 | AUC: 0.9453281745178567\n",
      "(504519, 504) (504519,) (86021, 504) (86021,)\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.960409\tvalid_1's auc: 0.931359\n",
      "[400]\ttraining's auc: 0.986409\tvalid_1's auc: 0.945054\n",
      "[600]\ttraining's auc: 0.99452\tvalid_1's auc: 0.94879\n",
      "[800]\ttraining's auc: 0.997629\tvalid_1's auc: 0.950011\n",
      "[1000]\ttraining's auc: 0.99897\tvalid_1's auc: 0.950367\n",
      "[1200]\ttraining's auc: 0.999547\tvalid_1's auc: 0.950625\n",
      "[1400]\ttraining's auc: 0.999804\tvalid_1's auc: 0.950386\n",
      "[1600]\ttraining's auc: 0.999919\tvalid_1's auc: 0.950095\n",
      "Early stopping, best iteration is:\n",
      "[1299]\ttraining's auc: 0.999701\tvalid_1's auc: 0.950702\n",
      "Fold 3 | AUC: 0.950702125882442\n",
      "(488908, 504) (488908,) (101632, 504) (101632,)\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.963488\tvalid_1's auc: 0.921874\n",
      "[400]\ttraining's auc: 0.987885\tvalid_1's auc: 0.9373\n",
      "[600]\ttraining's auc: 0.995563\tvalid_1's auc: 0.94293\n",
      "[800]\ttraining's auc: 0.998284\tvalid_1's auc: 0.945012\n",
      "[1000]\ttraining's auc: 0.99929\tvalid_1's auc: 0.945944\n",
      "[1200]\ttraining's auc: 0.999711\tvalid_1's auc: 0.946365\n",
      "[1400]\ttraining's auc: 0.999879\tvalid_1's auc: 0.946586\n",
      "[1600]\ttraining's auc: 0.999952\tvalid_1's auc: 0.946446\n",
      "[1800]\ttraining's auc: 0.999981\tvalid_1's auc: 0.946513\n",
      "Early stopping, best iteration is:\n",
      "[1408]\ttraining's auc: 0.999883\tvalid_1's auc: 0.946609\n",
      "Fold 4 | AUC: 0.9466090565628372\n",
      "(506885, 504) (506885,) (83655, 504) (83655,)\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.958731\tvalid_1's auc: 0.924021\n",
      "[400]\ttraining's auc: 0.985573\tvalid_1's auc: 0.94472\n",
      "[600]\ttraining's auc: 0.994403\tvalid_1's auc: 0.951854\n",
      "[800]\ttraining's auc: 0.997573\tvalid_1's auc: 0.954805\n",
      "[1000]\ttraining's auc: 0.998915\tvalid_1's auc: 0.955858\n",
      "[1200]\ttraining's auc: 0.999534\tvalid_1's auc: 0.956526\n",
      "[1400]\ttraining's auc: 0.999789\tvalid_1's auc: 0.956817\n",
      "[1600]\ttraining's auc: 0.999907\tvalid_1's auc: 0.956865\n",
      "[1800]\ttraining's auc: 0.999963\tvalid_1's auc: 0.956699\n",
      "[2000]\ttraining's auc: 0.999985\tvalid_1's auc: 0.956781\n",
      "Early stopping, best iteration is:\n",
      "[1550]\ttraining's auc: 0.999886\tvalid_1's auc: 0.956954\n",
      "Fold 5 | AUC: 0.9569537730794198\n",
      "(501214, 504) (501214,) (89326, 504) (89326,)\n",
      "Training until validation scores don't improve for 500 rounds.\n",
      "[200]\ttraining's auc: 0.961112\tvalid_1's auc: 0.916188\n",
      "[400]\ttraining's auc: 0.986525\tvalid_1's auc: 0.933298\n",
      "[600]\ttraining's auc: 0.994626\tvalid_1's auc: 0.939575\n",
      "[800]\ttraining's auc: 0.997839\tvalid_1's auc: 0.94201\n",
      "[1000]\ttraining's auc: 0.99908\tvalid_1's auc: 0.942704\n",
      "[1200]\ttraining's auc: 0.99961\tvalid_1's auc: 0.943062\n",
      "[1400]\ttraining's auc: 0.999826\tvalid_1's auc: 0.943024\n",
      "[1600]\ttraining's auc: 0.999927\tvalid_1's auc: 0.942911\n",
      "[1800]\ttraining's auc: 0.999967\tvalid_1's auc: 0.942676\n",
      "[2000]\ttraining's auc: 0.999987\tvalid_1's auc: 0.942591\n",
      "Early stopping, best iteration is:\n",
      "[1531]\ttraining's auc: 0.999902\tvalid_1's auc: 0.943145\n",
      "Fold 6 | AUC: 0.9431450680598834\n",
      "\n",
      "Mean AUC = 0.9440898412296787\n"
     ]
    }
   ],
   "source": [
    "y_preds = np.zeros(df_test.shape[0])\n",
    "score = 0\n",
    "\n",
    "feature_importances = pd.DataFrame()\n",
    "feature_importances['feature'] = df_train.columns\n",
    "\n",
    "NFOLDS = len(np.unique(dt_m)) # 6\n",
    "\n",
    "params = model_params()\n",
    "\n",
    "for fold_n, month in enumerate(np.unique(dt_m)):\n",
    "    X_train, y_train = df_train[~dt_m.isin([month])], y[~dt_m.isin([month])]\n",
    "    X_valid, y_valid = df_train[dt_m.isin([month])], y[dt_m.isin([month])]\n",
    "    print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)\n",
    "\n",
    "    dtrain = lgb.Dataset(X_train, label=y_train)\n",
    "    dvalid = lgb.Dataset(X_valid, label=y_valid)\n",
    "\n",
    "    clf = lgb.train(params, dtrain, 10000, valid_sets = [dtrain, dvalid], verbose_eval=200, early_stopping_rounds=500)\n",
    "    \n",
    "    feature_importances[f'fold_{fold_n + 1}'] = clf.feature_importance()\n",
    "    \n",
    "    y_pred_valid = clf.predict(X_valid)\n",
    "    save_to_disk(y_pred_valid, 'y_pred_valid_fold{}.pkl'.format(fold_n))\n",
    "    print(f\"Fold {fold_n + 1} | AUC: {roc_auc_score(y_valid, y_pred_valid)}\")\n",
    "    \n",
    "    score += roc_auc_score(y_valid, y_pred_valid) / NFOLDS\n",
    "    \n",
    "    y_pred_test = clf.predict(df_test)\n",
    "    save_to_disk(y_pred_test, 'y_pred_test_fold{}.pkl'.format(fold_n))\n",
    "    y_preds += y_pred_test / NFOLDS\n",
    "    \n",
    "    del X_train, X_valid, y_train, y_valid\n",
    "    gc.collect()\n",
    "\n",
    "print(f\"\\nMean AUC = {score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')\n",
    "sub['isFraud'] = y_preds\n",
    "sub.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances['average'] = feature_importances[[f'fold_{fold_n + 1}' for fold_n in range(NFOLDS)]].mean(axis=1)\n",
    "feature_importances.to_csv('feature_importances.csv')\n",
    "\n",
    "plt.figure(figsize=(16, 16))\n",
    "sns.barplot(data=feature_importances.sort_values(by='average', ascending=False).head(50), x='average', y='feature');\n",
    "plt.title('50 TOP feature importance over {} folds average'.format(NFOLDS));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}