Elo - ML model - 3.690.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Load-imports-and-dfs\" data-toc-modified-id=\"Load-imports-and-dfs-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Load imports and dfs</a></span></li><li><span><a href=\"#Blend\" data-toc-modified-id=\"Blend-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Blend</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(120000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 120 seconds\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 4\n",
    "%autosave 120"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load imports and dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.io import *\n",
    "from fastai.structured import *\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from pandas_summary import DataFrameSummary\n",
    "from IPython.display import display\n",
    "from sklearn import metrics\n",
    "import feather\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df2 = feather.read_dataframe('train_df_alpha')\n",
    "test_df2 = feather.read_dataframe('test_df_alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [train_df2, test_df2]:\n",
    "    for f in ['purchase_date_max','purchase_date_min','purchase_date_max_old',\\\n",
    "                     'purchase_date_min_old', 'observation_date_old']:\n",
    "        df[f] = df[f].astype(np.int64) * 1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_excluded = ['purchase_date_max', 'purchase_date_max_old', 'card_id', 'first_active_month',\n",
    "                 'target','outliers','card_id_size', 'card_id_size_old', \n",
    "                 'purchase_date_min', 'purchase_date_min_old','first_active_monthYear',\n",
    "                 'first_active_monthMonth',\n",
    "                 'first_active_monthWeek',\n",
    "                 'first_active_monthDay',\n",
    "                 'first_active_monthDayofweek',\n",
    "                 'first_active_monthDayofyear',\n",
    "                 'first_active_monthIs_month_end',\n",
    "                 'first_active_monthIs_month_start',\n",
    "                 'first_active_monthIs_quarter_end',\n",
    "                 'first_active_monthIs_quarter_start',\n",
    "                 'first_active_monthIs_year_end',\n",
    "                 'first_active_monthIs_year_start']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "cols_included = ['feature_1','feature_2','feature_3','transactions_count','subsector_id_nunique','merchant_id_nunique','merchant_category_id_nunique','purchase_Month_mean','purchase_Month_min','purchase_Month_max','purchase_Week_nunique','purchase_Week_mean','purchase_Week_min','purchase_Week_max','purchase_Dayofweek_mean','purchase_Dayofweek_min','purchase_Dayofweek_max','purchase_Day_nunique','purchase_Day_mean','purchase_Day_min','purchase_Day_max','purchase_Hour_nunique','purchase_Hour_mean','purchase_Hour_min','purchase_Hour_max','purchase_amount_sum','purchase_amount_max','purchase_amount_min','purchase_amount_mean','purchase_amount_var','purchase_amount_skew','installments_sum','installments_max','installments_mean','installments_var','installments_skew','month_lag_max','month_lag_min','month_lag_mean','month_lag_var','month_lag_skew','month_diff_mean','month_diff_var','month_diff_skew','purchased_on_weekend_mean','category_1_mean','category_2_mean','category_3_mean','card_id_count','price_mean','price_max','price_min','price_var','Christmas_Day_2017_mean','Children_day_2017_mean','Black_Friday_2017_mean','Mothers_Day_2018_mean','duration_mean','duration_min','duration_max','duration_var','duration_skew','amount_month_ratio_mean','amount_month_ratio_min','amount_month_ratio_max','amount_month_ratio_var','amount_month_ratio_skew','category_2_mean_mean','category_3_mean_mean','purchase_date_diff','purchase_date_average','purchase_date_uptonow','purchase_date_uptomin','transactions_count_old','subsector_id_nunique_old','merchant_id_nunique_old','merchant_category_id_nunique_old','purchase_Month_nunique','purchase_Month_mean_old','purchase_Month_min_old','purchase_Month_max_old','purchase_Week_nunique_old','purchase_Week_mean_old','purchase_Week_min_old','purchase_Week_max_old','purchase_Dayofweek_mean_old','purchase_Day_nunique_old','purchase_Day_mean_old','purchase_Day_min_old','purchase_Hour_nunique_old','purchase_Hour_mean_old','purchase_Hour_min_old','purchase_Hour_max_old','purchase_amount_sum_old','purchase_amount_max_old','purchase_amount_min_old','purchase_amount_mean_old','purchase_amount_var_old','purchase_amount_skew_old','installments_sum_old','installments_max_old','installments_mean_old','installments_var_old','installments_skew_old','month_lag_max_old','month_lag_min_old','month_lag_mean_old','month_lag_var_old','month_lag_skew_old','month_diff_max','month_diff_min','month_diff_mean_old','month_diff_var_old','month_diff_skew_old','authorized_flag_mean','purchased_on_weekend_mean_old','category_1_mean_old','category_2_mean_old','category_3_mean_old','card_id_count_old','price_sum','price_mean_old','price_max_old','price_min_old','price_var_old','Christmas_Day_2017_mean_old','Mothers_Day_2017_mean','fathers_day_2017_mean','Children_day_2017_mean_old','Valentine_Day_2017_mean','Black_Friday_2017_mean_old','Mothers_Day_2018_mean_old','duration_mean_old','duration_min_old','duration_max_old','duration_var_old','duration_skew_old','amount_month_ratio_mean_old','amount_month_ratio_min_old','amount_month_ratio_max_old','amount_month_ratio_var_old','amount_month_ratio_skew_old','category_2_mean_mean_old','category_3_mean_mean_old','purchase_date_diff_old','purchase_date_average_old','purchase_date_uptonow_old','purchase_date_uptomin_old','quarter','observed_elapsed_time','days_feature1','days_feature2','days_feature3','days_feature1_ratio','days_feature2_ratio','days_feature3_ratio','feature_sum','feature_mean','feature_max','feature_min','feature_var','card_id_total','card_id_count_total','card_id_count_ratio','purchase_amount_total','purchase_amount_total_mean','purchase_amount_total_max','purchase_amount_total_min','purchase_amount_sum_ratio','hist_first_buy','new_first_buy','hist_last_buy','new_last_buy','month_diff_ratio','installments_total','installments_ratio','price_total','CLV','CLV_old','CLV_ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_columns = [c for c in cols_included if c not in cols_excluded]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_columns = [c for c in train_df2.columns.values if c not in cols_excluded and c in test_df2.columns.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = train_df2['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((201917, 354), (123623, 352))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df2.shape, test_df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_df2['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['first_active_month', 'card_id', 'feature_1', 'feature_2', 'feature_3', 'transactions_count',\n",
       "       'subsector_id_nunique', 'merchant_id_nunique', 'merchant_category_id_nunique', 'purchase_Month_mean',\n",
       "       'purchase_Month_min', 'purchase_Month_max', 'purchase_Week_nunique', 'purchase_Week_mean',\n",
       "       'purchase_Week_min', 'purchase_Week_max', 'purchase_Dayofweek_mean', 'purchase_Dayofweek_min',\n",
       "       'purchase_Dayofweek_max', 'purchase_Day_nunique', 'purchase_Day_mean', 'purchase_Day_min',\n",
       "       'purchase_Day_max', 'purchase_Hour_nunique', 'purchase_Hour_mean', 'purchase_Hour_min',\n",
       "       'purchase_Hour_max', 'purchase_amount_sum', 'purchase_amount_pct_75', 'purchase_amount_pct_25',\n",
       "       'purchase_amount_mean', 'purchase_amount_median', 'purchase_amount_max', 'purchase_amount_min',\n",
       "       'purchase_amount_var', 'purchase_amount_skew', 'installments_sum', 'installments_max',\n",
       "       'installments_mean', 'installments_var', 'installments_skew', 'purchase_date_max',\n",
       "       'purchase_date_min', 'month_lag_max', 'month_lag_min', 'month_lag_mean', 'month_lag_var',\n",
       "       'month_lag_skew', 'month_diff_mean', 'month_diff_var', 'month_diff_skew', 'purchased_on_weekend_mean',\n",
       "       'category_1_mean', 'category_2_mean', 'category_3_mean', 'card_id_size', 'card_id_count',\n",
       "       'price_mean', 'price_max', 'price_min', 'price_var', 'Christmas_Day_2017_mean',\n",
       "       'Children_day_2017_mean', 'Black_Friday_2017_mean', 'Mothers_Day_2018_mean', 'duration_mean',\n",
       "       'duration_min', 'duration_max', 'duration_var', 'duration_skew', 'amount_month_ratio_mean',\n",
       "       'amount_month_ratio_min', 'amount_month_ratio_max', 'amount_month_ratio_var',\n",
       "       'amount_month_ratio_skew', 'purchase_date_successive_diff_mean', 'category_2_mean_mean',\n",
       "       'category_3_mean_mean', 'purchase_date_diff', 'purchase_date_average', 'purchase_date_uptonow',\n",
       "       'purchase_date_uptomin', 'inverse_avg_transactions_per_day', 'days_since_last_transaction',\n",
       "       'repurchase_merchant_rate', 'merchant_category_repurchase', 'avg_spend_per_merchant',\n",
       "       'avg_trans_per_merchant', 'avg_spend_per_transaction', 'transactions_count_old',\n",
       "       'subsector_id_nunique_old', 'merchant_id_nunique_old', 'merchant_category_id_nunique_old',\n",
       "       'purchase_Month_nunique', 'purchase_Month_mean_old', 'purchase_Month_min_old',\n",
       "       'purchase_Month_max_old', 'purchase_Week_nunique_old', 'purchase_Week_mean_old',\n",
       "       'purchase_Week_min_old', 'purchase_Week_max_old', 'purchase_Dayofweek_mean_old',\n",
       "       'purchase_Day_nunique_old', 'purchase_Day_mean_old', 'purchase_Day_min_old',\n",
       "       'purchase_Hour_nunique_old', 'purchase_Hour_mean_old', 'purchase_Hour_min_old',\n",
       "       'purchase_Hour_max_old', 'purchase_amount_sum_old', 'purchase_amount_pct_75_old',\n",
       "       'purchase_amount_pct_25_old', 'purchase_amount_mean_old', 'purchase_amount_median_old',\n",
       "       'purchase_amount_max_old', 'purchase_amount_min_old', 'purchase_amount_var_old',\n",
       "       'purchase_amount_skew_old', 'installments_sum_old', 'installments_max_old', 'installments_mean_old',\n",
       "       'installments_var_old', 'installments_skew_old', 'purchase_date_max_old', 'purchase_date_min_old',\n",
       "       'month_lag_max_old', 'month_lag_min_old', 'month_lag_mean_old', 'month_lag_var_old',\n",
       "       'month_lag_skew_old', 'month_diff_max', 'month_diff_min', 'month_diff_mean_old', 'month_diff_var_old',\n",
       "       'month_diff_skew_old', 'authorized_flag_mean', 'purchased_on_weekend_mean_old', 'category_1_mean_old',\n",
       "       'category_2_mean_old', 'category_3_mean_old', 'card_id_size_old', 'card_id_count_old', 'price_sum',\n",
       "       'price_mean_old', 'price_max_old', 'price_min_old', 'price_var_old', 'Christmas_Day_2017_mean_old',\n",
       "       'Mothers_Day_2017_mean', 'fathers_day_2017_mean', 'Children_day_2017_mean_old',\n",
       "       'Valentine_Day_2017_mean', 'Black_Friday_2017_mean_old', 'Mothers_Day_2018_mean_old',\n",
       "       'duration_mean_old', 'duration_min_old', 'duration_max_old', 'duration_var_old', 'duration_skew_old',\n",
       "       'amount_month_ratio_mean_old', 'amount_month_ratio_min_old', 'amount_month_ratio_max_old',\n",
       "       'amount_month_ratio_var_old', 'amount_month_ratio_skew_old', 'purchase_date_successive_diff_mean_old',\n",
       "       'category_2_mean_mean_old', 'category_3_mean_mean_old', 'purchase_date_diff_old',\n",
       "       'purchase_date_average_old', 'purchase_date_uptonow_old', 'purchase_date_uptomin_old',\n",
       "       'inverse_avg_transactions_per_day_old', 'days_since_last_transaction_old',\n",
       "       'repurchase_merchant_rate_old', 'merchant_category_repurchase_old', 'avg_spend_per_merchant_old',\n",
       "       'avg_trans_per_merchant_old', 'avg_spend_per_transaction_old', 'purchase_amount_count_mean',\n",
       "       'purchase_amount_count_sum', 'purchase_amount_count_ptp', 'purchase_amount_count_max',\n",
       "       'purchase_amount_sum_mean', 'purchase_amount_sum_sum', 'purchase_amount_sum_ptp',\n",
       "       'purchase_amount_sum_max', 'purchase_amount_max_mean', 'purchase_amount_max_sum',\n",
       "       'purchase_amount_max_ptp', 'purchase_amount_max_max', 'purchase_amount_mean_mean',\n",
       "       'purchase_amount_mean_sum', 'purchase_amount_mean_ptp', 'purchase_amount_mean_max',\n",
       "       'category_1_0_purchase_amount_mean', 'category_1_1_purchase_amount_mean',\n",
       "       'category_1_0_purchase_amount_sum', 'category_1_1_purchase_amount_sum',\n",
       "       'category_2_1.0_purchase_amount_mean', 'category_2_2.0_purchase_amount_mean',\n",
       "       'category_2_3.0_purchase_amount_mean', 'category_2_4.0_purchase_amount_mean',\n",
       "       'category_2_5.0_purchase_amount_mean', 'category_2_1.0_purchase_amount_sum',\n",
       "       'category_2_2.0_purchase_amount_sum', 'category_2_3.0_purchase_amount_sum',\n",
       "       'category_2_4.0_purchase_amount_sum', 'category_2_5.0_purchase_amount_sum',\n",
       "       'category_3_0_purchase_amount_mean', 'category_3_1_purchase_amount_mean',\n",
       "       'category_3_2_purchase_amount_mean', 'category_3_0_purchase_amount_sum',\n",
       "       'category_3_1_purchase_amount_sum', 'category_3_2_purchase_amount_sum',\n",
       "       'authorized_flag_0_purchase_amount_mean', 'authorized_flag_1_purchase_amount_mean',\n",
       "       'authorized_flag_0_purchase_amount_sum', 'authorized_flag_1_purchase_amount_sum', 'quarter',\n",
       "       'elapsed_time', 'days_feature1', 'days_feature2', 'days_feature3', 'days_feature1_ratio',\n",
       "       'days_feature2_ratio', 'days_feature3_ratio', 'feature_sum', 'feature_mean', 'feature_max',\n",
       "       'feature_min', 'feature_var', 'card_id_total', 'card_id_count_total', 'card_id_count_ratio',\n",
       "       'purchase_amount_total', 'purchase_amount_total_mean', 'purchase_amount_total_max',\n",
       "       'purchase_amount_total_min', 'purchase_amount_sum_ratio', 'hist_first_buy', 'new_first_buy',\n",
       "       'hist_last_buy', 'new_last_buy', 'purchased_before_issue', 'month_diff_ratio', 'installments_total',\n",
       "       'installments_ratio', 'price_total', 'CLV', 'CLV_old', 'CLV_ratio', 'outliers'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df2.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_cols = ['observed_elapsed_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_columns = df_train_columns + exp_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_cols =['purchase_amount_max',\n",
    " 'avg_trans_per_merchant_old',\n",
    " 'duration_min_old',\n",
    " 'purchase_date_uptonow',\n",
    " 'category_1_mean',\n",
    " 'authorized_flag_mean',\n",
    " 'purchase_date_uptonow_old',\n",
    " 'purchase_Week_mean',\n",
    " 'purchase_amount_mean',\n",
    " 'purchase_Month_nunique',\n",
    " 'month_diff_mean_old',\n",
    " 'category_1_mean_old',\n",
    " 'duration_mean_old',\n",
    " 'month_lag_mean_old',\n",
    " 'purchase_date_average_old',\n",
    " 'fathers_day_2017_mean',\n",
    " 'purchase_Month_mean',\n",
    " 'installments_sum_old',\n",
    " 'month_lag_mean',\n",
    " 'days_feature1',\n",
    " 'purchase_Week_max',\n",
    " 'hist_last_buy',\n",
    " 'month_lag_skew',\n",
    " 'purchase_Week_nunique_old',\n",
    " 'Mothers_Day_2017_mean',\n",
    " 'purchase_Dayofweek_mean_old',\n",
    " 'merchant_category_id_nunique_old',\n",
    " 'new_last_buy',\n",
    " 'duration_max_old',\n",
    " 'new_first_buy',\n",
    " 'month_lag_max',\n",
    " 'price_var',\n",
    " 'installments_var_old',\n",
    " 'price_min',\n",
    " 'purchase_amount_sum_old',\n",
    " 'merchant_id_nunique',\n",
    " 'purchase_Hour_nunique',\n",
    " 'purchase_Day_nunique']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train_columns = final_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_fold =5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 0\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.63985\tvalid_1's rmse: 3.70698\n",
      "[200]\ttraining's rmse: 3.55248\tvalid_1's rmse: 3.66841\n",
      "[300]\ttraining's rmse: 3.49739\tvalid_1's rmse: 3.65687\n",
      "[400]\ttraining's rmse: 3.46129\tvalid_1's rmse: 3.65235\n",
      "[500]\ttraining's rmse: 3.43423\tvalid_1's rmse: 3.64995\n",
      "[600]\ttraining's rmse: 3.41124\tvalid_1's rmse: 3.64911\n",
      "[700]\ttraining's rmse: 3.39093\tvalid_1's rmse: 3.64896\n",
      "[800]\ttraining's rmse: 3.37138\tvalid_1's rmse: 3.64894\n",
      "[900]\ttraining's rmse: 3.35388\tvalid_1's rmse: 3.649\n",
      "[1000]\ttraining's rmse: 3.33773\tvalid_1's rmse: 3.6491\n",
      "Early stopping, best iteration is:\n",
      "[844]\ttraining's rmse: 3.3633\tvalid_1's rmse: 3.64868\n",
      "fold 1\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.64115\tvalid_1's rmse: 3.69605\n",
      "[200]\ttraining's rmse: 3.55215\tvalid_1's rmse: 3.66095\n",
      "[300]\ttraining's rmse: 3.50055\tvalid_1's rmse: 3.64983\n",
      "[400]\ttraining's rmse: 3.46496\tvalid_1's rmse: 3.64609\n",
      "[500]\ttraining's rmse: 3.43674\tvalid_1's rmse: 3.64487\n",
      "[600]\ttraining's rmse: 3.41479\tvalid_1's rmse: 3.64448\n",
      "[700]\ttraining's rmse: 3.39466\tvalid_1's rmse: 3.64347\n",
      "[800]\ttraining's rmse: 3.37502\tvalid_1's rmse: 3.64274\n",
      "[900]\ttraining's rmse: 3.35817\tvalid_1's rmse: 3.64313\n",
      "Early stopping, best iteration is:\n",
      "[797]\ttraining's rmse: 3.37569\tvalid_1's rmse: 3.64273\n",
      "fold 2\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.63837\tvalid_1's rmse: 3.70715\n",
      "[200]\ttraining's rmse: 3.54942\tvalid_1's rmse: 3.67222\n",
      "[300]\ttraining's rmse: 3.49912\tvalid_1's rmse: 3.66175\n",
      "[400]\ttraining's rmse: 3.46244\tvalid_1's rmse: 3.65763\n",
      "[500]\ttraining's rmse: 3.43324\tvalid_1's rmse: 3.655\n",
      "[600]\ttraining's rmse: 3.41234\tvalid_1's rmse: 3.65387\n",
      "[700]\ttraining's rmse: 3.3927\tvalid_1's rmse: 3.65358\n",
      "[800]\ttraining's rmse: 3.37339\tvalid_1's rmse: 3.65327\n",
      "[900]\ttraining's rmse: 3.35595\tvalid_1's rmse: 3.65256\n",
      "[1000]\ttraining's rmse: 3.33901\tvalid_1's rmse: 3.65221\n",
      "[1100]\ttraining's rmse: 3.32235\tvalid_1's rmse: 3.65196\n",
      "[1200]\ttraining's rmse: 3.30614\tvalid_1's rmse: 3.6519\n",
      "Early stopping, best iteration is:\n",
      "[1081]\ttraining's rmse: 3.32545\tvalid_1's rmse: 3.65176\n",
      "fold 3\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.63896\tvalid_1's rmse: 3.7008\n",
      "[200]\ttraining's rmse: 3.54878\tvalid_1's rmse: 3.66747\n",
      "[300]\ttraining's rmse: 3.49605\tvalid_1's rmse: 3.65849\n",
      "[400]\ttraining's rmse: 3.45834\tvalid_1's rmse: 3.65448\n",
      "[500]\ttraining's rmse: 3.44013\tvalid_1's rmse: 3.66314\n",
      "[600]\ttraining's rmse: 3.40198\tvalid_1's rmse: 3.65283\n",
      "[700]\ttraining's rmse: 3.37646\tvalid_1's rmse: 3.65096\n",
      "[800]\ttraining's rmse: 3.35789\tvalid_1's rmse: 3.65091\n",
      "[900]\ttraining's rmse: 3.3439\tvalid_1's rmse: 3.65055\n",
      "[1000]\ttraining's rmse: 3.32713\tvalid_1's rmse: 3.65053\n",
      "[1100]\ttraining's rmse: 3.31242\tvalid_1's rmse: 3.65026\n",
      "[1200]\ttraining's rmse: 3.29681\tvalid_1's rmse: 3.64975\n",
      "[1300]\ttraining's rmse: 3.28075\tvalid_1's rmse: 3.64971\n",
      "[1400]\ttraining's rmse: 3.26511\tvalid_1's rmse: 3.65006\n",
      "Early stopping, best iteration is:\n",
      "[1278]\ttraining's rmse: 3.28393\tvalid_1's rmse: 3.64959\n",
      "fold 4\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.6393\tvalid_1's rmse: 3.69143\n",
      "[200]\ttraining's rmse: 3.549\tvalid_1's rmse: 3.66153\n",
      "[300]\ttraining's rmse: 3.49311\tvalid_1's rmse: 3.65355\n",
      "[400]\ttraining's rmse: 3.45685\tvalid_1's rmse: 3.65187\n",
      "[500]\ttraining's rmse: 3.4294\tvalid_1's rmse: 3.65038\n",
      "[600]\ttraining's rmse: 3.40792\tvalid_1's rmse: 3.64947\n",
      "[700]\ttraining's rmse: 3.38889\tvalid_1's rmse: 3.64957\n",
      "[800]\ttraining's rmse: 3.37184\tvalid_1's rmse: 3.65001\n",
      "Early stopping, best iteration is:\n",
      "[670]\ttraining's rmse: 3.39445\tvalid_1's rmse: 3.64933\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.648418646163904"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param = {\n",
    "        'task': 'train',\n",
    "        'boosting': 'goss',\n",
    "        'objective': 'regression',\n",
    "        'metric': 'rmse',\n",
    "        'learning_rate': 0.01,\n",
    "        'subsample': 0.9855232997390695,\n",
    "        'max_depth': 7,\n",
    "        'top_rate': 0.9064148448434349,\n",
    "        'num_leaves': 123,\n",
    "        'min_child_weight': 41.9612869171337,\n",
    "        'other_rate': 0.0721768246018207,\n",
    "        'reg_alpha': 9.677537745007898,\n",
    "        'colsample_bytree': 0.5665320670155495,\n",
    "        'min_split_gain': 9.820197773625843,\n",
    "        'reg_lambda': 8.2532317400459,\n",
    "        'min_data_in_leaf': 21,\n",
    "        'verbose': -1,\n",
    "        'seed':int(2**n_fold),\n",
    "        'bagging_seed':int(2**n_fold),\n",
    "        'drop_seed':int(2**n_fold)\n",
    "        }\n",
    "folds = StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=4590)\n",
    "oof = np.zeros(len(train_df2))\n",
    "predictions = np.zeros(len(test_df2))\n",
    "feature_importance_df = pd.DataFrame()\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_df2,train_df2['outliers'].values)):\n",
    "    print(\"fold {}\".format(fold_))\n",
    "    trn_data = lgb.Dataset(train_df2.iloc[trn_idx][df_train_columns], label=target.iloc[trn_idx])\n",
    "    val_data = lgb.Dataset(train_df2.iloc[val_idx][df_train_columns], label=target.iloc[val_idx])\n",
    "\n",
    "    num_round = 10000\n",
    "    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=100, early_stopping_rounds = 200)\n",
    "    oof[val_idx] = clf.predict(train_df2.iloc[val_idx][df_train_columns], num_iteration=clf.best_iteration)\n",
    "    \n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"Feature\"] = df_train_columns\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importance()\n",
    "    fold_importance_df[\"fold\"] = fold_ + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "    \n",
    "    predictions += clf.predict(test_df2[df_train_columns], num_iteration=clf.best_iteration) / folds.n_splits\n",
    "\n",
    "np.sqrt(mean_squared_error(oof, target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Base feats - f1,f2,f3 - 3.8421399267932315\n",
    "2. 3.6552368169417204, 3.692\n",
    "3. 3.654460362787254, 3.692\n",
    "4. 3.6538177315106624, 3.691\n",
    "5. 3.6552638224814196- after trimming purchase_amount\n",
    "6. 3.6544381493750677 - base_cols 180\n",
    "7. 3.653984525732515 - added purchase_amount_pct_25_old\n",
    "8. 3.653161955069063 - base_cols - 180 - 16th feb - 181 has first_active_monthElapsed score 3.653410894233743\n",
    "9. 3.6535590767314026 - added 'avg_trans_per_merchant_old','purchase_amount_sum_mean', 'category_2_5.0_purchase_amount_mean'\n",
    "10. 3.6573988258459713 - with 38 cols\n",
    "11. 3.655024925251293 - with 241 cols\n",
    "12. 3.6527864665829197 - with 283 cols - added missed out agg in train, test & added observed date, lb - 3.816\n",
    "13. 3.6535891905746243 - with 181 cols - base + observed_elapsed time, **3.691** better than 3.693 of 180 base cols\n",
    "14. 3.653084106006047 - 414 cols \n",
    "15. 3.648418646163904 - rescaled purchase_amount "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = (feature_importance_df[[\"Feature\", \"importance\"]]\n",
    "        .groupby(\"Feature\")\n",
    "        .mean()\n",
    "        .sort_values(by=\"importance\", ascending=False)[:1000].index)\n",
    "\n",
    "best_features = feature_importance_df.loc[feature_importance_df.Feature.isin(cols)]\n",
    "\n",
    "plt.figure(figsize=(14,25))\n",
    "sns.barplot(x=\"importance\",\n",
    "            y=\"Feature\",\n",
    "            data=best_features.sort_values(by=\"importance\",\n",
    "                                           ascending=False))\n",
    "plt.title('LightGBM Features (avg over folds)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('lgbm_importances.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "169"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(best_features.sort_values(by='importance', ascending=False)[:800]['Feature'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_ft_df = best_features.groupby('Feature').agg({'importance': ['mean']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_ft_df.columns = ['_'.join(c).strip() for c in best_ft_df.columns.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_ft_df = best_ft_df.reset_index().sort_values('importance_mean', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fts_sorted_by_imp = best_ft_df.sort_values('importance_mean', ascending=False)['Feature'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>index</th>\n",
       "      <th>Feature</th>\n",
       "      <th>importance_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158</td>\n",
       "      <td>purchase_amount_sum_ratio</td>\n",
       "      <td>1949.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>171</td>\n",
       "      <td>purchase_date_uptonow</td>\n",
       "      <td>1405.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84</td>\n",
       "      <td>month_diff_mean_old</td>\n",
       "      <td>1372.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>172</td>\n",
       "      <td>purchase_date_uptonow_old</td>\n",
       "      <td>1363.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>authorized_flag_mean</td>\n",
       "      <td>1330.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>category_1_mean_old</td>\n",
       "      <td>1212.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>94</td>\n",
       "      <td>month_lag_mean_old</td>\n",
       "      <td>1092.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>139</td>\n",
       "      <td>purchase_Month_nunique</td>\n",
       "      <td>1063.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>CLV</td>\n",
       "      <td>1055.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>168</td>\n",
       "      <td>purchase_date_diff_old</td>\n",
       "      <td>1002.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>142</td>\n",
       "      <td>purchase_Week_mean</td>\n",
       "      <td>956.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55</td>\n",
       "      <td>fathers_day_2017_mean</td>\n",
       "      <td>906.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>115</td>\n",
       "      <td>purchase_Day_mean</td>\n",
       "      <td>838.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>147</td>\n",
       "      <td>purchase_Week_nunique_old</td>\n",
       "      <td>814.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>93</td>\n",
       "      <td>month_lag_mean</td>\n",
       "      <td>804.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>167</td>\n",
       "      <td>purchase_date_diff</td>\n",
       "      <td>757.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12</td>\n",
       "      <td>Valentine_Day_2017_mean</td>\n",
       "      <td>749.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6</td>\n",
       "      <td>Children_day_2017_mean_old</td>\n",
       "      <td>739.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>29</td>\n",
       "      <td>category_1_mean</td>\n",
       "      <td>737.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>CLV_ratio</td>\n",
       "      <td>728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>month_lag_var_old</td>\n",
       "      <td>724.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>135</td>\n",
       "      <td>purchase_Month_mean</td>\n",
       "      <td>720.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>81</td>\n",
       "      <td>merchant_id_nunique_old</td>\n",
       "      <td>719.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>74</td>\n",
       "      <td>installments_sum_old</td>\n",
       "      <td>694.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>64</td>\n",
       "      <td>hist_first_buy</td>\n",
       "      <td>685.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>98</td>\n",
       "      <td>month_lag_skew_old</td>\n",
       "      <td>680.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>156</td>\n",
       "      <td>purchase_amount_sum</td>\n",
       "      <td>670.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>65</td>\n",
       "      <td>hist_last_buy</td>\n",
       "      <td>670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>140</td>\n",
       "      <td>purchase_Week_max</td>\n",
       "      <td>669.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>128</td>\n",
       "      <td>purchase_Hour_mean_old</td>\n",
       "      <td>666.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>113</td>\n",
       "      <td>price_var_old</td>\n",
       "      <td>643.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>170</td>\n",
       "      <td>purchase_date_uptomin_old</td>\n",
       "      <td>643.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>9</td>\n",
       "      <td>Mothers_Day_2017_mean</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>127</td>\n",
       "      <td>purchase_Hour_mean</td>\n",
       "      <td>628.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>169</td>\n",
       "      <td>purchase_date_uptomin</td>\n",
       "      <td>624.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>166</td>\n",
       "      <td>purchase_date_average_old</td>\n",
       "      <td>616.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>116</td>\n",
       "      <td>purchase_Day_mean_old</td>\n",
       "      <td>614.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>123</td>\n",
       "      <td>purchase_Dayofweek_mean_old</td>\n",
       "      <td>607.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>88</td>\n",
       "      <td>month_diff_skew_old</td>\n",
       "      <td>568.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>10</td>\n",
       "      <td>Mothers_Day_2018_mean</td>\n",
       "      <td>563.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>165</td>\n",
       "      <td>purchase_date_average</td>\n",
       "      <td>558.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7</td>\n",
       "      <td>Christmas_Day_2017_mean</td>\n",
       "      <td>542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>79</td>\n",
       "      <td>merchant_category_id_nunique_old</td>\n",
       "      <td>521.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>41</td>\n",
       "      <td>days_feature2</td>\n",
       "      <td>518.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>50</td>\n",
       "      <td>duration_min_old</td>\n",
       "      <td>505.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>144</td>\n",
       "      <td>purchase_Week_min</td>\n",
       "      <td>504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>174</td>\n",
       "      <td>purchased_on_weekend_mean_old</td>\n",
       "      <td>496.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>26</td>\n",
       "      <td>card_id_count_ratio</td>\n",
       "      <td>495.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>18</td>\n",
       "      <td>amount_month_ratio_min_old</td>\n",
       "      <td>494.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>179</td>\n",
       "      <td>transactions_count_old</td>\n",
       "      <td>493.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>77</td>\n",
       "      <td>installments_var_old</td>\n",
       "      <td>492.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>39</td>\n",
       "      <td>days_feature1</td>\n",
       "      <td>489.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>duration_var</td>\n",
       "      <td>486.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>69</td>\n",
       "      <td>installments_mean_old</td>\n",
       "      <td>483.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>Black_Friday_2017_mean_old</td>\n",
       "      <td>472.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>Black_Friday_2017_mean</td>\n",
       "      <td>470.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>114</td>\n",
       "      <td>purchase_Day_max</td>\n",
       "      <td>466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>13</td>\n",
       "      <td>amount_month_ratio_max</td>\n",
       "      <td>457.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>37</td>\n",
       "      <td>category_3_mean_mean_old</td>\n",
       "      <td>452.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>157</td>\n",
       "      <td>purchase_amount_sum_old</td>\n",
       "      <td>449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>28</td>\n",
       "      <td>card_id_total</td>\n",
       "      <td>448.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>45</td>\n",
       "      <td>duration_max</td>\n",
       "      <td>440.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>148</td>\n",
       "      <td>purchase_amount_max</td>\n",
       "      <td>438.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>52</td>\n",
       "      <td>duration_skew_old</td>\n",
       "      <td>429.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>3</td>\n",
       "      <td>CLV_old</td>\n",
       "      <td>424.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>70</td>\n",
       "      <td>installments_ratio</td>\n",
       "      <td>424.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>48</td>\n",
       "      <td>duration_mean_old</td>\n",
       "      <td>414.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>40</td>\n",
       "      <td>days_feature1_ratio</td>\n",
       "      <td>414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>38</td>\n",
       "      <td>category_3_mean_old</td>\n",
       "      <td>411.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>16</td>\n",
       "      <td>amount_month_ratio_mean_old</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>42</td>\n",
       "      <td>days_feature2_ratio</td>\n",
       "      <td>394.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>136</td>\n",
       "      <td>purchase_Month_mean_old</td>\n",
       "      <td>392.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>155</td>\n",
       "      <td>purchase_amount_skew_old</td>\n",
       "      <td>390.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>43</td>\n",
       "      <td>days_feature3</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>installments_total</td>\n",
       "      <td>377.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>20</td>\n",
       "      <td>amount_month_ratio_skew_old</td>\n",
       "      <td>376.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>153</td>\n",
       "      <td>purchase_amount_min_old</td>\n",
       "      <td>375.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>163</td>\n",
       "      <td>purchase_amount_var</td>\n",
       "      <td>375.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>8</td>\n",
       "      <td>Christmas_Day_2017_mean_old</td>\n",
       "      <td>374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>86</td>\n",
       "      <td>month_diff_ratio</td>\n",
       "      <td>372.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>111</td>\n",
       "      <td>price_total</td>\n",
       "      <td>369.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>72</td>\n",
       "      <td>installments_skew_old</td>\n",
       "      <td>369.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>21</td>\n",
       "      <td>amount_month_ratio_var</td>\n",
       "      <td>362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>108</td>\n",
       "      <td>price_min</td>\n",
       "      <td>361.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>106</td>\n",
       "      <td>price_mean</td>\n",
       "      <td>359.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>101</td>\n",
       "      <td>new_first_buy</td>\n",
       "      <td>359.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>117</td>\n",
       "      <td>purchase_Day_min</td>\n",
       "      <td>358.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>82</td>\n",
       "      <td>month_diff_max</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>47</td>\n",
       "      <td>duration_mean</td>\n",
       "      <td>350.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>83</td>\n",
       "      <td>month_diff_mean</td>\n",
       "      <td>348.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>162</td>\n",
       "      <td>purchase_amount_total_min</td>\n",
       "      <td>348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>145</td>\n",
       "      <td>purchase_Week_min_old</td>\n",
       "      <td>344.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>177</td>\n",
       "      <td>subsector_id_nunique_old</td>\n",
       "      <td>343.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>143</td>\n",
       "      <td>purchase_Week_mean_old</td>\n",
       "      <td>337.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>109</td>\n",
       "      <td>price_min_old</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5</td>\n",
       "      <td>Children_day_2017_mean</td>\n",
       "      <td>332.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>104</td>\n",
       "      <td>price_max</td>\n",
       "      <td>329.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>107</td>\n",
       "      <td>price_mean_old</td>\n",
       "      <td>328.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>151</td>\n",
       "      <td>purchase_amount_mean_old</td>\n",
       "      <td>328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>129</td>\n",
       "      <td>purchase_Hour_min</td>\n",
       "      <td>324.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>34</td>\n",
       "      <td>category_2_mean_old</td>\n",
       "      <td>321.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>49</td>\n",
       "      <td>duration_min</td>\n",
       "      <td>320.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>15</td>\n",
       "      <td>amount_month_ratio_mean</td>\n",
       "      <td>319.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>33</td>\n",
       "      <td>category_2_mean_mean_old</td>\n",
       "      <td>316.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>102</td>\n",
       "      <td>new_last_buy</td>\n",
       "      <td>309.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>99</td>\n",
       "      <td>month_lag_var</td>\n",
       "      <td>302.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>90</td>\n",
       "      <td>month_diff_var_old</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>68</td>\n",
       "      <td>installments_mean</td>\n",
       "      <td>300.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>141</td>\n",
       "      <td>purchase_Week_max_old</td>\n",
       "      <td>295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>105</td>\n",
       "      <td>price_max_old</td>\n",
       "      <td>292.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>122</td>\n",
       "      <td>purchase_Dayofweek_mean</td>\n",
       "      <td>291.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>54</td>\n",
       "      <td>duration_var_old</td>\n",
       "      <td>286.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>11</td>\n",
       "      <td>Mothers_Day_2018_mean_old</td>\n",
       "      <td>283.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>46</td>\n",
       "      <td>duration_max_old</td>\n",
       "      <td>281.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>17</td>\n",
       "      <td>amount_month_ratio_min</td>\n",
       "      <td>279.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>22</td>\n",
       "      <td>amount_month_ratio_var_old</td>\n",
       "      <td>275.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>132</td>\n",
       "      <td>purchase_Hour_nunique_old</td>\n",
       "      <td>271.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>150</td>\n",
       "      <td>purchase_amount_mean</td>\n",
       "      <td>270.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>112</td>\n",
       "      <td>price_var</td>\n",
       "      <td>265.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>110</td>\n",
       "      <td>price_sum</td>\n",
       "      <td>264.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>152</td>\n",
       "      <td>purchase_amount_min</td>\n",
       "      <td>261.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>14</td>\n",
       "      <td>amount_month_ratio_max_old</td>\n",
       "      <td>260.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>159</td>\n",
       "      <td>purchase_amount_total</td>\n",
       "      <td>259.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>56</td>\n",
       "      <td>feature_1</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>97</td>\n",
       "      <td>month_lag_skew</td>\n",
       "      <td>255.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>120</td>\n",
       "      <td>purchase_Day_nunique_old</td>\n",
       "      <td>254.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>36</td>\n",
       "      <td>category_3_mean_mean</td>\n",
       "      <td>251.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>149</td>\n",
       "      <td>purchase_amount_max_old</td>\n",
       "      <td>244.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>44</td>\n",
       "      <td>days_feature3_ratio</td>\n",
       "      <td>241.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>164</td>\n",
       "      <td>purchase_amount_var_old</td>\n",
       "      <td>240.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>161</td>\n",
       "      <td>purchase_amount_total_mean</td>\n",
       "      <td>233.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>103</td>\n",
       "      <td>observed_elapsed_time</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>purchase_Month_max</td>\n",
       "      <td>223.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>25</td>\n",
       "      <td>card_id_count_old</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>160</td>\n",
       "      <td>purchase_amount_total_max</td>\n",
       "      <td>213.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>19</td>\n",
       "      <td>amount_month_ratio_skew</td>\n",
       "      <td>207.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>76</td>\n",
       "      <td>installments_var</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>154</td>\n",
       "      <td>purchase_amount_skew</td>\n",
       "      <td>205.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>51</td>\n",
       "      <td>duration_skew</td>\n",
       "      <td>198.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>85</td>\n",
       "      <td>month_diff_min</td>\n",
       "      <td>195.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>27</td>\n",
       "      <td>card_id_count_total</td>\n",
       "      <td>175.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>96</td>\n",
       "      <td>month_lag_min_old</td>\n",
       "      <td>169.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>89</td>\n",
       "      <td>month_diff_var</td>\n",
       "      <td>169.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>130</td>\n",
       "      <td>purchase_Hour_min_old</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>63</td>\n",
       "      <td>feature_var</td>\n",
       "      <td>150.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>92</td>\n",
       "      <td>month_lag_max_old</td>\n",
       "      <td>135.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>32</td>\n",
       "      <td>category_2_mean_mean</td>\n",
       "      <td>134.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>71</td>\n",
       "      <td>installments_skew</td>\n",
       "      <td>129.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>125</td>\n",
       "      <td>purchase_Hour_max</td>\n",
       "      <td>119.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>35</td>\n",
       "      <td>category_3_mean</td>\n",
       "      <td>117.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>78</td>\n",
       "      <td>merchant_category_id_nunique</td>\n",
       "      <td>114.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>91</td>\n",
       "      <td>month_lag_max</td>\n",
       "      <td>113.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>67</td>\n",
       "      <td>installments_max_old</td>\n",
       "      <td>112.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>87</td>\n",
       "      <td>month_diff_skew</td>\n",
       "      <td>106.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>73</td>\n",
       "      <td>installments_sum</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>173</td>\n",
       "      <td>purchased_on_weekend_mean</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>118</td>\n",
       "      <td>purchase_Day_min_old</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>137</td>\n",
       "      <td>purchase_Month_min</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>62</td>\n",
       "      <td>feature_sum</td>\n",
       "      <td>94.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>66</td>\n",
       "      <td>installments_max</td>\n",
       "      <td>91.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>124</td>\n",
       "      <td>purchase_Dayofweek_min</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>31</td>\n",
       "      <td>category_2_mean</td>\n",
       "      <td>88.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>138</td>\n",
       "      <td>purchase_Month_min_old</td>\n",
       "      <td>83.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>57</td>\n",
       "      <td>feature_2</td>\n",
       "      <td>83.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>178</td>\n",
       "      <td>transactions_count</td>\n",
       "      <td>77.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>146</td>\n",
       "      <td>purchase_Week_nunique</td>\n",
       "      <td>74.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>176</td>\n",
       "      <td>subsector_id_nunique</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>80</td>\n",
       "      <td>merchant_id_nunique</td>\n",
       "      <td>67.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>175</td>\n",
       "      <td>quarter</td>\n",
       "      <td>67.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>134</td>\n",
       "      <td>purchase_Month_max_old</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>126</td>\n",
       "      <td>purchase_Hour_max_old</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>60</td>\n",
       "      <td>feature_mean</td>\n",
       "      <td>47.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>59</td>\n",
       "      <td>feature_max</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>95</td>\n",
       "      <td>month_lag_min</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>131</td>\n",
       "      <td>purchase_Hour_nunique</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>121</td>\n",
       "      <td>purchase_Dayofweek_max</td>\n",
       "      <td>37.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>119</td>\n",
       "      <td>purchase_Day_nunique</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>24</td>\n",
       "      <td>card_id_count</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>58</td>\n",
       "      <td>feature_3</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>61</td>\n",
       "      <td>feature_min</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                           Feature  importance_mean\n",
       "0      158         purchase_amount_sum_ratio           1949.8\n",
       "1      171             purchase_date_uptonow           1405.8\n",
       "2       84               month_diff_mean_old           1372.8\n",
       "3      172         purchase_date_uptonow_old           1363.6\n",
       "4       23              authorized_flag_mean           1330.6\n",
       "5       30               category_1_mean_old           1212.2\n",
       "6       94                month_lag_mean_old           1092.8\n",
       "7      139            purchase_Month_nunique           1063.2\n",
       "8        2                               CLV           1055.8\n",
       "9      168            purchase_date_diff_old           1002.6\n",
       "10     142                purchase_Week_mean            956.8\n",
       "11      55             fathers_day_2017_mean            906.2\n",
       "12     115                 purchase_Day_mean            838.4\n",
       "13     147         purchase_Week_nunique_old            814.8\n",
       "14      93                    month_lag_mean            804.4\n",
       "15     167                purchase_date_diff            757.4\n",
       "16      12           Valentine_Day_2017_mean            749.2\n",
       "17       6        Children_day_2017_mean_old            739.8\n",
       "18      29                   category_1_mean            737.8\n",
       "19       4                         CLV_ratio            728.0\n",
       "20     100                 month_lag_var_old            724.4\n",
       "21     135               purchase_Month_mean            720.6\n",
       "22      81           merchant_id_nunique_old            719.6\n",
       "23      74              installments_sum_old            694.4\n",
       "24      64                    hist_first_buy            685.4\n",
       "25      98                month_lag_skew_old            680.8\n",
       "26     156               purchase_amount_sum            670.2\n",
       "27      65                     hist_last_buy            670.0\n",
       "28     140                 purchase_Week_max            669.8\n",
       "29     128            purchase_Hour_mean_old            666.6\n",
       "30     113                     price_var_old            643.8\n",
       "31     170         purchase_date_uptomin_old            643.4\n",
       "32       9             Mothers_Day_2017_mean            637.0\n",
       "33     127                purchase_Hour_mean            628.4\n",
       "34     169             purchase_date_uptomin            624.4\n",
       "35     166         purchase_date_average_old            616.6\n",
       "36     116             purchase_Day_mean_old            614.8\n",
       "37     123       purchase_Dayofweek_mean_old            607.6\n",
       "38      88               month_diff_skew_old            568.2\n",
       "39      10             Mothers_Day_2018_mean            563.0\n",
       "40     165             purchase_date_average            558.8\n",
       "41       7           Christmas_Day_2017_mean            542.0\n",
       "42      79  merchant_category_id_nunique_old            521.8\n",
       "43      41                     days_feature2            518.6\n",
       "44      50                  duration_min_old            505.6\n",
       "45     144                 purchase_Week_min            504.0\n",
       "46     174     purchased_on_weekend_mean_old            496.6\n",
       "47      26               card_id_count_ratio            495.8\n",
       "48      18        amount_month_ratio_min_old            494.4\n",
       "49     179            transactions_count_old            493.2\n",
       "50      77              installments_var_old            492.8\n",
       "51      39                     days_feature1            489.4\n",
       "52      53                      duration_var            486.8\n",
       "53      69             installments_mean_old            483.6\n",
       "54       1        Black_Friday_2017_mean_old            472.6\n",
       "55       0            Black_Friday_2017_mean            470.4\n",
       "56     114                  purchase_Day_max            466.0\n",
       "57      13            amount_month_ratio_max            457.8\n",
       "58      37          category_3_mean_mean_old            452.4\n",
       "59     157           purchase_amount_sum_old            449.0\n",
       "60      28                     card_id_total            448.4\n",
       "61      45                      duration_max            440.4\n",
       "62     148               purchase_amount_max            438.4\n",
       "63      52                 duration_skew_old            429.4\n",
       "64       3                           CLV_old            424.4\n",
       "65      70                installments_ratio            424.2\n",
       "66      48                 duration_mean_old            414.2\n",
       "67      40               days_feature1_ratio            414.0\n",
       "68      38               category_3_mean_old            411.6\n",
       "69      16       amount_month_ratio_mean_old            399.0\n",
       "70      42               days_feature2_ratio            394.6\n",
       "71     136           purchase_Month_mean_old            392.6\n",
       "72     155          purchase_amount_skew_old            390.4\n",
       "73      43                     days_feature3            390.0\n",
       "74      75                installments_total            377.4\n",
       "75      20       amount_month_ratio_skew_old            376.4\n",
       "76     153           purchase_amount_min_old            375.8\n",
       "77     163               purchase_amount_var            375.2\n",
       "78       8       Christmas_Day_2017_mean_old            374.0\n",
       "79      86                  month_diff_ratio            372.2\n",
       "80     111                       price_total            369.6\n",
       "81      72             installments_skew_old            369.4\n",
       "82      21            amount_month_ratio_var            362.0\n",
       "83     108                         price_min            361.8\n",
       "84     106                        price_mean            359.6\n",
       "85     101                     new_first_buy            359.2\n",
       "86     117                  purchase_Day_min            358.8\n",
       "87      82                    month_diff_max            355.0\n",
       "88      47                     duration_mean            350.8\n",
       "89      83                   month_diff_mean            348.2\n",
       "90     162         purchase_amount_total_min            348.0\n",
       "91     145             purchase_Week_min_old            344.6\n",
       "92     177          subsector_id_nunique_old            343.4\n",
       "93     143            purchase_Week_mean_old            337.4\n",
       "94     109                     price_min_old            333.0\n",
       "95       5            Children_day_2017_mean            332.2\n",
       "96     104                         price_max            329.0\n",
       "97     107                    price_mean_old            328.8\n",
       "98     151          purchase_amount_mean_old            328.0\n",
       "99     129                 purchase_Hour_min            324.8\n",
       "100     34               category_2_mean_old            321.6\n",
       "101     49                      duration_min            320.8\n",
       "102     15           amount_month_ratio_mean            319.4\n",
       "103     33          category_2_mean_mean_old            316.8\n",
       "104    102                      new_last_buy            309.6\n",
       "105     99                     month_lag_var            302.4\n",
       "106     90                month_diff_var_old            301.0\n",
       "107     68                 installments_mean            300.2\n",
       "108    141             purchase_Week_max_old            295.0\n",
       "109    105                     price_max_old            292.4\n",
       "110    122           purchase_Dayofweek_mean            291.4\n",
       "111     54                  duration_var_old            286.8\n",
       "112     11         Mothers_Day_2018_mean_old            283.2\n",
       "113     46                  duration_max_old            281.2\n",
       "114     17            amount_month_ratio_min            279.6\n",
       "115     22        amount_month_ratio_var_old            275.6\n",
       "116    132         purchase_Hour_nunique_old            271.0\n",
       "117    150              purchase_amount_mean            270.6\n",
       "118    112                         price_var            265.2\n",
       "119    110                         price_sum            264.2\n",
       "120    152               purchase_amount_min            261.2\n",
       "121     14        amount_month_ratio_max_old            260.2\n",
       "122    159             purchase_amount_total            259.6\n",
       "123     56                         feature_1            259.0\n",
       "124     97                    month_lag_skew            255.8\n",
       "125    120          purchase_Day_nunique_old            254.8\n",
       "126     36              category_3_mean_mean            251.8\n",
       "127    149           purchase_amount_max_old            244.6\n",
       "128     44               days_feature3_ratio            241.6\n",
       "129    164           purchase_amount_var_old            240.8\n",
       "130    161        purchase_amount_total_mean            233.4\n",
       "131    103             observed_elapsed_time            225.0\n",
       "132    133                purchase_Month_max            223.8\n",
       "133     25                 card_id_count_old            223.0\n",
       "134    160         purchase_amount_total_max            213.8\n",
       "135     19           amount_month_ratio_skew            207.6\n",
       "136     76                  installments_var            207.0\n",
       "137    154              purchase_amount_skew            205.6\n",
       "138     51                     duration_skew            198.8\n",
       "139     85                    month_diff_min            195.2\n",
       "140     27               card_id_count_total            175.8\n",
       "141     96                 month_lag_min_old            169.8\n",
       "142     89                    month_diff_var            169.4\n",
       "143    130             purchase_Hour_min_old            153.0\n",
       "144     63                       feature_var            150.4\n",
       "145     92                 month_lag_max_old            135.4\n",
       "146     32              category_2_mean_mean            134.4\n",
       "147     71                 installments_skew            129.6\n",
       "148    125                 purchase_Hour_max            119.4\n",
       "149     35                   category_3_mean            117.4\n",
       "150     78      merchant_category_id_nunique            114.8\n",
       "151     91                     month_lag_max            113.8\n",
       "152     67              installments_max_old            112.8\n",
       "153     87                   month_diff_skew            106.8\n",
       "154     73                  installments_sum            106.0\n",
       "155    173         purchased_on_weekend_mean            103.0\n",
       "156    118              purchase_Day_min_old            101.0\n",
       "157    137                purchase_Month_min             98.0\n",
       "158     62                       feature_sum             94.6\n",
       "159     66                  installments_max             91.8\n",
       "160    124            purchase_Dayofweek_min             90.0\n",
       "161     31                   category_2_mean             88.2\n",
       "162    138            purchase_Month_min_old             83.8\n",
       "163     57                         feature_2             83.6\n",
       "164    178                transactions_count             77.8\n",
       "165    146             purchase_Week_nunique             74.2\n",
       "166    176              subsector_id_nunique             68.8\n",
       "167     80               merchant_id_nunique             67.6\n",
       "168    175                           quarter             67.4\n",
       "169    134            purchase_Month_max_old             61.0\n",
       "170    126             purchase_Hour_max_old             49.2\n",
       "171     60                      feature_mean             47.2\n",
       "172     59                       feature_max             47.0\n",
       "173     95                     month_lag_min             44.8\n",
       "174    131             purchase_Hour_nunique             42.0\n",
       "175    121            purchase_Dayofweek_max             37.4\n",
       "176    119              purchase_Day_nunique             34.8\n",
       "177     24                     card_id_count             23.8\n",
       "178     58                         feature_3              5.4\n",
       "179     61                       feature_min              1.8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_ft_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['purchase_amount_new_sum_ratio', 'purchase_date_uptonow', 'purchase_Month_nunique',\n",
       "       'repurchase_merchant_rate_old', 'duration_min_old', 'authorized_flag_mean', 'month_diff_mean_old',\n",
       "       'purchase_date_uptonow_old', 'purchase_Week_mean', 'category_1_1_purchase_amount_sum',\n",
       "       'authorized_flag_0_purchase_amount_sum', 'purchase_date_diff', 'purchase_date_diff_old',\n",
       "       'month_lag_mean_old', 'purchase_Day_mean', 'category_1_1_purchase_amount_sum_old',\n",
       "       'purchase_Month_mean', 'purchase_amount_new_sum_old', 'month_lag_var_old', 'days_feature2',\n",
       "       'purchase_amount_new_mean_ratio', 'purchase_date_uptomin', 'fathers_day_2017_mean',\n",
       "       'days_since_last_transaction', 'days_since_last_transaction_old', 'amount_month_ratio_min_old',\n",
       "       'month_lag_mean', 'purchase_Week_max', 'category_1_mean', 'authorized_flag_0_purchase_amount_mean',\n",
       "       'category_3_1_purchase_amount_sum', 'duration_min', 'month_lag_skew', 'month_lag_max',\n",
       "       'Mothers_Day_2018_mean', 'category_1_mean_old', 'purchase_amount_new_before_after_ratio',\n",
       "       'days_feature1', 'Children_day_2017_mean_old', 'category_1_1_purchase_amount_mean',\n",
       "       'purchase_date_uptomin_old', 'Valentine_Day_2017_mean', 'purchase_Week_nunique_old',\n",
       "       'month_diff_mean', 'avg_trans_per_merchant_old', 'installments_sum_old', 'hist_last_buy',\n",
       "       'category_3_1_purchase_amount_mean', 'purchase_date_successive_diff_mean',\n",
       "       'purchase_amount_new_first_last_diff', 'purchase_Hour_mean_old', 'purchase_Week_min',\n",
       "       'Mothers_Day_2017_mean', 'days_feature3', 'month_lag_skew_old', 'category_3_2_purchase_amount_sum',\n",
       "       'category_3_2_purchase_amount_mean', 'hist_first_buy', 'duration_mean_old', 'month_diff_ratio',\n",
       "       'Christmas_Day_2017_mean', 'purchase_amount_new_pct_75_ratio', 'purchase_Dayofweek_mean_old',\n",
       "       'card_id_count_ratio', 'days_feature1_ratio', 'purchase_Hour_mean',\n",
       "       'category_2_1.0_purchase_amount_sum', 'price_skew', 'purchase_date_average', 'month_diff_skew_old',\n",
       "       'merchant_category_repurchase_old', 'month_lag_var', 'category_3_0_purchase_amount_sum_old',\n",
       "       'days_feature2_ratio', 'category_3_0_purchase_amount_mean_old', 'Black_Friday_2017_mean',\n",
       "       'purchase_Day_mean_old', 'duration_var', 'category_2_1.0_purchase_amount_sum_ratio',\n",
       "       'purchase_Day_min', 'purchase_amount_new_median_ratio', 'purchase_date_successive_diff_mean_old',\n",
       "       'purchase_date_average_old', 'purchase_Day_max', 'avg_spend_per_merchant_old',\n",
       "       'Black_Friday_2017_mean_old', 'purchased_on_weekend_mean_old', 'category_2_1.0_purchase_amount_mean',\n",
       "       'new_first_buy', 'category_3_0_purchase_amount_mean', 'category_2_1.0_purchase_amount_mean_old',\n",
       "       'merchant_id_nunique_old', 'observed_elapsed_time', 'purchase_amount_new_fi_last_old_total',\n",
       "       'installments_ratio', 'month_diff_var_old', 'category_3_1_purchase_amount_sum_ratio',\n",
       "       'amount_month_ratio_min', 'new_last_buy', 'price_var_old', 'category_1_1_purchase_amount_sum_ratio',\n",
       "       'category_1_0_purchase_amount_sum_ratio', 'category_1_0_purchase_amount_mean_ratio',\n",
       "       'purchase_amount_skew_old', 'category_1_1_purchase_amount_mean_old', 'purchase_Week_mean_old',\n",
       "       'category_1_1_purchase_amount_mean_ratio', 'purchase_Month_mean_old',\n",
       "       'purchase_amount_new_gmean_ratio', 'price_max', 'merchant_category_id_nunique_old',\n",
       "       'amount_month_ratio_var', 'purchase_amount_new_inter_quartile_ratio', 'price_var',\n",
       "       'purchase_amount_new_sum', 'duration_skew_old', 'category_1_1_purchase_amount_sum_total',\n",
       "       'category_2_1.0_purchase_amount_mean_ratio', 'Children_day_2017_mean', 'Christmas_Day_2017_mean_old',\n",
       "       'category_3_0_purchase_amount_mean_ratio', 'category_3_1_purchase_amount_mean_old',\n",
       "       'purchase_Week_min_old', 'category_3_0_purchase_amount_sum', 'purchase_amount_fi_last_new_total',\n",
       "       'month_diff_max', 'category_2_1.0_purchase_amount_sum_old', 'duration_mean',\n",
       "       'purchase_amount_first_last_diff', 'purchase_amount_new_hmean', 'installments_var_old',\n",
       "       'purchase_amount_sum_ptp', 'purchase_amount_new_hmean_ratio',\n",
       "       'category_3_1_purchase_amount_mean_total', 'month_lag_min', 'installments_total',\n",
       "       'purchase_amount_max', 'purchase_Hour_min', 'category_1_0_purchase_amount_sum_old',\n",
       "       'purchase_amount_new_pct_25_ratio', 'category_3_0_purchase_amount_sum_ratio',\n",
       "       'category_1_0_purchase_amount_sum', 'purchase_amount_pct_75_ratio', 'feature_1',\n",
       "       'purchase_amount_min_old', 'purchase_amount_sum_max', 'amount_month_ratio_skew_old',\n",
       "       'purchase_amount_new_head_sum_old', 'purchase_Week_max_old', 'purchase_amount_count_ptp',\n",
       "       'purchase_amount_var', 'category_1_1_purchase_amount_mean_total', 'installments_skew_old',\n",
       "       'Mothers_Day_2018_mean_old', 'purchase_amount_mean_ptp', 'category_2_1.0_purchase_amount_sum_total',\n",
       "       'category_1_0_purchase_amount_sum_total', 'purchase_amount_head_max_old',\n",
       "       'category_1_0_purchase_amount_mean_old', 'category_3_1_purchase_amount_sum_old', 'duration_skew',\n",
       "       'purchase_amount_before_after_ratio', 'installments_pct_75_old',\n",
       "       'category_2_1.0_purchase_amount_mean_total', 'month_diff_skew', 'price_min',\n",
       "       'purchase_amount_max_mean_old', 'category_3_mean_old', 'CLV_ratio', 'category_3_mean_mean_old',\n",
       "       'purchase_Dayofweek_mean', 'installments_mean_old', 'purchase_amount_max_ptp', 'category_2_mean_old',\n",
       "       'category_3_0_purchase_amount_mean_total', 'installments_mean', 'purchase_amount_tail_sum_old',\n",
       "       'subsector_id_nunique_old', 'category_3_1_purchase_amount_sum_total', 'purchase_amount_tail_max_old',\n",
       "       'purchase_Month_max', 'purchase_amount_skew', 'purchase_amount_head_sum_old',\n",
       "       'purchase_amount_pct_25_old', 'purchase_amount_new_skew_ratio', 'price_sum',\n",
       "       'purchase_amount_skew_ratio', 'purchase_amount_inter_quartile_ratio', 'purchase_amount_max_sum_old',\n",
       "       'inverse_avg_transactions_per_day', 'days_feature3_ratio', 'purchase_amount_sum_max_old',\n",
       "       'category_1_0_purchase_amount_mean_total', 'category_2_5.0_purchase_amount_sum_old', 'CLV',\n",
       "       'installments_var', 'category_2_5.0_purchase_amount_sum', 'purchase_amount_median_ratio',\n",
       "       'category_1_0_purchase_amount_mean', 'category_3_0_purchase_amount_sum_total',\n",
       "       'category_2_mean_mean_old', 'purchase_amount_new_fi_last_new_total', 'purchase_amount_max_ptp_old',\n",
       "       'purchase_amount_median_old', 'authorized_flag_1_purchase_amount_mean', 'purchase_amount_new_skew',\n",
       "       'category_3_1_purchase_amount_mean_ratio', 'purchase_amount_sum_ratio', 'purchase_amount_count_max',\n",
       "       'installments_head_sum_old', 'inverse_avg_transactions_per_day_old', 'price_mean_old',\n",
       "       'amount_month_ratio_mean_old', 'installments_tail_sum_old', 'purchase_amount_mean_ratio',\n",
       "       'purchase_amount_new_head_sum', 'category_3_mean_mean', 'category_3_2_purchase_amount_mean_old',\n",
       "       'purchase_amount_mean_sum_old', 'month_lag_min_old', 'purchase_amount_new_tail_sum_old',\n",
       "       'category_3_2_purchase_amount_sum_old', 'purchase_amount_sum_ptp_old',\n",
       "       'category_3_2_purchase_amount_mean_total', 'purchase_Hour_nunique_old', 'month_diff_var',\n",
       "       'purchase_amount_new_var_old', 'authorized_flag_1_purchase_amount_sum', 'purchase_amount_new_max_old',\n",
       "       'purchase_amount_sum_mean_old', 'purchase_amount_min', 'purchase_amount_new_sum_total',\n",
       "       'purchase_amount_new_tail_max', 'purchase_amount_new_hmean_old', 'purchase_amount_mean_max_old',\n",
       "       'purchase_amount_new_inter_quartile_old', 'purchase_amount_new_tail_sum',\n",
       "       'purchase_amount_new_inter_quartile', 'category_2_5.0_purchase_amount_mean',\n",
       "       'purchase_amount_mean_ptp_old', 'category_3_2_purchase_amount_sum_ratio', 'price_mean',\n",
       "       'duration_max_old', 'amount_month_ratio_skew', 'purchase_amount_pct_25_ratio',\n",
       "       'purchase_amount_max_max_old', 'category_2_2.0_purchase_amount_mean', 'month_diff_min',\n",
       "       'category_2_3.0_purchase_amount_mean_old', 'purchase_Day_nunique_old',\n",
       "       'category_2_5.0_purchase_amount_mean_old', 'category_3_2_purchase_amount_mean_ratio', 'price_max_old',\n",
       "       'card_id_total', 'purchase_amount_total_min', 'price_min_old', 'purchase_Hour_min_old',\n",
       "       'installments_pct_75', 'purchase_amount_new_gmean', 'category_2_3.0_purchase_amount_mean',\n",
       "       'merchant_category_id_nunique', 'month_lag_max_old', 'CLV_old', 'purchase_amount_count_mean',\n",
       "       'purchase_amount_max_sum', 'purchase_amount_new_hmean_total', 'purchase_amount_tail_max',\n",
       "       'purchase_amount_head_max', 'purchase_amount_pct_25', 'purchase_amount_new_head_max',\n",
       "       'purchase_Month_min', 'category_2_3.0_purchase_amount_sum', 'category_2_4.0_purchase_amount_sum',\n",
       "       'purchase_amount_count_mean_old', 'feature_var', 'installments_max', 'purchase_amount_new_min',\n",
       "       'purchase_amount_total_max', 'purchase_amount_new_pct_25', 'category_3_2_purchase_amount_sum_total',\n",
       "       'transactions_count_old', 'amount_month_ratio_var_old', 'duration_max', 'amount_month_ratio_max_old',\n",
       "       'feature_sum', 'category_2_4.0_purchase_amount_mean', 'purchase_amount_median',\n",
       "       'amount_month_ratio_mean', 'purchase_amount_max_mean', 'purchase_amount_new_skew_old',\n",
       "       'purchase_amount_new_gmean_total', 'purchase_amount_pct_75_old', 'purchase_amount_tail_sum',\n",
       "       'duration_var_old', 'price_total', 'purchase_amount_mean_mean', 'purchase_amount_head_sum',\n",
       "       'category_2_3.0_purchase_amount_sum_ratio', 'purchase_amount_sum_mean', 'purchase_amount_max_old',\n",
       "       'purchase_amount_inter_quartile_old', 'amount_month_ratio_max', 'purchase_amount_new_median',\n",
       "       'purchase_amount_inter_quartile', 'purchase_amount_mean_sum', 'purchase_amount_mean_max',\n",
       "       'purchase_amount_fi_last_old_total', 'installments_median', 'purchase_amount_var_old',\n",
       "       'purchase_amount_sum', 'purchase_amount_count_max_old', 'purchase_Week_nunique',\n",
       "       'purchase_amount_pct_75', 'purchase_Hour_max', 'installments_tail_sum',\n",
       "       'category_2_2.0_purchase_amount_sum', 'installments_skew', 'category_2_5.0_purchase_amount_sum_total',\n",
       "       'purchase_amount_new_min_total', 'purchase_amount_sum_old',\n",
       "       'category_2_3.0_purchase_amount_mean_ratio', 'purchase_amount_new_tail_max_old', 'feature_2',\n",
       "       'card_id_count_old', 'purchased_on_weekend_mean', 'observation_date_old',\n",
       "       'purchase_amount_new_min_old', 'purchase_Dayofweek_min', 'category_2_5.0_purchase_amount_sum_ratio',\n",
       "       'purchase_amount_new_pct_25_old', 'purchase_amount_new_gmean_old', 'purchase_Month_min_old',\n",
       "       'purchase_amount_mean_mean_old', 'avg_spend_per_transaction', 'transactions_count', 'quarter',\n",
       "       'category_2_mean_mean', 'category_2_3.0_purchase_amount_mean_total', 'purchase_amount_total',\n",
       "       'category_2_3.0_purchase_amount_sum_total', 'category_2_5.0_purchase_amount_mean_ratio',\n",
       "       'category_2_5.0_purchase_amount_mean_total', 'purchase_amount_mean',\n",
       "       'purchase_amount_new_head_max_old', 'installments_sum', 'subsector_id_nunique', 'card_id_count_total',\n",
       "       'category_2_2.0_purchase_amount_mean_old', 'purchase_amount_new_pct_75', 'installments_tail_max',\n",
       "       'purchase_amount_new_max_total', 'purchase_amount_mean_old', 'installments_max_old',\n",
       "       'installments_pct_25', 'purchase_amount_total_mean', 'purchase_Day_nunique',\n",
       "       'installments_tail_max_old', 'category_2_4.0_purchase_amount_mean_old', 'purchase_Month_max_old',\n",
       "       'avg_spend_per_merchant', 'category_2_4.0_purchase_amount_sum_ratio', 'purchase_amount_min_total',\n",
       "       'category_2_4.0_purchase_amount_sum_old', 'purchase_amount_new_pct_75_old',\n",
       "       'purchase_amount_new_median_old', 'category_2_mean', 'feature_mean',\n",
       "       'category_2_3.0_purchase_amount_sum_old', 'merchant_id_nunique',\n",
       "       'category_2_4.0_purchase_amount_mean_total', 'purchase_Day_min_old',\n",
       "       'category_2_4.0_purchase_amount_mean_ratio', 'installments_pct_25_old', 'category_3_mean',\n",
       "       'purchase_amount_sum_sum', 'installments_head_max', 'category_2_2.0_purchase_amount_sum_old',\n",
       "       'purchase_amount_sum_sum_old', 'purchase_amount_new_mean_old', 'purchase_Hour_max_old',\n",
       "       'installments_head_max_old', 'purchase_amount_new_mean_total', 'purchase_amount_sum_total',\n",
       "       'installments_head_sum', 'purchase_Hour_nunique', 'purchase_amount_count_sum',\n",
       "       'category_2_2.0_purchase_amount_mean_total', 'installments_min', 'feature_max',\n",
       "       'merchant_category_repurchase', 'purchase_amount_max_max', 'category_2_4.0_purchase_amount_sum_total',\n",
       "       'category_2_2.0_purchase_amount_sum_ratio', 'purchase_amount_new_var',\n",
       "       'purchase_amount_count_ptp_old', 'purchase_amount_max_total', 'purchase_amount_new_max',\n",
       "       'card_id_count', 'purchase_Dayofweek_max', 'category_2_2.0_purchase_amount_mean_ratio',\n",
       "       'purchase_amount_new_mean', 'avg_spend_per_transaction_old', 'purchase_amount_count_sum_old',\n",
       "       'purchased_before_issue', 'purchase_amount_mean_total', 'authorized_flag_1_purchase_amount_mean_old',\n",
       "       'category_2_2.0_purchase_amount_sum_total', 'installments_median_old',\n",
       "       'authorized_flag_1_purchase_amount_sum_old', 'feature_3', 'repurchase_merchant_rate', 'feature_min',\n",
       "       'avg_trans_per_merchant', 'installments_min_old'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fts_sorted_by_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "picked_feats = best_features.sort_values(by='importance', ascending=False)[:800]['Feature'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(y_true, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kfold_lightgbm(train_df, test_df, num_folds, stratified = False, debug= False):\n",
    "    print(\"Starting LightGBM. Train shape: {}, test shape: {}\".format(train_df.shape, test_df.shape))\n",
    "\n",
    "    # Cross validation model\n",
    "    if stratified:\n",
    "        folds = StratifiedKFold(n_splits= num_folds, shuffle=True, random_state=326)\n",
    "    else:\n",
    "        folds = KFold(n_splits= num_folds, shuffle=True, random_state=326)\n",
    "\n",
    "    # Create arrays and dataframes to store results\n",
    "    oof_preds = np.zeros(train_df.shape[0])\n",
    "    sub_preds = np.zeros(test_df.shape[0])\n",
    "    feature_importance_df = pd.DataFrame()\n",
    "    feats = [f for f in train_df.columns if f not in cols_excluded]\n",
    "\n",
    "    # k-fold\n",
    "    for n_fold, (train_idx, valid_idx) in enumerate(folds.split(train_df[feats], train_df['outliers'])):\n",
    "        train_x, train_y = train_df[feats].iloc[train_idx], train_df['target'].iloc[train_idx]\n",
    "        valid_x, valid_y = train_df[feats].iloc[valid_idx], train_df['target'].iloc[valid_idx]\n",
    "\n",
    "        # set data structure\n",
    "        lgb_train = lgb.Dataset(train_x,\n",
    "                                label=train_y,\n",
    "                                free_raw_data=False)\n",
    "        lgb_test = lgb.Dataset(valid_x,\n",
    "                               label=valid_y,\n",
    "                               free_raw_data=False)\n",
    "\n",
    "        # params optimized by optuna\n",
    "        params ={\n",
    "                'task': 'train',\n",
    "                'boosting': 'goss',\n",
    "                'objective': 'regression',\n",
    "                'metric': 'rmse',\n",
    "                'learning_rate': 0.01,\n",
    "                'subsample': 0.9855232997390695,\n",
    "                'max_depth': 7,\n",
    "                'top_rate': 0.9064148448434349,\n",
    "                'num_leaves': 63,\n",
    "                'min_child_weight': 41.9612869171337,\n",
    "                'other_rate': 0.0721768246018207,\n",
    "                'reg_alpha': 9.677537745007898,\n",
    "                'colsample_bytree': 0.5665320670155495,\n",
    "                'min_split_gain': 9.820197773625843,\n",
    "                'reg_lambda': 8.2532317400459,\n",
    "                'min_data_in_leaf': 21,\n",
    "                'verbose': -1,\n",
    "                'seed':int(2**n_fold),\n",
    "                'bagging_seed':int(2**n_fold),\n",
    "                'drop_seed':int(2**n_fold)\n",
    "                }\n",
    "\n",
    "        reg = lgb.train(\n",
    "                        params,\n",
    "                        lgb_train,\n",
    "                        valid_sets=[lgb_train, lgb_test],\n",
    "                        valid_names=['train', 'test'],\n",
    "                        num_boost_round=10000,\n",
    "                        early_stopping_rounds= 200,\n",
    "                        verbose_eval=100\n",
    "                        )\n",
    "\n",
    "        oof_preds[valid_idx] = reg.predict(valid_x, num_iteration=reg.best_iteration)\n",
    "        sub_preds += reg.predict(test_df[feats], num_iteration=reg.best_iteration) / folds.n_splits\n",
    "\n",
    "        fold_importance_df = pd.DataFrame()\n",
    "        fold_importance_df[\"feature\"] = feats\n",
    "        fold_importance_df[\"importance\"] = np.log1p(reg.feature_importance(importance_type='gain', iteration=reg.best_iteration))\n",
    "        fold_importance_df[\"fold\"] = n_fold + 1\n",
    "        feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "        print('Fold %2d RMSE : %.6f' % (n_fold + 1, rmse(valid_y, oof_preds[valid_idx])))\n",
    "        del reg, train_x, train_y, valid_x, valid_y\n",
    "        gc.collect()\n",
    "\n",
    "    # display importances\n",
    "    display_importances(feature_importance_df)\n",
    "\n",
    "    if not debug:\n",
    "        # save submission file\n",
    "        submission_name = pd.to_datetime(\"today\")\n",
    "        sub_df = pd.DataFrame({\"card_id\":test_df2[\"card_id\"].values})\n",
    "        sub_df[\"target\"] = sub_preds\n",
    "        sub_df.to_csv(f'submission_kfold-{submission_name}.csv', index=False)\n",
    "#         test_df.loc[:,'target'] = sub_preds\n",
    "#         test_df = test_df.reset_index()\n",
    "#         test_df[['card_id', 'target']].to_csv(submission_name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display/plot feature importance\n",
    "def display_importances(feature_importance_df_):\n",
    "    cols = feature_importance_df_[[\"feature\", \"importance\"]].groupby(\"feature\").mean().sort_values(by=\"importance\", ascending=False)[:40].index\n",
    "    best_features = feature_importance_df_.loc[feature_importance_df_.feature.isin(cols)]\n",
    "\n",
    "    plt.figure(figsize=(8, 10))\n",
    "    sns.barplot(x=\"importance\", y=\"feature\", data=best_features.sort_values(by=\"importance\", ascending=False))\n",
    "    plt.title('LightGBM Features (avg over folds)')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('lgbm_importances.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(y_true, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51043"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting LightGBM. Train shape: (201917, 252), test shape: (123623, 249)\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'target'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3077\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3078\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3079\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'target'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-75f9825b2274>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkfold_lightgbm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_df2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_folds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstratified\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-42-706fb5a6f518>\u001b[0m in \u001b[0;36mkfold_lightgbm\u001b[0;34m(train_df, test_df, num_folds, stratified, debug)\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;31m# k-fold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mn_fold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_idx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeats\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'outliers'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeats\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m         \u001b[0mvalid_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeats\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2686\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2687\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2688\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2690\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2693\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2695\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2697\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2487\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2488\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2489\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2490\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2491\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   4113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4114\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4115\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4116\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3078\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'target'"
     ]
    }
   ],
   "source": [
    "kfold_lightgbm(train_df2, test_df2, num_folds=11, stratified=False, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_name = pd.to_datetime(\"today\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = pd.DataFrame({\"card_id\":test_df2[\"card_id\"].values})\n",
    "sub_df[\"target\"] = predictions\n",
    "sub_df.to_csv(f'submission-{submission_name}.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Blending submissions.ipynb'\r\n",
      "'Elo - ML model - 3.693.ipynb'\r\n",
      "'Elo - ML model - 3.695 beta.ipynb'\r\n",
      "'Elo - ML model - 3.695.ipynb'\r\n",
      "'Elo - ML model - 3.696.ipynb'\r\n",
      "'Elo - ML model - beta.ipynb'\r\n",
      "'Elo - ML model - cat aggregates.ipynb'\r\n",
      "'Elo - ML model 3.691.ipynb'\r\n",
      "'Elo - ML model clean.ipynb'\r\n",
      "'Elo - ML model proc_df.ipynb'\r\n",
      "'Elo - ML model-RF copy.ipynb'\r\n",
      "'Elo - ML model-backup.ipynb'\r\n",
      "'Elo - ML model-new.ipynb'\r\n",
      "'Elo RF- New.ipynb'\r\n",
      "'Elo RF.ipynb'\r\n",
      "'Elo end 2 end.ipynb'\r\n",
      "'Elo feature engineering - 3.691.ipynb'\r\n",
      "'Elo feature engineering - 3.696.ipynb'\r\n",
      "'Elo feature engineering 3.693 .ipynb'\r\n",
      "'Elo feature engineering 3.695 beta.ipynb'\r\n",
      "'Elo feature engineering 3.695.ipynb'\r\n",
      "'Elo feature engineering beta.ipynb'\r\n",
      "'Elo feature engineering using agg.ipynb'\r\n",
      "'Elo feature engineering-backup.ipynb'\r\n",
      "'Elo feature engineering-proc_df - new-Copy1.ipynb'\r\n",
      "'Elo feature engineering-proc_df - new.ipynb'\r\n",
      "'Elo feature engineering-proc_df-OLD.ipynb'\r\n",
      "'Elo feature engineering-proc_df.ipynb'\r\n",
      "'Elo merchant category reco - using RF.ipynb'\r\n",
      "'Elo outlier predictions & blending .ipynb'\r\n",
      "'Feature selection using Boruta.ipynb'\r\n",
      "'Feature selection.ipynb'\r\n",
      "'Null importances - beta.ipynb'\r\n",
      "'Outlier classification using Autoencoder.ipynb'\r\n",
      " README.md\r\n",
      "'Scrap pad.ipynb'\r\n",
      "'Scrap pad.py'\r\n",
      "'TS Fresh.ipynb'\r\n",
      "'TS Fresh.py'\r\n",
      "'Train & test clean.ipynb'\r\n",
      "'Using DL.ipynb'\r\n",
      "'Using Feature tools - old.ipynb'\r\n",
      "'Using feature tools-Copy1.ipynb'\r\n",
      "'Using feature tools.ipynb'\r\n",
      " actual_importances_ditribution_rf.csv\r\n",
      " adf\r\n",
      " alla.pkl\r\n",
      " authorised_trans_proc_agg\r\n",
      "'blend-2019-02-02 23:24:52.864854.csv'\r\n",
      " blend-{submission_name}\r\n",
      " blend_kfol_st.csv\r\n",
      " chandu_mean1.csv\r\n",
      " cols_keep.npy\r\n",
      " combining_submission.csv\r\n",
      " corr_scores_null_importances.csv\r\n",
      " correlation\r\n",
      " data\r\n",
      " df_auth\r\n",
      " df_hot_dropped_all\r\n",
      " df_hot_ext\r\n",
      " df_hot_test\r\n",
      " df_hot_test_ext\r\n",
      " df_new\r\n",
      " df_non_auth\r\n",
      " df_proc_dfed\r\n",
      " df_test_n_o\r\n",
      " df_xo\r\n",
      " fastai\r\n",
      " fm_new_hist\r\n",
      " hist_trans\r\n",
      " hist_trans_3_696\r\n",
      " hist_trans_agg\r\n",
      " hist_trans_agg_3_696\r\n",
      " hist_trans_agg_beta\r\n",
      " hist_trans_agg_cat\r\n",
      " hist_trans_agg_month\r\n",
      " hist_trans_agg_n\r\n",
      " hist_trans_agg_proc\r\n",
      " hist_trans_beta\r\n",
      "'kfold lightgbm.ipynb'\r\n",
      " latent_representation.png\r\n",
      " lgbm_importances.png\r\n",
      " lla\r\n",
      " new_hist_prc\r\n",
      " new_hist_trans\r\n",
      " new_hist_trans_3_696\r\n",
      " new_hist_trans_agg\r\n",
      " new_hist_trans_agg_3_696\r\n",
      " new_hist_trans_agg_beta\r\n",
      " new_hist_trans_agg_cat\r\n",
      " new_hist_trans_agg_month\r\n",
      " new_hist_trans_agg_n\r\n",
      " new_hist_trans_beta\r\n",
      " new_trans_agg_proc\r\n",
      " new_trans_proc_agg\r\n",
      " non_authorised_trans_proc\r\n",
      " non_authorised_trans_proc_agg\r\n",
      " null_importances_distribution_rf.csv\r\n",
      " oof_lgb_3.npy\r\n",
      " oof_xgb_3.npy\r\n",
      " original.png\r\n",
      " predictions_lgb_3.npy\r\n",
      " predictions_xgb_3.npy\r\n",
      "'scrap pad 2.ipynb'\r\n",
      "'submission-2019-01-11 09:49:53.716164.csv'\r\n",
      "'submission-2019-01-11 10:18:09.909061.csv'\r\n",
      "'submission-2019-01-12 13:53:27.215115.csv'\r\n",
      "'submission-2019-01-12 16:27:31.239044.csv'\r\n",
      "'submission-2019-01-12 17:25:56.678553.csv'\r\n",
      "'submission-2019-01-12 17:33:29.475835.csv'\r\n",
      "'submission-2019-01-12 17:42:01.350278.csv'\r\n",
      "'submission-2019-01-12 22:12:59.947458.csv'\r\n",
      "'submission-2019-01-13 15:59:45.792347.csv'\r\n",
      "'submission-2019-01-13 18:24:51.237991.csv'\r\n",
      "'submission-2019-01-14 19:04:33.028609.csv'\r\n",
      "'submission-2019-01-14 19:56:09.906235.csv'\r\n",
      "'submission-2019-01-15 22:18:34.294443.csv'\r\n",
      "'submission-2019-01-15 22:24:38.071972.csv'\r\n",
      "'submission-2019-01-15 22:29:20.144417.csv'\r\n",
      "'submission-2019-01-16 00:35:46.490517.csv'\r\n",
      "'submission-2019-01-16 00:36:26.004443.csv'\r\n",
      "'submission-2019-01-16 12:20:00.957329.csv'\r\n",
      "'submission-2019-01-16 21:37:16.421927.csv'\r\n",
      "'submission-2019-01-17 10:20:06.724992.csv'\r\n",
      "'submission-2019-01-17 11:09:17.495216.csv'\r\n",
      "'submission-2019-01-17 23:23:57.743505.csv'\r\n",
      "'submission-2019-01-18 01:18:08.182115.csv'\r\n",
      "'submission-2019-01-18 01:42:20.587834.csv'\r\n",
      "'submission-2019-01-18 01:47:04.024727.csv'\r\n",
      "'submission-2019-01-18 16:16:45.475456.csv'\r\n",
      "'submission-2019-01-18 16:27:58.560480.csv'\r\n",
      "'submission-2019-01-18 17:22:46.188675.csv'\r\n",
      "'submission-2019-01-20 00:30:10.643280.csv'\r\n",
      "'submission-2019-01-20 01:28:12.077657.csv'\r\n",
      "'submission-2019-01-20 01:46:39.187580.csv'\r\n",
      "'submission-2019-01-20 02:32:16.439302.csv'\r\n",
      "'submission-2019-01-20 17:01:54.207459.csv'\r\n",
      "'submission-2019-01-20 17:27:46.797180.csv'\r\n",
      "'submission-2019-01-20 18:29:37.773532.csv'\r\n",
      "'submission-2019-01-20 19:11:58.593189.csv'\r\n",
      "'submission-2019-01-20 19:35:24.892043.csv'\r\n",
      "'submission-2019-01-20 20:47:32.743729.csv'\r\n",
      "'submission-2019-01-22 09:33:02.341491.csv'\r\n",
      "'submission-2019-01-22 10:35:57.465665.csv'\r\n",
      "'submission-2019-01-31 22:00:10.810901.csv'\r\n",
      "'submission-2019-01-31 22:04:41.815902.csv'\r\n",
      "'submission-2019-02-02 23:13:59.670630.csv'\r\n",
      "'submission-2019-02-02 23:24:52.864854.csv'\r\n",
      "'submission-2019-02-03 14:42:22.636262.csv'\r\n",
      "'submission-2019-02-03 14:43:29.797523.csv'\r\n",
      "'submission-2019-02-10 14:58:45.312994.csv'\r\n",
      "'submission-2019-02-10 17:36:17.986042.csv'\r\n",
      "'submission-2019-02-10 18:01:58.093819.csv'\r\n",
      "'submission-2019-02-12 22:57:39.862967.csv'\r\n",
      "'submission-2019-02-13 00:42:12.274968.csv'\r\n",
      "'submission-2019-02-13 01:51:25.440947.csv'\r\n",
      "'submission-2019-02-13 22:48:45.005871.csv'\r\n",
      "'submission-2019-02-15 14:22:06.881490.csv'\r\n",
      "'submission_kfold-2019-02-13 00:55:25.132146.csv'\r\n",
      "'submission_kfold-2019-02-13 01:29:28.856630.csv'\r\n",
      "'submission_kfold-2019-02-13 01:59:08.971995.csv'\r\n",
      "'submission_kfold-2019-02-13 23:21:24.051900.csv'\r\n",
      " submissions\r\n",
      " target_all\r\n",
      " target_outliers_removed\r\n",
      " test_clean.csv\r\n",
      " test_clean.csv.zip\r\n",
      " test_df\r\n",
      " test_df_3.695\r\n",
      " test_df_3_696\r\n",
      " test_df_beta\r\n",
      " test_df_proc\r\n",
      " test_df_proc_new\r\n",
      " test_df_proc_new_m\r\n",
      " train_clean.csv\r\n",
      " train_clean.csv.zip\r\n",
      " train_clean0\r\n",
      " train_df\r\n",
      " train_df_3_696\r\n",
      " train_df_beta\r\n",
      " train_df_proc\r\n",
      " train_df_proc_new\r\n",
      " train_df_proc_new_m\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href='submission-2019-02-19 00:14:48.737899.csv' target='_blank'>submission-2019-02-19 00:14:48.737899.csv</a><br>"
      ],
      "text/plain": [
       "/home/chandrasekhar/private/kaggle/submission-2019-02-19 00:14:48.737899.csv"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileLink(f'submission-{submission_name}.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Blend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = pd.read_csv(f'submission-{submission_name}.csv')\n",
    "p2 = pd.read_csv('submission-2019-02-02 23:13:59.670630.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>card_id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_ID_0ab67a22ab</td>\n",
       "      <td>-2.960024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_ID_130fd0cbdd</td>\n",
       "      <td>-0.421305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_ID_b709037bc5</td>\n",
       "      <td>-1.011111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_ID_d27d835a9f</td>\n",
       "      <td>-0.141994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_ID_2b5e3df5c2</td>\n",
       "      <td>-1.270823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           card_id    target\n",
       "0  C_ID_0ab67a22ab -2.960024\n",
       "1  C_ID_130fd0cbdd -0.421305\n",
       "2  C_ID_b709037bc5 -1.011111\n",
       "3  C_ID_d27d835a9f -0.141994\n",
       "4  C_ID_2b5e3df5c2 -1.270823"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>card_id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_ID_0ab67a22ab</td>\n",
       "      <td>-3.072875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_ID_130fd0cbdd</td>\n",
       "      <td>-0.348145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_ID_b709037bc5</td>\n",
       "      <td>-0.949767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_ID_d27d835a9f</td>\n",
       "      <td>-0.149493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_ID_2b5e3df5c2</td>\n",
       "      <td>-1.212316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           card_id    target\n",
       "0  C_ID_0ab67a22ab -3.072875\n",
       "1  C_ID_130fd0cbdd -0.348145\n",
       "2  C_ID_b709037bc5 -0.949767\n",
       "3  C_ID_d27d835a9f -0.149493\n",
       "4  C_ID_2b5e3df5c2 -1.212316"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "p3 = p1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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",
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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>card_id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_ID_0ab67a22ab</td>\n",
       "      <td>-2.960024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_ID_130fd0cbdd</td>\n",
       "      <td>-0.421305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_ID_b709037bc5</td>\n",
       "      <td>-1.011111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_ID_d27d835a9f</td>\n",
       "      <td>-0.141994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_ID_2b5e3df5c2</td>\n",
       "      <td>-1.270823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           card_id    target\n",
       "0  C_ID_0ab67a22ab -2.960024\n",
       "1  C_ID_130fd0cbdd -0.421305\n",
       "2  C_ID_b709037bc5 -1.011111\n",
       "3  C_ID_d27d835a9f -0.141994\n",
       "4  C_ID_2b5e3df5c2 -1.270823"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "p3 = pd.DataFrame({\"card_id\":test_df2[\"card_id\"].values})\n",
    "p3[\"target\"] = p1['target']*.5 + p2['target']*.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>card_id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_ID_0ab67a22ab</td>\n",
       "      <td>-3.016449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_ID_130fd0cbdd</td>\n",
       "      <td>-0.384725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_ID_b709037bc5</td>\n",
       "      <td>-0.980439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_ID_d27d835a9f</td>\n",
       "      <td>-0.145743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_ID_2b5e3df5c2</td>\n",
       "      <td>-1.241569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           card_id    target\n",
       "0  C_ID_0ab67a22ab -3.016449\n",
       "1  C_ID_130fd0cbdd -0.384725\n",
       "2  C_ID_b709037bc5 -0.980439\n",
       "3  C_ID_d27d835a9f -0.145743\n",
       "4  C_ID_2b5e3df5c2 -1.241569"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "p3.to_csv('blend_kfol_st.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "message = 'using blend kfold + stratified, purchase amount trimmed'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>card_id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_ID_0ab67a22ab</td>\n",
       "      <td>-3.016449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_ID_130fd0cbdd</td>\n",
       "      <td>-0.384725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_ID_b709037bc5</td>\n",
       "      <td>-0.980439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_ID_d27d835a9f</td>\n",
       "      <td>-0.145743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_ID_2b5e3df5c2</td>\n",
       "      <td>-1.241569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           card_id    target\n",
       "0  C_ID_0ab67a22ab -3.016449\n",
       "1  C_ID_130fd0cbdd -0.384725\n",
       "2  C_ID_b709037bc5 -0.980439\n",
       "3  C_ID_d27d835a9f -0.145743\n",
       "4  C_ID_2b5e3df5c2 -1.241569"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = f'submission-{submission_name}.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'submission-2019-02-19 00:14:48.737899.csv'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|#######################################| 4.24M/4.24M [00:10<00:00, 440kB/s]\n",
      "Successfully submitted to Elo Merchant Category Recommendation"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit elo-merchant-category-recommendation -f 'submission-2019-02-19 00:14:48.737899.csv' -m \"rescaled purchase amount with old 180 cols & observed time\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train_df2.copy()\n",
    "train['rounded_target'] = train['target'].round(0)\n",
    "train = train.sort_values('rounded_target').reset_index(drop=True)\n",
    "vc = train['rounded_target'].value_counts()\n",
    "vc = dict(sorted(vc.items()))\n",
    "df = pd.DataFrame()\n",
    "train['indexcol'],i = 0,1\n",
    "for k,v in vc.items():\n",
    "    step = train.shape[0]/v\n",
    "    indent = train.shape[0]/(v+1)\n",
    "    df2 = train[train['rounded_target'] == k].sample(v, random_state=120).reset_index(drop=True)\n",
    "    for j in range(0, v):\n",
    "        df2.at[j, 'indexcol'] = indent + j*step + 0.000001*i\n",
    "    df = pd.concat([df2,df])\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.sort_values('indexcol', ascending=True).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>first_active_month</th>\n",
       "      <td>2017-12-01 00:00:00</td>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>2016-09-01 00:00:00</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_id</th>\n",
       "      <td>C_ID_176ecc65bd</td>\n",
       "      <td>C_ID_6082929d3a</td>\n",
       "      <td>C_ID_a87bbd85a7</td>\n",
       "      <td>C_ID_92e4809531</td>\n",
       "      <td>C_ID_d2dd6bbf9a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_1</th>\n",
       "      <td>0.0131446</td>\n",
       "      <td>0.0107116</td>\n",
       "      <td>0.0104794</td>\n",
       "      <td>0.00805849</td>\n",
       "      <td>0.0107116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_2</th>\n",
       "      <td>0.0113848</td>\n",
       "      <td>0.0113848</td>\n",
       "      <td>0.0113848</td>\n",
       "      <td>0.0113848</td>\n",
       "      <td>0.0113848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_3</th>\n",
       "      <td>0.0114275</td>\n",
       "      <td>0.0102828</td>\n",
       "      <td>0.0114275</td>\n",
       "      <td>0.0102828</td>\n",
       "      <td>0.0102828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.279039</td>\n",
       "      <td>-0.926039</td>\n",
       "      <td>0.475326</td>\n",
       "      <td>0.516026</td>\n",
       "      <td>-0.100927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthYear</th>\n",
       "      <td>2017</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthMonth</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthWeek</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>35</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthDay</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthDayofweek</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthDayofyear</th>\n",
       "      <td>335</td>\n",
       "      <td>336</td>\n",
       "      <td>245</td>\n",
       "      <td>305</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_month_end</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_month_start</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_quarter_end</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_quarter_start</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_year_end</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_year_start</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthElapsed</th>\n",
       "      <td>1512086400</td>\n",
       "      <td>1480550400</td>\n",
       "      <td>1472688000</td>\n",
       "      <td>1509494400</td>\n",
       "      <td>1509494400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactions_count</th>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_id_nunique</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_category_id_nunique</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_id_nunique</th>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_id_nunique</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subsector_id_nunique</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_Year_nunique</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_Month_nunique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_Week_nunique</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_Day_nunique</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_sum</th>\n",
       "      <td>-0.450675</td>\n",
       "      <td>-22.5638</td>\n",
       "      <td>-15.5147</td>\n",
       "      <td>-3.1597</td>\n",
       "      <td>-4.21238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_sum_sum</th>\n",
       "      <td>7.36008</td>\n",
       "      <td>-390.031</td>\n",
       "      <td>-322.03</td>\n",
       "      <td>-14.6139</td>\n",
       "      <td>-14.7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_sum_ptp</th>\n",
       "      <td>23.4604</td>\n",
       "      <td>358.046</td>\n",
       "      <td>284.74</td>\n",
       "      <td>12.2301</td>\n",
       "      <td>1.36254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_sum_max</th>\n",
       "      <td>15.4103</td>\n",
       "      <td>-0.619333</td>\n",
       "      <td>-18.6449</td>\n",
       "      <td>-1.1919</td>\n",
       "      <td>-6.70246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_mean</th>\n",
       "      <td>9.49366</td>\n",
       "      <td>2.61874</td>\n",
       "      <td>-0.588055</td>\n",
       "      <td>1.90307</td>\n",
       "      <td>3.17877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_sum</th>\n",
       "      <td>18.9873</td>\n",
       "      <td>7.85621</td>\n",
       "      <td>-1.17611</td>\n",
       "      <td>3.80614</td>\n",
       "      <td>6.35754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_ptp</th>\n",
       "      <td>14.2301</td>\n",
       "      <td>8.07011</td>\n",
       "      <td>0.129754</td>\n",
       "      <td>1.82123</td>\n",
       "      <td>4.16685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_max</th>\n",
       "      <td>16.6087</td>\n",
       "      <td>7.45078</td>\n",
       "      <td>-0.523178</td>\n",
       "      <td>2.81368</td>\n",
       "      <td>5.26219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_mean_mean</th>\n",
       "      <td>-0.04237</td>\n",
       "      <td>-0.593192</td>\n",
       "      <td>-0.712977</td>\n",
       "      <td>-0.280704</td>\n",
       "      <td>-0.284128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_mean_sum</th>\n",
       "      <td>-0.08474</td>\n",
       "      <td>-1.77958</td>\n",
       "      <td>-1.42595</td>\n",
       "      <td>-0.561408</td>\n",
       "      <td>-0.568255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_mean_ptp</th>\n",
       "      <td>0.490273</td>\n",
       "      <td>0.0499709</td>\n",
       "      <td>0.00826695</td>\n",
       "      <td>0.164107</td>\n",
       "      <td>0.269552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_mean_max</th>\n",
       "      <td>0.202767</td>\n",
       "      <td>-0.569362</td>\n",
       "      <td>-0.708844</td>\n",
       "      <td>-0.19865</td>\n",
       "      <td>-0.149352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elapsed_time</th>\n",
       "      <td>426</td>\n",
       "      <td>791</td>\n",
       "      <td>882</td>\n",
       "      <td>456</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_id_total</th>\n",
       "      <td>114</td>\n",
       "      <td>708</td>\n",
       "      <td>476</td>\n",
       "      <td>49</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_id_count_total</th>\n",
       "      <td>114</td>\n",
       "      <td>708</td>\n",
       "      <td>476</td>\n",
       "      <td>49</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_total</th>\n",
       "      <td>6.9094</td>\n",
       "      <td>-412.594</td>\n",
       "      <td>-337.545</td>\n",
       "      <td>-17.7736</td>\n",
       "      <td>-18.9798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_total_mean</th>\n",
       "      <td>0.0257024</td>\n",
       "      <td>-1.07969</td>\n",
       "      <td>-1.41453</td>\n",
       "      <td>-0.866476</td>\n",
       "      <td>-0.474238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installments_total</th>\n",
       "      <td>124</td>\n",
       "      <td>742</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hist_first_buy</th>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>125</td>\n",
       "      <td>6</td>\n",
       "      <td>-62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_first_buy</th>\n",
       "      <td>90</td>\n",
       "      <td>465</td>\n",
       "      <td>551</td>\n",
       "      <td>121</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hist_last_buy</th>\n",
       "      <td>89</td>\n",
       "      <td>454</td>\n",
       "      <td>540</td>\n",
       "      <td>117</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_last_buy</th>\n",
       "      <td>132</td>\n",
       "      <td>515</td>\n",
       "      <td>598</td>\n",
       "      <td>172</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_total</th>\n",
       "      <td>3.01714</td>\n",
       "      <td>0.574974</td>\n",
       "      <td>-1.31004</td>\n",
       "      <td>-0.932425</td>\n",
       "      <td>0.144826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchased_before_issue</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outliers</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days</th>\n",
       "      <td>62</td>\n",
       "      <td>427</td>\n",
       "      <td>518</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_feature1</th>\n",
       "      <td>310</td>\n",
       "      <td>1708</td>\n",
       "      <td>1554</td>\n",
       "      <td>92</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_feature2</th>\n",
       "      <td>62</td>\n",
       "      <td>427</td>\n",
       "      <td>518</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_feature3</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>518</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rounded_target</th>\n",
       "      <td>-0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indexcol</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0                    1  \\\n",
       "first_active_month                  2017-12-01 00:00:00  2016-12-01 00:00:00   \n",
       "card_id                                 C_ID_176ecc65bd      C_ID_6082929d3a   \n",
       "feature_1                                     0.0131446            0.0107116   \n",
       "feature_2                                     0.0113848            0.0113848   \n",
       "feature_3                                     0.0114275            0.0102828   \n",
       "target                                        -0.279039            -0.926039   \n",
       "first_active_monthYear                             2017                 2016   \n",
       "first_active_monthMonth                              12                   12   \n",
       "first_active_monthWeek                               48                   48   \n",
       "first_active_monthDay                                 1                    1   \n",
       "first_active_monthDayofweek                           4                    3   \n",
       "first_active_monthDayofyear                         335                  336   \n",
       "first_active_monthIs_month_end                    False                False   \n",
       "first_active_monthIs_month_start                   True                 True   \n",
       "first_active_monthIs_quarter_end                  False                False   \n",
       "first_active_monthIs_quarter_start                False                False   \n",
       "first_active_monthIs_year_end                     False                False   \n",
       "first_active_monthIs_year_start                   False                False   \n",
       "first_active_monthElapsed                    1512086400           1480550400   \n",
       "transactions_count                                   10                   46   \n",
       "city_id_nunique                                       5                   15   \n",
       "merchant_category_id_nunique                          7                   20   \n",
       "merchant_id_nunique                                  10                   46   \n",
       "state_id_nunique                                      2                    8   \n",
       "subsector_id_nunique                                  7                   13   \n",
       "purchase_Year_nunique                                 1                    1   \n",
       "purchase_Month_nunique                                2                    2   \n",
       "purchase_Week_nunique                                 5                    9   \n",
       "purchase_Day_nunique                                  7                   17   \n",
       "purchase_amount_sum                           -0.450675             -22.5638   \n",
       "...                                                 ...                  ...   \n",
       "purchase_amount_sum_sum                         7.36008             -390.031   \n",
       "purchase_amount_sum_ptp                         23.4604              358.046   \n",
       "purchase_amount_sum_max                         15.4103            -0.619333   \n",
       "purchase_amount_max_mean                        9.49366              2.61874   \n",
       "purchase_amount_max_sum                         18.9873              7.85621   \n",
       "purchase_amount_max_ptp                         14.2301              8.07011   \n",
       "purchase_amount_max_max                         16.6087              7.45078   \n",
       "purchase_amount_mean_mean                      -0.04237            -0.593192   \n",
       "purchase_amount_mean_sum                       -0.08474             -1.77958   \n",
       "purchase_amount_mean_ptp                       0.490273            0.0499709   \n",
       "purchase_amount_mean_max                       0.202767            -0.569362   \n",
       "elapsed_time                                        426                  791   \n",
       "card_id_total                                       114                  708   \n",
       "card_id_count_total                                 114                  708   \n",
       "purchase_amount_total                            6.9094             -412.594   \n",
       "purchase_amount_total_mean                    0.0257024             -1.07969   \n",
       "installments_total                                  124                  742   \n",
       "hist_first_buy                                        4                   31   \n",
       "new_first_buy                                        90                  465   \n",
       "hist_last_buy                                        89                  454   \n",
       "new_last_buy                                        132                  515   \n",
       "purchase_amount_max_total                       3.01714             0.574974   \n",
       "purchased_before_issue                            False                False   \n",
       "outliers                                              0                    0   \n",
       "days                                                 62                  427   \n",
       "days_feature1                                       310                 1708   \n",
       "days_feature2                                        62                  427   \n",
       "days_feature3                                        62                    0   \n",
       "rounded_target                                       -0                   -1   \n",
       "indexcol                                              2                    5   \n",
       "\n",
       "                                                      2                    3  \\\n",
       "first_active_month                  2016-09-01 00:00:00  2017-11-01 00:00:00   \n",
       "card_id                                 C_ID_a87bbd85a7      C_ID_92e4809531   \n",
       "feature_1                                     0.0104794           0.00805849   \n",
       "feature_2                                     0.0113848            0.0113848   \n",
       "feature_3                                     0.0114275            0.0102828   \n",
       "target                                         0.475326             0.516026   \n",
       "first_active_monthYear                             2016                 2017   \n",
       "first_active_monthMonth                               9                   11   \n",
       "first_active_monthWeek                               35                   44   \n",
       "first_active_monthDay                                 1                    1   \n",
       "first_active_monthDayofweek                           3                    2   \n",
       "first_active_monthDayofyear                         245                  305   \n",
       "first_active_monthIs_month_end                    False                False   \n",
       "first_active_monthIs_month_start                   True                 True   \n",
       "first_active_monthIs_quarter_end                  False                False   \n",
       "first_active_monthIs_quarter_start                False                False   \n",
       "first_active_monthIs_year_end                     False                False   \n",
       "first_active_monthIs_year_start                   False                False   \n",
       "first_active_monthElapsed                    1472688000           1509494400   \n",
       "transactions_count                                   22                    6   \n",
       "city_id_nunique                                       3                    5   \n",
       "merchant_category_id_nunique                         17                    4   \n",
       "merchant_id_nunique                                  22                    6   \n",
       "state_id_nunique                                      1                    3   \n",
       "subsector_id_nunique                                 11                    4   \n",
       "purchase_Year_nunique                                 1                    1   \n",
       "purchase_Month_nunique                                2                    2   \n",
       "purchase_Week_nunique                                 5                    5   \n",
       "purchase_Day_nunique                                 13                    6   \n",
       "purchase_amount_sum                            -15.5147              -3.1597   \n",
       "...                                                 ...                  ...   \n",
       "purchase_amount_sum_sum                         -322.03             -14.6139   \n",
       "purchase_amount_sum_ptp                          284.74              12.2301   \n",
       "purchase_amount_sum_max                        -18.6449              -1.1919   \n",
       "purchase_amount_max_mean                      -0.588055              1.90307   \n",
       "purchase_amount_max_sum                        -1.17611              3.80614   \n",
       "purchase_amount_max_ptp                        0.129754              1.82123   \n",
       "purchase_amount_max_max                       -0.523178              2.81368   \n",
       "purchase_amount_mean_mean                     -0.712977            -0.280704   \n",
       "purchase_amount_mean_sum                       -1.42595            -0.561408   \n",
       "purchase_amount_mean_ptp                     0.00826695             0.164107   \n",
       "purchase_amount_mean_max                      -0.708844             -0.19865   \n",
       "elapsed_time                                        882                  456   \n",
       "card_id_total                                       476                   49   \n",
       "card_id_count_total                                 476                   49   \n",
       "purchase_amount_total                          -337.545             -17.7736   \n",
       "purchase_amount_total_mean                     -1.41453            -0.866476   \n",
       "installments_total                                    0                   74   \n",
       "hist_first_buy                                      125                    6   \n",
       "new_first_buy                                       551                  121   \n",
       "hist_last_buy                                       540                  117   \n",
       "new_last_buy                                        598                  172   \n",
       "purchase_amount_max_total                      -1.31004            -0.932425   \n",
       "purchased_before_issue                            False                False   \n",
       "outliers                                              0                    0   \n",
       "days                                                518                   92   \n",
       "days_feature1                                      1554                   92   \n",
       "days_feature2                                       518                   92   \n",
       "days_feature3                                       518                    0   \n",
       "rounded_target                                        0                    1   \n",
       "indexcol                                              5                    5   \n",
       "\n",
       "                                                      4  \n",
       "first_active_month                  2017-11-01 00:00:00  \n",
       "card_id                                 C_ID_d2dd6bbf9a  \n",
       "feature_1                                     0.0107116  \n",
       "feature_2                                     0.0113848  \n",
       "feature_3                                     0.0102828  \n",
       "target                                        -0.100927  \n",
       "first_active_monthYear                             2017  \n",
       "first_active_monthMonth                              11  \n",
       "first_active_monthWeek                               44  \n",
       "first_active_monthDay                                 1  \n",
       "first_active_monthDayofweek                           2  \n",
       "first_active_monthDayofyear                         305  \n",
       "first_active_monthIs_month_end                    False  \n",
       "first_active_monthIs_month_start                   True  \n",
       "first_active_monthIs_quarter_end                  False  \n",
       "first_active_monthIs_quarter_start                False  \n",
       "first_active_monthIs_year_end                     False  \n",
       "first_active_monthIs_year_start                   False  \n",
       "first_active_monthElapsed                    1509494400  \n",
       "transactions_count                                   16  \n",
       "city_id_nunique                                       6  \n",
       "merchant_category_id_nunique                         13  \n",
       "merchant_id_nunique                                  16  \n",
       "state_id_nunique                                      3  \n",
       "subsector_id_nunique                                  8  \n",
       "purchase_Year_nunique                                 1  \n",
       "purchase_Month_nunique                                2  \n",
       "purchase_Week_nunique                                 6  \n",
       "purchase_Day_nunique                                  9  \n",
       "purchase_amount_sum                            -4.21238  \n",
       "...                                                 ...  \n",
       "purchase_amount_sum_sum                        -14.7675  \n",
       "purchase_amount_sum_ptp                         1.36254  \n",
       "purchase_amount_sum_max                        -6.70246  \n",
       "purchase_amount_max_mean                        3.17877  \n",
       "purchase_amount_max_sum                         6.35754  \n",
       "purchase_amount_max_ptp                         4.16685  \n",
       "purchase_amount_max_max                         5.26219  \n",
       "purchase_amount_mean_mean                     -0.284128  \n",
       "purchase_amount_mean_sum                      -0.568255  \n",
       "purchase_amount_mean_ptp                       0.269552  \n",
       "purchase_amount_mean_max                      -0.149352  \n",
       "elapsed_time                                        456  \n",
       "card_id_total                                        86  \n",
       "card_id_count_total                                  86  \n",
       "purchase_amount_total                          -18.9798  \n",
       "purchase_amount_total_mean                    -0.474238  \n",
       "installments_total                                  180  \n",
       "hist_first_buy                                      -62  \n",
       "new_first_buy                                       140  \n",
       "hist_last_buy                                       116  \n",
       "new_last_buy                                        180  \n",
       "purchase_amount_max_total                      0.144826  \n",
       "purchased_before_issue                             True  \n",
       "outliers                                              0  \n",
       "days                                                 92  \n",
       "days_feature1                                       368  \n",
       "days_feature2                                        92  \n",
       "days_feature3                                         0  \n",
       "rounded_target                                       -0  \n",
       "indexcol                                              8  \n",
       "\n",
       "[170 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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