Elo - ML model 3.691 beta new.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": 1,
   "metadata": {},
   "outputs": [
    {
     "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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df2 = feather.read_dataframe('train_df_3691')\n",
    "test_df2 = feather.read_dataframe('test_df_3691')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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']:\n",
    "        df[f] = df[f].astype(np.int64) * 1e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_columns = [c for c in train_df2.columns if c not in cols_excluded]\n",
    "target = train_df2['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((201917, 203), (123623, 201))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df2.shape, test_df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201917,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_df2['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['feature_1',\n",
       " 'feature_2',\n",
       " 'feature_3',\n",
       " 'first_active_monthElapsed',\n",
       " 'transactions_count',\n",
       " 'subsector_id_nunique',\n",
       " 'merchant_id_nunique',\n",
       " 'merchant_category_id_nunique',\n",
       " 'purchase_Month_mean',\n",
       " 'purchase_Month_min',\n",
       " 'purchase_Month_max',\n",
       " 'purchase_Week_nunique',\n",
       " 'purchase_Week_mean',\n",
       " 'purchase_Week_min',\n",
       " 'purchase_Week_max',\n",
       " 'purchase_Dayofweek_mean',\n",
       " 'purchase_Dayofweek_min',\n",
       " 'purchase_Dayofweek_max',\n",
       " 'purchase_Day_nunique',\n",
       " 'purchase_Day_mean',\n",
       " 'purchase_Day_min',\n",
       " 'purchase_Day_max',\n",
       " 'purchase_Hour_nunique',\n",
       " 'purchase_Hour_mean',\n",
       " 'purchase_Hour_min',\n",
       " 'purchase_Hour_max',\n",
       " 'purchase_amount_sum',\n",
       " 'purchase_amount_max',\n",
       " 'purchase_amount_min',\n",
       " 'purchase_amount_mean',\n",
       " 'purchase_amount_var',\n",
       " 'purchase_amount_skew',\n",
       " 'installments_sum',\n",
       " 'installments_max',\n",
       " 'installments_mean',\n",
       " 'installments_var',\n",
       " 'installments_skew',\n",
       " 'month_lag_max',\n",
       " 'month_lag_min',\n",
       " 'month_lag_mean',\n",
       " 'month_lag_var',\n",
       " 'month_lag_skew',\n",
       " 'month_diff_mean',\n",
       " 'month_diff_var',\n",
       " 'month_diff_skew',\n",
       " 'purchased_on_weekend_mean',\n",
       " 'category_1_mean',\n",
       " 'category_2_mean',\n",
       " 'category_3_mean',\n",
       " 'card_id_count',\n",
       " 'price_mean',\n",
       " 'price_max',\n",
       " 'price_min',\n",
       " 'price_var',\n",
       " 'Christmas_Day_2017_mean',\n",
       " 'Children_day_2017_mean',\n",
       " 'Black_Friday_2017_mean',\n",
       " 'Mothers_Day_2018_mean',\n",
       " 'duration_mean',\n",
       " 'duration_min',\n",
       " 'duration_max',\n",
       " 'duration_var',\n",
       " 'duration_skew',\n",
       " 'amount_month_ratio_mean',\n",
       " 'amount_month_ratio_min',\n",
       " 'amount_month_ratio_max',\n",
       " 'amount_month_ratio_var',\n",
       " 'amount_month_ratio_skew',\n",
       " 'category_2_mean_mean',\n",
       " 'category_3_mean_mean',\n",
       " 'purchase_date_diff',\n",
       " 'purchase_date_average',\n",
       " 'purchase_date_uptonow',\n",
       " 'purchase_date_uptomin',\n",
       " 'transactions_count_old',\n",
       " 'subsector_id_nunique_old',\n",
       " 'merchant_id_nunique_old',\n",
       " 'merchant_category_id_nunique_old',\n",
       " 'purchase_Month_nunique',\n",
       " 'purchase_Month_mean_old',\n",
       " 'purchase_Month_min_old',\n",
       " 'purchase_Month_max_old',\n",
       " 'purchase_Week_nunique_old',\n",
       " 'purchase_Week_mean_old',\n",
       " 'purchase_Week_min_old',\n",
       " 'purchase_Week_max_old',\n",
       " 'purchase_Dayofweek_mean_old',\n",
       " 'purchase_Day_nunique_old',\n",
       " 'purchase_Day_mean_old',\n",
       " 'purchase_Day_min_old',\n",
       " 'purchase_Hour_nunique_old',\n",
       " 'purchase_Hour_mean_old',\n",
       " 'purchase_Hour_min_old',\n",
       " 'purchase_Hour_max_old',\n",
       " 'purchase_amount_sum_old',\n",
       " 'purchase_amount_max_old',\n",
       " 'purchase_amount_min_old',\n",
       " 'purchase_amount_mean_old',\n",
       " 'purchase_amount_var_old',\n",
       " 'purchase_amount_skew_old',\n",
       " 'installments_sum_old',\n",
       " 'installments_max_old',\n",
       " 'installments_mean_old',\n",
       " 'installments_var_old',\n",
       " 'installments_skew_old',\n",
       " 'month_lag_max_old',\n",
       " 'month_lag_min_old',\n",
       " 'month_lag_mean_old',\n",
       " 'month_lag_var_old',\n",
       " 'month_lag_skew_old',\n",
       " 'month_diff_max',\n",
       " 'month_diff_min',\n",
       " 'month_diff_mean_old',\n",
       " 'month_diff_var_old',\n",
       " 'month_diff_skew_old',\n",
       " 'authorized_flag_mean',\n",
       " 'purchased_on_weekend_mean_old',\n",
       " 'category_1_mean_old',\n",
       " 'category_2_mean_old',\n",
       " 'category_3_mean_old',\n",
       " 'card_id_count_old',\n",
       " 'price_sum',\n",
       " 'price_mean_old',\n",
       " 'price_max_old',\n",
       " 'price_min_old',\n",
       " 'price_var_old',\n",
       " 'Christmas_Day_2017_mean_old',\n",
       " 'Mothers_Day_2017_mean',\n",
       " 'fathers_day_2017_mean',\n",
       " 'Children_day_2017_mean_old',\n",
       " 'Valentine_Day_2017_mean',\n",
       " 'Black_Friday_2017_mean_old',\n",
       " 'Mothers_Day_2018_mean_old',\n",
       " 'duration_mean_old',\n",
       " 'duration_min_old',\n",
       " 'duration_max_old',\n",
       " 'duration_var_old',\n",
       " 'duration_skew_old',\n",
       " 'amount_month_ratio_mean_old',\n",
       " 'amount_month_ratio_min_old',\n",
       " 'amount_month_ratio_max_old',\n",
       " 'amount_month_ratio_var_old',\n",
       " 'amount_month_ratio_skew_old',\n",
       " 'category_2_mean_mean_old',\n",
       " 'category_3_mean_mean_old',\n",
       " 'purchase_date_diff_old',\n",
       " 'purchase_date_average_old',\n",
       " 'purchase_date_uptonow_old',\n",
       " 'purchase_date_uptomin_old',\n",
       " 'card_id_total',\n",
       " 'card_id_count_total',\n",
       " 'card_id_count_ratio',\n",
       " 'purchase_amount_total',\n",
       " 'purchase_amount_total_mean',\n",
       " 'purchase_amount_total_max',\n",
       " 'purchase_amount_total_min',\n",
       " 'purchase_amount_sum_ratio',\n",
       " 'hist_first_buy',\n",
       " 'new_first_buy',\n",
       " 'hist_last_buy',\n",
       " 'new_last_buy',\n",
       " 'month_diff_ratio',\n",
       " 'installments_total',\n",
       " 'installments_ratio',\n",
       " 'price_total',\n",
       " 'CLV',\n",
       " 'CLV_old',\n",
       " 'CLV_ratio',\n",
       " 'quarter',\n",
       " 'elapsed_time',\n",
       " 'days_feature1',\n",
       " 'days_feature2',\n",
       " 'days_feature3',\n",
       " 'days_feature1_ratio',\n",
       " 'days_feature2_ratio',\n",
       " 'days_feature3_ratio',\n",
       " 'feature_sum',\n",
       " 'feature_mean',\n",
       " 'feature_max',\n",
       " 'feature_min',\n",
       " 'feature_var']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_fold =5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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: 3.64895\tvalid_1's rmse: 3.71321\n",
      "[200]\ttraining's rmse: 3.56154\tvalid_1's rmse: 3.6754\n",
      "[300]\ttraining's rmse: 3.50825\tvalid_1's rmse: 3.66249\n",
      "[400]\ttraining's rmse: 3.47226\tvalid_1's rmse: 3.65733\n",
      "[500]\ttraining's rmse: 3.44095\tvalid_1's rmse: 3.65516\n",
      "[600]\ttraining's rmse: 3.41556\tvalid_1's rmse: 3.65344\n",
      "[700]\ttraining's rmse: 3.39453\tvalid_1's rmse: 3.65281\n",
      "[800]\ttraining's rmse: 3.37522\tvalid_1's rmse: 3.65213\n",
      "[900]\ttraining's rmse: 3.35713\tvalid_1's rmse: 3.65221\n",
      "[1000]\ttraining's rmse: 3.33981\tvalid_1's rmse: 3.65217\n",
      "[1100]\ttraining's rmse: 3.32304\tvalid_1's rmse: 3.65241\n",
      "Early stopping, best iteration is:\n",
      "[950]\ttraining's rmse: 3.34887\tvalid_1's rmse: 3.65192\n",
      "fold 1\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.64968\tvalid_1's rmse: 3.70194\n",
      "[200]\ttraining's rmse: 3.56091\tvalid_1's rmse: 3.6661\n",
      "[300]\ttraining's rmse: 3.50831\tvalid_1's rmse: 3.65502\n",
      "[400]\ttraining's rmse: 3.47091\tvalid_1's rmse: 3.65197\n",
      "[500]\ttraining's rmse: 3.44058\tvalid_1's rmse: 3.65009\n",
      "[600]\ttraining's rmse: 3.41717\tvalid_1's rmse: 3.64919\n",
      "[700]\ttraining's rmse: 3.39471\tvalid_1's rmse: 3.64835\n",
      "[800]\ttraining's rmse: 3.37616\tvalid_1's rmse: 3.64787\n",
      "[900]\ttraining's rmse: 3.3565\tvalid_1's rmse: 3.64781\n",
      "[1000]\ttraining's rmse: 3.33843\tvalid_1's rmse: 3.64774\n",
      "Early stopping, best iteration is:\n",
      "[837]\ttraining's rmse: 3.36924\tvalid_1's rmse: 3.64769\n",
      "fold 2\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.64754\tvalid_1's rmse: 3.71021\n",
      "[200]\ttraining's rmse: 3.56003\tvalid_1's rmse: 3.6756\n",
      "[300]\ttraining's rmse: 3.50751\tvalid_1's rmse: 3.66394\n",
      "[400]\ttraining's rmse: 3.47076\tvalid_1's rmse: 3.6599\n",
      "[500]\ttraining's rmse: 3.44446\tvalid_1's rmse: 3.65775\n",
      "[600]\ttraining's rmse: 3.42151\tvalid_1's rmse: 3.65652\n",
      "[700]\ttraining's rmse: 3.4002\tvalid_1's rmse: 3.65655\n",
      "[800]\ttraining's rmse: 3.38096\tvalid_1's rmse: 3.65614\n",
      "[900]\ttraining's rmse: 3.36334\tvalid_1's rmse: 3.65621\n",
      "[1000]\ttraining's rmse: 3.34672\tvalid_1's rmse: 3.65608\n",
      "[1100]\ttraining's rmse: 3.3299\tvalid_1's rmse: 3.65588\n",
      "[1200]\ttraining's rmse: 3.31387\tvalid_1's rmse: 3.656\n",
      "[1300]\ttraining's rmse: 3.29648\tvalid_1's rmse: 3.6565\n",
      "Early stopping, best iteration is:\n",
      "[1125]\ttraining's rmse: 3.32589\tvalid_1's rmse: 3.65576\n",
      "fold 3\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.64444\tvalid_1's rmse: 3.70686\n",
      "[200]\ttraining's rmse: 3.55389\tvalid_1's rmse: 3.67757\n",
      "[300]\ttraining's rmse: 3.50113\tvalid_1's rmse: 3.66871\n",
      "[400]\ttraining's rmse: 3.46471\tvalid_1's rmse: 3.665\n",
      "[500]\ttraining's rmse: 3.43864\tvalid_1's rmse: 3.66231\n",
      "[600]\ttraining's rmse: 3.41603\tvalid_1's rmse: 3.66128\n",
      "[700]\ttraining's rmse: 3.39468\tvalid_1's rmse: 3.66034\n",
      "[800]\ttraining's rmse: 3.37722\tvalid_1's rmse: 3.65968\n",
      "[900]\ttraining's rmse: 3.35925\tvalid_1's rmse: 3.65949\n",
      "[1000]\ttraining's rmse: 3.33931\tvalid_1's rmse: 3.65959\n",
      "[1100]\ttraining's rmse: 3.32071\tvalid_1's rmse: 3.65939\n",
      "[1200]\ttraining's rmse: 3.30318\tvalid_1's rmse: 3.6593\n",
      "[1300]\ttraining's rmse: 3.28416\tvalid_1's rmse: 3.65903\n",
      "[1400]\ttraining's rmse: 3.26872\tvalid_1's rmse: 3.65933\n",
      "Early stopping, best iteration is:\n",
      "[1261]\ttraining's rmse: 3.29134\tvalid_1's rmse: 3.65897\n",
      "fold 4\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.64738\tvalid_1's rmse: 3.69663\n",
      "[200]\ttraining's rmse: 3.55756\tvalid_1's rmse: 3.66832\n",
      "[300]\ttraining's rmse: 3.50193\tvalid_1's rmse: 3.65911\n",
      "[400]\ttraining's rmse: 3.46394\tvalid_1's rmse: 3.65567\n",
      "[500]\ttraining's rmse: 3.4384\tvalid_1's rmse: 3.65411\n",
      "[600]\ttraining's rmse: 3.41551\tvalid_1's rmse: 3.65299\n",
      "[700]\ttraining's rmse: 3.39547\tvalid_1's rmse: 3.65297\n",
      "[800]\ttraining's rmse: 3.3759\tvalid_1's rmse: 3.6529\n",
      "[900]\ttraining's rmse: 3.35626\tvalid_1's rmse: 3.65295\n",
      "Early stopping, best iteration is:\n",
      "[769]\ttraining's rmse: 3.38146\tvalid_1's rmse: 3.65271\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.653410894233743"
      ]
     },
     "execution_count": 12,
     "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 - 13th Feb\n",
    "5. 3.654063570445142 - 15th Feb\n",
    "6. 3.653410894233743 - 16th Feb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(best_features.sort_values(by='importance', ascending=False)[:800]['Feature'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>fold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purchase_amount_max</td>\n",
       "      <td>1003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>duration_min_old</td>\n",
       "      <td>779</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>authorized_flag_mean_old</td>\n",
       "      <td>865</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_Week_mean</td>\n",
       "      <td>608</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>month_diff_mean_old</td>\n",
       "      <td>637</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Feature  importance  fold\n",
       "0       purchase_amount_max        1003     1\n",
       "1          duration_min_old         779     1\n",
       "2  authorized_flag_mean_old         865     1\n",
       "3        purchase_Week_mean         608     1\n",
       "4       month_diff_mean_old         637     1"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_features.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "picked_feats = best_features.sort_values(by='importance', ascending=False)[:800]['Feature'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "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": null,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33488"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "kfold_lightgbm(train_df2, test_df2, num_folds=11, stratified=False, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_name = pd.to_datetime(\"today\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Blend with df outliers.ipynb'\r\n",
      "'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 - alpha.ipynb'\r\n",
      "'Elo - ML model - cat aggregates.ipynb'\r\n",
      "'Elo - ML model 3.691 beta new.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 - beta.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 alpha.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-Copy1.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",
      " comb_15.csv\r\n",
      "'combined_submission-2019-02-15 15:07:09.944221.csv'\r\n",
      "'combined_submission-2019-02-15 15:09:20.708055.csv'\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-2019-02-15 14:45:36.932530.csv'\r\n",
      "'submission-2019-02-15 18:08:33.904515.csv'\r\n",
      "'submission-2019-02-16 13:14:48.321120.csv'\r\n",
      "'submission-2019-02-16 13:22:43.779348.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_3691\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_3691\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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href='submission-2019-02-13 22:48:45.005871.csv' target='_blank'>submission-2019-02-13 22:48:45.005871.csv</a><br>"
      ],
      "text/plain": [
       "/home/chandrasekhar/private/kaggle/submission-2019-02-13 22:48:45.005871.csv"
      ]
     },
     "execution_count": 18,
     "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",
       "        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>-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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = f'submission-{submission_name}.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'submission-2019-02-16 13:14:48.321120.csv'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|#######################################| 4.24M/4.24M [00:21<00:00, 203kB/s]\n",
      "Successfully submitted to Elo Merchant Category Recommendation"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit elo-merchant-category-recommendation -f 'submission-2019-02-16 13:14:48.321120.csv' -m 'lc'"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_180 = ['feature_1',\n",
    "         'feature_2',\n",
    "         'feature_3',\n",
    "         'transactions_count',\n",
    "         'subsector_id_nunique',\n",
    "         'merchant_id_nunique',\n",
    "         'merchant_category_id_nunique',\n",
    "         'purchase_Month_mean',\n",
    "         'purchase_Month_min',\n",
    "         'purchase_Month_max',\n",
    "         'purchase_Week_nunique',\n",
    "         'purchase_Week_mean',\n",
    "         'purchase_Week_min',\n",
    "         'purchase_Week_max',\n",
    "         'purchase_Dayofweek_mean',\n",
    "         'purchase_Dayofweek_min',\n",
    "         'purchase_Dayofweek_max',\n",
    "         'purchase_Day_nunique',\n",
    "         'purchase_Day_mean',\n",
    "         'purchase_Day_min',\n",
    "         'purchase_Day_max',\n",
    "         'purchase_Hour_nunique',\n",
    "         'purchase_Hour_mean',\n",
    "         'purchase_Hour_min',\n",
    "         'purchase_Hour_max',\n",
    "         'purchase_amount_sum',\n",
    "         'purchase_amount_max',\n",
    "         'purchase_amount_min',\n",
    "         'purchase_amount_mean',\n",
    "         'purchase_amount_var',\n",
    "         'purchase_amount_skew',\n",
    "         'installments_sum',\n",
    "         'installments_max',\n",
    "         'installments_mean',\n",
    "         'installments_var',\n",
    "         'installments_skew',\n",
    "         'month_lag_max',\n",
    "         'month_lag_min',\n",
    "         'month_lag_mean',\n",
    "         'month_lag_var',\n",
    "         'month_lag_skew',\n",
    "         'month_diff_mean',\n",
    "         'month_diff_var',\n",
    "         'month_diff_skew',\n",
    "         'purchased_on_weekend_mean',\n",
    "         'category_1_mean',\n",
    "         'category_2_mean',\n",
    "         'category_3_mean',\n",
    "         'card_id_count',\n",
    "         'price_mean',\n",
    "         'price_max',\n",
    "         'price_min',\n",
    "         'price_var',\n",
    "         'Christmas_Day_2017_mean',\n",
    "         'Children_day_2017_mean',\n",
    "         'Black_Friday_2017_mean',\n",
    "         'Mothers_Day_2018_mean',\n",
    "         'duration_mean',\n",
    "         'duration_min',\n",
    "         'duration_max',\n",
    "         'duration_var',\n",
    "         'duration_skew',\n",
    "         'amount_month_ratio_mean',\n",
    "         'amount_month_ratio_min',\n",
    "         'amount_month_ratio_max',\n",
    "         'amount_month_ratio_var',\n",
    "         'amount_month_ratio_skew',\n",
    "         'category_2_mean_mean',\n",
    "         'category_3_mean_mean',\n",
    "         'purchase_date_diff',\n",
    "         'purchase_date_average',\n",
    "         'purchase_date_uptonow',\n",
    "         'purchase_date_uptomin',\n",
    "         'transactions_count_old',\n",
    "         'subsector_id_nunique_old',\n",
    "         'merchant_id_nunique_old',\n",
    "         'merchant_category_id_nunique_old',\n",
    "         'purchase_Month_nunique',\n",
    "         'purchase_Month_mean_old',\n",
    "         'purchase_Month_min_old',\n",
    "         'purchase_Month_max_old',\n",
    "         'purchase_Week_nunique_old',\n",
    "         'purchase_Week_mean_old',\n",
    "         'purchase_Week_min_old',\n",
    "         'purchase_Week_max_old',\n",
    "         'purchase_Dayofweek_mean_old',\n",
    "         'purchase_Day_nunique_old',\n",
    "         'purchase_Day_mean_old',\n",
    "         'purchase_Day_min_old',\n",
    "         'purchase_Hour_nunique_old',\n",
    "         'purchase_Hour_mean_old',\n",
    "         'purchase_Hour_min_old',\n",
    "         'purchase_Hour_max_old',\n",
    "         'purchase_amount_sum_old',\n",
    "         'purchase_amount_max_old',\n",
    "         'purchase_amount_min_old',\n",
    "         'purchase_amount_mean_old',\n",
    "         'purchase_amount_var_old',\n",
    "         'purchase_amount_skew_old',\n",
    "         'installments_sum_old',\n",
    "         'installments_max_old',\n",
    "         'installments_mean_old',\n",
    "         'installments_var_old',\n",
    "         'installments_skew_old',\n",
    "         'month_lag_max_old',\n",
    "         'month_lag_min_old',\n",
    "         'month_lag_mean_old',\n",
    "         'month_lag_var_old',\n",
    "         'month_lag_skew_old',\n",
    "         'month_diff_max',\n",
    "         'month_diff_min',\n",
    "         'month_diff_mean_old',\n",
    "         'month_diff_var_old',\n",
    "         'month_diff_skew_old',\n",
    "         'authorized_flag_mean',\n",
    "         'purchased_on_weekend_mean_old',\n",
    "         'category_1_mean_old',\n",
    "         'category_2_mean_old',\n",
    "         'category_3_mean_old',\n",
    "         'card_id_count_old',\n",
    "         'price_sum',\n",
    "         'price_mean_old',\n",
    "         'price_max_old',\n",
    "         'price_min_old',\n",
    "         'price_var_old',\n",
    "         'Christmas_Day_2017_mean_old',\n",
    "         'Mothers_Day_2017_mean',\n",
    "         'fathers_day_2017_mean',\n",
    "         'Children_day_2017_mean_old',\n",
    "         'Valentine_Day_2017_mean',\n",
    "         'Black_Friday_2017_mean_old',\n",
    "         'Mothers_Day_2018_mean_old',\n",
    "         'duration_mean_old',\n",
    "         'duration_min_old',\n",
    "         'duration_max_old',\n",
    "         'duration_var_old',\n",
    "         'duration_skew_old',\n",
    "         'amount_month_ratio_mean_old',\n",
    "         'amount_month_ratio_min_old',\n",
    "         'amount_month_ratio_max_old',\n",
    "         'amount_month_ratio_var_old',\n",
    "         'amount_month_ratio_skew_old',\n",
    "         'category_2_mean_mean_old',\n",
    "         'category_3_mean_mean_old',\n",
    "         'purchase_date_diff_old',\n",
    "         'purchase_date_average_old',\n",
    "         'purchase_date_uptonow_old',\n",
    "         'purchase_date_uptomin_old',\n",
    "         'quarter',\n",
    "         'elapsed_time',\n",
    "         'days_feature1',\n",
    "         'days_feature2',\n",
    "         'days_feature3',\n",
    "         'days_feature1_ratio',\n",
    "         'days_feature2_ratio',\n",
    "         'days_feature3_ratio',\n",
    "         'feature_sum',\n",
    "         'feature_mean',\n",
    "         'feature_max',\n",
    "         'feature_min',\n",
    "         'feature_var',\n",
    "         'card_id_total',\n",
    "         'card_id_count_total',\n",
    "         'card_id_count_ratio',\n",
    "         'purchase_amount_total',\n",
    "         'purchase_amount_total_mean',\n",
    "         'purchase_amount_total_max',\n",
    "         'purchase_amount_total_min',\n",
    "         'purchase_amount_sum_ratio',\n",
    "         'hist_first_buy',\n",
    "         'new_first_buy',\n",
    "         'hist_last_buy',\n",
    "         'new_last_buy',\n",
    "         'month_diff_ratio',\n",
    "         'installments_total',\n",
    "         'installments_ratio',\n",
    "         'price_total',\n",
    "         'CLV',\n",
    "         'CLV_old',\n",
    "         'CLV_ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_181 = ['feature_1',\n",
    " 'feature_2',\n",
    " 'feature_3',\n",
    " 'first_active_monthElapsed',\n",
    " 'transactions_count',\n",
    " 'subsector_id_nunique',\n",
    " 'merchant_id_nunique',\n",
    " 'merchant_category_id_nunique',\n",
    " 'purchase_Month_mean',\n",
    " 'purchase_Month_min',\n",
    " 'purchase_Month_max',\n",
    " 'purchase_Week_nunique',\n",
    " 'purchase_Week_mean',\n",
    " 'purchase_Week_min',\n",
    " 'purchase_Week_max',\n",
    " 'purchase_Dayofweek_mean',\n",
    " 'purchase_Dayofweek_min',\n",
    " 'purchase_Dayofweek_max',\n",
    " 'purchase_Day_nunique',\n",
    " 'purchase_Day_mean',\n",
    " 'purchase_Day_min',\n",
    " 'purchase_Day_max',\n",
    " 'purchase_Hour_nunique',\n",
    " 'purchase_Hour_mean',\n",
    " 'purchase_Hour_min',\n",
    " 'purchase_Hour_max',\n",
    " 'purchase_amount_sum',\n",
    " 'purchase_amount_max',\n",
    " 'purchase_amount_min',\n",
    " 'purchase_amount_mean',\n",
    " 'purchase_amount_var',\n",
    " 'purchase_amount_skew',\n",
    " 'installments_sum',\n",
    " 'installments_max',\n",
    " 'installments_mean',\n",
    " 'installments_var',\n",
    " 'installments_skew',\n",
    " 'month_lag_max',\n",
    " 'month_lag_min',\n",
    " 'month_lag_mean',\n",
    " 'month_lag_var',\n",
    " 'month_lag_skew',\n",
    " 'month_diff_mean',\n",
    " 'month_diff_var',\n",
    " 'month_diff_skew',\n",
    " 'purchased_on_weekend_mean',\n",
    " 'category_1_mean',\n",
    " 'category_2_mean',\n",
    " 'category_3_mean',\n",
    " 'card_id_count',\n",
    " 'price_mean',\n",
    " 'price_max',\n",
    " 'price_min',\n",
    " 'price_var',\n",
    " 'Christmas_Day_2017_mean',\n",
    " 'Children_day_2017_mean',\n",
    " 'Black_Friday_2017_mean',\n",
    " 'Mothers_Day_2018_mean',\n",
    " 'duration_mean',\n",
    " 'duration_min',\n",
    " 'duration_max',\n",
    " 'duration_var',\n",
    " 'duration_skew',\n",
    " 'amount_month_ratio_mean',\n",
    " 'amount_month_ratio_min',\n",
    " 'amount_month_ratio_max',\n",
    " 'amount_month_ratio_var',\n",
    " 'amount_month_ratio_skew',\n",
    " 'category_2_mean_mean',\n",
    " 'category_3_mean_mean',\n",
    " 'purchase_date_diff',\n",
    " 'purchase_date_average',\n",
    " 'purchase_date_uptonow',\n",
    " 'purchase_date_uptomin',\n",
    " 'transactions_count_old',\n",
    " 'subsector_id_nunique_old',\n",
    " 'merchant_id_nunique_old',\n",
    " 'merchant_category_id_nunique_old',\n",
    " 'purchase_Month_nunique',\n",
    " 'purchase_Month_mean_old',\n",
    " 'purchase_Month_min_old',\n",
    " 'purchase_Month_max_old',\n",
    " 'purchase_Week_nunique_old',\n",
    " 'purchase_Week_mean_old',\n",
    " 'purchase_Week_min_old',\n",
    " 'purchase_Week_max_old',\n",
    " 'purchase_Dayofweek_mean_old',\n",
    " 'purchase_Day_nunique_old',\n",
    " 'purchase_Day_mean_old',\n",
    " 'purchase_Day_min_old',\n",
    " 'purchase_Hour_nunique_old',\n",
    " 'purchase_Hour_mean_old',\n",
    " 'purchase_Hour_min_old',\n",
    " 'purchase_Hour_max_old',\n",
    " 'purchase_amount_sum_old',\n",
    " 'purchase_amount_max_old',\n",
    " 'purchase_amount_min_old',\n",
    " 'purchase_amount_mean_old',\n",
    " 'purchase_amount_var_old',\n",
    " 'purchase_amount_skew_old',\n",
    " 'installments_sum_old',\n",
    " 'installments_max_old',\n",
    " 'installments_mean_old',\n",
    " 'installments_var_old',\n",
    " 'installments_skew_old',\n",
    " 'month_lag_max_old',\n",
    " 'month_lag_min_old',\n",
    " 'month_lag_mean_old',\n",
    " 'month_lag_var_old',\n",
    " 'month_lag_skew_old',\n",
    " 'month_diff_max',\n",
    " 'month_diff_min',\n",
    " 'month_diff_mean_old',\n",
    " 'month_diff_var_old',\n",
    " 'month_diff_skew_old',\n",
    " 'authorized_flag_mean',\n",
    " 'purchased_on_weekend_mean_old',\n",
    " 'category_1_mean_old',\n",
    " 'category_2_mean_old',\n",
    " 'category_3_mean_old',\n",
    " 'card_id_count_old',\n",
    " 'price_sum',\n",
    " 'price_mean_old',\n",
    " 'price_max_old',\n",
    " 'price_min_old',\n",
    " 'price_var_old',\n",
    " 'Christmas_Day_2017_mean_old',\n",
    " 'Mothers_Day_2017_mean',\n",
    " 'fathers_day_2017_mean',\n",
    " 'Children_day_2017_mean_old',\n",
    " 'Valentine_Day_2017_mean',\n",
    " 'Black_Friday_2017_mean_old',\n",
    " 'Mothers_Day_2018_mean_old',\n",
    " 'duration_mean_old',\n",
    " 'duration_min_old',\n",
    " 'duration_max_old',\n",
    " 'duration_var_old',\n",
    " 'duration_skew_old',\n",
    " 'amount_month_ratio_mean_old',\n",
    " 'amount_month_ratio_min_old',\n",
    " 'amount_month_ratio_max_old',\n",
    " 'amount_month_ratio_var_old',\n",
    " 'amount_month_ratio_skew_old',\n",
    " 'category_2_mean_mean_old',\n",
    " 'category_3_mean_mean_old',\n",
    " 'purchase_date_diff_old',\n",
    " 'purchase_date_average_old',\n",
    " 'purchase_date_uptonow_old',\n",
    " 'purchase_date_uptomin_old',\n",
    " 'card_id_total',\n",
    " 'card_id_count_total',\n",
    " 'card_id_count_ratio',\n",
    " 'purchase_amount_total',\n",
    " 'purchase_amount_total_mean',\n",
    " 'purchase_amount_total_max',\n",
    " 'purchase_amount_total_min',\n",
    " 'purchase_amount_sum_ratio',\n",
    " 'hist_first_buy',\n",
    " 'new_first_buy',\n",
    " 'hist_last_buy',\n",
    " 'new_last_buy',\n",
    " 'month_diff_ratio',\n",
    " 'installments_total',\n",
    " 'installments_ratio',\n",
    " 'price_total',\n",
    " 'CLV',\n",
    " 'CLV_old',\n",
    " 'CLV_ratio',\n",
    " 'quarter',\n",
    " 'elapsed_time',\n",
    " 'days_feature1',\n",
    " 'days_feature2',\n",
    " 'days_feature3',\n",
    " 'days_feature1_ratio',\n",
    " 'days_feature2_ratio',\n",
    " 'days_feature3_ratio',\n",
    " 'feature_sum',\n",
    " 'feature_mean',\n",
    " 'feature_max',\n",
    " 'feature_min',\n",
    " 'feature_var']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['first_active_monthElapsed']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[c for c in df_181 if c not in df_180]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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