Elo - ML model 3.691.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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "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": 119,
     "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: 3.6486\tvalid_1's rmse: 3.71202\n",
      "[200]\ttraining's rmse: 3.56235\tvalid_1's rmse: 3.67577\n",
      "[300]\ttraining's rmse: 3.50776\tvalid_1's rmse: 3.66326\n",
      "[400]\ttraining's rmse: 3.47179\tvalid_1's rmse: 3.6586\n",
      "[500]\ttraining's rmse: 3.43961\tvalid_1's rmse: 3.65613\n",
      "[600]\ttraining's rmse: 3.41592\tvalid_1's rmse: 3.65473\n",
      "[700]\ttraining's rmse: 3.39402\tvalid_1's rmse: 3.6543\n",
      "[800]\ttraining's rmse: 3.37392\tvalid_1's rmse: 3.65429\n",
      "[900]\ttraining's rmse: 3.35561\tvalid_1's rmse: 3.65406\n",
      "Early stopping, best iteration is:\n",
      "[741]\ttraining's rmse: 3.38555\tvalid_1's rmse: 3.65395\n",
      "fold 1\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.64985\tvalid_1's rmse: 3.70171\n",
      "[200]\ttraining's rmse: 3.56086\tvalid_1's rmse: 3.66717\n",
      "[300]\ttraining's rmse: 3.50689\tvalid_1's rmse: 3.65637\n",
      "[400]\ttraining's rmse: 3.46837\tvalid_1's rmse: 3.65305\n",
      "[500]\ttraining's rmse: 3.43843\tvalid_1's rmse: 3.65101\n",
      "[600]\ttraining's rmse: 3.41229\tvalid_1's rmse: 3.64981\n",
      "[700]\ttraining's rmse: 3.39128\tvalid_1's rmse: 3.64922\n",
      "[800]\ttraining's rmse: 3.37192\tvalid_1's rmse: 3.64876\n",
      "[900]\ttraining's rmse: 3.35331\tvalid_1's rmse: 3.64901\n",
      "Early stopping, best iteration is:\n",
      "[792]\ttraining's rmse: 3.37344\tvalid_1's rmse: 3.64869\n",
      "fold 2\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.64712\tvalid_1's rmse: 3.71001\n",
      "[200]\ttraining's rmse: 3.55876\tvalid_1's rmse: 3.67467\n",
      "[300]\ttraining's rmse: 3.50595\tvalid_1's rmse: 3.66287\n",
      "[400]\ttraining's rmse: 3.46904\tvalid_1's rmse: 3.65877\n",
      "[500]\ttraining's rmse: 3.44276\tvalid_1's rmse: 3.65712\n",
      "[600]\ttraining's rmse: 3.42144\tvalid_1's rmse: 3.65604\n",
      "[700]\ttraining's rmse: 3.40222\tvalid_1's rmse: 3.65579\n",
      "[800]\ttraining's rmse: 3.38305\tvalid_1's rmse: 3.65584\n",
      "Early stopping, best iteration is:\n",
      "[633]\ttraining's rmse: 3.41487\tvalid_1's rmse: 3.65567\n",
      "fold 3\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.6443\tvalid_1's rmse: 3.70682\n",
      "[200]\ttraining's rmse: 3.55463\tvalid_1's rmse: 3.67677\n",
      "[300]\ttraining's rmse: 3.50082\tvalid_1's rmse: 3.66747\n",
      "[400]\ttraining's rmse: 3.46385\tvalid_1's rmse: 3.66392\n",
      "[500]\ttraining's rmse: 3.43834\tvalid_1's rmse: 3.6616\n",
      "[600]\ttraining's rmse: 3.41558\tvalid_1's rmse: 3.66016\n",
      "[700]\ttraining's rmse: 3.39529\tvalid_1's rmse: 3.65937\n",
      "[800]\ttraining's rmse: 3.37749\tvalid_1's rmse: 3.65881\n",
      "[900]\ttraining's rmse: 3.36052\tvalid_1's rmse: 3.6585\n",
      "[1000]\ttraining's rmse: 3.34167\tvalid_1's rmse: 3.65867\n",
      "Early stopping, best iteration is:\n",
      "[891]\ttraining's rmse: 3.36217\tvalid_1's rmse: 3.6584\n",
      "fold 4\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's rmse: 3.64689\tvalid_1's rmse: 3.69685\n",
      "[200]\ttraining's rmse: 3.55694\tvalid_1's rmse: 3.66798\n",
      "[300]\ttraining's rmse: 3.50257\tvalid_1's rmse: 3.6593\n",
      "[400]\ttraining's rmse: 3.46639\tvalid_1's rmse: 3.65618\n",
      "[500]\ttraining's rmse: 3.44004\tvalid_1's rmse: 3.65497\n",
      "[600]\ttraining's rmse: 3.41871\tvalid_1's rmse: 3.65413\n",
      "[700]\ttraining's rmse: 3.39857\tvalid_1's rmse: 3.65372\n",
      "[800]\ttraining's rmse: 3.38013\tvalid_1's rmse: 3.65392\n",
      "[900]\ttraining's rmse: 3.36186\tvalid_1's rmse: 3.65386\n",
      "Early stopping, best iteration is:\n",
      "[707]\ttraining's rmse: 3.39718\tvalid_1's rmse: 3.6536\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.654063570445142"
      ]
     },
     "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"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Blending submissions.ipynb'\r\n",
      "'Elo - ML model - 3.693.ipynb'\r\n",
      "'Elo - ML model - 3.695 beta.ipynb'\r\n",
      "'Elo - ML model - 3.695.ipynb'\r\n",
      "'Elo - ML model - 3.696.ipynb'\r\n",
      "'Elo - ML model - beta.ipynb'\r\n",
      "'Elo - ML model - cat aggregates.ipynb'\r\n",
      "'Elo - ML model 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 - 3.696.ipynb'\r\n",
      "'Elo feature engineering 3.693 .ipynb'\r\n",
      "'Elo feature engineering 3.695 beta.ipynb'\r\n",
      "'Elo feature engineering 3.695.ipynb'\r\n",
      "'Elo feature engineering beta.ipynb'\r\n",
      "'Elo feature engineering using agg.ipynb'\r\n",
      "'Elo feature engineering-backup.ipynb'\r\n",
      "'Elo feature engineering-proc_df - new-Copy1.ipynb'\r\n",
      "'Elo feature engineering-proc_df - new.ipynb'\r\n",
      "'Elo feature engineering-proc_df-OLD.ipynb'\r\n",
      "'Elo feature engineering-proc_df.ipynb'\r\n",
      "'Elo merchant category reco - using RF.ipynb'\r\n",
      "'Elo outlier predictions & blending .ipynb'\r\n",
      "'Feature selection using Boruta.ipynb'\r\n",
      "'Null importances - beta.ipynb'\r\n",
      "'Outlier classification using Autoencoder.ipynb'\r\n",
      " README.md\r\n",
      "'Scrap pad.ipynb'\r\n",
      "'Scrap pad.py'\r\n",
      "'TS Fresh.ipynb'\r\n",
      "'TS Fresh.py'\r\n",
      "'Train & test clean.ipynb'\r\n",
      "'Using DL.ipynb'\r\n",
      "'Using Feature tools - old.ipynb'\r\n",
      "'Using feature tools-Copy1.ipynb'\r\n",
      "'Using feature tools.ipynb'\r\n",
      " actual_importances_ditribution_rf.csv\r\n",
      " adf\r\n",
      " alla.pkl\r\n",
      " authorised_trans_proc_agg\r\n",
      "'blend-2019-02-02 23:24:52.864854.csv'\r\n",
      " blend-{submission_name}\r\n",
      " blend_kfol_st.csv\r\n",
      " chandu_mean1.csv\r\n",
      " cols_keep.npy\r\n",
      " combining_submission.csv\r\n",
      " corr_scores_null_importances.csv\r\n",
      " correlation\r\n",
      " data\r\n",
      " df_auth\r\n",
      " df_hot_dropped_all\r\n",
      " df_hot_ext\r\n",
      " df_hot_test\r\n",
      " df_hot_test_ext\r\n",
      " df_new\r\n",
      " df_non_auth\r\n",
      " df_proc_dfed\r\n",
      " df_test_n_o\r\n",
      " df_xo\r\n",
      " fastai\r\n",
      " fm_new_hist\r\n",
      " hist_trans\r\n",
      " hist_trans_3_696\r\n",
      " hist_trans_agg\r\n",
      " hist_trans_agg_3_696\r\n",
      " hist_trans_agg_beta\r\n",
      " hist_trans_agg_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_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_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",
      " submissions\r\n",
      " target_all\r\n",
      " target_outliers_removed\r\n",
      " test_clean.csv\r\n",
      " test_clean.csv.zip\r\n",
      " test_df\r\n",
      " test_df_3.695\r\n",
      " test_df_3_696\r\n",
      " test_df_beta\r\n",
      " test_df_proc\r\n",
      " test_df_proc_new\r\n",
      " test_df_proc_new_m\r\n",
      " train_clean.csv\r\n",
      " train_clean.csv.zip\r\n",
      " train_clean0\r\n",
      " train_df\r\n",
      " train_df_3_696\r\n",
      " train_df_beta\r\n",
      " train_df_proc\r\n",
      " train_df_proc_new\r\n",
      " train_df_proc_new_m\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</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",
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       "<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",
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       "    <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",
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       "\n",
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       "</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",
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       "    <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",
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       "\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",
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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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = f'submission-{submission_name}.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'submission-2019-02-15 18:08:33.904515.csv'"
      ]
     },
     "execution_count": 17,
     "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-2019-02-15 18:08:33.904515.csv' -m 'better be good'"
   ]
  },
  {
   "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",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>first_active_month</th>\n",
       "      <td>2017-12-01 00:00:00</td>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>2016-09-01 00:00:00</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "      <td>2017-11-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_id</th>\n",
       "      <td>C_ID_176ecc65bd</td>\n",
       "      <td>C_ID_6082929d3a</td>\n",
       "      <td>C_ID_a87bbd85a7</td>\n",
       "      <td>C_ID_92e4809531</td>\n",
       "      <td>C_ID_d2dd6bbf9a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_1</th>\n",
       "      <td>0.0131446</td>\n",
       "      <td>0.0107116</td>\n",
       "      <td>0.0104794</td>\n",
       "      <td>0.00805849</td>\n",
       "      <td>0.0107116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_2</th>\n",
       "      <td>0.0113848</td>\n",
       "      <td>0.0113848</td>\n",
       "      <td>0.0113848</td>\n",
       "      <td>0.0113848</td>\n",
       "      <td>0.0113848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_3</th>\n",
       "      <td>0.0114275</td>\n",
       "      <td>0.0102828</td>\n",
       "      <td>0.0114275</td>\n",
       "      <td>0.0102828</td>\n",
       "      <td>0.0102828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.279039</td>\n",
       "      <td>-0.926039</td>\n",
       "      <td>0.475326</td>\n",
       "      <td>0.516026</td>\n",
       "      <td>-0.100927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthYear</th>\n",
       "      <td>2017</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthMonth</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthWeek</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>35</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthDay</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthDayofweek</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthDayofyear</th>\n",
       "      <td>335</td>\n",
       "      <td>336</td>\n",
       "      <td>245</td>\n",
       "      <td>305</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_month_end</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_month_start</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_quarter_end</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_quarter_start</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_year_end</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthIs_year_start</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_active_monthElapsed</th>\n",
       "      <td>1512086400</td>\n",
       "      <td>1480550400</td>\n",
       "      <td>1472688000</td>\n",
       "      <td>1509494400</td>\n",
       "      <td>1509494400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactions_count</th>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_id_nunique</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_category_id_nunique</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_id_nunique</th>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_id_nunique</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subsector_id_nunique</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_Year_nunique</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_Month_nunique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_Week_nunique</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_Day_nunique</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_sum</th>\n",
       "      <td>-0.450675</td>\n",
       "      <td>-22.5638</td>\n",
       "      <td>-15.5147</td>\n",
       "      <td>-3.1597</td>\n",
       "      <td>-4.21238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_sum_sum</th>\n",
       "      <td>7.36008</td>\n",
       "      <td>-390.031</td>\n",
       "      <td>-322.03</td>\n",
       "      <td>-14.6139</td>\n",
       "      <td>-14.7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_sum_ptp</th>\n",
       "      <td>23.4604</td>\n",
       "      <td>358.046</td>\n",
       "      <td>284.74</td>\n",
       "      <td>12.2301</td>\n",
       "      <td>1.36254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_sum_max</th>\n",
       "      <td>15.4103</td>\n",
       "      <td>-0.619333</td>\n",
       "      <td>-18.6449</td>\n",
       "      <td>-1.1919</td>\n",
       "      <td>-6.70246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_mean</th>\n",
       "      <td>9.49366</td>\n",
       "      <td>2.61874</td>\n",
       "      <td>-0.588055</td>\n",
       "      <td>1.90307</td>\n",
       "      <td>3.17877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_sum</th>\n",
       "      <td>18.9873</td>\n",
       "      <td>7.85621</td>\n",
       "      <td>-1.17611</td>\n",
       "      <td>3.80614</td>\n",
       "      <td>6.35754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_ptp</th>\n",
       "      <td>14.2301</td>\n",
       "      <td>8.07011</td>\n",
       "      <td>0.129754</td>\n",
       "      <td>1.82123</td>\n",
       "      <td>4.16685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_max</th>\n",
       "      <td>16.6087</td>\n",
       "      <td>7.45078</td>\n",
       "      <td>-0.523178</td>\n",
       "      <td>2.81368</td>\n",
       "      <td>5.26219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_mean_mean</th>\n",
       "      <td>-0.04237</td>\n",
       "      <td>-0.593192</td>\n",
       "      <td>-0.712977</td>\n",
       "      <td>-0.280704</td>\n",
       "      <td>-0.284128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_mean_sum</th>\n",
       "      <td>-0.08474</td>\n",
       "      <td>-1.77958</td>\n",
       "      <td>-1.42595</td>\n",
       "      <td>-0.561408</td>\n",
       "      <td>-0.568255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_mean_ptp</th>\n",
       "      <td>0.490273</td>\n",
       "      <td>0.0499709</td>\n",
       "      <td>0.00826695</td>\n",
       "      <td>0.164107</td>\n",
       "      <td>0.269552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_mean_max</th>\n",
       "      <td>0.202767</td>\n",
       "      <td>-0.569362</td>\n",
       "      <td>-0.708844</td>\n",
       "      <td>-0.19865</td>\n",
       "      <td>-0.149352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elapsed_time</th>\n",
       "      <td>426</td>\n",
       "      <td>791</td>\n",
       "      <td>882</td>\n",
       "      <td>456</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_id_total</th>\n",
       "      <td>114</td>\n",
       "      <td>708</td>\n",
       "      <td>476</td>\n",
       "      <td>49</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_id_count_total</th>\n",
       "      <td>114</td>\n",
       "      <td>708</td>\n",
       "      <td>476</td>\n",
       "      <td>49</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_total</th>\n",
       "      <td>6.9094</td>\n",
       "      <td>-412.594</td>\n",
       "      <td>-337.545</td>\n",
       "      <td>-17.7736</td>\n",
       "      <td>-18.9798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_total_mean</th>\n",
       "      <td>0.0257024</td>\n",
       "      <td>-1.07969</td>\n",
       "      <td>-1.41453</td>\n",
       "      <td>-0.866476</td>\n",
       "      <td>-0.474238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installments_total</th>\n",
       "      <td>124</td>\n",
       "      <td>742</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hist_first_buy</th>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>125</td>\n",
       "      <td>6</td>\n",
       "      <td>-62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_first_buy</th>\n",
       "      <td>90</td>\n",
       "      <td>465</td>\n",
       "      <td>551</td>\n",
       "      <td>121</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hist_last_buy</th>\n",
       "      <td>89</td>\n",
       "      <td>454</td>\n",
       "      <td>540</td>\n",
       "      <td>117</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_last_buy</th>\n",
       "      <td>132</td>\n",
       "      <td>515</td>\n",
       "      <td>598</td>\n",
       "      <td>172</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase_amount_max_total</th>\n",
       "      <td>3.01714</td>\n",
       "      <td>0.574974</td>\n",
       "      <td>-1.31004</td>\n",
       "      <td>-0.932425</td>\n",
       "      <td>0.144826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchased_before_issue</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outliers</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days</th>\n",
       "      <td>62</td>\n",
       "      <td>427</td>\n",
       "      <td>518</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_feature1</th>\n",
       "      <td>310</td>\n",
       "      <td>1708</td>\n",
       "      <td>1554</td>\n",
       "      <td>92</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_feature2</th>\n",
       "      <td>62</td>\n",
       "      <td>427</td>\n",
       "      <td>518</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_feature3</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>518</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rounded_target</th>\n",
       "      <td>-0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indexcol</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0                    1  \\\n",
       "first_active_month                  2017-12-01 00:00:00  2016-12-01 00:00:00   \n",
       "card_id                                 C_ID_176ecc65bd      C_ID_6082929d3a   \n",
       "feature_1                                     0.0131446            0.0107116   \n",
       "feature_2                                     0.0113848            0.0113848   \n",
       "feature_3                                     0.0114275            0.0102828   \n",
       "target                                        -0.279039            -0.926039   \n",
       "first_active_monthYear                             2017                 2016   \n",
       "first_active_monthMonth                              12                   12   \n",
       "first_active_monthWeek                               48                   48   \n",
       "first_active_monthDay                                 1                    1   \n",
       "first_active_monthDayofweek                           4                    3   \n",
       "first_active_monthDayofyear                         335                  336   \n",
       "first_active_monthIs_month_end                    False                False   \n",
       "first_active_monthIs_month_start                   True                 True   \n",
       "first_active_monthIs_quarter_end                  False                False   \n",
       "first_active_monthIs_quarter_start                False                False   \n",
       "first_active_monthIs_year_end                     False                False   \n",
       "first_active_monthIs_year_start                   False                False   \n",
       "first_active_monthElapsed                    1512086400           1480550400   \n",
       "transactions_count                                   10                   46   \n",
       "city_id_nunique                                       5                   15   \n",
       "merchant_category_id_nunique                          7                   20   \n",
       "merchant_id_nunique                                  10                   46   \n",
       "state_id_nunique                                      2                    8   \n",
       "subsector_id_nunique                                  7                   13   \n",
       "purchase_Year_nunique                                 1                    1   \n",
       "purchase_Month_nunique                                2                    2   \n",
       "purchase_Week_nunique                                 5                    9   \n",
       "purchase_Day_nunique                                  7                   17   \n",
       "purchase_amount_sum                           -0.450675             -22.5638   \n",
       "...                                                 ...                  ...   \n",
       "purchase_amount_sum_sum                         7.36008             -390.031   \n",
       "purchase_amount_sum_ptp                         23.4604              358.046   \n",
       "purchase_amount_sum_max                         15.4103            -0.619333   \n",
       "purchase_amount_max_mean                        9.49366              2.61874   \n",
       "purchase_amount_max_sum                         18.9873              7.85621   \n",
       "purchase_amount_max_ptp                         14.2301              8.07011   \n",
       "purchase_amount_max_max                         16.6087              7.45078   \n",
       "purchase_amount_mean_mean                      -0.04237            -0.593192   \n",
       "purchase_amount_mean_sum                       -0.08474             -1.77958   \n",
       "purchase_amount_mean_ptp                       0.490273            0.0499709   \n",
       "purchase_amount_mean_max                       0.202767            -0.569362   \n",
       "elapsed_time                                        426                  791   \n",
       "card_id_total                                       114                  708   \n",
       "card_id_count_total                                 114                  708   \n",
       "purchase_amount_total                            6.9094             -412.594   \n",
       "purchase_amount_total_mean                    0.0257024             -1.07969   \n",
       "installments_total                                  124                  742   \n",
       "hist_first_buy                                        4                   31   \n",
       "new_first_buy                                        90                  465   \n",
       "hist_last_buy                                        89                  454   \n",
       "new_last_buy                                        132                  515   \n",
       "purchase_amount_max_total                       3.01714             0.574974   \n",
       "purchased_before_issue                            False                False   \n",
       "outliers                                              0                    0   \n",
       "days                                                 62                  427   \n",
       "days_feature1                                       310                 1708   \n",
       "days_feature2                                        62                  427   \n",
       "days_feature3                                        62                    0   \n",
       "rounded_target                                       -0                   -1   \n",
       "indexcol                                              2                    5   \n",
       "\n",
       "                                                      2                    3  \\\n",
       "first_active_month                  2016-09-01 00:00:00  2017-11-01 00:00:00   \n",
       "card_id                                 C_ID_a87bbd85a7      C_ID_92e4809531   \n",
       "feature_1                                     0.0104794           0.00805849   \n",
       "feature_2                                     0.0113848            0.0113848   \n",
       "feature_3                                     0.0114275            0.0102828   \n",
       "target                                         0.475326             0.516026   \n",
       "first_active_monthYear                             2016                 2017   \n",
       "first_active_monthMonth                               9                   11   \n",
       "first_active_monthWeek                               35                   44   \n",
       "first_active_monthDay                                 1                    1   \n",
       "first_active_monthDayofweek                           3                    2   \n",
       "first_active_monthDayofyear                         245                  305   \n",
       "first_active_monthIs_month_end                    False                False   \n",
       "first_active_monthIs_month_start                   True                 True   \n",
       "first_active_monthIs_quarter_end                  False                False   \n",
       "first_active_monthIs_quarter_start                False                False   \n",
       "first_active_monthIs_year_end                     False                False   \n",
       "first_active_monthIs_year_start                   False                False   \n",
       "first_active_monthElapsed                    1472688000           1509494400   \n",
       "transactions_count                                   22                    6   \n",
       "city_id_nunique                                       3                    5   \n",
       "merchant_category_id_nunique                         17                    4   \n",
       "merchant_id_nunique                                  22                    6   \n",
       "state_id_nunique                                      1                    3   \n",
       "subsector_id_nunique                                 11                    4   \n",
       "purchase_Year_nunique                                 1                    1   \n",
       "purchase_Month_nunique                                2                    2   \n",
       "purchase_Week_nunique                                 5                    5   \n",
       "purchase_Day_nunique                                 13                    6   \n",
       "purchase_amount_sum                            -15.5147              -3.1597   \n",
       "...                                                 ...                  ...   \n",
       "purchase_amount_sum_sum                         -322.03             -14.6139   \n",
       "purchase_amount_sum_ptp                          284.74              12.2301   \n",
       "purchase_amount_sum_max                        -18.6449              -1.1919   \n",
       "purchase_amount_max_mean                      -0.588055              1.90307   \n",
       "purchase_amount_max_sum                        -1.17611              3.80614   \n",
       "purchase_amount_max_ptp                        0.129754              1.82123   \n",
       "purchase_amount_max_max                       -0.523178              2.81368   \n",
       "purchase_amount_mean_mean                     -0.712977            -0.280704   \n",
       "purchase_amount_mean_sum                       -1.42595            -0.561408   \n",
       "purchase_amount_mean_ptp                     0.00826695             0.164107   \n",
       "purchase_amount_mean_max                      -0.708844             -0.19865   \n",
       "elapsed_time                                        882                  456   \n",
       "card_id_total                                       476                   49   \n",
       "card_id_count_total                                 476                   49   \n",
       "purchase_amount_total                          -337.545             -17.7736   \n",
       "purchase_amount_total_mean                     -1.41453            -0.866476   \n",
       "installments_total                                    0                   74   \n",
       "hist_first_buy                                      125                    6   \n",
       "new_first_buy                                       551                  121   \n",
       "hist_last_buy                                       540                  117   \n",
       "new_last_buy                                        598                  172   \n",
       "purchase_amount_max_total                      -1.31004            -0.932425   \n",
       "purchased_before_issue                            False                False   \n",
       "outliers                                              0                    0   \n",
       "days                                                518                   92   \n",
       "days_feature1                                      1554                   92   \n",
       "days_feature2                                       518                   92   \n",
       "days_feature3                                       518                    0   \n",
       "rounded_target                                        0                    1   \n",
       "indexcol                                              5                    5   \n",
       "\n",
       "                                                      4  \n",
       "first_active_month                  2017-11-01 00:00:00  \n",
       "card_id                                 C_ID_d2dd6bbf9a  \n",
       "feature_1                                     0.0107116  \n",
       "feature_2                                     0.0113848  \n",
       "feature_3                                     0.0102828  \n",
       "target                                        -0.100927  \n",
       "first_active_monthYear                             2017  \n",
       "first_active_monthMonth                              11  \n",
       "first_active_monthWeek                               44  \n",
       "first_active_monthDay                                 1  \n",
       "first_active_monthDayofweek                           2  \n",
       "first_active_monthDayofyear                         305  \n",
       "first_active_monthIs_month_end                    False  \n",
       "first_active_monthIs_month_start                   True  \n",
       "first_active_monthIs_quarter_end                  False  \n",
       "first_active_monthIs_quarter_start                False  \n",
       "first_active_monthIs_year_end                     False  \n",
       "first_active_monthIs_year_start                   False  \n",
       "first_active_monthElapsed                    1509494400  \n",
       "transactions_count                                   16  \n",
       "city_id_nunique                                       6  \n",
       "merchant_category_id_nunique                         13  \n",
       "merchant_id_nunique                                  16  \n",
       "state_id_nunique                                      3  \n",
       "subsector_id_nunique                                  8  \n",
       "purchase_Year_nunique                                 1  \n",
       "purchase_Month_nunique                                2  \n",
       "purchase_Week_nunique                                 6  \n",
       "purchase_Day_nunique                                  9  \n",
       "purchase_amount_sum                            -4.21238  \n",
       "...                                                 ...  \n",
       "purchase_amount_sum_sum                        -14.7675  \n",
       "purchase_amount_sum_ptp                         1.36254  \n",
       "purchase_amount_sum_max                        -6.70246  \n",
       "purchase_amount_max_mean                        3.17877  \n",
       "purchase_amount_max_sum                         6.35754  \n",
       "purchase_amount_max_ptp                         4.16685  \n",
       "purchase_amount_max_max                         5.26219  \n",
       "purchase_amount_mean_mean                     -0.284128  \n",
       "purchase_amount_mean_sum                      -0.568255  \n",
       "purchase_amount_mean_ptp                       0.269552  \n",
       "purchase_amount_mean_max                      -0.149352  \n",
       "elapsed_time                                        456  \n",
       "card_id_total                                        86  \n",
       "card_id_count_total                                  86  \n",
       "purchase_amount_total                          -18.9798  \n",
       "purchase_amount_total_mean                    -0.474238  \n",
       "installments_total                                  180  \n",
       "hist_first_buy                                      -62  \n",
       "new_first_buy                                       140  \n",
       "hist_last_buy                                       116  \n",
       "new_last_buy                                        180  \n",
       "purchase_amount_max_total                      0.144826  \n",
       "purchased_before_issue                             True  \n",
       "outliers                                              0  \n",
       "days                                                 92  \n",
       "days_feature1                                       368  \n",
       "days_feature2                                        92  \n",
       "days_feature3                                         0  \n",
       "rounded_target                                       -0  \n",
       "indexcol                                              8  \n",
       "\n",
       "[170 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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