Trunc norm mean .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\"></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\n"
   ]
  },
  {
   "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, RepeatedKFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = './'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Blend with df outliers.ipynb'\r\n",
      "'Blending submissions.ipynb'\r\n",
      "'Combining model without outlier.ipynb'\r\n",
      "'Continuous data processing.ipynb'\r\n",
      "'DL on cont data-Copy1.ipynb'\r\n",
      "'DL on cont data.ipynb'\r\n",
      "'Elo - ML model - 3.690.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 3.690.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",
      " Merchants.ipynb\r\n",
      "'Model - DL & LGBM regression.ipynb'\r\n",
      "'Model - XG boost.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",
      " Untitled.ipynb\r\n",
      "'Using DL beta.ipynb'\r\n",
      "'Using DL last try.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",
      " all_card_merch\r\n",
      " all_cards\r\n",
      " all_test_cont\r\n",
      " all_train_cont\r\n",
      " all_trans\r\n",
      " all_trans_merch\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",
      " blend_top8_20feb.csv\r\n",
      " blend_top8_22feb.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",
      "'combined_submission-2019-02-20 00:21:17.597315.csv'\r\n",
      "'combined_submission-2019-02-20 00:25:02.521379.csv'\r\n",
      "'combined_submission-2019-02-20 00:26:39.776651.csv'\r\n",
      "'combined_submission-2019-02-21 13:55:21.772806.csv'\r\n",
      "'combined_submission-2019-02-22 03:43:50.172624.csv'\r\n",
      "'combined_submission-2019-02-22 20:49:55.088273.csv'\r\n",
      "'combined_submission-2019-02-22 21:40:05.269919.csv'\r\n",
      "'combined_submission-2019-02-26 22:47:23.452323.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_cont_proc\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",
      " dl_df_beta\r\n",
      " dl_df_t_beta\r\n",
      "'dl_submission-2019-02-22 21:30:24.851272.csv'\r\n",
      "'dl_submission_stacked_lgb-2019-02-22 21:37:59.515079.csv'\r\n",
      "'dl_submission_stacked_lgb-2019-02-26 22:44:02.780493.csv'\r\n",
      "'dl_xgb_submission_stacked_lgb-2019-02-26 22:45:24.597929.csv'\r\n",
      " fastai\r\n",
      " final_cols_352.txt\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",
      " hist_trans_purchase_aggs_alpha\r\n",
      "'kfold lightgbm.ipynb'\r\n",
      " latent_representation.png\r\n",
      " lgbm_importances.png\r\n",
      " lla\r\n",
      " merchants_df\r\n",
      " merchants_minimised\r\n",
      " models\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_hist_trans_purchase_aggs_alpha\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_dl.npy\r\n",
      " oof_lgb_3.npy\r\n",
      " oof_lgbm.npy\r\n",
      " oof_xgb_3.npy\r\n",
      " oof_xgb_3_2.npy\r\n",
      " original.png\r\n",
      "'outlier_combining_submission-2019-02-19 02:12:16.662625.csv'\r\n",
      "'outlier_combining_submission-2019-02-19 02:28:13.811057.csv'\r\n",
      "'outlier_combining_submission-2019-02-19 02:48:44.830259.csv'\r\n",
      "'outlier_combining_submission-2019-02-26 23:20:23.680409.csv'\r\n",
      " predictions_dl.npy\r\n",
      " predictions_lgb_3.npy\r\n",
      " predictions_lgbm.npy\r\n",
      " predictions_xgb_3.npy\r\n",
      " predictions_xgb_3_2.npy\r\n",
      "'scrap pad 2.ipynb'\r\n",
      " selecte_cold_prune.npy\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-2019-02-16 23:41:55.685750.csv'\r\n",
      "'submission-2019-02-16 23:52:03.148358.csv'\r\n",
      "'submission-2019-02-19 00:14:48.737899.csv'\r\n",
      "'submission-2019-02-19 00:42:33.872294.csv'\r\n",
      "'submission-2019-02-19 00:51:12.314011.csv'\r\n",
      "'submission-2019-02-19 00:51:48.972772.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",
      "'submission_kfold-2019-02-20 00:58:19.913926.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_alpha\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",
      " tmp\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_alpha\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",
      "'xg_submission-2019-02-21 11:18:00.066865.csv'\r\n",
      "'xg_submission_stacked_lgb-2019-02-21 13:41:48.181414.csv'\r\n",
      "'xg_submission_stacked_lgb-2019-02-22 03:43:06.565696.csv'\r\n",
      "'xg_submission_stacked_lgb-2019-02-22 20:48:54.579689.csv'\r\n",
      "'xg_submission_stacked_lgb-2019-02-22 21:37:47.622625.csv'\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'combined_submission-2019-02-26 22:47:23.452323.csv' - 3.684\n",
    "'dl_xgb_submission_stacked_lgb-2019-02-26 22:45:24.597929.csv' - 3.687\n",
    "'combined_submission-2019-02-22 21:40:05.269919.csv' - 3.685\n",
    "'dl_submission_stacked_lgb-2019-02-22 21:37:59.515079.csv' - 3.685\n",
    "'dl_submission-2019-02-22 21:30:24.851272.csv' - 3.745\n",
    "'outlier_combining_submission-2019-02-19 02:48:44.830259.csv'- 3.690\n",
    "'outlier_combining_submission-2019-02-19 02:28:13.811057.csv' - 3.689\n",
    "'submission-2019-02-19 00:51:48.972772.csv' - 3.717\n",
    "'blend_kfol_st.csv' - 3.693\n",
    "'submission-2019-02-13 22:48:45.005871.csv' - 3.691\n",
    "'submission-2019-01-12 13:53:27.215115.csv'- 3.696\n",
    "'submission-2019-01-17 23:23:57.743505.csv' - 3.696\n",
    "'submission-2019-01-20 19:35:24.892043.csv' - 3.695\n",
    "'chandu_mean1.csv' - 3.694\n",
    "'submission-2019-02-02 23:13:59.670630.csv' - 3.693\n",
    "'submission-2019-02-13 00:42:12.274968.csv' - 3.692\n",
    "'submission_kfold-2019-02-13 00:55:25.132146.csv' - 3.692\n",
    "'submission-2019-02-15 14:22:06.881490.csv' - 3.695\n",
    "'outlier_combining_submission-2019-02-19 02:12:16.662625.csv' - 3.689\n",
    "'combined_submission-2019-02-20 00:21:17.597315.csv'- 3.686\n",
    "'xg_submission_stacked_lgb-2019-02-21 13:41:48.181414.csv' - 3.688\n",
    "'combined_submission-2019-02-21 13:55:21.772806.csv' - 3.684\n",
    "'combined_submission-2019-02-22 20:49:55.088273.csv' - 3.684\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3_684_452323 = pd.read_csv('combined_submission-2019-02-26 22:47:23.452323.csv', names=['card_id', 'df_3_684_452323'], skiprows=[0], header=None) \n",
    "df_3_687_597929 = pd.read_csv('dl_xgb_submission_stacked_lgb-2019-02-26 22:45:24.597929.csv', names=['card_id', 'df_3_687_597929'], skiprows=[0], header=None) \n",
    "df_3_685_269919 = pd.read_csv('combined_submission-2019-02-22 21:40:05.269919.csv', names=['card_id', 'df_3_685_269919'], skiprows=[0], header=None) \n",
    "df_3_685_515079 = pd.read_csv('dl_submission_stacked_lgb-2019-02-22 21:37:59.515079.csv', names=['card_id', 'df_3_685_515079'], skiprows=[0], header=None) \n",
    "df_3_745_851272 = pd.read_csv('dl_submission-2019-02-22 21:30:24.851272.csv', names=['card_id', 'df_3_745_851272'], skiprows=[0], header=None) \n",
    "df_3_690_830259 = pd.read_csv('outlier_combining_submission-2019-02-19 02:48:44.830259.csv', names=['card_id', '3_690_830259'], skiprows=[0], header=None)\n",
    "df_3_689_811057 = pd.read_csv('outlier_combining_submission-2019-02-19 02:28:13.811057.csv', names=['card_id', 'df_3_689_811057'], skiprows=[0], header=None) \n",
    "df_3_717_972772 = pd.read_csv('submission-2019-02-19 00:51:48.972772.csv', names=['card_id', 'df_3_717_972772'], skiprows=[0], header=None) \n",
    "df_3_693_blend_kfol_st = pd.read_csv('blend_kfol_st.csv', names=['card_id', 'df_3_693_blend_kfol_st'], skiprows=[0], header=None) \n",
    "df_3_691_005871 = pd.read_csv('submission-2019-02-13 22:48:45.005871.csv', names=['card_id', 'df_3_691_005871'], skiprows=[0], header=None) \n",
    "df_3_696_215115 = pd.read_csv('submission-2019-01-12 13:53:27.215115.csv', names=['card_id', '3_696_215115'], skiprows=[0], header=None)\n",
    "df_3_696_743505 = pd.read_csv('submission-2019-01-17 23:23:57.743505.csv', names=['card_id', 'df_3_696_743505'], skiprows=[0], header=None) \n",
    "df_3_695_892043 = pd.read_csv('submission-2019-01-20 19:35:24.892043.csv', names=['card_id', 'df_3_695_892043'], skiprows=[0], header=None) \n",
    "df_3_694_chandu_mean1 = pd.read_csv('chandu_mean1.csv', names=['card_id', 'df_3_694_chandu_mean1'], skiprows=[0], header=None) \n",
    "df_3_693_670630 = pd.read_csv('submission-2019-02-02 23:13:59.670630.csv', names=['card_id', 'df_3_693_670630'], skiprows=[0], header=None) \n",
    "df_3_692_274968 = pd.read_csv('submission-2019-02-13 00:42:12.274968.csv', names=['card_id', 'df_3_692_274968'], skiprows=[0], header=None) \n",
    "df_3_692_132146 = pd.read_csv('submission_kfold-2019-02-13 00:55:25.132146.csv', names=['card_id', 'df_3_692_132146'], skiprows=[0], header=None) \n",
    "df_3_695_881490 = pd.read_csv('submission-2019-02-15 14:22:06.881490.csv', names=['card_id', 'df_3_695_881490'], skiprows=[0], header=None) \n",
    "df_3_689_662625 = pd.read_csv('outlier_combining_submission-2019-02-19 02:12:16.662625.csv', names=['card_id', 'df_3_689_662625'], skiprows=[0], header=None) \n",
    "df_3_686_597315 = pd.read_csv('combined_submission-2019-02-20 00:21:17.597315.csv', names=['card_id', '3_686_597315'], skiprows=[0], header=None)\n",
    "df_3_688_181414 = pd.read_csv('xg_submission_stacked_lgb-2019-02-21 13:41:48.181414.csv', names=['card_id', 'df_3_688_181414'], skiprows=[0], header=None) \n",
    "df_3_684_772806 = pd.read_csv('combined_submission-2019-02-21 13:55:21.772806.csv', names=['card_id', 'df_3_684_772806'], skiprows=[0], header=None) \n",
    "df_3_684_088273 = pd.read_csv('combined_submission-2019-02-22 20:49:55.088273.csv', names=['card_id', 'df_3_684_088273'], skiprows=[0], header=None) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base = df_3_684_088273"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base = pd.merge(df_base, df_3_684_452323, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_687_597929, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_685_269919, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_685_515079, how='inner', on='card_id')\n",
    "# df_base = pd.merge(df_base, df_3_745_851272, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_690_830259, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_689_811057, how='inner', on='card_id')\n",
    "# df_base = pd.merge(df_base, df_3_717_972772, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_693_blend_kfol_st, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_691_005871, how='inner', on='card_id')\n",
    "# df_base = pd.merge(df_base, df_3_696_215115, how='inner', on='card_id')\n",
    "# df_base = pd.merge(df_base, df_3_696_743505, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_695_892043, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_694_chandu_mean1, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_693_670630, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_692_274968, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_692_132146, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_695_881490, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_689_662625, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_686_597315, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_688_181414, how='inner', on='card_id')\n",
    "df_base = pd.merge(df_base, df_3_684_772806, how='inner', on='card_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d2242e588>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,12))\n",
    "sns.heatmap(df_base.iloc[:,1:].corr(),annot=True,fmt=\".2f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = np.zeros([df_base.iloc[:,1:].shape[1],df_base.iloc[:,1:].shape[1]])\n",
    "for i in np.arange(M.shape[1]):\n",
    "    for j in np.arange(M.shape[1]):\n",
    "        M[i,j] = np.sqrt(metrics.mean_squared_error(df_base.iloc[:,i+1], df_base.iloc[:,j+1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base['target'] = df_base.iloc[:,1:].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_base['target'] = df_3_745_851272['df_3_745_851272']* 0.1 + df_base['target'] * 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base[['card_id','target']].to_csv(\"mean_of_all_final.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|#######################################| 4.24M/4.24M [00:22<00:00, 197kB/s]\n",
      "Successfully submitted to Elo Merchant Category Recommendation"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit elo-merchant-category-recommendation -f 'mean_of_all_final.csv' -m \"blending top 14 submissions\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_base['target'] = df_3_745_851272['df_3_745_851272']* 0.1 + df_base['target'] * 0.9\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(df_3_745_851272['df_3_745_851272'],orient='v')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(df_base['target'], orient='v')\n",
    "plt.show()\n",
    "# df_base[['card_id','target']].to_csv(\"blend3.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import truncnorm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((123623, 25), (123623, 2))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_base.shape, df_3_684_088273.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>df_3_684_088273</th>\n",
       "      <th>df_3_684_452323</th>\n",
       "      <th>df_3_687_597929</th>\n",
       "      <th>df_3_685_269919</th>\n",
       "      <th>df_3_685_515079</th>\n",
       "      <th>df_3_745_851272</th>\n",
       "      <th>3_690_830259</th>\n",
       "      <th>df_3_689_811057</th>\n",
       "      <th>df_3_717_972772</th>\n",
       "      <th>...</th>\n",
       "      <th>df_3_694_chandu_mean1</th>\n",
       "      <th>df_3_693_670630</th>\n",
       "      <th>df_3_692_274968</th>\n",
       "      <th>df_3_692_132146</th>\n",
       "      <th>df_3_695_881490</th>\n",
       "      <th>df_3_689_662625</th>\n",
       "      <th>3_686_597315</th>\n",
       "      <th>df_3_688_181414</th>\n",
       "      <th>df_3_684_772806</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>-2.302681</td>\n",
       "      <td>-2.312583</td>\n",
       "      <td>-2.122841</td>\n",
       "      <td>-2.415547</td>\n",
       "      <td>-2.328769</td>\n",
       "      <td>-1.867310</td>\n",
       "      <td>-2.427491</td>\n",
       "      <td>-2.427491</td>\n",
       "      <td>-2.272456</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.840921</td>\n",
       "      <td>-3.072875</td>\n",
       "      <td>-3.234466</td>\n",
       "      <td>-3.168636</td>\n",
       "      <td>-3.407407</td>\n",
       "      <td>-2.427491</td>\n",
       "      <td>-2.464909</td>\n",
       "      <td>-2.147446</td>\n",
       "      <td>-2.324886</td>\n",
       "      <td>-2.534741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_ID_130fd0cbdd</td>\n",
       "      <td>-0.314882</td>\n",
       "      <td>-0.326738</td>\n",
       "      <td>-0.286171</td>\n",
       "      <td>-0.338663</td>\n",
       "      <td>-0.310020</td>\n",
       "      <td>-0.530126</td>\n",
       "      <td>-0.324981</td>\n",
       "      <td>-0.254059</td>\n",
       "      <td>-0.376396</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.310089</td>\n",
       "      <td>-0.348145</td>\n",
       "      <td>-0.246711</td>\n",
       "      <td>-0.269033</td>\n",
       "      <td>-0.274402</td>\n",
       "      <td>-0.254059</td>\n",
       "      <td>-0.346143</td>\n",
       "      <td>-0.292596</td>\n",
       "      <td>-0.329951</td>\n",
       "      <td>-0.333404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_ID_b709037bc5</td>\n",
       "      <td>-0.838809</td>\n",
       "      <td>-0.844756</td>\n",
       "      <td>-0.889205</td>\n",
       "      <td>-0.859591</td>\n",
       "      <td>-0.918874</td>\n",
       "      <td>-0.851647</td>\n",
       "      <td>-0.930830</td>\n",
       "      <td>-0.367841</td>\n",
       "      <td>-1.300530</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.057735</td>\n",
       "      <td>-0.949767</td>\n",
       "      <td>-0.888107</td>\n",
       "      <td>-1.012365</td>\n",
       "      <td>-0.954982</td>\n",
       "      <td>-0.367841</td>\n",
       "      <td>-0.865569</td>\n",
       "      <td>-0.912420</td>\n",
       "      <td>-0.856364</td>\n",
       "      <td>-0.890216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_ID_d27d835a9f</td>\n",
       "      <td>-0.090521</td>\n",
       "      <td>-0.084800</td>\n",
       "      <td>-0.060729</td>\n",
       "      <td>-0.087357</td>\n",
       "      <td>-0.065844</td>\n",
       "      <td>-0.074054</td>\n",
       "      <td>-0.082940</td>\n",
       "      <td>-0.089059</td>\n",
       "      <td>-0.333307</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.132511</td>\n",
       "      <td>-0.149493</td>\n",
       "      <td>-0.129507</td>\n",
       "      <td>-0.117934</td>\n",
       "      <td>-0.159088</td>\n",
       "      <td>-0.089059</td>\n",
       "      <td>-0.095905</td>\n",
       "      <td>-0.077765</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>-0.114255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_ID_2b5e3df5c2</td>\n",
       "      <td>-1.417952</td>\n",
       "      <td>-1.420726</td>\n",
       "      <td>-1.781597</td>\n",
       "      <td>-1.452571</td>\n",
       "      <td>-1.845287</td>\n",
       "      <td>-1.433802</td>\n",
       "      <td>-1.934582</td>\n",
       "      <td>-1.337903</td>\n",
       "      <td>-1.790302</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.248852</td>\n",
       "      <td>-1.212316</td>\n",
       "      <td>-1.695469</td>\n",
       "      <td>-1.500328</td>\n",
       "      <td>-1.796945</td>\n",
       "      <td>-1.337903</td>\n",
       "      <td>-1.497219</td>\n",
       "      <td>-1.832296</td>\n",
       "      <td>-1.446076</td>\n",
       "      <td>-1.501269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           card_id  df_3_684_088273  df_3_684_452323  df_3_687_597929  \\\n",
       "0  C_ID_0ab67a22ab        -2.302681        -2.312583        -2.122841   \n",
       "1  C_ID_130fd0cbdd        -0.314882        -0.326738        -0.286171   \n",
       "2  C_ID_b709037bc5        -0.838809        -0.844756        -0.889205   \n",
       "3  C_ID_d27d835a9f        -0.090521        -0.084800        -0.060729   \n",
       "4  C_ID_2b5e3df5c2        -1.417952        -1.420726        -1.781597   \n",
       "\n",
       "   df_3_685_269919  df_3_685_515079  df_3_745_851272  3_690_830259  \\\n",
       "0        -2.415547        -2.328769        -1.867310     -2.427491   \n",
       "1        -0.338663        -0.310020        -0.530126     -0.324981   \n",
       "2        -0.859591        -0.918874        -0.851647     -0.930830   \n",
       "3        -0.087357        -0.065844        -0.074054     -0.082940   \n",
       "4        -1.452571        -1.845287        -1.433802     -1.934582   \n",
       "\n",
       "   df_3_689_811057  df_3_717_972772  ...  df_3_694_chandu_mean1  \\\n",
       "0        -2.427491        -2.272456  ...              -2.840921   \n",
       "1        -0.254059        -0.376396  ...              -0.310089   \n",
       "2        -0.367841        -1.300530  ...              -1.057735   \n",
       "3        -0.089059        -0.333307  ...              -0.132511   \n",
       "4        -1.337903        -1.790302  ...              -1.248852   \n",
       "\n",
       "   df_3_693_670630  df_3_692_274968  df_3_692_132146  df_3_695_881490  \\\n",
       "0        -3.072875        -3.234466        -3.168636        -3.407407   \n",
       "1        -0.348145        -0.246711        -0.269033        -0.274402   \n",
       "2        -0.949767        -0.888107        -1.012365        -0.954982   \n",
       "3        -0.149493        -0.129507        -0.117934        -0.159088   \n",
       "4        -1.212316        -1.695469        -1.500328        -1.796945   \n",
       "\n",
       "   df_3_689_662625  3_686_597315  df_3_688_181414  df_3_684_772806    target  \n",
       "0        -2.427491     -2.464909        -2.147446        -2.324886 -2.534741  \n",
       "1        -0.254059     -0.346143        -0.292596        -0.329951 -0.333404  \n",
       "2        -0.367841     -0.865569        -0.912420        -0.856364 -0.890216  \n",
       "3        -0.089059     -0.095905        -0.077765        -0.093318 -0.114255  \n",
       "4        -1.337903     -1.497219        -1.832296        -1.446076 -1.501269  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_base.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base1 = df_base[['card_id', 'target']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((123623, 2), (123623, 2))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_base1.shape, df_3_684_088273.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.315821326743784"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truncnorm.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -2.122841\n",
       "1   -0.286171\n",
       "2   -0.889205\n",
       "3   -0.060729\n",
       "4   -1.781597\n",
       "Name: df_3_687_597929, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3_687_597929['df_3_687_597929'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123623"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_3_684_088273['df_3_684_088273'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123623"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_3_687_597929['df_3_687_597929'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (49494,) (123623,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-61a91e699bd7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtruncnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_3_684_088273\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'df_3_684_088273'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_3_687_597929\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'df_3_687_597929'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m   1215\u001b[0m         \"\"\"\n\u001b[1;32m   1216\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'moments'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'm'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\u001b[0m in \u001b[0;36mstats\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m   1011\u001b[0m                                               **{'moments': moments})\n\u001b[1;32m   1012\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m                 \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgoodargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mg1\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m                 \u001b[0mmu3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/_continuous_distns.py\u001b[0m in \u001b[0;36m_stats\u001b[0;34m(self, a, b)\u001b[0m\n\u001b[1;32m   6369\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnB\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnA\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6370\u001b[0m         \u001b[0mpA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpB\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_norm_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_norm_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6371\u001b[0;31m         \u001b[0mmu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpA\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpB\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0md\u001b[0m   \u001b[0;31m# correction sign\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6372\u001b[0m         \u001b[0mmu2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpA\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpB\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6373\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (49494,) (123623,) "
     ]
    }
   ],
   "source": [
    "truncnorm.mean(df_3_684_088273['df_3_684_088273'].values.tolist(), df_3_687_597929['df_3_687_597929'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chandrasekhar/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/_continuous_distns.py:6348: RuntimeWarning: invalid value encountered in log\n",
      "  self._logdelta = np.log(self._delta)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (37950,) (123623,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-6ba6f9663724>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_base3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtruncnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_3_684_088273\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'df_3_684_088273'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf_base1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m   1215\u001b[0m         \"\"\"\n\u001b[1;32m   1216\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'moments'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'm'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\u001b[0m in \u001b[0;36mstats\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m   1011\u001b[0m                                               **{'moments': moments})\n\u001b[1;32m   1012\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m                 \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgoodargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mg1\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m                 \u001b[0mmu3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/scipy/stats/_continuous_distns.py\u001b[0m in \u001b[0;36m_stats\u001b[0;34m(self, a, b)\u001b[0m\n\u001b[1;32m   6369\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnB\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnA\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6370\u001b[0m         \u001b[0mpA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpB\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_norm_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_norm_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6371\u001b[0;31m         \u001b[0mmu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpA\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpB\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0md\u001b[0m   \u001b[0;31m# correction sign\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6372\u001b[0m         \u001b[0mmu2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpA\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpB\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6373\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (37950,) (123623,) "
     ]
    }
   ],
   "source": [
    "df_base3['target'] = truncnorm.mean(df_3_684_088273['df_3_684_088273'],df_base1['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base[['card_id','target']].to_csv(\"blend3_truncnorm.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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