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'active_months_lag12_2.0', 'city_id_x_261', 'city_id_x_128', 'city_id_x_287', 'city_id_x_313', 'merchant_category_id_x_593', 'city_id_x_81', 'merchant_category_id_x_334', 'merchant_category_id_x_803', 'merchant_category_id_x_725', 'merchant_category_id_x_790', 'merchant_category_id_x_281', 'merchant_category_id_x_789', 'merchant_category_id_x_780', 'merchant_category_id_x_322', 'merchant_category_id_x_741', 'merchant_category_id_x_718', 'merchant_category_id_x_743', 'merchant_category_id_x_268', 'merchant_category_id_x_31', 'merchant_category_id_x_276', 'merchant_category_id_x_311', 'merchant_category_id_x_714', 'merchant_category_id_x_713', 'merchant_category_id_x_712', 'merchant_category_id_x_757', 'merchant_category_id_x_266', 'merchant_category_id_x_27', 'merchant_category_id_x_261', 'merchant_category_id_x_86', 'merchant_category_id_x_805', 'subsector_id_x_28', 'merchant_category_id_x_18', 'subsector_id_x_14', 'merchant_category_id_x_164', 'merchant_category_id_x_162', 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'city_id_x_306', 'merchant_category_id_x_471', 'merchant_category_id_x_580', 'merchant_category_id_x_583', 'merchant_category_id_x_587', 'city_id_x_197', 'merchant_category_id_x_59', 'merchant_category_id_x_469', 'merchant_category_id_x_464', 'city_id_x_309', 'city_id_x_92', 'merchant_category_id_x_599', 'city_id_x_310', 'city_id_x_185', 'merchant_category_id_x_601', 'city_id_x_315', 'merchant_category_id_x_603', 'city_id_x_178', 'city_id_x_175', 'city_id_x_203', 'city_id_x_293', 'city_id_x_290', 'merchant_category_id_x_579', 'merchant_category_id_x_526', 'merchant_category_id_x_523', 'city_id_x_237', 'merchant_category_id_x_530', 'merchant_category_id_x_52', 'merchant_category_id_x_514', 'city_id_x_265', 'merchant_category_id_x_513', 'city_id_x_222', 'city_id_x_273', 'merchant_category_id_x_504', 'merchant_category_id_x_542', 'merchant_category_id_x_551', 'merchant_category_id_x_498', 'merchant_category_id_x_49', 'merchant_category_id_x_482', 'city_id_x_207', 'city_id_x_316', 'city_id_x_319', 'merchant_category_id_x_457', 'city_id_x_129', 'merchant_category_id_x_662', 'merchant_category_id_x_664', 'city_id_x_122', 'city_id_x_83', 'city_id_x_84', 'merchant_category_id_x_67', 'city_id_x_86', 'city_id_x_114', 'merchant_category_id_x_351', 'city_id_x_112', 'merchant_category_id_x_348', 'city_id_x_89', 'merchant_category_id_x_345', 'merchant_category_id_x_343', 'merchant_category_id_x_685', 'city_id_x_105', 'city_id_x_104', 'merchant_category_id_x_653', 'merchant_category_id_x_115', 'merchant_category_id_x_448', 'merchant_category_id_x_358', 'merchant_category_id_x_436', 'merchant_category_id_x_421', 'merchant_category_id_x_401', 'city_id_x_44', 'merchant_category_id_x_628', 'city_id_x_49', 'merchant_category_id_x_393', 'merchant_category_id_x_634', 'city_id_x_145', 'merchant_category_id_x_381', 'city_id_x_65', 'merchant_category_id_x_37', 'city_id_x_68', 'city_id_x_139', 'merchant_category_id_x_648', 'merchant_category_id_x_36', 'merchant_category_id_x_652', 'active_months_lag12_1.0']\nAll_featrures = ['hist_month_diff_mean', 'hist_purchase_date_max', 'new_hist_purchase_amount_max', 'new_hist_purchase_date_max', 'authorized_flag_N', 'new_hist_purchase_amount_var', 'new_hist_purchase_date_diff', 'hist_purchase_date_min', 'new_hist_purchase_amount_mean', 'hist_month_lag_mean', 'new_hist_month_lag_mean', 'new_hist_purchase_date_min', 'new_hist_purchase_date_uptonow', 'hist_first_buy', 'month_lag_-4', 'new_hist_purchase_date_average', 'month_lag_0', 'hist_month_lag_skew', 'hist_month_lag_var', 'hist_purchase_date_average', 'hist_purchase_amount_median', 'merchant_category_id_x_755', 'new_hist_first_buy', 'subsector_id_x_8', 'hist_purchase_amount_max', 'new_hist_purchase_amount_median', 'hist_month_nunique', 'new_hist_purchase_amount_sum', 'month_lag_-5', 'month_lag_-6', 'hist_weekend_mean', 'hist_purchase_date_diff', 'month_lag_-1', 'new_hist_purchase_amount_min', 'hist_purchase_amount_mean', 'avg_purchases_lag3_min', 'category_4_N', 'category_1_y_Y', 'hist_purchase_amount_skew', 'new_hist_category_1_mean', 'category_3_B', 'purchase_amount_skew', 'hist_category_1_mean', 'category_1_x_Y', 'new_hist_month_diff_mean', 'month_lag_1', 'month_lag_-2', 'hist_installments_sum', 'month_lag_-3', 'category_4_Y', 'subsector_id_x_37', 'hist_installments_var', 'purchase_amount_median', 'hist_installments_mean', 'numerical_2_skew', 'month_lag_-8', 'hist_merchant_id_nunique', 'new_hist_purchase_amount_skew', 'category_3_C', 'most_recent_purchases_range_B', 'month_lag_-7', 'merchant_category_id_x_661', 'hist_category_1_sum', 'subsector_id_x_7', 'avg_purchases_lag12_median', 'numerical_1_skew', 'hist_category_3_mean_mean', 'avg_purchases_lag6_min', 'most_recent_sales_range_C', 'avg_purchases_lag3_median', 'merchant_category_id_x_705', 'category_3_A', 'most_recent_sales_range_B', 'category_2_y_1.0', 'hist_purchase_amount_var', 'hist_purchase_amount_min', 'hist_installments_skew', 'purchase_amount_min', 'subsector_id_x_29', 'avg_purchases_lag6_median', 'category_2_x_1.0', 'avg_sales_lag3_min', 'most_recent_purchases_range_D', 'installments_3', 'merchant_category_id_x_879', 'merchant_category_id_x_278', 'avg_sales_lag12_min', 'installments_2', 'most_recent_sales_range_D', 'subsector_id_x_34', 'state_id_x_9', 'most_recent_purchases_range_C', 'avg_purchases_lag3_skew', 'subsector_id_x_27', 'avg_sales_lag12_skew', 'hist_weekofyear_nunique', 'city_id_x_69', 'merchant_category_id_x_511', 'avg_sales_lag3_skew', 'month_lag_-9', 'new_hist_month_lag_var', 'avg_sales_lag12_median', 'most_recent_purchases_range_A', 'avg_sales_lag6_min', 'most_recent_sales_range_A', 'state_id_x_-1', 'purchase_amount_mean', 'avg_sales_lag3_mean', 'numerical_2_mean', 'purchase_amount_std', 'purchase_amount_total', 'avg_sales_lag6_median', 'merchant_category_id_x_307', 'weekofyear', 'hist_purchase_amount_sum', 'new_hist_category_1_sum', 'most_recent_purchases_range_E', 'hist_category_2_mean_mean', 'elapsed_time', 'avg_sales_lag3_std', 'numerical_1_std', 'category_1_x_N', 'avg_purchases_lag6_skew', 'purchase_amount_max', 'avg_purchases_lag12_min', 'avg_purchases_lag3_mean', 'avg_sales_lag6_skew', 'category_1_y_N', 'hist_merchant_category_id_nunique', 'avg_purchases_lag3_std', 'most_recent_sales_range_E', 'merchant_category_id_x_80', 'avg_purchases_lag12_skew', 'avg_sales_lag12_mean', 'new_hist_month_lag_skew', 'subsector_id_x_19', 'subsector_id_x_33', 'hist_purchase_date_uptonow', 'new_hist_category_3_mean_mean', 'new_hist_installments_var', 'merchant_category_id_x_451', 'avg_sales_lag3_median', 'merchant_category_id_x_367', 'city_id_x_-1', 'subsector_id_x_22', 'month_lag_-10', 'numerical_1_median', 'numerical_1_mean', 'merchant_category_id_x_560', 'month_lag_2', 'month_lag_-11', 'numerical_1_max', 'active_months_lag12_7.0', 'avg_sales_lag6_mean', 'merchant_category_id_x_574', 'new_hist_installments_skew', 'city_id_x_158', 'subsector_id_x_21', 'month', 'avg_purchases_lag12_mean', 'merchant_category_id_x_683', 'merchant_category_id_x_839', 'subsector_id_x_1', 'numerical_2_median', 'subsector_id_x_16', 'avg_purchases_lag6_std', 'feature_1_x', 'hist_weekend_sum', 'avg_sales_lag12_std', 'hist_subsector_id_nunique', 'new_hist_weekend_mean', 'merchant_category_id_x_437', 'authorized_flag_Y', 'state_id_x_15', 'installments_4', 'merchant_category_id_x_884', 'state_id_x_16', 'avg_purchases_lag6_mean', 'merchant_category_id_x_108', 'hist_month_lag_median', 'new_hist_installments_mean', 'avg_sales_lag6_std', 'new_hist_merchant_category_id_nunique', 'merchant_category_id_x_690', 'hist_hour_nunique', 'city_id_x_283', 'city_id_x_19', 'subsector_id_x_18', 'category_2_y_4.0', 'merchant_category_id_x_68', 'merchant_category_id_x_302', 'subsector_id_x_25', 'merchant_category_id_x_422', 'category_2_x_5.0', 'card_id_total', 'subsector_id_x_20', 'dayofweek', 'category_2_x_3.0', 'merchant_category_id_x_506', 'avg_purchases_lag12_std', 'installments_6', 'merchant_category_id_x_360', 'merchant_category_id_x_178', 'new_hist_installments_sum', 'month_lag_-12', 'merchant_category_id_x_414', 'installments_12', 'category_2_x_4.0', 'subsector_id_x_36', 'new_hist_category_2_mean_mean', 'installments_5', 'merchant_category_id_x_842', 'city_id_x_233', 'numerical_2_std', 'city_id_x_213', 'merchant_category_id_x_222', 'state_id_x_22', 'category_2_x_2.0', 'category_2_y_2.0', 'installments_10', 'city_id_x_138', 'state_id_x_20', 'new_hist_weekofyear_nunique', 'state_id_x_12', 'new_hist_subsector_id_nunique', 'merchant_category_id_x_210', 'category_2_y_3.0', 'merchant_category_id_x_87', 'city_id_x_330', 'city_id_x_149', 'merchant_category_id_x_557', 'subsector_id_x_17', 'new_hist_installments_min', 'avg_sales_lag12_max', 'merchant_category_id_x_454', 'city_id_x_331', 'merchant_category_id_x_45', 'category_2_y_5.0', 'city_id_x_53', 'city_id_x_88', 'new_hist_hour_nunique', 'subsector_id_x_31', 'merchant_category_id_x_783', 'city_id_x_286', 'avg_purchases_lag6_max', 'new_hist_month_lag_max', 'state_id_x_5', 'merchant_category_id_x_111', 'merchant_category_id_x_836', 'avg_purchases_lag3_max', 'city_id_x_333', 'city_id_x_212', 'new_hist_merchant_id_nunique', 'merchant_category_id_x_195', 'city_id_x_3', 'avg_sales_lag3_max', 'state_id_x_24', 'merchant_category_id_x_771', 'city_id_x_116', 'feature_2_x', 'state_id_x_10', 'merchant_category_id_x_734', 'merchant_category_id_x_792', 'hist_card_id_size', 'merchant_category_id_x_90', 'city_id_x_79', 'city_id_x_308', 'avg_purchases_lag12_max', 'city_id_x_110', 'hist_installments_max', 'merchant_category_id_x_273', 'avg_sales_lag6_max', 'merchant_category_id_x_665', 'merchant_category_id_x_309', 'merchant_category_id_x_217', 'state_id_x_19', 'merchant_category_id_x_497', 'city_id_x_322', 'hist_month_diff_median', 'merchant_category_id_x_650', 'state_id_x_4', 'state_id_x_21', 'subsector_id_x_3', 'city_id_x_91', 'merchant_category_id_x_19', 'merchant_category_id_x_373', 'city_id_x_107', 'city_id_x_291', 'city_id_x_126', 'merchant_category_id_x_398', 'state_id_x_14', 'city_id_x_334', 'active_months_lag12_12.0', 'city_id_x_87', 'new_hist_installments_max', 'state_id_x_2', 'merchant_category_id_x_332', 'city_id_x_137', 'new_hist_year_nunique', 'active_months_lag3_3.0', 'installments_-1', 'merchant_category_id_x_834', 'subsector_id_x_32', 'merchant_category_id_x_427', 'merchant_category_id_x_818', 'city_id_x_96', 'new_hist_dayofweek_nunique', 'merchant_category_id_x_419', 'merchant_category_id_x_434', 'state_id_x_3', 'city_id_x_143', 'subsector_id_x_41', 'numerical_2_max', 'city_id_x_266', 'city_id_x_195', 'subsector_id_x_10', 'city_id_x_277', 'city_id_x_267', 'city_id_x_244', 'hist_month_lag_min', 'state_id_x_11', 'merchant_category_id_x_656', 'merchant_category_id_x_299', 'active_months_lag12_10.0', 'subsector_id_x_4', 'city_id_x_60', 'hist_month_lag_max', 'subsector_id_x_2', 'new_hist_weekend_sum', 'merchant_category_id_x_317', 'city_id_x_20', 'city_id_x_8', 'new_hist_installments_median', 'state_id_x_7', 'city_id_x_274', 'city_id_x_102', 'merchant_category_id_x_607', 'city_id_x_337', 'installments_1', 'merchant_category_id_x_779', 'month_lag_-13', 'merchant_category_id_x_544', 'merchant_category_id_x_518', 'subsector_id_x_38', 'hist_installments_min', 'city_id_x_188', 'new_hist_card_id_size', 'subsector_id_x_35', 'merchant_category_id_x_840', 'merchant_category_id_x_356', 'state_id_x_1', 'city_id_x_57', 'merchant_category_id_x_706', 'state_id_x_13', 'city_id_x_21', 'merchant_category_id_x_561', 'merchant_category_id_x_130', 'merchant_category_id_x_290', 'city_id_x_340', 'merchant_category_id_x_387', 'subsector_id_x_12', 'city_id_x_124', 'city_id_x_318', 'city_id_x_1', 'city_id_x_325', 'city_id_x_216', 'feature_3_x', 'city_id_x_329', 'merchant_category_id_x_184', 'merchant_category_id_x_550', 'merchant_category_id_x_298', 'merchant_category_id_x_528', 'merchant_category_id_x_63', 'merchant_category_id_x_2', 'merchant_category_id_x_206', 'city_id_x_38', 'city_id_x_168', 'city_id_x_4', 'city_id_x_253', 'city_id_x_117', 'city_id_x_296', 'merchant_category_id_x_598', 'city_id_x_179', 'merchant_category_id_x_411', 'installments_7', 'city_id_x_344', 'city_id_x_56', 'feature_2_y', 'city_id_x_242', 'state_id_x_8', 'merchant_category_id_x_34', 'hist_installments_median', 'city_id_x_52', 'merchant_category_id_x_813', 'merchant_category_id_x_489', 'state_id_x_18', 'merchant_category_id_x_415', 'feature_1_y', 'state_id_x_23', 'city_id_x_223', 'merchant_category_id_x_793', 'merchant_category_id_x_769', 'city_id_x_182', 'city_id_x_299', 'city_id_x_347', 'installments_8', 'merchant_category_id_x_369', 'city_id_x_163', 'city_id_x_14', 'city_id_x_90', 'merchant_category_id_x_667', 'city_id_x_231', 'city_id_x_12', 'subsector_id_x_26', 'city_id_x_171', 'city_id_x_170', 'merchant_category_id_x_529', 'city_id_x_47', 'merchant_category_id_x_38', 'merchant_category_id_x_-1', 'active_months_lag12_9.0', 'merchant_category_id_x_763', 'merchant_category_id_x_40', 'subsector_id_x_9', 'city_id_x_289', 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'city_id_x_129', 'active_months_lag12_2.0', 'merchant_category_id_x_140', 'city_id_x_178', 'merchant_category_id_x_662', 'merchant_category_id_x_652', 'merchant_category_id_x_648', 'merchant_category_id_x_634', 'merchant_category_id_x_628', 'merchant_category_id_x_603', 'merchant_category_id_x_601', 'merchant_category_id_x_599', 'merchant_category_id_x_59', 'merchant_category_id_x_587', 'merchant_category_id_x_583', 'merchant_category_id_x_580', 'merchant_category_id_x_579', 'merchant_category_id_x_551', 'merchant_category_id_x_542', 'merchant_category_id_x_530', 'merchant_category_id_x_526', 'merchant_category_id_x_523', 'merchant_category_id_x_52', 'merchant_category_id_x_513', 'merchant_category_id_x_509', 'merchant_category_id_x_49', 'merchant_category_id_x_482', 'merchant_category_id_x_464', 'merchant_category_id_x_457', 'merchant_category_id_x_448', 'merchant_category_id_x_653', 'merchant_category_id_x_664', 'merchant_category_id_x_421', 'merchant_category_id_x_67', 'subsector_id_x_28', 'merchant_category_id_x_889', 'merchant_category_id_x_882', 'merchant_category_id_x_878', 'merchant_category_id_x_875', 'merchant_category_id_x_86', 'merchant_category_id_x_847', 'merchant_category_id_x_83', 'merchant_category_id_x_817', 'merchant_category_id_x_814', 'merchant_category_id_x_805', 'merchant_category_id_x_803', 'merchant_category_id_x_790', 'merchant_category_id_x_789', 'merchant_category_id_x_780', 'merchant_category_id_x_757', 'merchant_category_id_x_743', 'merchant_category_id_x_741', 'merchant_category_id_x_725', 'merchant_category_id_x_718', 'merchant_category_id_x_714', 'merchant_category_id_x_712', 'merchant_category_id_x_687', 'merchant_category_id_x_686', 'merchant_category_id_x_676', 'merchant_category_id_x_436', 'merchant_category_id_x_401', 'city_id_x_185', 'merchant_category_id_x_164', 'merchant_category_id_x_153', 'merchant_category_id_x_145', 'merchant_category_id_x_141', 'merchant_category_id_x_131', 'merchant_category_id_x_11', 'merchant_category_id_x_109', 'merchant_category_id_x_101', 'installments_999', 'city_id_x_92', 'city_id_x_89', 'city_id_x_84', 'city_id_x_65', 'city_id_x_321', 'city_id_x_319', 'city_id_x_316', 'city_id_x_309', 'city_id_x_306', 'city_id_x_28', 'city_id_x_273', 'city_id_x_255', 'city_id_x_237', 'city_id_x_222', 'city_id_x_215', 'city_id_x_207', 'city_id_x_197', 'merchant_category_id_x_162', 'merchant_category_id_x_18', 'merchant_category_id_x_393', 'merchant_category_id_x_183', 'merchant_category_id_x_381', 'merchant_category_id_x_37', 'merchant_category_id_x_358', 'merchant_category_id_x_355', 'merchant_category_id_x_345', 'merchant_category_id_x_343', 'merchant_category_id_x_334', 'merchant_category_id_x_322', 'merchant_category_id_x_311', 'merchant_category_id_x_31', 'merchant_category_id_x_281', 'merchant_category_id_x_27', 'merchant_category_id_x_268', 'merchant_category_id_x_266', 'merchant_category_id_x_261', 'merchant_category_id_x_260', 'merchant_category_id_x_249', 'merchant_category_id_x_245', 'merchant_category_id_x_226', 'merchant_category_id_x_223', 'merchant_category_id_x_220', 'merchant_category_id_x_201', 'merchant_category_id_x_192', 'merchant_category_id_x_190', 'merchant_category_id_x_187', 'active_months_lag12_1.0']\nAdditional_features = ['hist_category_1_median', 'hist_month_diff_median', 'hist_installments_skew', 'hist_purchase_amount_median', 'new_hist_month_lag_skew', 'new_hist_installments_median', 'new_hist_weekend_median', 'new_hist_month_lag_median', 'new_hist_purchase_amount_median', 'new_hist_category_1_median', 'hist_purchase_amount_skew', 'new_hist_month_diff_median', 'hist_installments_median', 'hist_month_lag_median', 'new_hist_purchase_amount_skew', 'hist_weekend_median', 'new_hist_installments_skew', 'hist_month_lag_skew']\nextra_columns_gaberial = ['new_hist_category_2_mean', 'hist_authorized_flag_max', 'hist_month_min', 'hist_category_2_max', 'new_hist_category_3_min', 'new_hist_weekend_min', 'new_hist_month_max', 'new_hist_month_diff_var', 'hist_month_median', 'hist_purchase_date_uptomin', 'new_hist_category_2_max', 'hist_weekend_max', 'new_hist_category_2_sum', 'new_hist_dayofweek_min', 'hist_dayofweek_mean', 'hist_year_mean', 'new_hist_weekofyear_mean', 'new_hist_hour_mean', 'hist_hour_max', 'hist_month_max', 'hist_category_3_sum', 'new_hist_year_min', 'new_hist_year_median', 'new_hist_last_buy', 'hist_category_1_min', 'hist_category_1_max', 'new_hist_month_diff_min', 'hist_category_3_max', 'hist_hour_mean', 'hist_dayofweek_median', 'hist_weekofyear_mean', 'new_hist_month_min', 'new_hist_weekofyear_max', 'hist_month_diff_max', 'new_hist_category_2_min', 'new_hist_category_1_min', 'new_hist_hour_min', 'hist_dayofweek_max', 'feature_3', 'hist_category_3_min', 'new_hist_month_median', 'hist_category_2_sum', 'hist_hour_median', 'hist_last_buy', 'new_hist_weekend_max', 'card_id', 'new_hist_category_1_max', 'hist_category_3_mean',  'new_hist_year_max', 'new_hist_category_3_max', 'hist_authorized_flag_min', 'is_month_start', 'new_hist_card_id_count', 'hist_weekofyear_median', 'feature_1', 'new_hist_dayofweek_mean', 'hist_year_median', 'hist_category_2_mean', 'hist_month_diff_var', 'new_hist_category_3_sum', 'new_hist_month_diff_max', 'hist_year_max', 'new_hist_weekofyear_median', 'hist_category_2_min', 'hist_hour_min', 'quarter', 'dayofyear', 'feature_2', 'hist_year_min', 'new_hist_year_mean', 'new_hist_category_3_mean', 'hist_month_mean', 'new_hist_hour_max', 'hist_weekend_min', 'hist_month_diff_min', 'new_hist_purchase_date_uptomin', 'new_hist_weekofyear_min', 'hist_weekofyear_max', 'new_hist_dayofweek_max', 'new_hist_hour_median', 'new_hist_month_mean', 'hist_card_id_count', 'new_hist_dayofweek_median', 'hist_authorized_flag_sum', 'hist_authorized_flag_mean', 'hist_dayofweek_min', 'hist_weekofyear_min']\nAll_feature_new_extra = ['new_hist_purchase_amount_max', 'hist_month_diff_mean', 'hist_purchase_date_max', 'new_hist_weekofyear_mean', 'hist_authorized_flag_mean_x', 'new_hist_purchase_date_max', 'new_hist_purchase_amount_var', 'new_hist_purchase_date_diff', 'new_hist_purchase_amount_median', 'new_hist_month_lag_mean', 'hist_month_nunique', 'authorized_flag_N', 'new_hist_month_mean', 'new_hist_weekofyear_max', 'month_lag_0', 'subsector_id_x_8', 'hist_purchase_date_average', 'merchant_category_id_x_755', 'new_hist_purchase_date_average', 'hist_purchase_amount_mean', 'new_hist_purchase_date_uptonow', 'hist_purchase_amount_median', 'new_hist_category_1_mean', 'hist_month_lag_mean', 'hist_purchase_date_min', 'category_1_x_Y', 'hist_first_buy', 'new_hist_weekofyear_min', 'hist_last_buy', 'month_lag_1', 'month_lag_-5', 'hist_purchase_amount_var', 'hist_month_lag_var', 'new_hist_purchase_amount_min', 'hist_month_diff_var', 'month_lag_-6', 'hist_category_1_mean', 'hist_month_lag_skew', 'purchase_amount_skew', 'new_hist_weekofyear_median', 'hist_hour_mean', 'hist_dayofweek_mean', 'category_1_y_Y', 'month_lag_-1', 'avg_purchases_lag3_min', 'new_hist_purchase_date_uptomin', 'hist_purchase_date_diff', 'merchant_category_id_x_661', 'hist_installments_mean', 'subsector_id_x_37', 'new_hist_hour_median', 'month_lag_-3', 'hist_purchase_amount_skew', 'hist_installments_var', 'category_3_C', 'hist_year_mean', 'hist_category_1_sum', 'category_4_Y', 'subsector_id_x_7', 'hist_installments_sum', 'hist_merchant_id_nunique', 'new_hist_category_1_sum', 'month_lag_-2', 'new_hist_last_buy', 'hist_category_3_sum', 'purchase_amount_median', 'month_lag_-8', 'new_hist_first_buy', 'hist_weekend_mean', 'purchase_amount_min', 'avg_sales_lag12_min', 'most_recent_sales_range_C', 'month_lag_-7', 'purchase_amount_std', 'numerical_2_skew', 'installments_3', 'subsector_id_x_33', 'most_recent_purchases_range_B', 'merchant_category_id_x_511', 'installments_0', 'hist_weekofyear_nunique', 'installments_2', 'subsector_id_x_29', 'subsector_id_x_34', 'avg_purchases_lag6_min', 'new_hist_purchase_amount_skew', 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'merchant_category_id_x_676', 'active_months_lag12_2.0', 'merchant_category_id_x_183', 'merchant_category_id_x_140', 'new_hist_category_3_max', 'merchant_category_id_x_882', 'city_id_x_65', 'subsector_id_x_28', 'city_id_x_112', 'merchant_category_id_x_67', 'merchant_category_id_x_145', 'merchant_category_id_x_18', 'merchant_category_id_x_141', 'city_id_x_84', 'new_hist_weekend_min', 'merchant_category_id_x_817', 'merchant_category_id_x_814', 'merchant_category_id_x_83', 'new_hist_year_min', 'merchant_category_id_x_805', 'merchant_category_id_x_803', 'city_id_x_77', 'new_hist_weekend_max', 'merchant_category_id_x_847', 'city_id_x_83', 'merchant_category_id_x_153', 'merchant_category_id_x_741', 'merchant_category_id_x_86', 'merchant_category_id_x_725', 'merchant_category_id_x_718', 'merchant_category_id_x_714', 'merchant_category_id_x_875', 'active_months_lag6_1.0', 'merchant_category_id_x_712', 'merchant_category_id_x_164', 'active_months_lag3_1.0', 'merchant_category_id_x_878', 'hist_category_3_max', 'merchant_category_id_x_187', 'city_id_x_165', 'merchant_category_id_x_664', 'merchant_category_id_x_27', 'hist_year_median', 'merchant_category_id_x_311', 'merchant_category_id_x_31', 'hist_year_max', 'hist_weekend_min', 'merchant_category_id_x_464', 'city_id_x_237', 'merchant_category_id_x_281', 'merchant_category_id_x_482', 'merchant_category_id_x_49', 'merchant_category_id_x_662', 'city_id_x_222', 'city_id_x_220', 'merchant_category_id_x_268', 'merchant_category_id_x_498', 'city_id_x_92', 'merchant_category_id_x_266', 'merchant_category_id_x_509', 'merchant_category_id_x_513', 'city_id_x_207', 'merchant_category_id_x_457', 'merchant_category_id_x_448', 'hist_year_min', 'merchant_category_id_x_436', 'merchant_category_id_x_343', 'merchant_category_id_x_322', 'city_id_x_306', 'merchant_category_id_x_345', 'merchant_category_id_x_351', 'city_id_x_309', 'merchant_category_id_x_355', 'merchant_category_id_x_358', 'city_id_x_313', 'merchant_category_id_x_37', 'city_id_x_28', 'merchant_category_id_x_381', 'city_id_x_315', 'merchant_category_id_x_393', 'city_id_x_316', 'city_id_x_273', 'merchant_category_id_x_401', 'city_id_x_319', 'merchant_category_id_x_421', 'city_id_x_203', 'merchant_category_id_x_52', 'merchant_category_id_x_523', 'merchant_category_id_x_599', 'merchant_category_id_x_603', 'merchant_category_id_x_220', 'city_id_x_49', 'city_id_x_145', 'hist_authorized_flag_max', 'merchant_category_id_x_628', 'merchant_category_id_x_634', 'merchant_category_id_x_648', 'merchant_category_id_x_201', 'merchant_category_id_x_192', 'merchant_category_id_x_190', 'city_id_x_58', 'city_id_x_129', 'city_id_x_128', 'hist_weekend_max', 'merchant_category_id_x_652', 'merchant_category_id_x_653', 'hist_authorized_flag_min', 'city_id_x_122', 'merchant_category_id_x_601', 'merchant_category_id_x_223', 'merchant_category_id_x_526', 'merchant_category_id_x_59', 'merchant_category_id_x_530', 'city_id_x_197', 'merchant_category_id_x_261', 'merchant_category_id_x_536', 'merchant_category_id_x_542', 'merchant_category_id_x_551', 'city_id_x_186', 'city_id_x_185', 'merchant_category_id_x_260', 'merchant_category_id_x_249', 'merchant_category_id_x_579', 'city_id_x_178', 'city_id_x_175', 'merchant_category_id_x_245', 'merchant_category_id_x_580', 'merchant_category_id_x_583', 'merchant_category_id_x_226', 'merchant_category_id_x_587', 'merchant_category_id_x_334', 'city_id_x_215']","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport datetime\nimport gc\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport lightgbm as lgb\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import mean_squared_error\nfrom scipy.stats import ks_2samp\nfrom tqdm import tqdm\nimport os\nprint(os.listdir(\"../input\"))\n\nprint(len(All_featrures_old))\nprint(len(All_featrures))\nprint(len(Additional_features))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"bdeb81f67b730faf628d937d420782b4a782ec86"},"cell_type":"code","source":"df_train_f = pd.read_csv(\"../input/elo-first-cut-data/train.csv\")\ndf_test_f = pd.read_csv(\"../input/elo-first-cut-data/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"4d262a86240e52cee66a9d935df1dad2c67e9f5e"},"cell_type":"code","source":"df_train_extra = pd.read_csv(\"../input/generating-feature/train_new_extra.csv\", usecols = extra_columns_gaberial)\ndf_test_extra = pd.read_csv(\"../input/generating-feature/test_new_extra.csv\", usecols = extra_columns_gaberial)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"28799360fbd8f99e9216b904d76dcffb22a793a8"},"cell_type":"code","source":"df_train = pd.merge(df_train_f, df_train_extra, on ='card_id')\ndf_test = pd.merge(df_test_f, df_test_extra, on ='card_id')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5cfe9890acb8e51833cbfccf8dbfb58a987a04e9"},"cell_type":"code","source":"len(df_train.columns)\nlen(df_test.columns)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"a28ebaa5276c0b83034913dde3f0902ab009f3ad"},"cell_type":"code","source":"df_train.to_pickle(\"train_old_features.pkl\")\ndf_test.to_pickle(\"test_old_features.pkl\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"028f80a7471508e32d0d7be45b30c5877e9771bb"},"cell_type":"code","source":"#'target' in list(df_train.columns)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"89169879056503eed463b25025b52f07acb7a180"},"cell_type":"code","source":"df_train_columns = All_feature_new_extra[0:100]\n\n#[c for c in df_train.columns if c not in ['card_id', 'first_active_month','target','outliers']]\n\n##All_featrures[0:105]# \n#=============================== Comparing distributions ===========================================\n\nlist_p_value =[]\n\nfor i in tqdm(df_train_columns):\n    list_p_value.append(ks_2samp(df_test[i] , df_train[i])[1])\n\nSe = pd.Series(list_p_value, index = df_train_columns).sort_values() \nlist_discarded = list(Se[Se < .1].index)\n\n#==============================\n\ndf_train_columns = list(set(df_train_columns) - set(list_discarded))\n\ntarget = df_train['target']\ndel df_train['target'] #, df_train['target_y']\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"98e7bb99d3782b6ef13cced4f6398090f0fa21dd"},"cell_type":"code","source":"\nparam = {'num_leaves':60,\n         'min_data_in_leaf': 30, \n         'objective':'regression',\n         'max_depth': 9,\n         'learning_rate': 0.5,\n         \"min_child_samples\": 20,\n         \"boosting\": \"gbdt\",\n         \"feature_fraction\": 0.9,\n         \"bagging_freq\": 1,\n         \"bagging_fraction\": 0.9 ,\n         \"bagging_seed\": 11,\n         \"metric\": 'rmse',\n         \"lambda_l1\": 0.1,\n         \"verbosity\": -1,\n         \"nthread\": 4,\n         \"random_state\": 4590,\n        \"max_bin\": 3000}\n\n\nfor i in range(1):\n    folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=4590)\n    oof = np.zeros(len(df_train))\n    predictions = np.zeros(len(df_test))\n    feature_importance_df = pd.DataFrame()\n\nlist_regressor = []\n#list_rmse = []\n\nfor fold_, (trn_idx, val_idx) in enumerate(folds.split(df_train,df_train['outliers'].values)):\n    print(\"fold {}\".format(fold_))\n    trn_data = lgb.Dataset(df_train.iloc[trn_idx][df_train_columns], label=target.iloc[trn_idx])#, categorical_feature=categorical_feats)\n    val_data = lgb.Dataset(df_train.iloc[val_idx][df_train_columns], label=target.iloc[val_idx])#, categorical_feature=categorical_feats)\n\n    num_round = 50000\n    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=100, early_stopping_rounds = 1000)\n    oof[val_idx] = clf.predict(df_train.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(df_test[df_train_columns], num_iteration=clf.best_iteration) / folds.n_splits\n\n    list_regressor.append(clf)\n\nprint(np.sqrt(mean_squared_error(oof, target)))\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3604858bf3aeb243971383ace2059fac0eba448e"},"cell_type":"code","source":"cols = (feature_importance_df[[\"Feature\", \"importance\"]]\n        .groupby(\"Feature\")\n        .mean()\n        .sort_values(by=\"importance\", ascending=False)[:1000].index)\n\nbest_features = feature_importance_df.loc[feature_importance_df.Feature.isin(cols)]\n\nplt.figure(figsize=(20,40))\nsns.barplot(x=\"importance\",\n            y=\"Feature\",\n            data=best_features.sort_values(by=\"importance\",\n                                           ascending=False))\nplt.title('LightGBM Features (avg over folds)')\nplt.tight_layout()\nplt.show()\nplt.savefig('lgbm_importances.png')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"fcb1b859e348fe26e5db213f781b10d88b5f0ae4"},"cell_type":"code","source":"fip = best_features.sort_values(by=\"importance\",ascending=False)\nplt.figure(figsize=(20,40))\nfip.groupby('Feature')['importance'].mean().sort_values(ascending=True).plot('barh')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"8f723b19840c30c81c5e66b4c4da17047ae4d944"},"cell_type":"code","source":"Se_imp = fip.groupby('Feature')['importance'].mean().sort_values(ascending=False)\nprint(list(Se_imp.index))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"793a17c876312b4a3ec0cc1347053bca2783b364"},"cell_type":"code","source":"for i in range(1):\n    sub_df = pd.DataFrame({\"card_id\":df_test[\"card_id\"].values})\n    sub_df[\"target\"] = predictions\n    sub_df.to_csv(\"submission.csv\", index=False)\n\n","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}