#all_no_test
import os
import time
import json
from collections import defaultdict
from functools import reduce
import pandas as pd
import numpy as np
import lightgbm as lgb
from lightgbm.callback import record_evaluation
from opt_utils import *

os.system('pip install neptune-client')
os.system('pip install neptune-lightgbm')
import neptune.new as neptune
from neptune.new.integrations.lightgbm import create_booster_summary
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
NEPTUNE_API_TOKEN = user_secrets.get_secret("NEPTUNE_API_TOKEN")

cfg = {
    "script_name": 'opt_train_v2_nep_op_85_enc',
    "path_features": '../input/generate-train-features-script-p10/p10_train.pkl', # Used in train mode
    "path_models": '',
    "path_data_raw": '../input/optiver-realized-volatility-prediction/',
    "neptune_project": 'chrisrichardmiles/optiver',
    "neptune_description": 'p10 features second run with enc. Notice but and will have to rerun with feature generation and training',
    "encode_time_cols":  [
        'real_vol_mean_decay_0.99_-1', 'real_vol_mean_decay_0.95_-1', 
        'real_vol_mean_decay_0.9_-1', 'real_vol_mean_decay_0.85_-1', 
        'ask_price1_real_vol', 'bid_price1_real_vol', 'spread_mean', 
        'size_momentum', 'size_real_vol', 'abs_log_return_max_sub_min', 
        'sum_bid_ask_real_vol', 'log_return_mean_decay', 'wap_last-first'
                        ],
    "encode_stock_cols": [],
    "encode_funcs": ['mean', 'std', 'max', 'min'], 
    "shake": False, 
    "shake_std": .5, 
    "prefix": '',
    "rerun": True,
    "drop_cols": ['row_id', 'stock_id', 'time_id', 'target'] + ['abs_log_return_std_450', 'wap_last_150', 'seconds_in_bucket_count_unique_350', 'wap_mean_decay_150', 'price_wap_diff_mean_decay_flip_450', 'is_pos_return_max_sub_min_150', 'is_pos_return_mean_decay_150', 'wap_mean_350', 'abs_log_return_mean_decay_350', 'is_neg_return_mean_decay_0', 'log_return_max_sub_min_450', 'size_amin_350', 'abs_log_return_mean_decay_flip_350', 'abs_log_return_sum_350', 'is_neg_return_sum_450', 'is_neg_return_std_450', 'abs_log_return_std_150', 'log_return_count_150', 'abs_log_return_mean_decay_flip_450', 'size_sum_150', 'wap_mean_decay_350', 'is_pos_return_sum_0', 'size_sum_350', 'is_pos_return_sum_350', 'is_neg_return_max_sub_min_450', 'seconds_in_bucket_count_unique_150', 'is_pos_return_max_sub_min_450', 'price_wap_diff_amax_0', 'wap_last_350', 'wap_mean_decay_flip_150', 'wap_momentum_350', 'wap_mean_150', 'wap_amin_350', 'is_pos_return_std_350', 'size_amax_350', 'log_return_realized_volatility_150', 'wap_mean_450', 'log_return_std_150', 'order_count_amax_150', 'log_return_mean_decay_350', 'log_return_count_0', 'abs_log_return_sum_150', 'wap_amin_450', 'is_neg_return_sum_350', 'wap_amax_450', 'log_return_realized_volatility_350', 'is_pos_return_max_sub_min_350', 'wap_first_350', 'wap_first_450', 'sum_ask_sub_sum_bid_scaled_mean_decay_450', 'log_return_count_450', 'wap_last_450', 'log_return_std_450', 'size_amax_450', 'is_pos_return_mean_decay_350', 'is_neg_return_mean_decay_350', 'real_vol_mean_decay_0.45_-1', 'real_vol_mean_decay_0.85_1', 'sum_ask_sub_sum_bid_vol_log_return_150', 'sum_ask_sub_sum_bid_scaled_mean_350', 'log_return_std_350', 'log_return_realized_volatility_450', 'wap_mean_decay_450', 'is_pos_return_mean_decay_0', 'price_wap_diff_momentum_150', 'size_amin_150', 'wap_mean_decay_0', 'is_neg_return_mean_decay_flip_450', 'is_pos_return_sum_150', 'is_neg_return_std_350', 'log_return_std_0', 'size_amin_0', 'log_return_count_350', 'abs_log_return_mean_decay_150', 'real_vol_mean_decay_0.65_1', 'is_neg_return_mean_decay_150', 'wap_mean_decay_flip_450', 'is_neg_return_sum_150', 'wap_amax_0', 'wap_amax_350', 'is_neg_return_sum_0', 'ask_size1_vol_log_return_450', 'sum_ask_sub_sum_bid_vol_log_return_350', 'is_pos_return_std_450', 'price_vol_log_return_450', 'seconds_in_bucket_count_unique_0', 'wap_mean_decay_flip_350', 'price_wap_diff_momentum_350', 'wap_momentum_450', 'wap_mean_0', 'is_pos_return_max_sub_min_0', 'real_vol_mean_decay_0.55_-1', 'sum_ask_sub_sum_bid_scaled_mean_450', 'real_vol_mean_decay_0.9_1', 'log_return_mean_decay_flip_450', 'sum_ask_sub_sum_bid_scaled_mean_decay_0', 'wap_amin_0', 'is_neg_return_max_sub_min_150', 'log_return_mean_decay_flip_350', 'sum_ask_vol_log_return_350', 'abs_log_return_mean_decay_450', 'wap_amax_150', 'log_return_realized_volatility_0', 'is_pos_return_mean_decay_flip_450', 'is_pos_return_std_150', 'is_pos_return_mean_decay_450', 'price_wap_diff_mean_decay_0', 'sum_ask_sub_sum_bid_scaled_mean_decay_150', 'is_neg_return_mean_decay_450', 'price_wap_diff_mean_decay_350', 'wap_amin_150', 'is_neg_return_max_sub_min_0', 'is_pos_return_sum_450', 'is_neg_return_max_sub_min_350', 'sum_ask_sub_sum_bid_scaled_mean_decay_350', 'dummy1', 'seconds_in_bucket_count_unique_450', 'abs_log_return_mean_decay_0', 'price_wap_diff_mean_decay_150', 'abs_log_return_sum_450', 'size_sum_450', 
                         'is_neg_return_std_150'], 
    "neptune_run_name": '',
    "lgb_params": {
        # https://lightgbm.readthedocs.io/en/latest/index.html
        "boosting_type": "gbdt",
        "objective": "rmse",
        "learning_rate": 0.05,
        "num_leaves": 255,
        "min_data_in_leaf": 255,
        "feature_fraction": 0.8,
        "bagging_fraction": .5, # Select bagging_fraction of rows every bagging_freq of iterations.
        "bagging_freq": 1,      # This speeds up training and underfits. Need both set to do anything.
        "n_estimators": 5000,
        "early_stopping_rounds": 50,
        "n_jobs": -1,
        "seed": 42,
        "verbose": -1, 
    },
}
with open('cfg.json', 'w') as f: 
    json.dump(cfg, f)

def rdf(train): 
    train['spe'] = ((train['target'] - train['pred']) / train['target']) ** 2
    return np.sqrt((train['spe'].sum()) / train.shape[0])

with open('../input/nepop76/cfg.json') as f: 
    cfg = json.load(f)

stocks = defaultdict(dict)

all_train = pd.read_pickle('../input/nepop76/enc_p8_train.pkl')
cols = [c for c in all_train.columns if not c.startswith('stock_id_')]
all_train = all_train[cols]
# all_train.columns.tolist()
all_preds = all_train[['stock_id', 'target']]
all_preds['pred'] = 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  

	stock_id	target	pred
0	0	0.004136	0
1	0	0.001445	0
2	0	0.002168	0
3	0	0.002195	0
4	0	0.001747	0
...	...	...	...
428927	126	0.003461	0
428928	126	0.003113	0
428929	126	0.004070	0
428930	126	0.003357	0
428931	126	0.002090	0

428932 rows × 3 columns

old_preds = all_train[['stock_id', 'target']]
old_preds['pred'] = np.load('../input/nepop76/oof_predictions.npy')

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  

sdf = pd.DataFrame(columns=['old', 'new'])

def main(stock_id): 
#     train = pd.read_pickle(cfg['path_features'])
#     train = encode_cols(train, cfg["encode_time_cols"], funcs=cfg['encode_funcs'], on='time_id')
    
    # Saving encoded stock columns
#     feat_file = 'enc_' + os.path.split(cfg['path_features'])[1]
#     tmp = encode_cols(train, cfg["encode_stock_cols"], funcs=cfg['encode_funcs'], on='stock_id')
#     tmp.to_pickle(os.path.join(cfg['path_models'], feat_file))
     
    train = all_train[all_train.stock_id == stock_id]
    drop_cols = [c for c in cfg['drop_cols'] if c in train.columns and c != 'stock_id' and c not in cfg['encode_stock_cols']]
    x = train.drop(drop_cols, axis = 1)
    y = train['target']
    
    oof_predictions = np.zeros(x.shape[0]) # Create out of folds array
    scores = [] # Keep track of scores for each fold and all oof at the end
    best_iterations = []
    training_best_scores = []
    valid_best_scores = [] # Same as scores in this script, but would be different with nested cv
    best_score_diffs = []
    dict_eval_logs = [] # For experimentation tracking
    booster_summaries = [] # For experimentation tracking
    dumb_features = []
    top_features = []
    
    for fold in range(5):
        trn_ind = x.fold != fold
        val_ind = x.fold == fold
        
        print(f'Training fold {fold}')
        x_train, x_val = x[trn_ind].drop('fold', axis=1), x[val_ind].drop('fold', axis=1)
        y_train, y_val = y[trn_ind], y[val_ind]
        
        x_train = encode_cols(x_train, 
                              cfg['encode_stock_cols'], 
                              funcs=cfg['encode_funcs'], 
                              shake=cfg['shake'], 
                              shake_std=cfg['shake_std']).drop('stock_id', axis=1)
        n_train_cols = x_train.shape[1]
        
        x_val = encode_cols(x_val, 
                            cfg['encode_stock_cols'], 
                            funcs=cfg['encode_funcs']).drop('stock_id', axis=1)
        
        train_weights = 1 / np.square(y_train) # Root mean squared percentage error weights
        val_weights = 1 / np.square(y_val)
        train_dataset = lgb.Dataset(x_train, y_train, weight=train_weights)
        val_dataset = lgb.Dataset(x_val, y_val, weight=val_weights, reference=train_dataset)
        
        dict_eval_log = {}
        model = lgb.train(params = cfg['lgb_params'], 
                          train_set = train_dataset, 
                          valid_sets = [val_dataset, train_dataset], 
                          valid_names = ['valid', 'train'], 
                          feval = feval_rmspe,
                          callbacks=[record_evaluation(dict_eval_log)],
                          verbose_eval = 50)
        
#         model.save_model(os.path.join(cfg['path_models'], f'{cfg["prefix"]}lgb_fold_{fold}.txt'))
        y_pred = model.predict(x_val)
        oof_predictions[val_ind] = y_pred
        scores.append(round(rmspe(y_val, y_pred), 3))
        
#         dumb_features.append(get_dumb_features(model))
#         top_features.append(get_top_features(model))
        
#         booster_summary = create_booster_summary(
#             booster=model,
#             log_importances=True,
#             max_num_features=25,
#             log_trees_as_dataframe=False, 
#             log_pickled_booster=True, 
#             y_true=y_val, 
#             y_pred=y_pred, 
#         )
#         train_score = model.best_score['train']['RMSPE']
#         valid_score = model.best_score['valid']['RMSPE']
#         best_iterations.append(model.best_iteration)
#         training_best_scores.append(round(train_score, 3))
#         valid_best_scores.append(round(valid_score, 3))
#         best_score_diffs.append(round(valid_score - train_score, 3))
        
#         booster_summaries.append(booster_summary)
#         dict_eval_logs.append(dict_eval_log)
#         del booster_summary, dict_eval_log
    
    
    rmspe_score = round(rmspe(y, oof_predictions), 3)
    old = rdf(old_preds[old_preds.stock_id == stock_id])
    sc = [old, rmspe_score]
    sdf.loc[stock_id] = sc
    all_preds.loc[all_preds.stock_id == stock_id, 'pred'] = oof_predictions
    print(f'Our out of folds RMSPE is {rmspe_score}, compared to {old}, giving gain {rmspe_score - old}')
    print(f'Our cv fold scores are {scores}')
#     np.save('oof_predictions', oof_predictions)
    
#     run = neptune.init(
#             project=cfg['neptune_project'],
#             api_token=NEPTUNE_API_TOKEN,
#             name=cfg['neptune_run_name'],    
#             description=cfg['neptune_description'],
#             tags=[cfg['path_features'], cfg['prefix']],
#             source_files=['cfg.json'],
#     )
#     run = stocks[stock_id]
# #     run['feat_id'] = feat_file
#     run['old_score'] = 
#     run['cfg'] = cfg
#     run['RMSPE'] = rmspe_score
#     run['RMSPE_oof_scores'] = scores
#     run['RMSPE_cv_std'] = np.std(scores)
    
#     run['best_iterations'] = best_iterations
#     best_iterations_mean = int(np.mean(best_iterations))
#     run['best_iterations_mean'] = best_iterations_mean
#     run['training_best_scores'] = training_best_scores
#     run['valid_best_scores'] = valid_best_scores
#     run['best_score_diffs'] = best_score_diffs
#     run['best_score_diffs_mean'] = round(np.mean(best_score_diffs), 3)
#     run['dumb_features'] = list(reduce(lambda a, b: set(a).intersection(set(b)), dumb_features))
#     run['top_features'] = list(reduce(lambda a, b: set(a).intersection(set(b)), top_features))
    
#     run[f'fold_{fold}'] = booster_summaries[fold]
#     run[f'dumb_features_{fold}'] = list(dumb_features[fold])
#     run[f'top_features_{fold}'] = list(top_features[fold])
    
    # Logs for each folds model
#     for fold in range(5):
#         run[f'lgbm_summaries/fold_{fold}'] = booster_summaries[fold]
#         run[f'lgbm_summaries/dumb_features_{fold}'] = list(dumb_features[fold])
#         run[f'lgbm_summaries/top_features_{fold}'] = list(top_features[fold])
#         dict_eval_log = dict_eval_logs[fold]
#         for valid_set, odict in dict_eval_log.items():
#             for metric, log in odict.items():
#                 for val in log:
#                     run[f'eval_logs/{fold}_{valid_set}_{metric}'].log(val)
#     run.stop()
    
#     if cfg['rerun']: 
#         print(f'retraining model with all data for {best_iterations} iterations')
#         params = cfg['lgb_params'].copy()
#         params['early_stopping_rounds'] = 0 # No valid set to stop with
        
#         x_train = x.drop(['fold'], axis=1)
#         x_train = encode_cols(x_train, 
#                               cfg['encode_stock_cols'], 
#                               funcs=cfg['encode_funcs'], 
#                               shake=cfg['shake'], 
#                               shake_std=cfg['shake_std']).drop('stock_id', axis=1)
#         y_train = y
        
#         assert(n_train_cols == x_train.shape[1])
        
#         train_weights = 1 / np.square(y_train) # Root mean squared percentage error weights
#         train_dataset = lgb.Dataset(x_train, y_train, weight=train_weights)
        
#         for fold, best_iter in enumerate(best_iterations): 
#             params['n_estimators'] = int(best_iter) # lgbm needs int here
#             model = lgb.train(params = params, 
#                               train_set = train_dataset)
#             model.save_model(os.path.join(cfg['path_models'], f'{cfg["prefix"]}rerun_lgb_{fold}.txt'))
    
# if __name__ == '__main__': 
#     main()

main(31)

Training fold 0
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000613071   train's RMSPE: 0.440295 valid's rmse: 0.000770026   valid's RMSPE: 0.444244
[100]   train's rmse: 0.000542566   train's RMSPE: 0.38966  valid's rmse: 0.000754667   valid's RMSPE: 0.435383
Early stopping, best iteration is:
[88]    train's rmse: 0.00055269    train's RMSPE: 0.396931 valid's rmse: 0.000751319   valid's RMSPE: 0.433451
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000601588   train's RMSPE: 0.419415 valid's rmse: 0.00116532    valid's RMSPE: 0.783223
Early stopping, best iteration is:
[16]    train's rmse: 0.000776851   train's RMSPE: 0.541605 valid's rmse: 0.0010316 valid's RMSPE: 0.693344
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000644589   train's RMSPE: 0.445608 valid's rmse: 0.000662557   valid's RMSPE: 0.461107
[100]   train's rmse: 0.000567772   train's RMSPE: 0.392504 valid's rmse: 0.000656094   valid's RMSPE: 0.456609
Early stopping, best iteration is:
[94]    train's rmse: 0.000574504   train's RMSPE: 0.397159 valid's rmse: 0.000650131   valid's RMSPE: 0.452459
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000668864   train's RMSPE: 0.440228 valid's rmse: 0.000588563   valid's RMSPE: 0.479311
[100]   train's rmse: 0.000600592   train's RMSPE: 0.395293 valid's rmse: 0.000617043   valid's RMSPE: 0.502504
Early stopping, best iteration is:
[57]    train's rmse: 0.000652315   train's RMSPE: 0.429336 valid's rmse: 0.000576923   valid's RMSPE: 0.469831
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000645764   train's RMSPE: 0.448808 valid's rmse: 0.000658246   valid's RMSPE: 0.448308
[100]   train's rmse: 0.000564414   train's RMSPE: 0.39227  valid's rmse: 0.000672598   valid's RMSPE: 0.458083
Early stopping, best iteration is:
[57]    train's rmse: 0.0006261 train's RMSPE: 0.435142 valid's rmse: 0.000649591   valid's RMSPE: 0.442413
Our out of folds RMSPE is 0.508, compared to 0.5555796745804047, giving gain -0.047579674580404685
Our cv fold scores are [0.433, 0.693, 0.452, 0.47, 0.442]

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)

for stock_id in all_preds.stock_id.unique(): 
    main(stock_id)

Training fold 0
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000708746   train's RMSPE: 0.275569 valid's rmse: 0.00077724    valid's RMSPE: 0.296384
[100]   train's rmse: 0.000678066   train's RMSPE: 0.26364  valid's rmse: 0.000767171   valid's RMSPE: 0.292545
[150]   train's rmse: 0.000661326   train's RMSPE: 0.257131 valid's rmse: 0.000765755   valid's RMSPE: 0.292005
[200]   train's rmse: 0.00064754    train's RMSPE: 0.251771 valid's rmse: 0.000763377   valid's RMSPE: 0.291098
[250]   train's rmse: 0.00063573    train's RMSPE: 0.247179 valid's rmse: 0.000760109   valid's RMSPE: 0.289852
[300]   train's rmse: 0.000625967   train's RMSPE: 0.243383 valid's rmse: 0.000758097   valid's RMSPE: 0.289085
[350]   train's rmse: 0.000615999   train's RMSPE: 0.239507 valid's rmse: 0.000757436   valid's RMSPE: 0.288833
[400]   train's rmse: 0.00060813    train's RMSPE: 0.236448 valid's rmse: 0.000755773   valid's RMSPE: 0.288198
[450]   train's rmse: 0.000601009   train's RMSPE: 0.233679 valid's rmse: 0.000753786   valid's RMSPE: 0.287441
[500]   train's rmse: 0.000594125   train's RMSPE: 0.231003 valid's rmse: 0.000754027   valid's RMSPE: 0.287533
Early stopping, best iteration is:
[470]   train's rmse: 0.000597956   train's RMSPE: 0.232492 valid's rmse: 0.000752422   valid's RMSPE: 0.286921
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000717123   train's RMSPE: 0.277548 valid's rmse: 0.000735287   valid's RMSPE: 0.285668
[100]   train's rmse: 0.000687241   train's RMSPE: 0.265983 valid's rmse: 0.000723043   valid's RMSPE: 0.280911
[150]   train's rmse: 0.000670054   train's RMSPE: 0.259331 valid's rmse: 0.000721038   valid's RMSPE: 0.280132
[200]   train's rmse: 0.00065537    train's RMSPE: 0.253647 valid's rmse: 0.000720285   valid's RMSPE: 0.27984
[250]   train's rmse: 0.000643291   train's RMSPE: 0.248973 valid's rmse: 0.000723496   valid's RMSPE: 0.281087
Early stopping, best iteration is:
[204]   train's rmse: 0.000653848   train's RMSPE: 0.253058 valid's rmse: 0.000719287   valid's RMSPE: 0.279452
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000721856   train's RMSPE: 0.2783   valid's rmse: 0.000716356   valid's RMSPE: 0.282543
[100]   train's rmse: 0.000690661   train's RMSPE: 0.266273 valid's rmse: 0.000703217   valid's RMSPE: 0.277361
[150]   train's rmse: 0.000671458   train's RMSPE: 0.258869 valid's rmse: 0.000702197   valid's RMSPE: 0.276959
Early stopping, best iteration is:
[141]   train's rmse: 0.000674685   train's RMSPE: 0.260114 valid's rmse: 0.000700929   valid's RMSPE: 0.276459
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000716965   train's RMSPE: 0.277472 valid's rmse: 0.000739081   valid's RMSPE: 0.287201
[100]   train's rmse: 0.000685413   train's RMSPE: 0.265261 valid's rmse: 0.000731112   valid's RMSPE: 0.284104
[150]   train's rmse: 0.000667796   train's RMSPE: 0.258443 valid's rmse: 0.000726732   valid's RMSPE: 0.282402
[200]   train's rmse: 0.00065308    train's RMSPE: 0.252748 valid's rmse: 0.000726079   valid's RMSPE: 0.282149
[250]   train's rmse: 0.000642084   train's RMSPE: 0.248492 valid's rmse: 0.00072531    valid's RMSPE: 0.28185
Early stopping, best iteration is:
[243]   train's rmse: 0.000643344   train's RMSPE: 0.24898  valid's rmse: 0.000723453   valid's RMSPE: 0.281128
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000721276   train's RMSPE: 0.280029 valid's rmse: 0.000723078   valid's RMSPE: 0.277408
[100]   train's rmse: 0.000691166   train's RMSPE: 0.268339 valid's rmse: 0.000703881   valid's RMSPE: 0.270043
[150]   train's rmse: 0.000674363   train's RMSPE: 0.261815 valid's rmse: 0.000702649   valid's RMSPE: 0.26957
[200]   train's rmse: 0.000661207   train's RMSPE: 0.256707 valid's rmse: 0.000700385   valid's RMSPE: 0.268701
[250]   train's rmse: 0.000649103   train's RMSPE: 0.252008 valid's rmse: 0.000697772   valid's RMSPE: 0.267699
Early stopping, best iteration is:
[241]   train's rmse: 0.000651073   train's RMSPE: 0.252773 valid's rmse: 0.000697554   valid's RMSPE: 0.267615

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.278, compared to 0.24694968849387877, giving gain 0.031050311506121253
Our cv fold scores are [0.287, 0.279, 0.276, 0.281, 0.268]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000663015   train's RMSPE: 0.218782 valid's rmse: 0.000694527   valid's RMSPE: 0.230997
[100]   train's rmse: 0.000626827   train's RMSPE: 0.206841 valid's rmse: 0.000664777   valid's RMSPE: 0.221102
[150]   train's rmse: 0.000611527   train's RMSPE: 0.201792 valid's rmse: 0.000657947   valid's RMSPE: 0.21883
[200]   train's rmse: 0.000598256   train's RMSPE: 0.197413 valid's rmse: 0.000653201   valid's RMSPE: 0.217252
[250]   train's rmse: 0.000586653   train's RMSPE: 0.193584 valid's rmse: 0.00065099    valid's RMSPE: 0.216516
[300]   train's rmse: 0.000577668   train's RMSPE: 0.19062  valid's rmse: 0.00064818    valid's RMSPE: 0.215582
[350]   train's rmse: 0.000569594   train's RMSPE: 0.187955 valid's rmse: 0.000646962   valid's RMSPE: 0.215177
[400]   train's rmse: 0.000562173   train's RMSPE: 0.185506 valid's rmse: 0.000646283   valid's RMSPE: 0.214951
Early stopping, best iteration is:
[365]   train's rmse: 0.000567366   train's RMSPE: 0.18722  valid's rmse: 0.000646171   valid's RMSPE: 0.214914
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00067065    train's RMSPE: 0.221116 valid's rmse: 0.000668631   valid's RMSPE: 0.223119
[100]   train's rmse: 0.000634159   train's RMSPE: 0.209084 valid's rmse: 0.000643784   valid's RMSPE: 0.214828
[150]   train's rmse: 0.000616275   train's RMSPE: 0.203188 valid's rmse: 0.000638892   valid's RMSPE: 0.213196
[200]   train's rmse: 0.00060468    train's RMSPE: 0.199365 valid's rmse: 0.000638823   valid's RMSPE: 0.213173
Early stopping, best iteration is:
[162]   train's rmse: 0.000612928   train's RMSPE: 0.202084 valid's rmse: 0.000637516   valid's RMSPE: 0.212737
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000661398   train's RMSPE: 0.218809 valid's rmse: 0.00068514    valid's RMSPE: 0.225556
[100]   train's rmse: 0.000624917   train's RMSPE: 0.20674  valid's rmse: 0.000668918   valid's RMSPE: 0.220215
[150]   train's rmse: 0.000609733   train's RMSPE: 0.201717 valid's rmse: 0.000666552   valid's RMSPE: 0.219437
Early stopping, best iteration is:
[129]   train's rmse: 0.000614757   train's RMSPE: 0.203379 valid's rmse: 0.000665544   valid's RMSPE: 0.219105
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000664549   train's RMSPE: 0.21977  valid's rmse: 0.000684257   valid's RMSPE: 0.225604
[100]   train's rmse: 0.000628342   train's RMSPE: 0.207796 valid's rmse: 0.000656102   valid's RMSPE: 0.216321
[150]   train's rmse: 0.000611617   train's RMSPE: 0.202265 valid's rmse: 0.00065246    valid's RMSPE: 0.21512
[200]   train's rmse: 0.00059894    train's RMSPE: 0.198072 valid's rmse: 0.00064975    valid's RMSPE: 0.214227
[250]   train's rmse: 0.00058807    train's RMSPE: 0.194478 valid's rmse: 0.000646565   valid's RMSPE: 0.213177
[300]   train's rmse: 0.000579363   train's RMSPE: 0.191598 valid's rmse: 0.000645649   valid's RMSPE: 0.212875
[350]   train's rmse: 0.000570791   train's RMSPE: 0.188763 valid's rmse: 0.000643303   valid's RMSPE: 0.212101
[400]   train's rmse: 0.000562965   train's RMSPE: 0.186175 valid's rmse: 0.000644943   valid's RMSPE: 0.212642
Early stopping, best iteration is:
[350]   train's rmse: 0.000570791   train's RMSPE: 0.188763 valid's rmse: 0.000643303   valid's RMSPE: 0.212101
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000663843   train's RMSPE: 0.219936 valid's rmse: 0.000673262   valid's RMSPE: 0.220343
[100]   train's rmse: 0.000629131   train's RMSPE: 0.208436 valid's rmse: 0.000656543   valid's RMSPE: 0.214872
[150]   train's rmse: 0.000614006   train's RMSPE: 0.203425 valid's rmse: 0.000652618   valid's RMSPE: 0.213587
[200]   train's rmse: 0.000602411   train's RMSPE: 0.199583 valid's rmse: 0.000649536   valid's RMSPE: 0.212578
[250]   train's rmse: 0.000591933   train's RMSPE: 0.196112 valid's rmse: 0.000648082   valid's RMSPE: 0.212102
[300]   train's rmse: 0.000582249   train's RMSPE: 0.192904 valid's rmse: 0.000644267   valid's RMSPE: 0.210854
[350]   train's rmse: 0.000574861   train's RMSPE: 0.190456 valid's rmse: 0.000643239   valid's RMSPE: 0.210517
[400]   train's rmse: 0.000567273   train's RMSPE: 0.187942 valid's rmse: 0.000641481   valid's RMSPE: 0.209942
[450]   train's rmse: 0.000560119   train's RMSPE: 0.185572 valid's rmse: 0.000641056   valid's RMSPE: 0.209803
[500]   train's rmse: 0.000554047   train's RMSPE: 0.18356  valid's rmse: 0.000641136   valid's RMSPE: 0.209829
Early stopping, best iteration is:
[473]   train's rmse: 0.000557142   train's RMSPE: 0.184585 valid's rmse: 0.000640663   valid's RMSPE: 0.209674
Our out of folds RMSPE is 0.214, compared to 0.19164992013891172, giving gain 0.02235007986108828
Our cv fold scores are [0.215, 0.213, 0.219, 0.212, 0.21]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000385367   train's RMSPE: 0.254042 valid's rmse: 0.0004076 valid's RMSPE: 0.26942
[100]   train's rmse: 0.000370853   train's RMSPE: 0.244474 valid's rmse: 0.000395757   valid's RMSPE: 0.261591
[150]   train's rmse: 0.000363434   train's RMSPE: 0.239583 valid's rmse: 0.000392467   valid's RMSPE: 0.259417
[200]   train's rmse: 0.000357173   train's RMSPE: 0.235456 valid's rmse: 0.000390607   valid's RMSPE: 0.258188
[250]   train's rmse: 0.000352057   train's RMSPE: 0.232084 valid's rmse: 0.000389412   valid's RMSPE: 0.257398
[300]   train's rmse: 0.00034762    train's RMSPE: 0.229158 valid's rmse: 0.000387807   valid's RMSPE: 0.256337
[350]   train's rmse: 0.000343856   train's RMSPE: 0.226677 valid's rmse: 0.000387619   valid's RMSPE: 0.256212
[400]   train's rmse: 0.000340726   train's RMSPE: 0.224614 valid's rmse: 0.000386394   valid's RMSPE: 0.255403
[450]   train's rmse: 0.000337471   train's RMSPE: 0.222468 valid's rmse: 0.000385527   valid's RMSPE: 0.25483
[500]   train's rmse: 0.000333853   train's RMSPE: 0.220083 valid's rmse: 0.000384959   valid's RMSPE: 0.254455
[550]   train's rmse: 0.000330805   train's RMSPE: 0.218073 valid's rmse: 0.000383802   valid's RMSPE: 0.25369
[600]   train's rmse: 0.000327949   train's RMSPE: 0.216191 valid's rmse: 0.00038304    valid's RMSPE: 0.253186
[650]   train's rmse: 0.000325399   train's RMSPE: 0.21451  valid's rmse: 0.0003822 valid's RMSPE: 0.25263
[700]   train's rmse: 0.000322875   train's RMSPE: 0.212846 valid's rmse: 0.000381819   valid's RMSPE: 0.252379
[750]   train's rmse: 0.00032028    train's RMSPE: 0.211136 valid's rmse: 0.00038167    valid's RMSPE: 0.25228
[800]   train's rmse: 0.000318113   train's RMSPE: 0.209707 valid's rmse: 0.000381441   valid's RMSPE: 0.252129
[850]   train's rmse: 0.000316257   train's RMSPE: 0.208483 valid's rmse: 0.000381708   valid's RMSPE: 0.252306
Early stopping, best iteration is:
[811]   train's rmse: 0.00031767    train's RMSPE: 0.209415 valid's rmse: 0.000381174   valid's RMSPE: 0.251952
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000382807   train's RMSPE: 0.252032 valid's rmse: 0.000410493   valid's RMSPE: 0.272706
[100]   train's rmse: 0.000368068   train's RMSPE: 0.242329 valid's rmse: 0.000398842   valid's RMSPE: 0.264966
[150]   train's rmse: 0.000360027   train's RMSPE: 0.237035 valid's rmse: 0.000394982   valid's RMSPE: 0.262401
[200]   train's rmse: 0.000353969   train's RMSPE: 0.233046 valid's rmse: 0.000392758   valid's RMSPE: 0.260924
[250]   train's rmse: 0.000348929   train's RMSPE: 0.229728 valid's rmse: 0.000391607   valid's RMSPE: 0.26016
[300]   train's rmse: 0.000344562   train's RMSPE: 0.226853 valid's rmse: 0.000390124   valid's RMSPE: 0.259174
[350]   train's rmse: 0.000340462   train's RMSPE: 0.224153 valid's rmse: 0.000389361   valid's RMSPE: 0.258667
[400]   train's rmse: 0.000337143   train's RMSPE: 0.221968 valid's rmse: 0.000388813   valid's RMSPE: 0.258303
[450]   train's rmse: 0.000333724   train's RMSPE: 0.219717 valid's rmse: 0.000388467   valid's RMSPE: 0.258073
[500]   train's rmse: 0.000330729   train's RMSPE: 0.217745 valid's rmse: 0.000388054   valid's RMSPE: 0.257799
[550]   train's rmse: 0.000328077   train's RMSPE: 0.215999 valid's rmse: 0.000387804   valid's RMSPE: 0.257633
[600]   train's rmse: 0.000325312   train's RMSPE: 0.214179 valid's rmse: 0.00038713    valid's RMSPE: 0.257185
[650]   train's rmse: 0.000323051   train's RMSPE: 0.21269  valid's rmse: 0.000387104   valid's RMSPE: 0.257168
Early stopping, best iteration is:
[624]   train's rmse: 0.000324124   train's RMSPE: 0.213397 valid's rmse: 0.000386491   valid's RMSPE: 0.256761
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000386336   train's RMSPE: 0.254397 valid's rmse: 0.000386773   valid's RMSPE: 0.256781
[100]   train's rmse: 0.000370459   train's RMSPE: 0.243943 valid's rmse: 0.000380171   valid's RMSPE: 0.252398
[150]   train's rmse: 0.000363626   train's RMSPE: 0.239444 valid's rmse: 0.000379187   valid's RMSPE: 0.251745
[200]   train's rmse: 0.000358025   train's RMSPE: 0.235755 valid's rmse: 0.000378275   valid's RMSPE: 0.251139
[250]   train's rmse: 0.000352804   train's RMSPE: 0.232317 valid's rmse: 0.000377376   valid's RMSPE: 0.250542
[300]   train's rmse: 0.000348202   train's RMSPE: 0.229287 valid's rmse: 0.000377415   valid's RMSPE: 0.250569
Early stopping, best iteration is:
[292]   train's rmse: 0.0003488 train's RMSPE: 0.229681 valid's rmse: 0.000377049   valid's RMSPE: 0.250326
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000393114   train's RMSPE: 0.259607 valid's rmse: 0.000367642   valid's RMSPE: 0.241292
[100]   train's rmse: 0.000377236   train's RMSPE: 0.249121 valid's rmse: 0.0003591 valid's RMSPE: 0.235685
[150]   train's rmse: 0.000370072   train's RMSPE: 0.244391 valid's rmse: 0.000356556   valid's RMSPE: 0.234016
[200]   train's rmse: 0.000364154   train's RMSPE: 0.240482 valid's rmse: 0.000354155   valid's RMSPE: 0.23244
[250]   train's rmse: 0.00035922    train's RMSPE: 0.237224 valid's rmse: 0.000352567   valid's RMSPE: 0.231398
[300]   train's rmse: 0.000354725   train's RMSPE: 0.234255 valid's rmse: 0.000350734   valid's RMSPE: 0.230194
[350]   train's rmse: 0.000351167   train's RMSPE: 0.231906 valid's rmse: 0.000349603   valid's RMSPE: 0.229452
[400]   train's rmse: 0.000347746   train's RMSPE: 0.229647 valid's rmse: 0.000348669   valid's RMSPE: 0.228839
[450]   train's rmse: 0.000344824   train's RMSPE: 0.227717 valid's rmse: 0.000348059   valid's RMSPE: 0.228439
[500]   train's rmse: 0.000341999   train's RMSPE: 0.225851 valid's rmse: 0.000347595   valid's RMSPE: 0.228134
[550]   train's rmse: 0.000339051   train's RMSPE: 0.223904 valid's rmse: 0.000347563   valid's RMSPE: 0.228114
Early stopping, best iteration is:
[509]   train's rmse: 0.000341371   train's RMSPE: 0.225436 valid's rmse: 0.000347164   valid's RMSPE: 0.227851
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000386987   train's RMSPE: 0.255952 valid's rmse: 0.000393253   valid's RMSPE: 0.256494
[100]   train's rmse: 0.000371233   train's RMSPE: 0.245532 valid's rmse: 0.000382655   valid's RMSPE: 0.249582
[150]   train's rmse: 0.000363756   train's RMSPE: 0.240587 valid's rmse: 0.00037907    valid's RMSPE: 0.247244
[200]   train's rmse: 0.000357675   train's RMSPE: 0.236565 valid's rmse: 0.000377367   valid's RMSPE: 0.246133
[250]   train's rmse: 0.000352391   train's RMSPE: 0.233071 valid's rmse: 0.000375788   valid's RMSPE: 0.245103
[300]   train's rmse: 0.000347854   train's RMSPE: 0.23007  valid's rmse: 0.000374972   valid's RMSPE: 0.244571
[350]   train's rmse: 0.000344208   train's RMSPE: 0.227658 valid's rmse: 0.000374364   valid's RMSPE: 0.244174
[400]   train's rmse: 0.000340407   train's RMSPE: 0.225144 valid's rmse: 0.000373648   valid's RMSPE: 0.243707
[450]   train's rmse: 0.00033703    train's RMSPE: 0.222911 valid's rmse: 0.000372698   valid's RMSPE: 0.243087
[500]   train's rmse: 0.000334398   train's RMSPE: 0.22117  valid's rmse: 0.000372722   valid's RMSPE: 0.243104
[550]   train's rmse: 0.000331644   train's RMSPE: 0.219348 valid's rmse: 0.000371867   valid's RMSPE: 0.242546
[600]   train's rmse: 0.000329187   train's RMSPE: 0.217723 valid's rmse: 0.000371062   valid's RMSPE: 0.24202
Early stopping, best iteration is:
[598]   train's rmse: 0.000329246   train's RMSPE: 0.217762 valid's rmse: 0.000371011   valid's RMSPE: 0.241987
Our out of folds RMSPE is 0.246, compared to 0.18952182769614576, giving gain 0.05647817230385424
Our cv fold scores are [0.252, 0.257, 0.25, 0.228, 0.242]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000973236   train's RMSPE: 0.229739 valid's rmse: 0.00102394    valid's RMSPE: 0.237663
[100]   train's rmse: 0.00091764    train's RMSPE: 0.216615 valid's rmse: 0.000997012   valid's RMSPE: 0.231413
[150]   train's rmse: 0.000890674   train's RMSPE: 0.21025  valid's rmse: 0.000994557   valid's RMSPE: 0.230843
[200]   train's rmse: 0.000868401   train's RMSPE: 0.204992 valid's rmse: 0.000993257   valid's RMSPE: 0.230542
Early stopping, best iteration is:
[187]   train's rmse: 0.000873797   train's RMSPE: 0.206266 valid's rmse: 0.00099259    valid's RMSPE: 0.230387
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000971754   train's RMSPE: 0.227947 valid's rmse: 0.000998896   valid's RMSPE: 0.237787
[100]   train's rmse: 0.000915413   train's RMSPE: 0.214731 valid's rmse: 0.00098596    valid's RMSPE: 0.234707
[150]   train's rmse: 0.000888289   train's RMSPE: 0.208368 valid's rmse: 0.000988451   valid's RMSPE: 0.2353
Early stopping, best iteration is:
[106]   train's rmse: 0.000911646   train's RMSPE: 0.213847 valid's rmse: 0.000984962   valid's RMSPE: 0.23447
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000982139   train's RMSPE: 0.229925 valid's rmse: 0.00101126    valid's RMSPE: 0.242577
[100]   train's rmse: 0.000925828   train's RMSPE: 0.216742 valid's rmse: 0.000970109   valid's RMSPE: 0.232706
[150]   train's rmse: 0.000900234   train's RMSPE: 0.210751 valid's rmse: 0.000968805   valid's RMSPE: 0.232394
Early stopping, best iteration is:
[141]   train's rmse: 0.000904525   train's RMSPE: 0.211755 valid's rmse: 0.000966091   valid's RMSPE: 0.231743
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000970443   train's RMSPE: 0.230288 valid's rmse: 0.00101834    valid's RMSPE: 0.231133
[100]   train's rmse: 0.000918926   train's RMSPE: 0.218063 valid's rmse: 0.000995422   valid's RMSPE: 0.225932
[150]   train's rmse: 0.000892506   train's RMSPE: 0.211793 valid's rmse: 0.000989623   valid's RMSPE: 0.224616
[200]   train's rmse: 0.000874237   train's RMSPE: 0.207458 valid's rmse: 0.000990818   valid's RMSPE: 0.224887
Early stopping, best iteration is:
[167]   train's rmse: 0.00088598    train's RMSPE: 0.210245 valid's rmse: 0.000988675   valid's RMSPE: 0.224401
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000980881   train's RMSPE: 0.229827 valid's rmse: 0.00101017    valid's RMSPE: 0.241528
[100]   train's rmse: 0.000926154   train's RMSPE: 0.217004 valid's rmse: 0.00097133    valid's RMSPE: 0.232241
[150]   train's rmse: 0.000899878   train's RMSPE: 0.210847 valid's rmse: 0.000968824   valid's RMSPE: 0.231641
[200]   train's rmse: 0.000879691   train's RMSPE: 0.206117 valid's rmse: 0.000970801   valid's RMSPE: 0.232114
Early stopping, best iteration is:
[159]   train's rmse: 0.000895304   train's RMSPE: 0.209775 valid's rmse: 0.000967081   valid's RMSPE: 0.231225
Our out of folds RMSPE is 0.23, compared to 0.21766147454252618, giving gain 0.012338525457473826
Our cv fold scores are [0.23, 0.234, 0.232, 0.224, 0.231]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000755692   train's RMSPE: 0.273927 valid's rmse: 0.000811946   valid's RMSPE: 0.289188
[100]   train's rmse: 0.000723597   train's RMSPE: 0.262293 valid's rmse: 0.000795207   valid's RMSPE: 0.283226
[150]   train's rmse: 0.000707182   train's RMSPE: 0.256343 valid's rmse: 0.000791371   valid's RMSPE: 0.28186
[200]   train's rmse: 0.000692183   train's RMSPE: 0.250906 valid's rmse: 0.000789591   valid's RMSPE: 0.281226
[250]   train's rmse: 0.00068035    train's RMSPE: 0.246617 valid's rmse: 0.000790391   valid's RMSPE: 0.281511
Early stopping, best iteration is:
[207]   train's rmse: 0.000690418   train's RMSPE: 0.250266 valid's rmse: 0.000789476   valid's RMSPE: 0.281185
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00076311    train's RMSPE: 0.275074 valid's rmse: 0.000782888   valid's RMSPE: 0.285189
[100]   train's rmse: 0.000730568   train's RMSPE: 0.263344 valid's rmse: 0.000762068   valid's RMSPE: 0.277605
[150]   train's rmse: 0.000712568   train's RMSPE: 0.256855 valid's rmse: 0.000758832   valid's RMSPE: 0.276426
[200]   train's rmse: 0.000698341   train's RMSPE: 0.251727 valid's rmse: 0.000759107   valid's RMSPE: 0.276526
Early stopping, best iteration is:
[181]   train's rmse: 0.000703557   train's RMSPE: 0.253607 valid's rmse: 0.000757376   valid's RMSPE: 0.275896
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000761674   train's RMSPE: 0.274614 valid's rmse: 0.000770253   valid's RMSPE: 0.280354
[100]   train's rmse: 0.000729076   train's RMSPE: 0.262861 valid's rmse: 0.000757299   valid's RMSPE: 0.275639
[150]   train's rmse: 0.000712713   train's RMSPE: 0.256962 valid's rmse: 0.000754344   valid's RMSPE: 0.274563
[200]   train's rmse: 0.00069845    train's RMSPE: 0.251819 valid's rmse: 0.000750683   valid's RMSPE: 0.273231
[250]   train's rmse: 0.000686699   train's RMSPE: 0.247583 valid's rmse: 0.00074819    valid's RMSPE: 0.272323
[300]   train's rmse: 0.000676821   train's RMSPE: 0.244021 valid's rmse: 0.000748485   valid's RMSPE: 0.272431
[350]   train's rmse: 0.000666601   train's RMSPE: 0.240336 valid's rmse: 0.000746721   valid's RMSPE: 0.271789
[400]   train's rmse: 0.00065758    train's RMSPE: 0.237084 valid's rmse: 0.000743606   valid's RMSPE: 0.270655
[450]   train's rmse: 0.000649884   train's RMSPE: 0.234309 valid's rmse: 0.000742612   valid's RMSPE: 0.270293
Early stopping, best iteration is:
[441]   train's rmse: 0.000651319   train's RMSPE: 0.234827 valid's rmse: 0.00074219    valid's RMSPE: 0.27014
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000767367   train's RMSPE: 0.277162 valid's rmse: 0.00076626    valid's RMSPE: 0.276933
[100]   train's rmse: 0.00073468    train's RMSPE: 0.265356 valid's rmse: 0.0007553 valid's RMSPE: 0.272972
[150]   train's rmse: 0.000718498   train's RMSPE: 0.259511 valid's rmse: 0.000750146   valid's RMSPE: 0.271109
[200]   train's rmse: 0.00070432    train's RMSPE: 0.25439  valid's rmse: 0.000747318   valid's RMSPE: 0.270087
[250]   train's rmse: 0.000690451   train's RMSPE: 0.249381 valid's rmse: 0.000747016   valid's RMSPE: 0.269978
[300]   train's rmse: 0.000679839   train's RMSPE: 0.245548 valid's rmse: 0.000743633   valid's RMSPE: 0.268755
[350]   train's rmse: 0.000669133   train's RMSPE: 0.241681 valid's rmse: 0.000741087   valid's RMSPE: 0.267835
[400]   train's rmse: 0.00065996    train's RMSPE: 0.238368 valid's rmse: 0.000741565   valid's RMSPE: 0.268008
Early stopping, best iteration is:
[354]   train's rmse: 0.00066817    train's RMSPE: 0.241333 valid's rmse: 0.00074056    valid's RMSPE: 0.267645
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00076205    train's RMSPE: 0.27546  valid's rmse: 0.000771792   valid's RMSPE: 0.278047
[100]   train's rmse: 0.000731108   train's RMSPE: 0.264275 valid's rmse: 0.000762482   valid's RMSPE: 0.274693
[150]   train's rmse: 0.000713089   train's RMSPE: 0.257762 valid's rmse: 0.000762368   valid's RMSPE: 0.274652
Early stopping, best iteration is:
[130]   train's rmse: 0.000720001   train's RMSPE: 0.26026  valid's rmse: 0.000759827   valid's RMSPE: 0.273736
Our out of folds RMSPE is 0.274, compared to 0.23933832319605133, giving gain 0.03466167680394869
Our cv fold scores are [0.281, 0.276, 0.27, 0.268, 0.274]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000944656   train's RMSPE: 0.266426 valid's rmse: 0.00097543    valid's RMSPE: 0.269382
[100]   train's rmse: 0.000899396   train's RMSPE: 0.253661 valid's rmse: 0.000957845   valid's RMSPE: 0.264525
[150]   train's rmse: 0.000872786   train's RMSPE: 0.246156 valid's rmse: 0.000953427   valid's RMSPE: 0.263305
[200]   train's rmse: 0.000852248   train's RMSPE: 0.240363 valid's rmse: 0.000946795   valid's RMSPE: 0.261474
[250]   train's rmse: 0.000834292   train's RMSPE: 0.235299 valid's rmse: 0.000943103   valid's RMSPE: 0.260454
[300]   train's rmse: 0.000819513   train's RMSPE: 0.231131 valid's rmse: 0.000939008   valid's RMSPE: 0.259323
[350]   train's rmse: 0.000806426   train's RMSPE: 0.22744  valid's rmse: 0.000942704   valid's RMSPE: 0.260344
Early stopping, best iteration is:
[305]   train's rmse: 0.000818266   train's RMSPE: 0.230779 valid's rmse: 0.000937696   valid's RMSPE: 0.258961
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000927945   train's RMSPE: 0.258831 valid's rmse: 0.00104221    valid's RMSPE: 0.300684
[100]   train's rmse: 0.000883927   train's RMSPE: 0.246553 valid's rmse: 0.00101045    valid's RMSPE: 0.29152
[150]   train's rmse: 0.00086138    train's RMSPE: 0.240264 valid's rmse: 0.00100192    valid's RMSPE: 0.28906
[200]   train's rmse: 0.000842614   train's RMSPE: 0.23503  valid's rmse: 0.00100184    valid's RMSPE: 0.289036
Early stopping, best iteration is:
[193]   train's rmse: 0.000845355   train's RMSPE: 0.235794 valid's rmse: 0.00100067    valid's RMSPE: 0.288698
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000942966   train's RMSPE: 0.265835 valid's rmse: 0.000955974   valid's RMSPE: 0.26448
[100]   train's rmse: 0.000895375   train's RMSPE: 0.252418 valid's rmse: 0.000941904   valid's RMSPE: 0.260587
[150]   train's rmse: 0.000869753   train's RMSPE: 0.245195 valid's rmse: 0.000938302   valid's RMSPE: 0.259591
Early stopping, best iteration is:
[135]   train's rmse: 0.000876947   train's RMSPE: 0.247223 valid's rmse: 0.000937079   valid's RMSPE: 0.259252
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000955162   train's RMSPE: 0.268909 valid's rmse: 0.00093659    valid's RMSPE: 0.260568
[100]   train's rmse: 0.000906527   train's RMSPE: 0.255217 valid's rmse: 0.00092687    valid's RMSPE: 0.257864
[150]   train's rmse: 0.000878069   train's RMSPE: 0.247205 valid's rmse: 0.00091908    valid's RMSPE: 0.255696
[200]   train's rmse: 0.000858229   train's RMSPE: 0.241619 valid's rmse: 0.000914347   valid's RMSPE: 0.254379
[250]   train's rmse: 0.000840302   train's RMSPE: 0.236572 valid's rmse: 0.000910522   valid's RMSPE: 0.253315
[300]   train's rmse: 0.000824868   train's RMSPE: 0.232227 valid's rmse: 0.000909547   valid's RMSPE: 0.253044
[350]   train's rmse: 0.000810734   train's RMSPE: 0.228248 valid's rmse: 0.00090975    valid's RMSPE: 0.2531
[400]   train's rmse: 0.00079829    train's RMSPE: 0.224744 valid's rmse: 0.000908251   valid's RMSPE: 0.252683
[450]   train's rmse: 0.000786947   train's RMSPE: 0.221551 valid's rmse: 0.000908615   valid's RMSPE: 0.252785
Early stopping, best iteration is:
[436]   train's rmse: 0.000790367   train's RMSPE: 0.222514 valid's rmse: 0.000907673   valid's RMSPE: 0.252523
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00095325    train's RMSPE: 0.266844 valid's rmse: 0.000951162   valid's RMSPE: 0.270701
[100]   train's rmse: 0.00090717    train's RMSPE: 0.253945 valid's rmse: 0.000919809   valid's RMSPE: 0.261778
[150]   train's rmse: 0.000882432   train's RMSPE: 0.24702  valid's rmse: 0.000911042   valid's RMSPE: 0.259283
[200]   train's rmse: 0.000861238   train's RMSPE: 0.241087 valid's rmse: 0.000906354   valid's RMSPE: 0.257949
[250]   train's rmse: 0.0008438 train's RMSPE: 0.236206 valid's rmse: 0.000906677   valid's RMSPE: 0.258041
Early stopping, best iteration is:
[237]   train's rmse: 0.000848025   train's RMSPE: 0.237388 valid's rmse: 0.000905404   valid's RMSPE: 0.257678

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.264, compared to 0.23908992165115264, giving gain 0.02491007834884737
Our cv fold scores are [0.259, 0.289, 0.259, 0.253, 0.258]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00108865    train's RMSPE: 0.212352 valid's rmse: 0.00115337    valid's RMSPE: 0.222546
[100]   train's rmse: 0.00102362    train's RMSPE: 0.199666 valid's rmse: 0.00111319    valid's RMSPE: 0.214792
[150]   train's rmse: 0.000990082   train's RMSPE: 0.193124 valid's rmse: 0.00110163    valid's RMSPE: 0.212562
[200]   train's rmse: 0.000965683   train's RMSPE: 0.188365 valid's rmse: 0.00109864    valid's RMSPE: 0.211984
[250]   train's rmse: 0.000946053   train's RMSPE: 0.184536 valid's rmse: 0.00109835    valid's RMSPE: 0.211929
Early stopping, best iteration is:
[211]   train's rmse: 0.00096107    train's RMSPE: 0.187466 valid's rmse: 0.00109718    valid's RMSPE: 0.211704
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00110252    train's RMSPE: 0.213866 valid's rmse: 0.00110269    valid's RMSPE: 0.217515
[100]   train's rmse: 0.0010343 train's RMSPE: 0.200634 valid's rmse: 0.00108394    valid's RMSPE: 0.213816
Early stopping, best iteration is:
[94]    train's rmse: 0.00103997    train's RMSPE: 0.201733 valid's rmse: 0.00107817    valid's RMSPE: 0.212678
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00109839    train's RMSPE: 0.212804 valid's rmse: 0.00110527    valid's RMSPE: 0.219052
[100]   train's rmse: 0.00103216    train's RMSPE: 0.199973 valid's rmse: 0.00107956    valid's RMSPE: 0.213956
[150]   train's rmse: 0.000997546   train's RMSPE: 0.193267 valid's rmse: 0.00107626    valid's RMSPE: 0.213301
[200]   train's rmse: 0.00096799    train's RMSPE: 0.187541 valid's rmse: 0.00107638    valid's RMSPE: 0.213326
Early stopping, best iteration is:
[184]   train's rmse: 0.000976383   train's RMSPE: 0.189167 valid's rmse: 0.00107376    valid's RMSPE: 0.212806
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00109017    train's RMSPE: 0.21377  valid's rmse: 0.00112714    valid's RMSPE: 0.212723
[100]   train's rmse: 0.00102221    train's RMSPE: 0.200445 valid's rmse: 0.00110785    valid's RMSPE: 0.209082
[150]   train's rmse: 0.000989757   train's RMSPE: 0.194081 valid's rmse: 0.00110981    valid's RMSPE: 0.209453
Early stopping, best iteration is:
[115]   train's rmse: 0.00101086    train's RMSPE: 0.19822  valid's rmse: 0.00110717    valid's RMSPE: 0.208954
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00108622    train's RMSPE: 0.211072 valid's rmse: 0.00119314    valid's RMSPE: 0.233762
[100]   train's rmse: 0.00102504    train's RMSPE: 0.199185 valid's rmse: 0.00113577    valid's RMSPE: 0.22252
Early stopping, best iteration is:
[89]    train's rmse: 0.00103343    train's RMSPE: 0.200815 valid's rmse: 0.00113289    valid's RMSPE: 0.221956

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.214, compared to 0.20317871450499694, giving gain 0.010821285495003052
Our cv fold scores are [0.212, 0.213, 0.213, 0.209, 0.222]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000573645   train's RMSPE: 0.254149 valid's rmse: 0.000620383   valid's RMSPE: 0.270708
[100]   train's rmse: 0.000547103   train's RMSPE: 0.242389 valid's rmse: 0.000605305   valid's RMSPE: 0.264128
[150]   train's rmse: 0.000533209   train's RMSPE: 0.236234 valid's rmse: 0.000602481   valid's RMSPE: 0.262896
[200]   train's rmse: 0.000522978   train's RMSPE: 0.231701 valid's rmse: 0.000601763   valid's RMSPE: 0.262583
[250]   train's rmse: 0.000512558   train's RMSPE: 0.227084 valid's rmse: 0.00059995    valid's RMSPE: 0.261792
[300]   train's rmse: 0.000503239   train's RMSPE: 0.222956 valid's rmse: 0.000600425   valid's RMSPE: 0.261999
Early stopping, best iteration is:
[256]   train's rmse: 0.000511293   train's RMSPE: 0.226524 valid's rmse: 0.000599902   valid's RMSPE: 0.261771
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000575339   train's RMSPE: 0.253678 valid's rmse: 0.0005984 valid's RMSPE: 0.26621
[100]   train's rmse: 0.000548005   train's RMSPE: 0.241626 valid's rmse: 0.000590675   valid's RMSPE: 0.262773
[150]   train's rmse: 0.000534878   train's RMSPE: 0.235838 valid's rmse: 0.000589252   valid's RMSPE: 0.26214
[200]   train's rmse: 0.000523637   train's RMSPE: 0.230882 valid's rmse: 0.000589124   valid's RMSPE: 0.262083
Early stopping, best iteration is:
[179]   train's rmse: 0.000527766   train's RMSPE: 0.232703 valid's rmse: 0.000588614   valid's RMSPE: 0.261856
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000577926   train's RMSPE: 0.255238 valid's rmse: 0.000591157   valid's RMSPE: 0.261282
[100]   train's rmse: 0.000551468   train's RMSPE: 0.243553 valid's rmse: 0.000580355   valid's RMSPE: 0.256507
[150]   train's rmse: 0.000537292   train's RMSPE: 0.237292 valid's rmse: 0.000579173   valid's RMSPE: 0.255985
Early stopping, best iteration is:
[148]   train's rmse: 0.000537834   train's RMSPE: 0.237531 valid's rmse: 0.000578989   valid's RMSPE: 0.255903
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000586955   train's RMSPE: 0.257438 valid's rmse: 0.000579631   valid's RMSPE: 0.263125
[100]   train's rmse: 0.00055955    train's RMSPE: 0.245418 valid's rmse: 0.000569319   valid's RMSPE: 0.258444
Early stopping, best iteration is:
[89]    train's rmse: 0.000563266   train's RMSPE: 0.247048 valid's rmse: 0.000565388   valid's RMSPE: 0.256659
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000576876   train's RMSPE: 0.256327 valid's rmse: 0.00059276    valid's RMSPE: 0.255515
[100]   train's rmse: 0.000550507   train's RMSPE: 0.24461  valid's rmse: 0.000577949   valid's RMSPE: 0.24913
[150]   train's rmse: 0.00053607    train's RMSPE: 0.238195 valid's rmse: 0.000577356   valid's RMSPE: 0.248875
[200]   train's rmse: 0.000525622   train's RMSPE: 0.233553 valid's rmse: 0.000576717   valid's RMSPE: 0.248599
Early stopping, best iteration is:
[176]   train's rmse: 0.00053042    train's RMSPE: 0.235685 valid's rmse: 0.000575464   valid's RMSPE: 0.248059
Our out of folds RMSPE is 0.257, compared to 0.2273039636391359, giving gain 0.029696036360864098
Our cv fold scores are [0.262, 0.262, 0.256, 0.257, 0.248]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000683591   train's RMSPE: 0.238078 valid's rmse: 0.000716843   valid's RMSPE: 0.248204
[100]   train's rmse: 0.000647358   train's RMSPE: 0.225459 valid's rmse: 0.000690402   valid's RMSPE: 0.239049
[150]   train's rmse: 0.000630882   train's RMSPE: 0.21972  valid's rmse: 0.000687774   valid's RMSPE: 0.238139
[200]   train's rmse: 0.000616818   train's RMSPE: 0.214822 valid's rmse: 0.00068629    valid's RMSPE: 0.237625
Early stopping, best iteration is:
[197]   train's rmse: 0.000617311   train's RMSPE: 0.214994 valid's rmse: 0.000685929   valid's RMSPE: 0.2375
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000686234   train's RMSPE: 0.239009 valid's rmse: 0.000708663   valid's RMSPE: 0.245325
[100]   train's rmse: 0.000650577   train's RMSPE: 0.22659  valid's rmse: 0.00068683    valid's RMSPE: 0.237767
[150]   train's rmse: 0.000633236   train's RMSPE: 0.22055  valid's rmse: 0.000682944   valid's RMSPE: 0.236421
[200]   train's rmse: 0.000620042   train's RMSPE: 0.215955 valid's rmse: 0.000678422   valid's RMSPE: 0.234856
[250]   train's rmse: 0.000608512   train's RMSPE: 0.211939 valid's rmse: 0.000677182   valid's RMSPE: 0.234427
[300]   train's rmse: 0.000597369   train's RMSPE: 0.208058 valid's rmse: 0.000674325   valid's RMSPE: 0.233438
Early stopping, best iteration is:
[276]   train's rmse: 0.000602036   train's RMSPE: 0.209684 valid's rmse: 0.000673706   valid's RMSPE: 0.233224
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000682659   train's RMSPE: 0.236975 valid's rmse: 0.000722046   valid's RMSPE: 0.25329
[100]   train's rmse: 0.000645468   train's RMSPE: 0.224064 valid's rmse: 0.000702473   valid's RMSPE: 0.246424
[150]   train's rmse: 0.000629283   train's RMSPE: 0.218446 valid's rmse: 0.000698327   valid's RMSPE: 0.244969
[200]   train's rmse: 0.000614941   train's RMSPE: 0.213467 valid's rmse: 0.000695825   valid's RMSPE: 0.244092
[250]   train's rmse: 0.000602504   train's RMSPE: 0.20915  valid's rmse: 0.000694437   valid's RMSPE: 0.243604
[300]   train's rmse: 0.000592626   train's RMSPE: 0.205721 valid's rmse: 0.000693096   valid's RMSPE: 0.243134
[350]   train's rmse: 0.000582644   train's RMSPE: 0.202256 valid's rmse: 0.000691468   valid's RMSPE: 0.242563
[400]   train's rmse: 0.000575027   train's RMSPE: 0.199612 valid's rmse: 0.000689889   valid's RMSPE: 0.242009
[450]   train's rmse: 0.000567723   train's RMSPE: 0.197076 valid's rmse: 0.000690564   valid's RMSPE: 0.242246
[500]   train's rmse: 0.000560671   train's RMSPE: 0.194629 valid's rmse: 0.000688332   valid's RMSPE: 0.241463
Early stopping, best iteration is:
[499]   train's rmse: 0.000560799   train's RMSPE: 0.194673 valid's rmse: 0.000688069   valid's RMSPE: 0.241371
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000692809   train's RMSPE: 0.240536 valid's rmse: 0.00068023    valid's RMSPE: 0.238474
[100]   train's rmse: 0.000654228   train's RMSPE: 0.227141 valid's rmse: 0.000662673   valid's RMSPE: 0.232319
[150]   train's rmse: 0.000635342   train's RMSPE: 0.220584 valid's rmse: 0.000658331   valid's RMSPE: 0.230797
[200]   train's rmse: 0.000621143   train's RMSPE: 0.215654 valid's rmse: 0.0006549 valid's RMSPE: 0.229594
[250]   train's rmse: 0.000609981   train's RMSPE: 0.211779 valid's rmse: 0.000654351   valid's RMSPE: 0.229401
[300]   train's rmse: 0.000598974   train's RMSPE: 0.207957 valid's rmse: 0.000652797   valid's RMSPE: 0.228857
[350]   train's rmse: 0.000589405   train's RMSPE: 0.204635 valid's rmse: 0.0006517 valid's RMSPE: 0.228472
[400]   train's rmse: 0.000581362   train's RMSPE: 0.201843 valid's rmse: 0.000650069   valid's RMSPE: 0.2279
Early stopping, best iteration is:
[388]   train's rmse: 0.000583179   train's RMSPE: 0.202474 valid's rmse: 0.00064992    valid's RMSPE: 0.227848
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000688292   train's RMSPE: 0.23984  valid's rmse: 0.000687628   valid's RMSPE: 0.237584
[100]   train's rmse: 0.000650501   train's RMSPE: 0.226672 valid's rmse: 0.000665065   valid's RMSPE: 0.229788
[150]   train's rmse: 0.000632791   train's RMSPE: 0.2205   valid's rmse: 0.000659404   valid's RMSPE: 0.227832
[200]   train's rmse: 0.000620417   train's RMSPE: 0.216188 valid's rmse: 0.000655457   valid's RMSPE: 0.226468
[250]   train's rmse: 0.000609194   train's RMSPE: 0.212278 valid's rmse: 0.000653756   valid's RMSPE: 0.225881
[300]   train's rmse: 0.000599015   train's RMSPE: 0.208731 valid's rmse: 0.000651064   valid's RMSPE: 0.224951
[350]   train's rmse: 0.000590011   train's RMSPE: 0.205593 valid's rmse: 0.000651666   valid's RMSPE: 0.225159
Early stopping, best iteration is:
[307]   train's rmse: 0.000597671   train's RMSPE: 0.208263 valid's rmse: 0.000650092   valid's RMSPE: 0.224615
Our out of folds RMSPE is 0.233, compared to 0.21572974617592797, giving gain 0.017270253824072046
Our cv fold scores are [0.237, 0.233, 0.241, 0.228, 0.225]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000996248   train's RMSPE: 0.256754 valid's rmse: 0.00104637    valid's RMSPE: 0.274374
[100]   train's rmse: 0.000948884   train's RMSPE: 0.244547 valid's rmse: 0.00102038    valid's RMSPE: 0.267559
[150]   train's rmse: 0.000922486   train's RMSPE: 0.237744 valid's rmse: 0.00101386    valid's RMSPE: 0.265849
[200]   train's rmse: 0.000900638   train's RMSPE: 0.232113 valid's rmse: 0.00101384    valid's RMSPE: 0.265843
[250]   train's rmse: 0.000883149   train's RMSPE: 0.227606 valid's rmse: 0.00101541    valid's RMSPE: 0.266255
Early stopping, best iteration is:
[212]   train's rmse: 0.000896171   train's RMSPE: 0.230962 valid's rmse: 0.0010127 valid's RMSPE: 0.265545
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000971785   train's RMSPE: 0.251525 valid's rmse: 0.00114474    valid's RMSPE: 0.295129
[100]   train's rmse: 0.000928191   train's RMSPE: 0.240242 valid's rmse: 0.00113156    valid's RMSPE: 0.291731
[150]   train's rmse: 0.000902794   train's RMSPE: 0.233669 valid's rmse: 0.00112029    valid's RMSPE: 0.288824
[200]   train's rmse: 0.000883852   train's RMSPE: 0.228766 valid's rmse: 0.00111993    valid's RMSPE: 0.288731
Early stopping, best iteration is:
[178]   train's rmse: 0.000892396   train's RMSPE: 0.230977 valid's rmse: 0.00111871    valid's RMSPE: 0.288418
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0010086 train's RMSPE: 0.260273 valid's rmse: 0.000986853   valid's RMSPE: 0.257465
[100]   train's rmse: 0.000961924   train's RMSPE: 0.248229 valid's rmse: 0.000966244   valid's RMSPE: 0.252089
[150]   train's rmse: 0.000935374   train's RMSPE: 0.241378 valid's rmse: 0.000961685   valid's RMSPE: 0.250899
[200]   train's rmse: 0.000911655   train's RMSPE: 0.235257 valid's rmse: 0.000953509   valid's RMSPE: 0.248766
[250]   train's rmse: 0.00089265    train's RMSPE: 0.230353 valid's rmse: 0.000952645   valid's RMSPE: 0.248541
Early stopping, best iteration is:
[243]   train's rmse: 0.000895242   train's RMSPE: 0.231022 valid's rmse: 0.000951661   valid's RMSPE: 0.248284
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.0010081 train's RMSPE: 0.261386 valid's rmse: 0.000967497   valid's RMSPE: 0.247646
[100]   train's rmse: 0.000961959   train's RMSPE: 0.249423 valid's rmse: 0.000945814   valid's RMSPE: 0.242096
Early stopping, best iteration is:
[89]    train's rmse: 0.000968588   train's RMSPE: 0.251142 valid's rmse: 0.000944815   valid's RMSPE: 0.24184
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00100379    train's RMSPE: 0.260216 valid's rmse: 0.00101585    valid's RMSPE: 0.260238
[100]   train's rmse: 0.00095478    train's RMSPE: 0.247511 valid's rmse: 0.000998499   valid's RMSPE: 0.255794
[150]   train's rmse: 0.000926798   train's RMSPE: 0.240257 valid's rmse: 0.00099781    valid's RMSPE: 0.255618
Early stopping, best iteration is:
[134]   train's rmse: 0.000933631   train's RMSPE: 0.242029 valid's rmse: 0.00099449    valid's RMSPE: 0.254767
Our out of folds RMSPE is 0.26, compared to 0.24636791823616067, giving gain 0.013632081763839343
Our cv fold scores are [0.266, 0.288, 0.248, 0.242, 0.255]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000502889   train's RMSPE: 0.216962 valid's rmse: 0.000535229   valid's RMSPE: 0.233057
[100]   train's rmse: 0.00047541    train's RMSPE: 0.205107 valid's rmse: 0.0005074 valid's RMSPE: 0.220939
[150]   train's rmse: 0.000465857   train's RMSPE: 0.200985 valid's rmse: 0.000504205   valid's RMSPE: 0.219548
[200]   train's rmse: 0.000457891   train's RMSPE: 0.197548 valid's rmse: 0.000501479   valid's RMSPE: 0.21836
Early stopping, best iteration is:
[194]   train's rmse: 0.000458492   train's RMSPE: 0.197807 valid's rmse: 0.000501248   valid's RMSPE: 0.21826
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000499855   train's RMSPE: 0.216876 valid's rmse: 0.000537696   valid's RMSPE: 0.228844
[100]   train's rmse: 0.000472025   train's RMSPE: 0.204801 valid's rmse: 0.000520909   valid's RMSPE: 0.221699
[150]   train's rmse: 0.000462356   train's RMSPE: 0.200606 valid's rmse: 0.000518513   valid's RMSPE: 0.220679
[200]   train's rmse: 0.000454856   train's RMSPE: 0.197351 valid's rmse: 0.000516055   valid's RMSPE: 0.219633
[250]   train's rmse: 0.000447781   train's RMSPE: 0.194282 valid's rmse: 0.000514675   valid's RMSPE: 0.219046
[300]   train's rmse: 0.000441385   train's RMSPE: 0.191507 valid's rmse: 0.000513542   valid's RMSPE: 0.218563
[350]   train's rmse: 0.000436165   train's RMSPE: 0.189242 valid's rmse: 0.000512305   valid's RMSPE: 0.218037
[400]   train's rmse: 0.000431183   train's RMSPE: 0.18708  valid's rmse: 0.000512007   valid's RMSPE: 0.21791
[450]   train's rmse: 0.000426894   train's RMSPE: 0.185219 valid's rmse: 0.000511126   valid's RMSPE: 0.217535
[500]   train's rmse: 0.000423103   train's RMSPE: 0.183575 valid's rmse: 0.000509688   valid's RMSPE: 0.216923
Early stopping, best iteration is:
[499]   train's rmse: 0.000423155   train's RMSPE: 0.183597 valid's rmse: 0.000509656   valid's RMSPE: 0.216909
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000510056   train's RMSPE: 0.219812 valid's rmse: 0.000478071   valid's RMSPE: 0.209066
[100]   train's rmse: 0.000483929   train's RMSPE: 0.208552 valid's rmse: 0.000453579   valid's RMSPE: 0.198355
[150]   train's rmse: 0.000474046   train's RMSPE: 0.204293 valid's rmse: 0.000450015   valid's RMSPE: 0.196796
[200]   train's rmse: 0.000465815   train's RMSPE: 0.200746 valid's rmse: 0.000448373   valid's RMSPE: 0.196078
[250]   train's rmse: 0.000459622   train's RMSPE: 0.198077 valid's rmse: 0.000447051   valid's RMSPE: 0.1955
[300]   train's rmse: 0.000453199   train's RMSPE: 0.195308 valid's rmse: 0.000444225   valid's RMSPE: 0.194264
Early stopping, best iteration is:
[299]   train's rmse: 0.000453294   train's RMSPE: 0.19535  valid's rmse: 0.00044418    valid's RMSPE: 0.194245
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000502998   train's RMSPE: 0.217518 valid's rmse: 0.000518909   valid's RMSPE: 0.223856
[100]   train's rmse: 0.000475193   train's RMSPE: 0.205494 valid's rmse: 0.000503684   valid's RMSPE: 0.217288
[150]   train's rmse: 0.000465766   train's RMSPE: 0.201417 valid's rmse: 0.00050207    valid's RMSPE: 0.216592
[200]   train's rmse: 0.00045832    train's RMSPE: 0.198197 valid's rmse: 0.000501178   valid's RMSPE: 0.216207
[250]   train's rmse: 0.000451999   train's RMSPE: 0.195464 valid's rmse: 0.000500343   valid's RMSPE: 0.215847
[300]   train's rmse: 0.000446455   train's RMSPE: 0.193066 valid's rmse: 0.000499361   valid's RMSPE: 0.215423
Early stopping, best iteration is:
[299]   train's rmse: 0.000446518   train's RMSPE: 0.193094 valid's rmse: 0.000499305   valid's RMSPE: 0.215399
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000504604   train's RMSPE: 0.21822  valid's rmse: 0.000506495   valid's RMSPE: 0.218469
[100]   train's rmse: 0.000478129   train's RMSPE: 0.206771 valid's rmse: 0.000490917   valid's RMSPE: 0.21175
[150]   train's rmse: 0.000468267   train's RMSPE: 0.202506 valid's rmse: 0.000488406   valid's RMSPE: 0.210666
[200]   train's rmse: 0.000460701   train's RMSPE: 0.199234 valid's rmse: 0.000487159   valid's RMSPE: 0.210129
[250]   train's rmse: 0.000453743   train's RMSPE: 0.196225 valid's rmse: 0.000486436   valid's RMSPE: 0.209817
[300]   train's rmse: 0.000447505   train's RMSPE: 0.193527 valid's rmse: 0.000485776   valid's RMSPE: 0.209532
Early stopping, best iteration is:
[285]   train's rmse: 0.000449415   train's RMSPE: 0.194353 valid's rmse: 0.000485267   valid's RMSPE: 0.209312

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.211, compared to 0.1731892288214724, giving gain 0.0378107711785276
Our cv fold scores are [0.218, 0.217, 0.194, 0.215, 0.209]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000736734   train's RMSPE: 0.249597 valid's rmse: 0.000776814   valid's RMSPE: 0.265348
[100]   train's rmse: 0.00070234    train's RMSPE: 0.237945 valid's rmse: 0.000752108   valid's RMSPE: 0.256909
[150]   train's rmse: 0.000685514   train's RMSPE: 0.232244 valid's rmse: 0.000750291   valid's RMSPE: 0.256288
[200]   train's rmse: 0.000672003   train's RMSPE: 0.227667 valid's rmse: 0.000748589   valid's RMSPE: 0.255707
[250]   train's rmse: 0.000659487   train's RMSPE: 0.223427 valid's rmse: 0.000750149   valid's RMSPE: 0.25624
Early stopping, best iteration is:
[209]   train's rmse: 0.000669683   train's RMSPE: 0.226881 valid's rmse: 0.00074801    valid's RMSPE: 0.255509
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000734301   train's RMSPE: 0.248285 valid's rmse: 0.000788026   valid's RMSPE: 0.271245
[100]   train's rmse: 0.000700522   train's RMSPE: 0.236864 valid's rmse: 0.000771452   valid's RMSPE: 0.26554
[150]   train's rmse: 0.000683773   train's RMSPE: 0.231201 valid's rmse: 0.000773926   valid's RMSPE: 0.266391
Early stopping, best iteration is:
[112]   train's rmse: 0.000696452   train's RMSPE: 0.235488 valid's rmse: 0.000771045   valid's RMSPE: 0.2654
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000742552   train's RMSPE: 0.252735 valid's rmse: 0.00074144    valid's RMSPE: 0.248589
[100]   train's rmse: 0.000709467   train's RMSPE: 0.241474 valid's rmse: 0.000725967   valid's RMSPE: 0.243402
[150]   train's rmse: 0.000692258   train's RMSPE: 0.235617 valid's rmse: 0.000721985   valid's RMSPE: 0.242067
[200]   train's rmse: 0.000678488   train's RMSPE: 0.23093  valid's rmse: 0.000719907   valid's RMSPE: 0.24137
[250]   train's rmse: 0.00066776    train's RMSPE: 0.227279 valid's rmse: 0.000719298   valid's RMSPE: 0.241166
[300]   train's rmse: 0.000657032   train's RMSPE: 0.223628 valid's rmse: 0.000718317   valid's RMSPE: 0.240837
[350]   train's rmse: 0.000648486   train's RMSPE: 0.220719 valid's rmse: 0.00071866    valid's RMSPE: 0.240952
Early stopping, best iteration is:
[320]   train's rmse: 0.000653656   train's RMSPE: 0.222478 valid's rmse: 0.000718023   valid's RMSPE: 0.240738
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000746463   train's RMSPE: 0.253615 valid's rmse: 0.000746741   valid's RMSPE: 0.252193
[100]   train's rmse: 0.000710827   train's RMSPE: 0.241507 valid's rmse: 0.000734354   valid's RMSPE: 0.24801
[150]   train's rmse: 0.000693849   train's RMSPE: 0.235739 valid's rmse: 0.000732339   valid's RMSPE: 0.247329
Early stopping, best iteration is:
[119]   train's rmse: 0.000703754   train's RMSPE: 0.239104 valid's rmse: 0.000731626   valid's RMSPE: 0.247088
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000747071   train's RMSPE: 0.253792 valid's rmse: 0.000752757   valid's RMSPE: 0.254341
[100]   train's rmse: 0.000713948   train's RMSPE: 0.24254  valid's rmse: 0.000733785   valid's RMSPE: 0.247931
[150]   train's rmse: 0.000697031   train's RMSPE: 0.236793 valid's rmse: 0.000728828   valid's RMSPE: 0.246256
[200]   train's rmse: 0.000683624   train's RMSPE: 0.232238 valid's rmse: 0.000728172   valid's RMSPE: 0.246034
[250]   train's rmse: 0.000672446   train's RMSPE: 0.228441 valid's rmse: 0.000727523   valid's RMSPE: 0.245815
[300]   train's rmse: 0.000662311   train's RMSPE: 0.224998 valid's rmse: 0.000726069   valid's RMSPE: 0.245324
[350]   train's rmse: 0.000653165   train's RMSPE: 0.221891 valid's rmse: 0.000725237   valid's RMSPE: 0.245042
[400]   train's rmse: 0.000644602   train's RMSPE: 0.218982 valid's rmse: 0.000724066   valid's RMSPE: 0.244647
Early stopping, best iteration is:
[388]   train's rmse: 0.000646679   train's RMSPE: 0.219688 valid's rmse: 0.000722934   valid's RMSPE: 0.244264

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.251, compared to 0.22177076465868406, giving gain 0.02922923534131594
Our cv fold scores are [0.256, 0.265, 0.241, 0.247, 0.244]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000381483   train's RMSPE: 0.238166 valid's rmse: 0.000426127   valid's RMSPE: 0.268764
[100]   train's rmse: 0.000360635   train's RMSPE: 0.22515  valid's rmse: 0.000409895   valid's RMSPE: 0.258526
[150]   train's rmse: 0.000351648   train's RMSPE: 0.219539 valid's rmse: 0.000405578   valid's RMSPE: 0.255803
[200]   train's rmse: 0.000344365   train's RMSPE: 0.214992 valid's rmse: 0.000405641   valid's RMSPE: 0.255843
[250]   train's rmse: 0.000338058   train's RMSPE: 0.211055 valid's rmse: 0.000405558   valid's RMSPE: 0.255791
Early stopping, best iteration is:
[219]   train's rmse: 0.000341802   train's RMSPE: 0.213392 valid's rmse: 0.000404283   valid's RMSPE: 0.254986
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000386419   train's RMSPE: 0.241393 valid's rmse: 0.000397816   valid's RMSPE: 0.250316
[100]   train's rmse: 0.000363913   train's RMSPE: 0.227334 valid's rmse: 0.000385238   valid's RMSPE: 0.242401
[150]   train's rmse: 0.000354043   train's RMSPE: 0.221168 valid's rmse: 0.00038238    valid's RMSPE: 0.240603
[200]   train's rmse: 0.000346513   train's RMSPE: 0.216464 valid's rmse: 0.000380247   valid's RMSPE: 0.239261
[250]   train's rmse: 0.000340254   train's RMSPE: 0.212554 valid's rmse: 0.000379501   valid's RMSPE: 0.238791
[300]   train's rmse: 0.000334169   train's RMSPE: 0.208753 valid's rmse: 0.000377718   valid's RMSPE: 0.23767
[350]   train's rmse: 0.000329556   train's RMSPE: 0.205871 valid's rmse: 0.00037714    valid's RMSPE: 0.237306
[400]   train's rmse: 0.000325274   train's RMSPE: 0.203196 valid's rmse: 0.000377477   valid's RMSPE: 0.237518
[450]   train's rmse: 0.000320997   train's RMSPE: 0.200524 valid's rmse: 0.000376427   valid's RMSPE: 0.236857
[500]   train's rmse: 0.000317178   train's RMSPE: 0.198139 valid's rmse: 0.000376007   valid's RMSPE: 0.236593
[550]   train's rmse: 0.00031384    train's RMSPE: 0.196053 valid's rmse: 0.000375351   valid's RMSPE: 0.23618
[600]   train's rmse: 0.000310546   train's RMSPE: 0.193996 valid's rmse: 0.000374953   valid's RMSPE: 0.23593
Early stopping, best iteration is:
[564]   train's rmse: 0.00031286    train's RMSPE: 0.195441 valid's rmse: 0.000374756   valid's RMSPE: 0.235806
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00038804    train's RMSPE: 0.242562 valid's rmse: 0.000385378   valid's RMSPE: 0.24187
[100]   train's rmse: 0.000367607   train's RMSPE: 0.229789 valid's rmse: 0.000371898   valid's RMSPE: 0.23341
[150]   train's rmse: 0.000357901   train's RMSPE: 0.223722 valid's rmse: 0.000369786   valid's RMSPE: 0.232084
[200]   train's rmse: 0.000349973   train's RMSPE: 0.218766 valid's rmse: 0.000368117   valid's RMSPE: 0.231037
[250]   train's rmse: 0.000343611   train's RMSPE: 0.214789 valid's rmse: 0.00036674    valid's RMSPE: 0.230172
[300]   train's rmse: 0.000338379   train's RMSPE: 0.211519 valid's rmse: 0.000366292   valid's RMSPE: 0.229891
[350]   train's rmse: 0.00033375    train's RMSPE: 0.208625 valid's rmse: 0.000365041   valid's RMSPE: 0.229106
[400]   train's rmse: 0.000329134   train's RMSPE: 0.20574  valid's rmse: 0.000363405   valid's RMSPE: 0.228079
[450]   train's rmse: 0.000325105   train's RMSPE: 0.203222 valid's rmse: 0.000362715   valid's RMSPE: 0.227646
[500]   train's rmse: 0.000321571   train's RMSPE: 0.201012 valid's rmse: 0.000362525   valid's RMSPE: 0.227527
[550]   train's rmse: 0.000317821   train's RMSPE: 0.198668 valid's rmse: 0.00036232    valid's RMSPE: 0.227398
Early stopping, best iteration is:
[521]   train's rmse: 0.000319792   train's RMSPE: 0.1999   valid's rmse: 0.00036192    valid's RMSPE: 0.227148
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000391112   train's RMSPE: 0.245045 valid's rmse: 0.000369371   valid's RMSPE: 0.229695
[100]   train's rmse: 0.000370136   train's RMSPE: 0.231902 valid's rmse: 0.000358857   valid's RMSPE: 0.223157
[150]   train's rmse: 0.000360863   train's RMSPE: 0.226093 valid's rmse: 0.000354215   valid's RMSPE: 0.22027
[200]   train's rmse: 0.000353379   train's RMSPE: 0.221404 valid's rmse: 0.000351162   valid's RMSPE: 0.218372
[250]   train's rmse: 0.000347449   train's RMSPE: 0.217689 valid's rmse: 0.000348718   valid's RMSPE: 0.216851
[300]   train's rmse: 0.00034225    train's RMSPE: 0.214431 valid's rmse: 0.00034885    valid's RMSPE: 0.216933
Early stopping, best iteration is:
[265]   train's rmse: 0.000345665   train's RMSPE: 0.216571 valid's rmse: 0.000348148   valid's RMSPE: 0.216497
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000386732   train's RMSPE: 0.24262  valid's rmse: 0.000398143   valid's RMSPE: 0.246256
[100]   train's rmse: 0.00036442    train's RMSPE: 0.228623 valid's rmse: 0.000387388   valid's RMSPE: 0.239604
[150]   train's rmse: 0.0003553 train's RMSPE: 0.222901 valid's rmse: 0.000385318   valid's RMSPE: 0.238323
[200]   train's rmse: 0.000348516   train's RMSPE: 0.218645 valid's rmse: 0.000384357   valid's RMSPE: 0.237729
[250]   train's rmse: 0.000342379   train's RMSPE: 0.214795 valid's rmse: 0.000383352   valid's RMSPE: 0.237108
[300]   train's rmse: 0.000337021   train's RMSPE: 0.211434 valid's rmse: 0.000382638   valid's RMSPE: 0.236666
[350]   train's rmse: 0.000332485   train's RMSPE: 0.208588 valid's rmse: 0.000381909   valid's RMSPE: 0.236215
[400]   train's rmse: 0.000328051   train's RMSPE: 0.205807 valid's rmse: 0.000381067   valid's RMSPE: 0.235694
Early stopping, best iteration is:
[386]   train's rmse: 0.000329215   train's RMSPE: 0.206537 valid's rmse: 0.00038052    valid's RMSPE: 0.235356

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.234, compared to 0.1930378405336289, giving gain 0.040962159466371106
Our cv fold scores are [0.255, 0.236, 0.227, 0.216, 0.235]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000384782   train's RMSPE: 0.216141 valid's rmse: 0.00041617    valid's RMSPE: 0.233972
[100]   train's rmse: 0.000360969   train's RMSPE: 0.202765 valid's rmse: 0.000395374   valid's RMSPE: 0.222281
[150]   train's rmse: 0.000352712   train's RMSPE: 0.198126 valid's rmse: 0.000390744   valid's RMSPE: 0.219678
[200]   train's rmse: 0.000344949   train's RMSPE: 0.193766 valid's rmse: 0.00038726    valid's RMSPE: 0.217719
[250]   train's rmse: 0.000339541   train's RMSPE: 0.190728 valid's rmse: 0.000385985   valid's RMSPE: 0.217003
[300]   train's rmse: 0.000335158   train's RMSPE: 0.188266 valid's rmse: 0.000384433   valid's RMSPE: 0.21613
[350]   train's rmse: 0.000330883   train's RMSPE: 0.185865 valid's rmse: 0.000383497   valid's RMSPE: 0.215604
[400]   train's rmse: 0.000326963   train's RMSPE: 0.183663 valid's rmse: 0.000382281   valid's RMSPE: 0.21492
[450]   train's rmse: 0.000322706   train's RMSPE: 0.181272 valid's rmse: 0.000382544   valid's RMSPE: 0.215068
Early stopping, best iteration is:
[403]   train's rmse: 0.000326348   train's RMSPE: 0.183317 valid's rmse: 0.000381603   valid's RMSPE: 0.214539
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000380577   train's RMSPE: 0.21289  valid's rmse: 0.000418214   valid's RMSPE: 0.238986
[100]   train's rmse: 0.00035824    train's RMSPE: 0.200395 valid's rmse: 0.00039854    valid's RMSPE: 0.227744
[150]   train's rmse: 0.00034969    train's RMSPE: 0.195612 valid's rmse: 0.000393661   valid's RMSPE: 0.224956
[200]   train's rmse: 0.000343398   train's RMSPE: 0.192093 valid's rmse: 0.000389981   valid's RMSPE: 0.222853
[250]   train's rmse: 0.000338058   train's RMSPE: 0.189105 valid's rmse: 0.000387312   valid's RMSPE: 0.221327
[300]   train's rmse: 0.00033245    train's RMSPE: 0.185969 valid's rmse: 0.000384984   valid's RMSPE: 0.219997
[350]   train's rmse: 0.000328569   train's RMSPE: 0.183798 valid's rmse: 0.000383814   valid's RMSPE: 0.219328
[400]   train's rmse: 0.000324602   train's RMSPE: 0.181578 valid's rmse: 0.00038238    valid's RMSPE: 0.218509
[450]   train's rmse: 0.000321023   train's RMSPE: 0.179576 valid's rmse: 0.000381534   valid's RMSPE: 0.218026
[500]   train's rmse: 0.000318089   train's RMSPE: 0.177935 valid's rmse: 0.000381131   valid's RMSPE: 0.217795
[550]   train's rmse: 0.000315386   train's RMSPE: 0.176423 valid's rmse: 0.000380522   valid's RMSPE: 0.217447
[600]   train's rmse: 0.00031261    train's RMSPE: 0.17487  valid's rmse: 0.000380056   valid's RMSPE: 0.217181
[650]   train's rmse: 0.000309752   train's RMSPE: 0.173272 valid's rmse: 0.0003795 valid's RMSPE: 0.216863
[700]   train's rmse: 0.000307186   train's RMSPE: 0.171836 valid's rmse: 0.000379806   valid's RMSPE: 0.217038
[750]   train's rmse: 0.000304376   train's RMSPE: 0.170264 valid's rmse: 0.000378732   valid's RMSPE: 0.216424
Early stopping, best iteration is:
[744]   train's rmse: 0.000304642   train's RMSPE: 0.170413 valid's rmse: 0.00037858    valid's RMSPE: 0.216338
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000389079   train's RMSPE: 0.218222 valid's rmse: 0.000365458   valid's RMSPE: 0.206706
[100]   train's rmse: 0.000365529   train's RMSPE: 0.205014 valid's rmse: 0.000350951   valid's RMSPE: 0.198501
[150]   train's rmse: 0.000356609   train's RMSPE: 0.200011 valid's rmse: 0.000348343   valid's RMSPE: 0.197026
[200]   train's rmse: 0.000349578   train's RMSPE: 0.196068 valid's rmse: 0.000346755   valid's RMSPE: 0.196127
[250]   train's rmse: 0.000344077   train's RMSPE: 0.192982 valid's rmse: 0.000345076   valid's RMSPE: 0.195177
[300]   train's rmse: 0.00033944    train's RMSPE: 0.190381 valid's rmse: 0.000345725   valid's RMSPE: 0.195545
Early stopping, best iteration is:
[251]   train's rmse: 0.000344019   train's RMSPE: 0.192949 valid's rmse: 0.00034503    valid's RMSPE: 0.195152
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000390033   train's RMSPE: 0.219327 valid's rmse: 0.000372524   valid's RMSPE: 0.208528
[100]   train's rmse: 0.000366111   train's RMSPE: 0.205875 valid's rmse: 0.000358462   valid's RMSPE: 0.200657
[150]   train's rmse: 0.000357868   train's RMSPE: 0.20124  valid's rmse: 0.000356217   valid's RMSPE: 0.199401
[200]   train's rmse: 0.000351282   train's RMSPE: 0.197536 valid's rmse: 0.000354192   valid's RMSPE: 0.198267
[250]   train's rmse: 0.000345907   train's RMSPE: 0.194514 valid's rmse: 0.000352985   valid's RMSPE: 0.197591
[300]   train's rmse: 0.000341495   train's RMSPE: 0.192033 valid's rmse: 0.000351283   valid's RMSPE: 0.196639
Early stopping, best iteration is:
[293]   train's rmse: 0.000342072   train's RMSPE: 0.192357 valid's rmse: 0.000350955   valid's RMSPE: 0.196455
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000386639   train's RMSPE: 0.218364 valid's rmse: 0.000402552   valid's RMSPE: 0.221341
[100]   train's rmse: 0.000363112   train's RMSPE: 0.205076 valid's rmse: 0.000384095   valid's RMSPE: 0.211192
[150]   train's rmse: 0.000353671   train's RMSPE: 0.199744 valid's rmse: 0.000380119   valid's RMSPE: 0.209006
[200]   train's rmse: 0.00034643    train's RMSPE: 0.195655 valid's rmse: 0.000378692   valid's RMSPE: 0.208221
[250]   train's rmse: 0.000340689   train's RMSPE: 0.192412 valid's rmse: 0.000376218   valid's RMSPE: 0.206861
[300]   train's rmse: 0.000335999   train's RMSPE: 0.189764 valid's rmse: 0.000375025   valid's RMSPE: 0.206205
[350]   train's rmse: 0.000331875   train's RMSPE: 0.187434 valid's rmse: 0.000375051   valid's RMSPE: 0.206219
[400]   train's rmse: 0.000327678   train's RMSPE: 0.185064 valid's rmse: 0.000373807   valid's RMSPE: 0.205536
[450]   train's rmse: 0.00032427    train's RMSPE: 0.183139 valid's rmse: 0.000374149   valid's RMSPE: 0.205724
Early stopping, best iteration is:
[446]   train's rmse: 0.000324529   train's RMSPE: 0.183286 valid's rmse: 0.000373668   valid's RMSPE: 0.205459
Our out of folds RMSPE is 0.206, compared to 0.1686554230321588, giving gain 0.037344576967841187
Our cv fold scores are [0.215, 0.216, 0.195, 0.196, 0.205]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000575982   train's RMSPE: 0.233616 valid's rmse: 0.000610009   valid's RMSPE: 0.2464
[100]   train's rmse: 0.000547459   train's RMSPE: 0.222047 valid's rmse: 0.000591339   valid's RMSPE: 0.238859
[150]   train's rmse: 0.000534472   train's RMSPE: 0.216779 valid's rmse: 0.000587469   valid's RMSPE: 0.237296
[200]   train's rmse: 0.00052523    train's RMSPE: 0.213031 valid's rmse: 0.000587013   valid's RMSPE: 0.237111
[250]   train's rmse: 0.000516824   train's RMSPE: 0.209621 valid's rmse: 0.000586106   valid's RMSPE: 0.236745
[300]   train's rmse: 0.000509715   train's RMSPE: 0.206738 valid's rmse: 0.00058524    valid's RMSPE: 0.236395
Early stopping, best iteration is:
[289]   train's rmse: 0.000511099   train's RMSPE: 0.207299 valid's rmse: 0.000584212   valid's RMSPE: 0.23598
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000575489   train's RMSPE: 0.232763 valid's rmse: 0.000604577   valid's RMSPE: 0.246941
[100]   train's rmse: 0.000546889   train's RMSPE: 0.221195 valid's rmse: 0.000586229   valid's RMSPE: 0.239447
[150]   train's rmse: 0.000533618   train's RMSPE: 0.215828 valid's rmse: 0.000581886   valid's RMSPE: 0.237673
[200]   train's rmse: 0.000524077   train's RMSPE: 0.211969 valid's rmse: 0.00057857    valid's RMSPE: 0.236318
[250]   train's rmse: 0.000515961   train's RMSPE: 0.208686 valid's rmse: 0.000576071   valid's RMSPE: 0.235298
[300]   train's rmse: 0.000507557   train's RMSPE: 0.205287 valid's rmse: 0.00057412    valid's RMSPE: 0.234501
[350]   train's rmse: 0.000500767   train's RMSPE: 0.202541 valid's rmse: 0.00057323    valid's RMSPE: 0.234137
Early stopping, best iteration is:
[334]   train's rmse: 0.000502837   train's RMSPE: 0.203378 valid's rmse: 0.000572651   valid's RMSPE: 0.233901
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000580629   train's RMSPE: 0.235061 valid's rmse: 0.000578487   valid's RMSPE: 0.235418
[100]   train's rmse: 0.000553119   train's RMSPE: 0.223924 valid's rmse: 0.000556155   valid's RMSPE: 0.22633
[150]   train's rmse: 0.000540631   train's RMSPE: 0.218868 valid's rmse: 0.000552012   valid's RMSPE: 0.224644
[200]   train's rmse: 0.000529465   train's RMSPE: 0.214348 valid's rmse: 0.000548324   valid's RMSPE: 0.223143
[250]   train's rmse: 0.000521872   train's RMSPE: 0.211274 valid's rmse: 0.000546144   valid's RMSPE: 0.222256
[300]   train's rmse: 0.000514498   train's RMSPE: 0.208288 valid's rmse: 0.000544662   valid's RMSPE: 0.221653
[350]   train's rmse: 0.00050858    train's RMSPE: 0.205893 valid's rmse: 0.000544609   valid's RMSPE: 0.221631
Early stopping, best iteration is:
[307]   train's rmse: 0.000513812   train's RMSPE: 0.208011 valid's rmse: 0.000544195   valid's RMSPE: 0.221463
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000582283   train's RMSPE: 0.235814 valid's rmse: 0.000575235   valid's RMSPE: 0.233765
[100]   train's rmse: 0.000551679   train's RMSPE: 0.22342  valid's rmse: 0.000556892   valid's RMSPE: 0.226311
[150]   train's rmse: 0.000538868   train's RMSPE: 0.218232 valid's rmse: 0.000555158   valid's RMSPE: 0.225606
[200]   train's rmse: 0.00052871    train's RMSPE: 0.214118 valid's rmse: 0.000552035   valid's RMSPE: 0.224337
[250]   train's rmse: 0.000519738   train's RMSPE: 0.210485 valid's rmse: 0.000547457   valid's RMSPE: 0.222477
[300]   train's rmse: 0.000512577   train's RMSPE: 0.207585 valid's rmse: 0.000545923   valid's RMSPE: 0.221853
[350]   train's rmse: 0.000505555   train's RMSPE: 0.204741 valid's rmse: 0.000543915   valid's RMSPE: 0.221037
[400]   train's rmse: 0.000499733   train's RMSPE: 0.202383 valid's rmse: 0.000542997   valid's RMSPE: 0.220664
[450]   train's rmse: 0.00049474    train's RMSPE: 0.200361 valid's rmse: 0.00054145    valid's RMSPE: 0.220036
[500]   train's rmse: 0.000489338   train's RMSPE: 0.198173 valid's rmse: 0.000539899   valid's RMSPE: 0.219405
[550]   train's rmse: 0.000484276   train's RMSPE: 0.196123 valid's rmse: 0.000539289   valid's RMSPE: 0.219157
[600]   train's rmse: 0.000479866   train's RMSPE: 0.194337 valid's rmse: 0.000538044   valid's RMSPE: 0.218651
[650]   train's rmse: 0.000475214   train's RMSPE: 0.192453 valid's rmse: 0.000537727   valid's RMSPE: 0.218523
Early stopping, best iteration is:
[610]   train's rmse: 0.000478767   train's RMSPE: 0.193892 valid's rmse: 0.000537542   valid's RMSPE: 0.218447
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000576429   train's RMSPE: 0.23428  valid's rmse: 0.000593098   valid's RMSPE: 0.237564
[100]   train's rmse: 0.000546956   train's RMSPE: 0.222301 valid's rmse: 0.000578114   valid's RMSPE: 0.231562
[150]   train's rmse: 0.000534339   train's RMSPE: 0.217173 valid's rmse: 0.000573977   valid's RMSPE: 0.229905
[200]   train's rmse: 0.00052516    train's RMSPE: 0.213442 valid's rmse: 0.000571255   valid's RMSPE: 0.228815
[250]   train's rmse: 0.000516511   train's RMSPE: 0.209927 valid's rmse: 0.000569413   valid's RMSPE: 0.228077
[300]   train's rmse: 0.000509189   train's RMSPE: 0.206951 valid's rmse: 0.000567924   valid's RMSPE: 0.22748
[350]   train's rmse: 0.000501732   train's RMSPE: 0.20392  valid's rmse: 0.000566379   valid's RMSPE: 0.226862
Early stopping, best iteration is:
[337]   train's rmse: 0.000503596   train's RMSPE: 0.204678 valid's rmse: 0.000566109   valid's RMSPE: 0.226754
Our out of folds RMSPE is 0.227, compared to 0.191652955156558, giving gain 0.035347044843442005
Our cv fold scores are [0.236, 0.234, 0.221, 0.218, 0.227]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000711837   train's RMSPE: 0.280035 valid's rmse: 0.000771399   valid's RMSPE: 0.305334
[100]   train's rmse: 0.000676695   train's RMSPE: 0.26621  valid's rmse: 0.000757647   valid's RMSPE: 0.299891
[150]   train's rmse: 0.000658101   train's RMSPE: 0.258895 valid's rmse: 0.000754218   valid's RMSPE: 0.298534
[200]   train's rmse: 0.00064415    train's RMSPE: 0.253407 valid's rmse: 0.000752087   valid's RMSPE: 0.29769
[250]   train's rmse: 0.000631555   train's RMSPE: 0.248452 valid's rmse: 0.000751979   valid's RMSPE: 0.297647
Early stopping, best iteration is:
[204]   train's rmse: 0.00064322    train's RMSPE: 0.253041 valid's rmse: 0.000751441   valid's RMSPE: 0.297434
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000718611   train's RMSPE: 0.280959 valid's rmse: 0.000763813   valid's RMSPE: 0.309579
[100]   train's rmse: 0.000686064   train's RMSPE: 0.268234 valid's rmse: 0.000733588   valid's RMSPE: 0.297328
[150]   train's rmse: 0.000669262   train's RMSPE: 0.261665 valid's rmse: 0.000731339   valid's RMSPE: 0.296417
Early stopping, best iteration is:
[124]   train's rmse: 0.000677614   train's RMSPE: 0.26493  valid's rmse: 0.000729048   valid's RMSPE: 0.295488
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000729827   train's RMSPE: 0.286404 valid's rmse: 0.000695522   valid's RMSPE: 0.277967
[100]   train's rmse: 0.000692909   train's RMSPE: 0.271916 valid's rmse: 0.000684463   valid's RMSPE: 0.273547
[150]   train's rmse: 0.000674148   train's RMSPE: 0.264554 valid's rmse: 0.000686471   valid's RMSPE: 0.27435
Early stopping, best iteration is:
[106]   train's rmse: 0.000690399   train's RMSPE: 0.270931 valid's rmse: 0.000682954   valid's RMSPE: 0.272944
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000722212   train's RMSPE: 0.286414 valid's rmse: 0.000745011   valid's RMSPE: 0.285272
[100]   train's rmse: 0.000685207   train's RMSPE: 0.271739 valid's rmse: 0.000736635   valid's RMSPE: 0.282065
[150]   train's rmse: 0.000665343   train's RMSPE: 0.263861 valid's rmse: 0.000730966   valid's RMSPE: 0.279894
[200]   train's rmse: 0.000650377   train's RMSPE: 0.257926 valid's rmse: 0.000731671   valid's RMSPE: 0.280164
Early stopping, best iteration is:
[175]   train's rmse: 0.000658039   train's RMSPE: 0.260964 valid's rmse: 0.000729852   valid's RMSPE: 0.279468
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000718978   train's RMSPE: 0.284721 valid's rmse: 0.000728316   valid's RMSPE: 0.280596
[100]   train's rmse: 0.00068428    train's RMSPE: 0.27098  valid's rmse: 0.000712443   valid's RMSPE: 0.274481
[150]   train's rmse: 0.000666736   train's RMSPE: 0.264033 valid's rmse: 0.000710709   valid's RMSPE: 0.273813
[200]   train's rmse: 0.000653682   train's RMSPE: 0.258863 valid's rmse: 0.000708121   valid's RMSPE: 0.272815
[250]   train's rmse: 0.000641891   train's RMSPE: 0.254194 valid's rmse: 0.000707535   valid's RMSPE: 0.27259
[300]   train's rmse: 0.000631877   train's RMSPE: 0.250228 valid's rmse: 0.000708484   valid's RMSPE: 0.272955
[350]   train's rmse: 0.00062199    train's RMSPE: 0.246313 valid's rmse: 0.00070626    valid's RMSPE: 0.272099
[400]   train's rmse: 0.000614042   train's RMSPE: 0.243165 valid's rmse: 0.00070629    valid's RMSPE: 0.27211
Early stopping, best iteration is:
[377]   train's rmse: 0.000617611   train's RMSPE: 0.244578 valid's rmse: 0.000705118   valid's RMSPE: 0.271659

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.284, compared to 0.25713568883373233, giving gain 0.02686431116626764
Our cv fold scores are [0.297, 0.295, 0.273, 0.279, 0.272]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000635952   train's RMSPE: 0.233423 valid's rmse: 0.000677687   valid's RMSPE: 0.253069
[100]   train's rmse: 0.000605958   train's RMSPE: 0.222414 valid's rmse: 0.000651236   valid's RMSPE: 0.243192
[150]   train's rmse: 0.000591471   train's RMSPE: 0.217097 valid's rmse: 0.000647573   valid's RMSPE: 0.241824
[200]   train's rmse: 0.000579884   train's RMSPE: 0.212843 valid's rmse: 0.000645845   valid's RMSPE: 0.241178
[250]   train's rmse: 0.00057113    train's RMSPE: 0.20963  valid's rmse: 0.000643455   valid's RMSPE: 0.240286
[300]   train's rmse: 0.000562645   train's RMSPE: 0.206516 valid's rmse: 0.000643038   valid's RMSPE: 0.24013
[350]   train's rmse: 0.000554556   train's RMSPE: 0.203547 valid's rmse: 0.000639853   valid's RMSPE: 0.238941
Early stopping, best iteration is:
[347]   train's rmse: 0.000554992   train's RMSPE: 0.203707 valid's rmse: 0.000639367   valid's RMSPE: 0.238759
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000631052   train's RMSPE: 0.23118  valid's rmse: 0.000689904   valid's RMSPE: 0.259534
[100]   train's rmse: 0.000599598   train's RMSPE: 0.219657 valid's rmse: 0.000666121   valid's RMSPE: 0.250587
[150]   train's rmse: 0.000585679   train's RMSPE: 0.214558 valid's rmse: 0.000659903   valid's RMSPE: 0.248247
[200]   train's rmse: 0.000574978   train's RMSPE: 0.210638 valid's rmse: 0.000655928   valid's RMSPE: 0.246752
[250]   train's rmse: 0.000566921   train's RMSPE: 0.207686 valid's rmse: 0.00065328    valid's RMSPE: 0.245756
[300]   train's rmse: 0.000558922   train's RMSPE: 0.204756 valid's rmse: 0.000649415   valid's RMSPE: 0.244302
[350]   train's rmse: 0.000551769   train's RMSPE: 0.202135 valid's rmse: 0.000646548   valid's RMSPE: 0.243223
[400]   train's rmse: 0.000545268   train's RMSPE: 0.199754 valid's rmse: 0.000645924   valid's RMSPE: 0.242989
[450]   train's rmse: 0.00053964    train's RMSPE: 0.197692 valid's rmse: 0.000643798   valid's RMSPE: 0.242189
[500]   train's rmse: 0.000533583   train's RMSPE: 0.195473 valid's rmse: 0.000641323   valid's RMSPE: 0.241258
[550]   train's rmse: 0.000528085   train's RMSPE: 0.193459 valid's rmse: 0.000640522   valid's RMSPE: 0.240956
[600]   train's rmse: 0.000523455   train's RMSPE: 0.191763 valid's rmse: 0.000639716   valid's RMSPE: 0.240653
[650]   train's rmse: 0.000518661   train's RMSPE: 0.190006 valid's rmse: 0.000638226   valid's RMSPE: 0.240093
[700]   train's rmse: 0.000513731   train's RMSPE: 0.188201 valid's rmse: 0.000638467   valid's RMSPE: 0.240184
Early stopping, best iteration is:
[664]   train's rmse: 0.000517075   train's RMSPE: 0.189425 valid's rmse: 0.000637786   valid's RMSPE: 0.239927
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000642865   train's RMSPE: 0.236321 valid's rmse: 0.000631448   valid's RMSPE: 0.234404
[100]   train's rmse: 0.000608943   train's RMSPE: 0.223851 valid's rmse: 0.000613907   valid's RMSPE: 0.227893
[150]   train's rmse: 0.000594986   train's RMSPE: 0.21872  valid's rmse: 0.000610803   valid's RMSPE: 0.22674
[200]   train's rmse: 0.000582996   train's RMSPE: 0.214313 valid's rmse: 0.000607715   valid's RMSPE: 0.225594
[250]   train's rmse: 0.000573542   train's RMSPE: 0.210838 valid's rmse: 0.000606709   valid's RMSPE: 0.225221
[300]   train's rmse: 0.000566387   train's RMSPE: 0.208207 valid's rmse: 0.000605567   valid's RMSPE: 0.224797
[350]   train's rmse: 0.000559827   train's RMSPE: 0.205796 valid's rmse: 0.000606535   valid's RMSPE: 0.225156
Early stopping, best iteration is:
[310]   train's rmse: 0.000565079   train's RMSPE: 0.207726 valid's rmse: 0.000605026   valid's RMSPE: 0.224596
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000648115   train's RMSPE: 0.239664 valid's rmse: 0.000605241   valid's RMSPE: 0.219369
[100]   train's rmse: 0.000615741   train's RMSPE: 0.227693 valid's rmse: 0.000594228   valid's RMSPE: 0.215378
[150]   train's rmse: 0.000602537   train's RMSPE: 0.22281  valid's rmse: 0.000592018   valid's RMSPE: 0.214577
[200]   train's rmse: 0.000591016   train's RMSPE: 0.21855  valid's rmse: 0.0005892 valid's RMSPE: 0.213555
[250]   train's rmse: 0.000581653   train's RMSPE: 0.215087 valid's rmse: 0.00058716    valid's RMSPE: 0.212816
[300]   train's rmse: 0.000571873   train's RMSPE: 0.211471 valid's rmse: 0.000586105   valid's RMSPE: 0.212434
[350]   train's rmse: 0.000563975   train's RMSPE: 0.208551 valid's rmse: 0.000584527   valid's RMSPE: 0.211862
[400]   train's rmse: 0.000556879   train's RMSPE: 0.205926 valid's rmse: 0.000583707   valid's RMSPE: 0.211564
Early stopping, best iteration is:
[385]   train's rmse: 0.000558868   train's RMSPE: 0.206662 valid's rmse: 0.000583442   valid's RMSPE: 0.211468
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000641123   train's RMSPE: 0.237765 valid's rmse: 0.000657114   valid's RMSPE: 0.235278
[100]   train's rmse: 0.000607932   train's RMSPE: 0.225456 valid's rmse: 0.000637019   valid's RMSPE: 0.228083
[150]   train's rmse: 0.000593093   train's RMSPE: 0.219953 valid's rmse: 0.000631381   valid's RMSPE: 0.226065
[200]   train's rmse: 0.000581679   train's RMSPE: 0.21572  valid's rmse: 0.000629575   valid's RMSPE: 0.225418
[250]   train's rmse: 0.000572791   train's RMSPE: 0.212424 valid's rmse: 0.000628712   valid's RMSPE: 0.225109
[300]   train's rmse: 0.000564822   train's RMSPE: 0.209468 valid's rmse: 0.00062704    valid's RMSPE: 0.22451
[350]   train's rmse: 0.00055659    train's RMSPE: 0.206416 valid's rmse: 0.000624631   valid's RMSPE: 0.223648
[400]   train's rmse: 0.000549678   train's RMSPE: 0.203852 valid's rmse: 0.000624158   valid's RMSPE: 0.223479
[450]   train's rmse: 0.000543746   train's RMSPE: 0.201652 valid's rmse: 0.000623013   valid's RMSPE: 0.223069
[500]   train's rmse: 0.000538405   train's RMSPE: 0.199671 valid's rmse: 0.000623647   valid's RMSPE: 0.223296
Early stopping, best iteration is:
[451]   train's rmse: 0.000543692   train's RMSPE: 0.201632 valid's rmse: 0.000622932   valid's RMSPE: 0.223039

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.228, compared to 0.19072875458746932, giving gain 0.037271245412530685
Our cv fold scores are [0.239, 0.24, 0.225, 0.211, 0.223]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00167119    train's RMSPE: 0.286459 valid's rmse: 0.00184806    valid's RMSPE: 0.311681
[100]   train's rmse: 0.00159208    train's RMSPE: 0.272899 valid's rmse: 0.00181748    valid's RMSPE: 0.306523
[150]   train's rmse: 0.00154664    train's RMSPE: 0.26511  valid's rmse: 0.00180777    valid's RMSPE: 0.304885
[200]   train's rmse: 0.00151132    train's RMSPE: 0.259056 valid's rmse: 0.00180815    valid's RMSPE: 0.304949
Early stopping, best iteration is:
[170]   train's rmse: 0.00153185    train's RMSPE: 0.262575 valid's rmse: 0.00180459    valid's RMSPE: 0.304348
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00168635    train's RMSPE: 0.28763  valid's rmse: 0.0017807 valid's RMSPE: 0.306375
[100]   train's rmse: 0.00160457    train's RMSPE: 0.273682 valid's rmse: 0.00174622    valid's RMSPE: 0.300443
[150]   train's rmse: 0.00156184    train's RMSPE: 0.266392 valid's rmse: 0.00174482    valid's RMSPE: 0.300201
Early stopping, best iteration is:
[125]   train's rmse: 0.00158212    train's RMSPE: 0.269851 valid's rmse: 0.00173887    valid's RMSPE: 0.299178
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00172558    train's RMSPE: 0.293414 valid's rmse: 0.00161225    valid's RMSPE: 0.280729
[100]   train's rmse: 0.00164543    train's RMSPE: 0.279785 valid's rmse: 0.00157121    valid's RMSPE: 0.273583
[150]   train's rmse: 0.00160099    train's RMSPE: 0.272228 valid's rmse: 0.00155786    valid's RMSPE: 0.271259
[200]   train's rmse: 0.0015686 train's RMSPE: 0.26672  valid's rmse: 0.00155391    valid's RMSPE: 0.270572
Early stopping, best iteration is:
[181]   train's rmse: 0.00157996    train's RMSPE: 0.268652 valid's rmse: 0.00155247    valid's RMSPE: 0.270321
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00167584    train's RMSPE: 0.285722 valid's rmse: 0.00180791    valid's RMSPE: 0.311545
[100]   train's rmse: 0.0015899 train's RMSPE: 0.271071 valid's rmse: 0.00178703    valid's RMSPE: 0.307947
[150]   train's rmse: 0.00154124    train's RMSPE: 0.262775 valid's rmse: 0.0017884 valid's RMSPE: 0.308183
Early stopping, best iteration is:
[102]   train's rmse: 0.00158667    train's RMSPE: 0.27052  valid's rmse: 0.00178602    valid's RMSPE: 0.307774
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.0016907 train's RMSPE: 0.290461 valid's rmse: 0.00177341    valid's RMSPE: 0.296269
[100]   train's rmse: 0.00161115    train's RMSPE: 0.276794 valid's rmse: 0.00174614    valid's RMSPE: 0.291713
[150]   train's rmse: 0.00156436    train's RMSPE: 0.268757 valid's rmse: 0.00174247    valid's RMSPE: 0.2911
Early stopping, best iteration is:
[146]   train's rmse: 0.00156782    train's RMSPE: 0.269351 valid's rmse: 0.00173899    valid's RMSPE: 0.290518
Our out of folds RMSPE is 0.295, compared to 0.2889225845421511, giving gain 0.0060774154578489
Our cv fold scores are [0.304, 0.299, 0.27, 0.308, 0.291]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000490788   train's RMSPE: 0.263215 valid's rmse: 0.000531108   valid's RMSPE: 0.283377
[100]   train's rmse: 0.000466599   train's RMSPE: 0.250242 valid's rmse: 0.000518328   valid's RMSPE: 0.276558
[150]   train's rmse: 0.000454113   train's RMSPE: 0.243546 valid's rmse: 0.000516021   valid's RMSPE: 0.275328
[200]   train's rmse: 0.000445361   train's RMSPE: 0.238851 valid's rmse: 0.00051365    valid's RMSPE: 0.274062
[250]   train's rmse: 0.000437869   train's RMSPE: 0.234833 valid's rmse: 0.000511702   valid's RMSPE: 0.273023
[300]   train's rmse: 0.000431259   train's RMSPE: 0.231289 valid's rmse: 0.000509312   valid's RMSPE: 0.271748
[350]   train's rmse: 0.000425126   train's RMSPE: 0.228    valid's rmse: 0.000508052   valid's RMSPE: 0.271076
[400]   train's rmse: 0.000419583   train's RMSPE: 0.225027 valid's rmse: 0.000508063   valid's RMSPE: 0.271082
Early stopping, best iteration is:
[354]   train's rmse: 0.000424598   train's RMSPE: 0.227716 valid's rmse: 0.000507699   valid's RMSPE: 0.270887
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000491334   train's RMSPE: 0.262405 valid's rmse: 0.000533633   valid's RMSPE: 0.289489
[100]   train's rmse: 0.000466528   train's RMSPE: 0.249157 valid's rmse: 0.000520872   valid's RMSPE: 0.282566
[150]   train's rmse: 0.000454126   train's RMSPE: 0.242533 valid's rmse: 0.000517467   valid's RMSPE: 0.280719
[200]   train's rmse: 0.000443623   train's RMSPE: 0.236924 valid's rmse: 0.00051588    valid's RMSPE: 0.279858
[250]   train's rmse: 0.000435741   train's RMSPE: 0.232714 valid's rmse: 0.000515847   valid's RMSPE: 0.27984
Early stopping, best iteration is:
[212]   train's rmse: 0.000441752   train's RMSPE: 0.235925 valid's rmse: 0.000515403   valid's RMSPE: 0.279599
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000503659   train's RMSPE: 0.269387 valid's rmse: 0.000481946   valid's RMSPE: 0.259938
[100]   train's rmse: 0.000478298   train's RMSPE: 0.255822 valid's rmse: 0.000466311   valid's RMSPE: 0.251505
[150]   train's rmse: 0.000464998   train's RMSPE: 0.248709 valid's rmse: 0.000466505   valid's RMSPE: 0.25161
Early stopping, best iteration is:
[111]   train's rmse: 0.000474784   train's RMSPE: 0.253943 valid's rmse: 0.000465048   valid's RMSPE: 0.250824
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000498307   train's RMSPE: 0.266603 valid's rmse: 0.000509034   valid's RMSPE: 0.274229
[100]   train's rmse: 0.000473999   train's RMSPE: 0.253598 valid's rmse: 0.000494233   valid's RMSPE: 0.266256
[150]   train's rmse: 0.000462714   train's RMSPE: 0.24756  valid's rmse: 0.000492027   valid's RMSPE: 0.265067
[200]   train's rmse: 0.000453425   train's RMSPE: 0.24259  valid's rmse: 0.000490754   valid's RMSPE: 0.264381
[250]   train's rmse: 0.000445749   train's RMSPE: 0.238484 valid's rmse: 0.000488495   valid's RMSPE: 0.263164
Early stopping, best iteration is:
[231]   train's rmse: 0.000448377   train's RMSPE: 0.239889 valid's rmse: 0.000487196   valid's RMSPE: 0.262465
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000501141   train's RMSPE: 0.269884 valid's rmse: 0.000492624   valid's RMSPE: 0.258381
[100]   train's rmse: 0.000476804   train's RMSPE: 0.256778 valid's rmse: 0.000478897   valid's RMSPE: 0.251181
[150]   train's rmse: 0.000463476   train's RMSPE: 0.2496   valid's rmse: 0.000476099   valid's RMSPE: 0.249714
[200]   train's rmse: 0.000452939   train's RMSPE: 0.243925 valid's rmse: 0.000474241   valid's RMSPE: 0.248739
Early stopping, best iteration is:
[187]   train's rmse: 0.000455319   train's RMSPE: 0.245207 valid's rmse: 0.000474061   valid's RMSPE: 0.248645
Our out of folds RMSPE is 0.263, compared to 0.22601895323506935, giving gain 0.036981046764930664
Our cv fold scores are [0.271, 0.28, 0.251, 0.262, 0.249]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000444933   train's RMSPE: 0.226682 valid's rmse: 0.000475867   valid's RMSPE: 0.241866
[100]   train's rmse: 0.000425009   train's RMSPE: 0.216532 valid's rmse: 0.000459049   valid's RMSPE: 0.233317
[150]   train's rmse: 0.000415276   train's RMSPE: 0.211573 valid's rmse: 0.000453479   valid's RMSPE: 0.230486
[200]   train's rmse: 0.000407785   train's RMSPE: 0.207756 valid's rmse: 0.000451113   valid's RMSPE: 0.229284
[250]   train's rmse: 0.000401481   train's RMSPE: 0.204544 valid's rmse: 0.000450142   valid's RMSPE: 0.228791
[300]   train's rmse: 0.00039584    train's RMSPE: 0.201671 valid's rmse: 0.00044851    valid's RMSPE: 0.227961
[350]   train's rmse: 0.000390753   train's RMSPE: 0.199079 valid's rmse: 0.00044793    valid's RMSPE: 0.227666
Early stopping, best iteration is:
[324]   train's rmse: 0.000393145   train's RMSPE: 0.200298 valid's rmse: 0.000447624   valid's RMSPE: 0.227511
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000440434   train's RMSPE: 0.224109 valid's rmse: 0.000482282   valid's RMSPE: 0.246356
[100]   train's rmse: 0.000419707   train's RMSPE: 0.213563 valid's rmse: 0.000469944   valid's RMSPE: 0.240053
[150]   train's rmse: 0.000410704   train's RMSPE: 0.208982 valid's rmse: 0.000468145   valid's RMSPE: 0.239135
[200]   train's rmse: 0.00040319    train's RMSPE: 0.205158 valid's rmse: 0.000465055   valid's RMSPE: 0.237556
[250]   train's rmse: 0.000397336   train's RMSPE: 0.202179 valid's rmse: 0.00046447    valid's RMSPE: 0.237257
Early stopping, best iteration is:
[243]   train's rmse: 0.000398031   train's RMSPE: 0.202533 valid's rmse: 0.000464112   valid's RMSPE: 0.237074
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00045182    train's RMSPE: 0.229304 valid's rmse: 0.000431247   valid's RMSPE: 0.222549
[100]   train's rmse: 0.000431161   train's RMSPE: 0.218819 valid's rmse: 0.000419763   valid's RMSPE: 0.216622
[150]   train's rmse: 0.000421159   train's RMSPE: 0.213743 valid's rmse: 0.000416146   valid's RMSPE: 0.214755
[200]   train's rmse: 0.000413843   train's RMSPE: 0.21003  valid's rmse: 0.000414329   valid's RMSPE: 0.213818
[250]   train's rmse: 0.000407864   train's RMSPE: 0.206996 valid's rmse: 0.000414628   valid's RMSPE: 0.213972
Early stopping, best iteration is:
[245]   train's rmse: 0.000408311   train's RMSPE: 0.207223 valid's rmse: 0.000414143   valid's RMSPE: 0.213722
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000456671   train's RMSPE: 0.232649 valid's rmse: 0.000420007   valid's RMSPE: 0.213522
[100]   train's rmse: 0.000435785   train's RMSPE: 0.222009 valid's rmse: 0.000408954   valid's RMSPE: 0.207903
[150]   train's rmse: 0.00042709    train's RMSPE: 0.217579 valid's rmse: 0.000407944   valid's RMSPE: 0.20739
[200]   train's rmse: 0.000419061   train's RMSPE: 0.213489 valid's rmse: 0.000408294   valid's RMSPE: 0.207568
Early stopping, best iteration is:
[174]   train's rmse: 0.000422751   train's RMSPE: 0.215369 valid's rmse: 0.000407498   valid's RMSPE: 0.207163
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000445781   train's RMSPE: 0.227744 valid's rmse: 0.000472346   valid's RMSPE: 0.237381
[100]   train's rmse: 0.000424741   train's RMSPE: 0.216995 valid's rmse: 0.000458404   valid's RMSPE: 0.230375
[150]   train's rmse: 0.000415616   train's RMSPE: 0.212333 valid's rmse: 0.000455008   valid's RMSPE: 0.228668
[200]   train's rmse: 0.000408374   train's RMSPE: 0.208634 valid's rmse: 0.000452808   valid's RMSPE: 0.227562
[250]   train's rmse: 0.000401722   train's RMSPE: 0.205235 valid's rmse: 0.000452202   valid's RMSPE: 0.227258
Early stopping, best iteration is:
[226]   train's rmse: 0.000404502   train's RMSPE: 0.206655 valid's rmse: 0.0004517 valid's RMSPE: 0.227005

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.223, compared to 0.17900678157963607, giving gain 0.04399321842036394
Our cv fold scores are [0.228, 0.237, 0.214, 0.207, 0.227]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000435184   train's RMSPE: 0.256744 valid's rmse: 0.000465168   valid's RMSPE: 0.276192
[100]   train's rmse: 0.00041287    train's RMSPE: 0.24358  valid's rmse: 0.000449542   valid's RMSPE: 0.266914
[150]   train's rmse: 0.000401982   train's RMSPE: 0.237156 valid's rmse: 0.000446324   valid's RMSPE: 0.265003
[200]   train's rmse: 0.000392722   train's RMSPE: 0.231693 valid's rmse: 0.000446187   valid's RMSPE: 0.264922
[250]   train's rmse: 0.000385982   train's RMSPE: 0.227717 valid's rmse: 0.000444877   valid's RMSPE: 0.264145
[300]   train's rmse: 0.000379589   train's RMSPE: 0.223945 valid's rmse: 0.000443365   valid's RMSPE: 0.263247
[350]   train's rmse: 0.000373994   train's RMSPE: 0.220644 valid's rmse: 0.000443324   valid's RMSPE: 0.263222
Early stopping, best iteration is:
[324]   train's rmse: 0.000376925   train's RMSPE: 0.222374 valid's rmse: 0.000442732   valid's RMSPE: 0.262871
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00043093    train's RMSPE: 0.255392 valid's rmse: 0.000471853   valid's RMSPE: 0.275065
[100]   train's rmse: 0.000407579   train's RMSPE: 0.241553 valid's rmse: 0.000458794   valid's RMSPE: 0.267453
[150]   train's rmse: 0.000396335   train's RMSPE: 0.23489  valid's rmse: 0.000455669   valid's RMSPE: 0.265631
[200]   train's rmse: 0.000387339   train's RMSPE: 0.229558 valid's rmse: 0.00045417    valid's RMSPE: 0.264757
[250]   train's rmse: 0.000379866   train's RMSPE: 0.225129 valid's rmse: 0.000452687   valid's RMSPE: 0.263893
[300]   train's rmse: 0.000373394   train's RMSPE: 0.221293 valid's rmse: 0.000452377   valid's RMSPE: 0.263712
Early stopping, best iteration is:
[269]   train's rmse: 0.000377272   train's RMSPE: 0.223592 valid's rmse: 0.000452205   valid's RMSPE: 0.263612
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000434788   train's RMSPE: 0.257213 valid's rmse: 0.000435545   valid's RMSPE: 0.255788
[100]   train's rmse: 0.000411495   train's RMSPE: 0.243434 valid's rmse: 0.000430635   valid's RMSPE: 0.252904
[150]   train's rmse: 0.00039988    train's RMSPE: 0.236562 valid's rmse: 0.00042907    valid's RMSPE: 0.251985
Early stopping, best iteration is:
[144]   train's rmse: 0.000400852   train's RMSPE: 0.237137 valid's rmse: 0.000428938   valid's RMSPE: 0.251908
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000435917   train's RMSPE: 0.25793  valid's rmse: 0.000453489   valid's RMSPE: 0.26612
[100]   train's rmse: 0.000412679   train's RMSPE: 0.244181 valid's rmse: 0.000441486   valid's RMSPE: 0.259076
[150]   train's rmse: 0.000401228   train's RMSPE: 0.237405 valid's rmse: 0.000441829   valid's RMSPE: 0.259277
Early stopping, best iteration is:
[114]   train's rmse: 0.000409088   train's RMSPE: 0.242055 valid's rmse: 0.000440896   valid's RMSPE: 0.25873
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000444483   train's RMSPE: 0.261228 valid's rmse: 0.000417535   valid's RMSPE: 0.251615
[100]   train's rmse: 0.000422477   train's RMSPE: 0.248295 valid's rmse: 0.000401623   valid's RMSPE: 0.242026
[150]   train's rmse: 0.000411233   train's RMSPE: 0.241687 valid's rmse: 0.000398365   valid's RMSPE: 0.240063
[200]   train's rmse: 0.000401979   train's RMSPE: 0.236249 valid's rmse: 0.000396598   valid's RMSPE: 0.238998
[250]   train's rmse: 0.000394447   train's RMSPE: 0.231822 valid's rmse: 0.000397063   valid's RMSPE: 0.239278
Early stopping, best iteration is:
[222]   train's rmse: 0.000398575   train's RMSPE: 0.234248 valid's rmse: 0.000396023   valid's RMSPE: 0.238651

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.255, compared to 0.22612054446229005, giving gain 0.028879455537709958
Our cv fold scores are [0.263, 0.264, 0.252, 0.259, 0.239]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000728622   train's RMSPE: 0.227569 valid's rmse: 0.000785335   valid's RMSPE: 0.240509
[100]   train's rmse: 0.000687906   train's RMSPE: 0.214853 valid's rmse: 0.000763836   valid's RMSPE: 0.233925
[150]   train's rmse: 0.000670188   train's RMSPE: 0.209319 valid's rmse: 0.000759741   valid's RMSPE: 0.232671
[200]   train's rmse: 0.00065475    train's RMSPE: 0.204497 valid's rmse: 0.000757188   valid's RMSPE: 0.231889
Early stopping, best iteration is:
[194]   train's rmse: 0.000656324   train's RMSPE: 0.204988 valid's rmse: 0.000755931   valid's RMSPE: 0.231504
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000728531   train's RMSPE: 0.225462 valid's rmse: 0.000777148   valid's RMSPE: 0.246844
[100]   train's rmse: 0.000687393   train's RMSPE: 0.21273  valid's rmse: 0.000758287   valid's RMSPE: 0.240853
[150]   train's rmse: 0.000669455   train's RMSPE: 0.207179 valid's rmse: 0.000755781   valid's RMSPE: 0.240057
[200]   train's rmse: 0.000654868   train's RMSPE: 0.202665 valid's rmse: 0.000754434   valid's RMSPE: 0.239629
[250]   train's rmse: 0.000641682   train's RMSPE: 0.198584 valid's rmse: 0.000754077   valid's RMSPE: 0.239516
Early stopping, best iteration is:
[205]   train's rmse: 0.000653458   train's RMSPE: 0.202229 valid's rmse: 0.000753145   valid's RMSPE: 0.23922
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000739777   train's RMSPE: 0.230105 valid's rmse: 0.000714744   valid's RMSPE: 0.222589
[100]   train's rmse: 0.000697432   train's RMSPE: 0.216934 valid's rmse: 0.000695711   valid's RMSPE: 0.216662
[150]   train's rmse: 0.000677796   train's RMSPE: 0.210826 valid's rmse: 0.00069396    valid's RMSPE: 0.216117
Early stopping, best iteration is:
[148]   train's rmse: 0.000678279   train's RMSPE: 0.210976 valid's rmse: 0.000693469   valid's RMSPE: 0.215964
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00072916    train's RMSPE: 0.22703  valid's rmse: 0.000773565   valid's RMSPE: 0.239941
[100]   train's rmse: 0.000686764   train's RMSPE: 0.21383  valid's rmse: 0.000763724   valid's RMSPE: 0.236888
Early stopping, best iteration is:
[76]    train's rmse: 0.000700067   train's RMSPE: 0.217972 valid's rmse: 0.000761842   valid's RMSPE: 0.236305
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000739768   train's RMSPE: 0.230361 valid's rmse: 0.000744123   valid's RMSPE: 0.230695
[100]   train's rmse: 0.000700814   train's RMSPE: 0.218231 valid's rmse: 0.000714484   valid's RMSPE: 0.221506
[150]   train's rmse: 0.000682654   train's RMSPE: 0.212576 valid's rmse: 0.000711031   valid's RMSPE: 0.220436
[200]   train's rmse: 0.00066642    train's RMSPE: 0.207521 valid's rmse: 0.000708605   valid's RMSPE: 0.219684
Early stopping, best iteration is:
[180]   train's rmse: 0.00067252    train's RMSPE: 0.20942  valid's rmse: 0.000707721   valid's RMSPE: 0.21941
Our out of folds RMSPE is 0.229, compared to 0.20676157404809947, giving gain 0.022238425951900537
Our cv fold scores are [0.232, 0.239, 0.216, 0.236, 0.219]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000694247   train's RMSPE: 0.236973 valid's rmse: 0.000743298   valid's RMSPE: 0.257054
[100]   train's rmse: 0.000657039   train's RMSPE: 0.224273 valid's rmse: 0.000722694   valid's RMSPE: 0.249928
[150]   train's rmse: 0.00063937    train's RMSPE: 0.218242 valid's rmse: 0.000719903   valid's RMSPE: 0.248963
[200]   train's rmse: 0.000625594   train's RMSPE: 0.213539 valid's rmse: 0.000719468   valid's RMSPE: 0.248813
[250]   train's rmse: 0.000613638   train's RMSPE: 0.209458 valid's rmse: 0.000718563   valid's RMSPE: 0.2485
[300]   train's rmse: 0.000604656   train's RMSPE: 0.206392 valid's rmse: 0.000718666   valid's RMSPE: 0.248535
Early stopping, best iteration is:
[267]   train's rmse: 0.000610311   train's RMSPE: 0.208323 valid's rmse: 0.00071782    valid's RMSPE: 0.248243
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000697113   train's RMSPE: 0.237124 valid's rmse: 0.000741569   valid's RMSPE: 0.259902
[100]   train's rmse: 0.000663173   train's RMSPE: 0.225579 valid's rmse: 0.000716087   valid's RMSPE: 0.250971
[150]   train's rmse: 0.000648547   train's RMSPE: 0.220604 valid's rmse: 0.000710754   valid's RMSPE: 0.249102
[200]   train's rmse: 0.000635601   train's RMSPE: 0.2162   valid's rmse: 0.00070875    valid's RMSPE: 0.2484
[250]   train's rmse: 0.000625343   train's RMSPE: 0.212711 valid's rmse: 0.000705913   valid's RMSPE: 0.247406
[300]   train's rmse: 0.000615569   train's RMSPE: 0.209387 valid's rmse: 0.000703559   valid's RMSPE: 0.246581
[350]   train's rmse: 0.000606403   train's RMSPE: 0.206269 valid's rmse: 0.000699992   valid's RMSPE: 0.245331
[400]   train's rmse: 0.000598168   train's RMSPE: 0.203468 valid's rmse: 0.000701231   valid's RMSPE: 0.245765
Early stopping, best iteration is:
[379]   train's rmse: 0.000600909   train's RMSPE: 0.2044   valid's rmse: 0.000699261   valid's RMSPE: 0.245074
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000708932   train's RMSPE: 0.242677 valid's rmse: 0.000674426   valid's RMSPE: 0.230621
[100]   train's rmse: 0.000672407   train's RMSPE: 0.230174 valid's rmse: 0.000656306   valid's RMSPE: 0.224425
[150]   train's rmse: 0.000655856   train's RMSPE: 0.224509 valid's rmse: 0.000654705   valid's RMSPE: 0.223877
[200]   train's rmse: 0.000643032   train's RMSPE: 0.220119 valid's rmse: 0.000653878   valid's RMSPE: 0.223594
[250]   train's rmse: 0.000631498   train's RMSPE: 0.21617  valid's rmse: 0.000651153   valid's RMSPE: 0.222663
Early stopping, best iteration is:
[245]   train's rmse: 0.000632475   train's RMSPE: 0.216505 valid's rmse: 0.000650815   valid's RMSPE: 0.222547
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000710283   train's RMSPE: 0.243296 valid's rmse: 0.000693382   valid's RMSPE: 0.236489
[100]   train's rmse: 0.000672329   train's RMSPE: 0.230296 valid's rmse: 0.000672201   valid's RMSPE: 0.229265
[150]   train's rmse: 0.000657181   train's RMSPE: 0.225107 valid's rmse: 0.000669294   valid's RMSPE: 0.228274
[200]   train's rmse: 0.000643908   train's RMSPE: 0.22056  valid's rmse: 0.000665188   valid's RMSPE: 0.226873
[250]   train's rmse: 0.000633346   train's RMSPE: 0.216943 valid's rmse: 0.000661013   valid's RMSPE: 0.22545
[300]   train's rmse: 0.000623373   train's RMSPE: 0.213527 valid's rmse: 0.00065929    valid's RMSPE: 0.224862
[350]   train's rmse: 0.000614738   train's RMSPE: 0.210569 valid's rmse: 0.000659222   valid's RMSPE: 0.224839
[400]   train's rmse: 0.000606861   train's RMSPE: 0.207871 valid's rmse: 0.000660092   valid's RMSPE: 0.225135
Early stopping, best iteration is:
[352]   train's rmse: 0.000614449   train's RMSPE: 0.21047  valid's rmse: 0.000658829   valid's RMSPE: 0.224705
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000699256   train's RMSPE: 0.241139 valid's rmse: 0.000733697   valid's RMSPE: 0.243292
[100]   train's rmse: 0.000665507   train's RMSPE: 0.2295   valid's rmse: 0.000712454   valid's RMSPE: 0.236248
[150]   train's rmse: 0.000649241   train's RMSPE: 0.223891 valid's rmse: 0.000707823   valid's RMSPE: 0.234712
[200]   train's rmse: 0.000635673   train's RMSPE: 0.219212 valid's rmse: 0.000704086   valid's RMSPE: 0.233473
[250]   train's rmse: 0.000623882   train's RMSPE: 0.215146 valid's rmse: 0.000704128   valid's RMSPE: 0.233487
Early stopping, best iteration is:
[224]   train's rmse: 0.000629302   train's RMSPE: 0.217015 valid's rmse: 0.000702665   valid's RMSPE: 0.233002
Our out of folds RMSPE is 0.235, compared to 0.19946161370414217, giving gain 0.035538386295857816
Our cv fold scores are [0.248, 0.245, 0.223, 0.225, 0.233]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000514385   train's RMSPE: 0.238296 valid's rmse: 0.000540053   valid's RMSPE: 0.247204
[100]   train's rmse: 0.000488623   train's RMSPE: 0.226361 valid's rmse: 0.000519593   valid's RMSPE: 0.237839
[150]   train's rmse: 0.000475888   train's RMSPE: 0.220461 valid's rmse: 0.000514632   valid's RMSPE: 0.235568
[200]   train's rmse: 0.00046544    train's RMSPE: 0.215621 valid's rmse: 0.000511899   valid's RMSPE: 0.234317
[250]   train's rmse: 0.000456955   train's RMSPE: 0.211691 valid's rmse: 0.000510592   valid's RMSPE: 0.233719
[300]   train's rmse: 0.000450052   train's RMSPE: 0.208493 valid's rmse: 0.000508884   valid's RMSPE: 0.232937
[350]   train's rmse: 0.000443583   train's RMSPE: 0.205496 valid's rmse: 0.000507774   valid's RMSPE: 0.232429
[400]   train's rmse: 0.000437796   train's RMSPE: 0.202815 valid's rmse: 0.000507792   valid's RMSPE: 0.232437
[450]   train's rmse: 0.000432336   train's RMSPE: 0.200285 valid's rmse: 0.000507046   valid's RMSPE: 0.232096
[500]   train's rmse: 0.000427492   train's RMSPE: 0.198041 valid's rmse: 0.000506456   valid's RMSPE: 0.231826
[550]   train's rmse: 0.000423445   train's RMSPE: 0.196166 valid's rmse: 0.000506216   valid's RMSPE: 0.231716
[600]   train's rmse: 0.000419621   train's RMSPE: 0.194395 valid's rmse: 0.000506205   valid's RMSPE: 0.231711
Early stopping, best iteration is:
[562]   train's rmse: 0.000422477   train's RMSPE: 0.195718 valid's rmse: 0.000505774   valid's RMSPE: 0.231514
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000506689   train's RMSPE: 0.233591 valid's rmse: 0.000568943   valid's RMSPE: 0.265546
[100]   train's rmse: 0.000482446   train's RMSPE: 0.222414 valid's rmse: 0.000552225   valid's RMSPE: 0.257743
[150]   train's rmse: 0.000472033   train's RMSPE: 0.217614 valid's rmse: 0.00054693    valid's RMSPE: 0.255272
[200]   train's rmse: 0.000463314   train's RMSPE: 0.213594 valid's rmse: 0.000541185   valid's RMSPE: 0.252591
[250]   train's rmse: 0.000455685   train's RMSPE: 0.210077 valid's rmse: 0.00053853    valid's RMSPE: 0.251351
[300]   train's rmse: 0.000449416   train's RMSPE: 0.207187 valid's rmse: 0.000536121   valid's RMSPE: 0.250227
[350]   train's rmse: 0.000443756   train's RMSPE: 0.204578 valid's rmse: 0.00053323    valid's RMSPE: 0.248878
[400]   train's rmse: 0.0004386 train's RMSPE: 0.202201 valid's rmse: 0.0005298 valid's RMSPE: 0.247277
[450]   train's rmse: 0.00043416    train's RMSPE: 0.200154 valid's rmse: 0.000530067   valid's RMSPE: 0.247402
[500]   train's rmse: 0.000429181   train's RMSPE: 0.197858 valid's rmse: 0.000529186   valid's RMSPE: 0.24699
[550]   train's rmse: 0.000424933   train's RMSPE: 0.1959   valid's rmse: 0.000527102   valid's RMSPE: 0.246018
[600]   train's rmse: 0.000420958   train's RMSPE: 0.194067 valid's rmse: 0.000525788   valid's RMSPE: 0.245404
[650]   train's rmse: 0.000417588   train's RMSPE: 0.192514 valid's rmse: 0.000524409   valid's RMSPE: 0.244761
[700]   train's rmse: 0.000414353   train's RMSPE: 0.191023 valid's rmse: 0.00052378    valid's RMSPE: 0.244467
[750]   train's rmse: 0.000411174   train's RMSPE: 0.189557 valid's rmse: 0.000522802   valid's RMSPE: 0.244011
[800]   train's rmse: 0.000407793   train's RMSPE: 0.187998 valid's rmse: 0.000521815   valid's RMSPE: 0.24355
[850]   train's rmse: 0.000404801   train's RMSPE: 0.186619 valid's rmse: 0.000521591   valid's RMSPE: 0.243446
[900]   train's rmse: 0.000401799   train's RMSPE: 0.185235 valid's rmse: 0.00051995    valid's RMSPE: 0.24268
[950]   train's rmse: 0.000398998   train's RMSPE: 0.183944 valid's rmse: 0.000519095   valid's RMSPE: 0.24228
[1000]  train's rmse: 0.000396138   train's RMSPE: 0.182625 valid's rmse: 0.000519466   valid's RMSPE: 0.242454
Early stopping, best iteration is:
[955]   train's rmse: 0.000398694   train's RMSPE: 0.183804 valid's rmse: 0.000518858   valid's RMSPE: 0.24217
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000522691   train's RMSPE: 0.241194 valid's rmse: 0.000500253   valid's RMSPE: 0.232629
[100]   train's rmse: 0.000496872   train's RMSPE: 0.22928  valid's rmse: 0.000479474   valid's RMSPE: 0.222966
[150]   train's rmse: 0.000483818   train's RMSPE: 0.223256 valid's rmse: 0.000473498   valid's RMSPE: 0.220188
[200]   train's rmse: 0.000473663   train's RMSPE: 0.21857  valid's rmse: 0.000470943   valid's RMSPE: 0.218999
[250]   train's rmse: 0.000465423   train's RMSPE: 0.214768 valid's rmse: 0.000469391   valid's RMSPE: 0.218278
[300]   train's rmse: 0.000458505   train's RMSPE: 0.211576 valid's rmse: 0.000469731   valid's RMSPE: 0.218436
Early stopping, best iteration is:
[272]   train's rmse: 0.000462177   train's RMSPE: 0.21327  valid's rmse: 0.000468464   valid's RMSPE: 0.217847
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000521042   train's RMSPE: 0.241032 valid's rmse: 0.000518277   valid's RMSPE: 0.238633
[100]   train's rmse: 0.000493491   train's RMSPE: 0.228286 valid's rmse: 0.000507818   valid's RMSPE: 0.233818
[150]   train's rmse: 0.000481685   train's RMSPE: 0.222825 valid's rmse: 0.000506517   valid's RMSPE: 0.233219
[200]   train's rmse: 0.000471486   train's RMSPE: 0.218107 valid's rmse: 0.000506149   valid's RMSPE: 0.233049
[250]   train's rmse: 0.000463572   train's RMSPE: 0.214446 valid's rmse: 0.000504795   valid's RMSPE: 0.232426
[300]   train's rmse: 0.000456354   train's RMSPE: 0.211107 valid's rmse: 0.000503055   valid's RMSPE: 0.231625
Early stopping, best iteration is:
[289]   train's rmse: 0.0004576 train's RMSPE: 0.211684 valid's rmse: 0.000502711   valid's RMSPE: 0.231466
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000522361   train's RMSPE: 0.241591 valid's rmse: 0.000513421   valid's RMSPE: 0.236598
[100]   train's rmse: 0.00049628    train's RMSPE: 0.229528 valid's rmse: 0.000496745   valid's RMSPE: 0.228913
[150]   train's rmse: 0.000482765   train's RMSPE: 0.223278 valid's rmse: 0.000490285   valid's RMSPE: 0.225936
[200]   train's rmse: 0.000472864   train's RMSPE: 0.218699 valid's rmse: 0.000488412   valid's RMSPE: 0.225073
[250]   train's rmse: 0.000464073   train's RMSPE: 0.214633 valid's rmse: 0.000487121   valid's RMSPE: 0.224478
[300]   train's rmse: 0.000456863   train's RMSPE: 0.211298 valid's rmse: 0.000485217   valid's RMSPE: 0.223601
[350]   train's rmse: 0.000450551   train's RMSPE: 0.208379 valid's rmse: 0.000482647   valid's RMSPE: 0.222416
[400]   train's rmse: 0.000444283   train's RMSPE: 0.20548  valid's rmse: 0.00048138    valid's RMSPE: 0.221832
[450]   train's rmse: 0.000438156   train's RMSPE: 0.202646 valid's rmse: 0.000480165   valid's RMSPE: 0.221273
[500]   train's rmse: 0.000433464   train's RMSPE: 0.200476 valid's rmse: 0.000479613   valid's RMSPE: 0.221018
Early stopping, best iteration is:
[477]   train's rmse: 0.000435858   train's RMSPE: 0.201583 valid's rmse: 0.000479369   valid's RMSPE: 0.220906

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.229, compared to 0.1941653502859443, giving gain 0.0348346497140557
Our cv fold scores are [0.232, 0.242, 0.218, 0.231, 0.221]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00121212    train's RMSPE: 0.27341  valid's rmse: 0.0014175 valid's RMSPE: 0.31535
[100]   train's rmse: 0.00116106    train's RMSPE: 0.261892 valid's rmse: 0.00139045    valid's RMSPE: 0.309334
[150]   train's rmse: 0.0011327 train's RMSPE: 0.255494 valid's rmse: 0.00139183    valid's RMSPE: 0.309641
Early stopping, best iteration is:
[109]   train's rmse: 0.001156  train's RMSPE: 0.26075  valid's rmse: 0.00138726    valid's RMSPE: 0.308624
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00123601    train's RMSPE: 0.277175 valid's rmse: 0.00127804    valid's RMSPE: 0.29103
[100]   train's rmse: 0.00117894    train's RMSPE: 0.264377 valid's rmse: 0.00125846    valid's RMSPE: 0.286571
[150]   train's rmse: 0.00114875    train's RMSPE: 0.257607 valid's rmse: 0.0012541 valid's RMSPE: 0.28558
[200]   train's rmse: 0.0011247 train's RMSPE: 0.252213 valid's rmse: 0.00125131    valid's RMSPE: 0.284944
[250]   train's rmse: 0.0011016 train's RMSPE: 0.247034 valid's rmse: 0.00124068    valid's RMSPE: 0.282523
[300]   train's rmse: 0.00108238    train's RMSPE: 0.242725 valid's rmse: 0.00123539    valid's RMSPE: 0.281319
[350]   train's rmse: 0.00106562    train's RMSPE: 0.238966 valid's rmse: 0.00123244    valid's RMSPE: 0.280647
Early stopping, best iteration is:
[332]   train's rmse: 0.00107157    train's RMSPE: 0.2403   valid's rmse: 0.00123023    valid's RMSPE: 0.280143
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00124766    train's RMSPE: 0.280951 valid's rmse: 0.00120655    valid's RMSPE: 0.270269
[100]   train's rmse: 0.00119283    train's RMSPE: 0.268605 valid's rmse: 0.00118769    valid's RMSPE: 0.266046
[150]   train's rmse: 0.00115673    train's RMSPE: 0.260475 valid's rmse: 0.00117915    valid's RMSPE: 0.264133
Early stopping, best iteration is:
[148]   train's rmse: 0.00115815    train's RMSPE: 0.260797 valid's rmse: 0.00117827    valid's RMSPE: 0.263936
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00124413    train's RMSPE: 0.279006 valid's rmse: 0.00127038    valid's RMSPE: 0.289246
[100]   train's rmse: 0.00118884    train's RMSPE: 0.266608 valid's rmse: 0.00125107    valid's RMSPE: 0.28485
[150]   train's rmse: 0.00115573    train's RMSPE: 0.259181 valid's rmse: 0.00124254    valid's RMSPE: 0.282908
[200]   train's rmse: 0.0011287 train's RMSPE: 0.253121 valid's rmse: 0.00123722    valid's RMSPE: 0.281696
[250]   train's rmse: 0.00110761    train's RMSPE: 0.248391 valid's rmse: 0.00122969    valid's RMSPE: 0.279981
Early stopping, best iteration is:
[234]   train's rmse: 0.00111411    train's RMSPE: 0.249848 valid's rmse: 0.00122877    valid's RMSPE: 0.279774
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00123855    train's RMSPE: 0.279269 valid's rmse: 0.00124188    valid's RMSPE: 0.276698
[100]   train's rmse: 0.00117726    train's RMSPE: 0.265449 valid's rmse: 0.00123112    valid's RMSPE: 0.2743
[150]   train's rmse: 0.0011406 train's RMSPE: 0.257183 valid's rmse: 0.00123193    valid's RMSPE: 0.27448
Early stopping, best iteration is:
[126]   train's rmse: 0.0011578 train's RMSPE: 0.261062 valid's rmse: 0.00122925    valid's RMSPE: 0.273883
Our out of folds RMSPE is 0.282, compared to 0.2661141171323659, giving gain 0.015885882867634094
Our cv fold scores are [0.309, 0.28, 0.264, 0.28, 0.274]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000473624   train's RMSPE: 0.248436 valid's rmse: 0.000490458   valid's RMSPE: 0.256469
[100]   train's rmse: 0.000450868   train's RMSPE: 0.2365   valid's rmse: 0.000473001   valid's RMSPE: 0.24734
[150]   train's rmse: 0.000441001   train's RMSPE: 0.231325 valid's rmse: 0.000468413   valid's RMSPE: 0.244941
[200]   train's rmse: 0.0004322 train's RMSPE: 0.226708 valid's rmse: 0.000465933   valid's RMSPE: 0.243644
Early stopping, best iteration is:
[195]   train's rmse: 0.000432779   train's RMSPE: 0.227012 valid's rmse: 0.000465682   valid's RMSPE: 0.243513
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000471483   train's RMSPE: 0.247971 valid's rmse: 0.000512537   valid's RMSPE: 0.265128
[100]   train's rmse: 0.0004481 train's RMSPE: 0.235673 valid's rmse: 0.000501623   valid's RMSPE: 0.259483
[150]   train's rmse: 0.000437621   train's RMSPE: 0.230161 valid's rmse: 0.000496677   valid's RMSPE: 0.256924
[200]   train's rmse: 0.000428756   train's RMSPE: 0.225499 valid's rmse: 0.000493929   valid's RMSPE: 0.255502
[250]   train's rmse: 0.00042233    train's RMSPE: 0.222119 valid's rmse: 0.000493317   valid's RMSPE: 0.255186
[300]   train's rmse: 0.000415843   train's RMSPE: 0.218707 valid's rmse: 0.000491041   valid's RMSPE: 0.254009
[350]   train's rmse: 0.000410075   train's RMSPE: 0.215674 valid's rmse: 0.000490252   valid's RMSPE: 0.253601
[400]   train's rmse: 0.000405327   train's RMSPE: 0.213177 valid's rmse: 0.000489291   valid's RMSPE: 0.253103
[450]   train's rmse: 0.000401137   train's RMSPE: 0.210973 valid's rmse: 0.000490256   valid's RMSPE: 0.253603
Early stopping, best iteration is:
[407]   train's rmse: 0.000404733   train's RMSPE: 0.212864 valid's rmse: 0.000489178   valid's RMSPE: 0.253045
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000479807   train's RMSPE: 0.250903 valid's rmse: 0.000466318   valid's RMSPE: 0.246852
[100]   train's rmse: 0.000455484   train's RMSPE: 0.238184 valid's rmse: 0.00045884    valid's RMSPE: 0.242893
[150]   train's rmse: 0.000443775   train's RMSPE: 0.232061 valid's rmse: 0.000456692   valid's RMSPE: 0.241756
[200]   train's rmse: 0.000434865   train's RMSPE: 0.227402 valid's rmse: 0.000457387   valid's RMSPE: 0.242124
Early stopping, best iteration is:
[167]   train's rmse: 0.000439945   train's RMSPE: 0.230058 valid's rmse: 0.000455344   valid's RMSPE: 0.241043
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00047802    train's RMSPE: 0.249662 valid's rmse: 0.000487006   valid's RMSPE: 0.25903
[100]   train's rmse: 0.000453355   train's RMSPE: 0.23678  valid's rmse: 0.000467893   valid's RMSPE: 0.248865
[150]   train's rmse: 0.00044361    train's RMSPE: 0.231691 valid's rmse: 0.000465544   valid's RMSPE: 0.247615
[200]   train's rmse: 0.000435754   train's RMSPE: 0.227588 valid's rmse: 0.000464494   valid's RMSPE: 0.247057
[250]   train's rmse: 0.000428603   train's RMSPE: 0.223853 valid's rmse: 0.000462918   valid's RMSPE: 0.246219
[300]   train's rmse: 0.000422096   train's RMSPE: 0.220454 valid's rmse: 0.000461172   valid's RMSPE: 0.24529
[350]   train's rmse: 0.000416235   train's RMSPE: 0.217393 valid's rmse: 0.000459158   valid's RMSPE: 0.244219
[400]   train's rmse: 0.000411506   train's RMSPE: 0.214923 valid's rmse: 0.000459324   valid's RMSPE: 0.244307
[450]   train's rmse: 0.000407049   train's RMSPE: 0.212596 valid's rmse: 0.000458507   valid's RMSPE: 0.243872
Early stopping, best iteration is:
[433]   train's rmse: 0.000408708   train's RMSPE: 0.213462 valid's rmse: 0.000458174   valid's RMSPE: 0.243695
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000478852   train's RMSPE: 0.251586 valid's rmse: 0.000485159   valid's RMSPE: 0.252038
[100]   train's rmse: 0.000456377   train's RMSPE: 0.239777 valid's rmse: 0.000471884   valid's RMSPE: 0.245141
[150]   train's rmse: 0.000444948   train's RMSPE: 0.233772 valid's rmse: 0.000469155   valid's RMSPE: 0.243724
[200]   train's rmse: 0.000435719   train's RMSPE: 0.228924 valid's rmse: 0.000465971   valid's RMSPE: 0.242069
[250]   train's rmse: 0.00042888    train's RMSPE: 0.225331 valid's rmse: 0.000465388   valid's RMSPE: 0.241767
[300]   train's rmse: 0.000422325   train's RMSPE: 0.221887 valid's rmse: 0.000464272   valid's RMSPE: 0.241187
[350]   train's rmse: 0.00041678    train's RMSPE: 0.218973 valid's rmse: 0.000465631   valid's RMSPE: 0.241893
Early stopping, best iteration is:
[318]   train's rmse: 0.000420318   train's RMSPE: 0.220832 valid's rmse: 0.000464113   valid's RMSPE: 0.241104
Our out of folds RMSPE is 0.245, compared to 0.2057642320364516, giving gain 0.039235767963548385
Our cv fold scores are [0.244, 0.253, 0.241, 0.244, 0.241]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000248786   train's RMSPE: 0.215369 valid's rmse: 0.000271734   valid's RMSPE: 0.238568
[100]   train's rmse: 0.000232294   train's RMSPE: 0.201092 valid's rmse: 0.000257682   valid's RMSPE: 0.226231
[150]   train's rmse: 0.000225665   train's RMSPE: 0.195353 valid's rmse: 0.000252378   valid's RMSPE: 0.221574
[200]   train's rmse: 0.000221167   train's RMSPE: 0.191459 valid's rmse: 0.000250959   valid's RMSPE: 0.220329
[250]   train's rmse: 0.000217874   train's RMSPE: 0.188609 valid's rmse: 0.000249193   valid's RMSPE: 0.218778
[300]   train's rmse: 0.000214876   train's RMSPE: 0.186014 valid's rmse: 0.000247871   valid's RMSPE: 0.217617
[350]   train's rmse: 0.000212475   train's RMSPE: 0.183935 valid's rmse: 0.000247221   valid's RMSPE: 0.217047
[400]   train's rmse: 0.000210441   train's RMSPE: 0.182174 valid's rmse: 0.000246752   valid's RMSPE: 0.216634
[450]   train's rmse: 0.000208473   train's RMSPE: 0.18047  valid's rmse: 0.00024672    valid's RMSPE: 0.216606
[500]   train's rmse: 0.000206312   train's RMSPE: 0.1786   valid's rmse: 0.000246064   valid's RMSPE: 0.216031
[550]   train's rmse: 0.000204213   train's RMSPE: 0.176783 valid's rmse: 0.000245143   valid's RMSPE: 0.215222
[600]   train's rmse: 0.000202111   train's RMSPE: 0.174964 valid's rmse: 0.000244718   valid's RMSPE: 0.214849
[650]   train's rmse: 0.000200457   train's RMSPE: 0.173531 valid's rmse: 0.000244197   valid's RMSPE: 0.214391
[700]   train's rmse: 0.000198929   train's RMSPE: 0.172209 valid's rmse: 0.00024366    valid's RMSPE: 0.213921
[750]   train's rmse: 0.000197442   train's RMSPE: 0.170921 valid's rmse: 0.000243314   valid's RMSPE: 0.213617
[800]   train's rmse: 0.00019593    train's RMSPE: 0.169612 valid's rmse: 0.000243098   valid's RMSPE: 0.213427
[850]   train's rmse: 0.000194457   train's RMSPE: 0.168337 valid's rmse: 0.000243259   valid's RMSPE: 0.213568
Early stopping, best iteration is:
[821]   train's rmse: 0.000195292   train's RMSPE: 0.16906  valid's rmse: 0.000242992   valid's RMSPE: 0.213333
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000248691   train's RMSPE: 0.2143   valid's rmse: 0.000266124   valid's RMSPE: 0.237757
[100]   train's rmse: 0.000231472   train's RMSPE: 0.199463 valid's rmse: 0.000251885   valid's RMSPE: 0.225036
[150]   train's rmse: 0.0002254 train's RMSPE: 0.194231 valid's rmse: 0.00024812    valid's RMSPE: 0.221673
[200]   train's rmse: 0.000220963   train's RMSPE: 0.190407 valid's rmse: 0.000246166   valid's RMSPE: 0.219926
[250]   train's rmse: 0.000217696   train's RMSPE: 0.187592 valid's rmse: 0.000244828   valid's RMSPE: 0.218731
[300]   train's rmse: 0.000214631   train's RMSPE: 0.18495  valid's rmse: 0.000243399   valid's RMSPE: 0.217454
[350]   train's rmse: 0.000212025   train's RMSPE: 0.182705 valid's rmse: 0.000243081   valid's RMSPE: 0.21717
[400]   train's rmse: 0.000209786   train's RMSPE: 0.180775 valid's rmse: 0.000242672   valid's RMSPE: 0.216805
[450]   train's rmse: 0.000207477   train's RMSPE: 0.178786 valid's rmse: 0.000242039   valid's RMSPE: 0.216239
Early stopping, best iteration is:
[425]   train's rmse: 0.000208511   train's RMSPE: 0.179677 valid's rmse: 0.000241778   valid's RMSPE: 0.216006
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000254411   train's RMSPE: 0.221016 valid's rmse: 0.00024132    valid's RMSPE: 0.208932
[100]   train's rmse: 0.000236131   train's RMSPE: 0.205135 valid's rmse: 0.000228149   valid's RMSPE: 0.197528
[150]   train's rmse: 0.000230733   train's RMSPE: 0.200445 valid's rmse: 0.00022641    valid's RMSPE: 0.196022
[200]   train's rmse: 0.000226901   train's RMSPE: 0.197116 valid's rmse: 0.00022519    valid's RMSPE: 0.194967
[250]   train's rmse: 0.000223782   train's RMSPE: 0.194407 valid's rmse: 0.000224741   valid's RMSPE: 0.194577
[300]   train's rmse: 0.000221129   train's RMSPE: 0.192102 valid's rmse: 0.000224573   valid's RMSPE: 0.194432
[350]   train's rmse: 0.000218603   train's RMSPE: 0.189908 valid's rmse: 0.000223615   valid's RMSPE: 0.193603
[400]   train's rmse: 0.000216427   train's RMSPE: 0.188018 valid's rmse: 0.000223729   valid's RMSPE: 0.193701
[450]   train's rmse: 0.000214145   train's RMSPE: 0.186035 valid's rmse: 0.000223391   valid's RMSPE: 0.193409
[500]   train's rmse: 0.000212182   train's RMSPE: 0.184329 valid's rmse: 0.000223059   valid's RMSPE: 0.193122
[550]   train's rmse: 0.000210149   train's RMSPE: 0.182564 valid's rmse: 0.000222775   valid's RMSPE: 0.192876
[600]   train's rmse: 0.000208179   train's RMSPE: 0.180852 valid's rmse: 0.000222323   valid's RMSPE: 0.192484
[650]   train's rmse: 0.00020648    train's RMSPE: 0.179376 valid's rmse: 0.000221952   valid's RMSPE: 0.192163
Early stopping, best iteration is:
[612]   train's rmse: 0.00020781    train's RMSPE: 0.180532 valid's rmse: 0.000221835   valid's RMSPE: 0.192061
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000252984   train's RMSPE: 0.220628 valid's rmse: 0.000248828   valid's RMSPE: 0.212035
[100]   train's rmse: 0.000235304   train's RMSPE: 0.205209 valid's rmse: 0.000233904   valid's RMSPE: 0.199318
[150]   train's rmse: 0.000229846   train's RMSPE: 0.200449 valid's rmse: 0.000230084   valid's RMSPE: 0.196062
[200]   train's rmse: 0.000225897   train's RMSPE: 0.197006 valid's rmse: 0.00022711    valid's RMSPE: 0.193528
[250]   train's rmse: 0.000222964   train's RMSPE: 0.194447 valid's rmse: 0.000226232   valid's RMSPE: 0.19278
[300]   train's rmse: 0.000220052   train's RMSPE: 0.191908 valid's rmse: 0.000224616   valid's RMSPE: 0.191403
[350]   train's rmse: 0.000217186   train's RMSPE: 0.189409 valid's rmse: 0.000223813   valid's RMSPE: 0.190718
[400]   train's rmse: 0.000214813   train's RMSPE: 0.187339 valid's rmse: 0.000223123   valid's RMSPE: 0.19013
[450]   train's rmse: 0.000212588   train's RMSPE: 0.185399 valid's rmse: 0.000222855   valid's RMSPE: 0.189902
[500]   train's rmse: 0.000210425   train's RMSPE: 0.183512 valid's rmse: 0.000222627   valid's RMSPE: 0.189708
[550]   train's rmse: 0.000208609   train's RMSPE: 0.181928 valid's rmse: 0.000222421   valid's RMSPE: 0.189532
[600]   train's rmse: 0.000206814   train's RMSPE: 0.180363 valid's rmse: 0.00022215    valid's RMSPE: 0.189302
[650]   train's rmse: 0.000205171   train's RMSPE: 0.17893  valid's rmse: 0.000221987   valid's RMSPE: 0.189163
Early stopping, best iteration is:
[632]   train's rmse: 0.000205779   train's RMSPE: 0.17946  valid's rmse: 0.000221852   valid's RMSPE: 0.189048
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000253122   train's RMSPE: 0.220836 valid's rmse: 0.000255566   valid's RMSPE: 0.217411
[100]   train's rmse: 0.000235659   train's RMSPE: 0.205601 valid's rmse: 0.000242469   valid's RMSPE: 0.206269
[150]   train's rmse: 0.000229598   train's RMSPE: 0.200313 valid's rmse: 0.000240242   valid's RMSPE: 0.204375
[200]   train's rmse: 0.000225233   train's RMSPE: 0.196505 valid's rmse: 0.000239054   valid's RMSPE: 0.203365
[250]   train's rmse: 0.00022147    train's RMSPE: 0.193222 valid's rmse: 0.000237821   valid's RMSPE: 0.202316
[300]   train's rmse: 0.000218541   train's RMSPE: 0.190666 valid's rmse: 0.000237553   valid's RMSPE: 0.202088
Early stopping, best iteration is:
[271]   train's rmse: 0.000220211   train's RMSPE: 0.192123 valid's rmse: 0.000237456   valid's RMSPE: 0.202005
Our out of folds RMSPE is 0.203, compared to 0.17478725390050795, giving gain 0.028212746099492064
Our cv fold scores are [0.213, 0.216, 0.192, 0.189, 0.202]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000690832   train's RMSPE: 0.289989 valid's rmse: 0.000763569   valid's RMSPE: 0.322017
[100]   train's rmse: 0.000663163   train's RMSPE: 0.278375 valid's rmse: 0.000746924   valid's RMSPE: 0.314997
[150]   train's rmse: 0.000647834   train's RMSPE: 0.27194  valid's rmse: 0.000743378   valid's RMSPE: 0.313502
[200]   train's rmse: 0.000635305   train's RMSPE: 0.266681 valid's rmse: 0.000737699   valid's RMSPE: 0.311107
[250]   train's rmse: 0.000625332   train's RMSPE: 0.262495 valid's rmse: 0.000737708   valid's RMSPE: 0.311111
[300]   train's rmse: 0.000615935   train's RMSPE: 0.25855  valid's rmse: 0.000736753   valid's RMSPE: 0.310708
Early stopping, best iteration is:
[278]   train's rmse: 0.000619826   train's RMSPE: 0.260183 valid's rmse: 0.000736159   valid's RMSPE: 0.310458
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000688508   train's RMSPE: 0.288444 valid's rmse: 0.000759902   valid's RMSPE: 0.322964
[100]   train's rmse: 0.000659715   train's RMSPE: 0.276381 valid's rmse: 0.000749398   valid's RMSPE: 0.3185
Early stopping, best iteration is:
[86]    train's rmse: 0.000664955   train's RMSPE: 0.278577 valid's rmse: 0.000749094   valid's RMSPE: 0.318371
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000706965   train's RMSPE: 0.295707 valid's rmse: 0.000684289   valid's RMSPE: 0.292612
[100]   train's rmse: 0.000675875   train's RMSPE: 0.282703 valid's rmse: 0.000671272   valid's RMSPE: 0.287046
[150]   train's rmse: 0.000660329   train's RMSPE: 0.2762   valid's rmse: 0.000668144   valid's RMSPE: 0.285709
[200]   train's rmse: 0.000647167   train's RMSPE: 0.270695 valid's rmse: 0.000667939   valid's RMSPE: 0.285621
[250]   train's rmse: 0.000635613   train's RMSPE: 0.265862 valid's rmse: 0.000667132   valid's RMSPE: 0.285276
[300]   train's rmse: 0.000624982   train's RMSPE: 0.261415 valid's rmse: 0.000666077   valid's RMSPE: 0.284825
Early stopping, best iteration is:
[273]   train's rmse: 0.000630414   train's RMSPE: 0.263687 valid's rmse: 0.000665048   valid's RMSPE: 0.284385
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000711571   train's RMSPE: 0.299744 valid's rmse: 0.000667515   valid's RMSPE: 0.277557
[100]   train's rmse: 0.000681277   train's RMSPE: 0.286983 valid's rmse: 0.000658838   valid's RMSPE: 0.273949
[150]   train's rmse: 0.000664825   train's RMSPE: 0.280052 valid's rmse: 0.000655814   valid's RMSPE: 0.272692
[200]   train's rmse: 0.000652344   train's RMSPE: 0.274795 valid's rmse: 0.000652173   valid's RMSPE: 0.271178
[250]   train's rmse: 0.000640346   train's RMSPE: 0.269741 valid's rmse: 0.000652518   valid's RMSPE: 0.271321
Early stopping, best iteration is:
[205]   train's rmse: 0.000650817   train's RMSPE: 0.274152 valid's rmse: 0.000651393   valid's RMSPE: 0.270853
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000703194   train's RMSPE: 0.297143 valid's rmse: 0.000721825   valid's RMSPE: 0.296248
[100]   train's rmse: 0.000674124   train's RMSPE: 0.284859 valid's rmse: 0.000707837   valid's RMSPE: 0.290507
[150]   train's rmse: 0.000658236   train's RMSPE: 0.278145 valid's rmse: 0.000707051   valid's RMSPE: 0.290185
[200]   train's rmse: 0.000645686   train's RMSPE: 0.272842 valid's rmse: 0.000706569   valid's RMSPE: 0.289987
Early stopping, best iteration is:
[171]   train's rmse: 0.000652471   train's RMSPE: 0.275709 valid's rmse: 0.000705291   valid's RMSPE: 0.289462
Our out of folds RMSPE is 0.295, compared to 0.25716377936657236, giving gain 0.03783622063342762
Our cv fold scores are [0.31, 0.318, 0.284, 0.271, 0.289]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000614869   train's RMSPE: 0.441586 valid's rmse: 0.000764693   valid's RMSPE: 0.441167
[100]   train's rmse: 0.000546937   train's RMSPE: 0.392799 valid's rmse: 0.000757734   valid's RMSPE: 0.437152
[150]   train's rmse: 0.00051293    train's RMSPE: 0.368376 valid's rmse: 0.000750625   valid's RMSPE: 0.433051
Early stopping, best iteration is:
[145]   train's rmse: 0.000514909   train's RMSPE: 0.369797 valid's rmse: 0.000749825   valid's RMSPE: 0.43259
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000601515   train's RMSPE: 0.419364 valid's rmse: 0.00116479    valid's RMSPE: 0.782863
Early stopping, best iteration is:
[16]    train's rmse: 0.000776851   train's RMSPE: 0.541605 valid's rmse: 0.0010316 valid's RMSPE: 0.693344
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000647022   train's RMSPE: 0.447291 valid's rmse: 0.000658346   valid's RMSPE: 0.458176
[100]   train's rmse: 0.000571828   train's RMSPE: 0.395309 valid's rmse: 0.0006871 valid's RMSPE: 0.478187
Early stopping, best iteration is:
[53]    train's rmse: 0.000638903   train's RMSPE: 0.441678 valid's rmse: 0.000655376   valid's RMSPE: 0.456109
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000666162   train's RMSPE: 0.438449 valid's rmse: 0.000576003   valid's RMSPE: 0.469082
[100]   train's rmse: 0.000594935   train's RMSPE: 0.391569 valid's rmse: 0.000554784   valid's RMSPE: 0.451802
Early stopping, best iteration is:
[94]    train's rmse: 0.000603128   train's RMSPE: 0.396962 valid's rmse: 0.000547952   valid's RMSPE: 0.446238
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000658642   train's RMSPE: 0.457759 valid's rmse: 0.000670324   valid's RMSPE: 0.456534
Early stopping, best iteration is:
[43]    train's rmse: 0.000670946   train's RMSPE: 0.46631  valid's rmse: 0.000666708   valid's RMSPE: 0.454071
Our out of folds RMSPE is 0.506, compared to 0.5555796745804047, giving gain -0.04957967458040469
Our cv fold scores are [0.433, 0.693, 0.456, 0.446, 0.454]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00039723    train's RMSPE: 0.226417 valid's rmse: 0.000439767   valid's RMSPE: 0.249683
[100]   train's rmse: 0.000369055   train's RMSPE: 0.210358 valid's rmse: 0.000418914   valid's RMSPE: 0.237844
[150]   train's rmse: 0.000359385   train's RMSPE: 0.204846 valid's rmse: 0.000414815   valid's RMSPE: 0.235516
[200]   train's rmse: 0.000351331   train's RMSPE: 0.200255 valid's rmse: 0.00041314    valid's RMSPE: 0.234565
[250]   train's rmse: 0.000344951   train's RMSPE: 0.196618 valid's rmse: 0.000411162   valid's RMSPE: 0.233442
[300]   train's rmse: 0.000339402   train's RMSPE: 0.193455 valid's rmse: 0.00040962    valid's RMSPE: 0.232566
[350]   train's rmse: 0.000334466   train's RMSPE: 0.190642 valid's rmse: 0.000409882   valid's RMSPE: 0.232716
Early stopping, best iteration is:
[328]   train's rmse: 0.000336732   train's RMSPE: 0.191934 valid's rmse: 0.000409114   valid's RMSPE: 0.23228
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000402896   train's RMSPE: 0.228173 valid's rmse: 0.000423411   valid's RMSPE: 0.246517
[100]   train's rmse: 0.000374615   train's RMSPE: 0.212157 valid's rmse: 0.00039617    valid's RMSPE: 0.230657
[150]   train's rmse: 0.000363968   train's RMSPE: 0.206127 valid's rmse: 0.000390854   valid's RMSPE: 0.227562
[200]   train's rmse: 0.000355846   train's RMSPE: 0.201527 valid's rmse: 0.000387348   valid's RMSPE: 0.22552
[250]   train's rmse: 0.000349067   train's RMSPE: 0.197688 valid's rmse: 0.000384118   valid's RMSPE: 0.22364
[300]   train's rmse: 0.000343017   train's RMSPE: 0.194262 valid's rmse: 0.000383293   valid's RMSPE: 0.223159
[350]   train's rmse: 0.000337753   train's RMSPE: 0.191281 valid's rmse: 0.000381529   valid's RMSPE: 0.222132
[400]   train's rmse: 0.000333333   train's RMSPE: 0.188778 valid's rmse: 0.000380727   valid's RMSPE: 0.221665
[450]   train's rmse: 0.000329207   train's RMSPE: 0.186441 valid's rmse: 0.000379729   valid's RMSPE: 0.221084
[500]   train's rmse: 0.000325146   train's RMSPE: 0.184141 valid's rmse: 0.000378746   valid's RMSPE: 0.220512
[550]   train's rmse: 0.000321359   train's RMSPE: 0.181996 valid's rmse: 0.000379008   valid's RMSPE: 0.220664
Early stopping, best iteration is:
[530]   train's rmse: 0.000323102   train's RMSPE: 0.182983 valid's rmse: 0.000378465   valid's RMSPE: 0.220348
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000405455   train's RMSPE: 0.230161 valid's rmse: 0.000386934   valid's RMSPE: 0.223268
[100]   train's rmse: 0.00037835    train's RMSPE: 0.214774 valid's rmse: 0.000366542   valid's RMSPE: 0.211501
[150]   train's rmse: 0.000367153   train's RMSPE: 0.208419 valid's rmse: 0.000362319   valid's RMSPE: 0.209065
[200]   train's rmse: 0.000359099   train's RMSPE: 0.203847 valid's rmse: 0.000359321   valid's RMSPE: 0.207334
[250]   train's rmse: 0.000352645   train's RMSPE: 0.200183 valid's rmse: 0.000358424   valid's RMSPE: 0.206817
[300]   train's rmse: 0.000347163   train's RMSPE: 0.197071 valid's rmse: 0.000357344   valid's RMSPE: 0.206194
[350]   train's rmse: 0.000341992   train's RMSPE: 0.194136 valid's rmse: 0.000356144   valid's RMSPE: 0.205502
[400]   train's rmse: 0.00033734    train's RMSPE: 0.191495 valid's rmse: 0.000356351   valid's RMSPE: 0.205621
Early stopping, best iteration is:
[355]   train's rmse: 0.000341555   train's RMSPE: 0.193887 valid's rmse: 0.000355747   valid's RMSPE: 0.205273
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000399879   train's RMSPE: 0.228842 valid's rmse: 0.000405203   valid's RMSPE: 0.226295
[100]   train's rmse: 0.000370735   train's RMSPE: 0.212164 valid's rmse: 0.000392929   valid's RMSPE: 0.21944
[150]   train's rmse: 0.000360031   train's RMSPE: 0.206038 valid's rmse: 0.000391477   valid's RMSPE: 0.218629
[200]   train's rmse: 0.000351761   train's RMSPE: 0.201306 valid's rmse: 0.000388342   valid's RMSPE: 0.216878
[250]   train's rmse: 0.000345449   train's RMSPE: 0.197693 valid's rmse: 0.000387485   valid's RMSPE: 0.216399
[300]   train's rmse: 0.000339815   train's RMSPE: 0.194469 valid's rmse: 0.000385555   valid's RMSPE: 0.215322
[350]   train's rmse: 0.000334853   train's RMSPE: 0.191629 valid's rmse: 0.000384336   valid's RMSPE: 0.214641
[400]   train's rmse: 0.000330034   train's RMSPE: 0.188872 valid's rmse: 0.000383217   valid's RMSPE: 0.214016
[450]   train's rmse: 0.000326632   train's RMSPE: 0.186925 valid's rmse: 0.000382196   valid's RMSPE: 0.213446
Early stopping, best iteration is:
[446]   train's rmse: 0.00032685    train's RMSPE: 0.18705  valid's rmse: 0.000382023   valid's RMSPE: 0.213349
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000402398   train's RMSPE: 0.229946 valid's rmse: 0.000406105   valid's RMSPE: 0.228192
[100]   train's rmse: 0.000375333   train's RMSPE: 0.21448  valid's rmse: 0.000385549   valid's RMSPE: 0.216642
[150]   train's rmse: 0.000364505   train's RMSPE: 0.208293 valid's rmse: 0.000383036   valid's RMSPE: 0.21523
[200]   train's rmse: 0.00035633    train's RMSPE: 0.203621 valid's rmse: 0.00038178    valid's RMSPE: 0.214524
[250]   train's rmse: 0.00034964    train's RMSPE: 0.199798 valid's rmse: 0.000380764   valid's RMSPE: 0.213953
[300]   train's rmse: 0.000343263   train's RMSPE: 0.196154 valid's rmse: 0.000378777   valid's RMSPE: 0.212836
[350]   train's rmse: 0.000337913   train's RMSPE: 0.193097 valid's rmse: 0.000376702   valid's RMSPE: 0.21167
[400]   train's rmse: 0.000333583   train's RMSPE: 0.190622 valid's rmse: 0.000375854   valid's RMSPE: 0.211194
Early stopping, best iteration is:
[398]   train's rmse: 0.00033375    train's RMSPE: 0.190718 valid's rmse: 0.000375718   valid's RMSPE: 0.211117
Our out of folds RMSPE is 0.217, compared to 0.1923845378320642, giving gain 0.02461546216793581
Our cv fold scores are [0.232, 0.22, 0.205, 0.213, 0.211]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0011175 train's RMSPE: 0.304442 valid's rmse: 0.00117706    valid's RMSPE: 0.311014
[100]   train's rmse: 0.00106958    train's RMSPE: 0.291388 valid's rmse: 0.00115566    valid's RMSPE: 0.305359
[150]   train's rmse: 0.0010402 train's RMSPE: 0.283383 valid's rmse: 0.00114694    valid's RMSPE: 0.303054
[200]   train's rmse: 0.00101648    train's RMSPE: 0.276922 valid's rmse: 0.0011412 valid's RMSPE: 0.301537
[250]   train's rmse: 0.000995729   train's RMSPE: 0.271268 valid's rmse: 0.00113983    valid's RMSPE: 0.301176
Early stopping, best iteration is:
[242]   train's rmse: 0.000998485   train's RMSPE: 0.272019 valid's rmse: 0.0011387 valid's RMSPE: 0.300876
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00111123    train's RMSPE: 0.299647 valid's rmse: 0.00116845    valid's RMSPE: 0.321783
[100]   train's rmse: 0.00106458    train's RMSPE: 0.287067 valid's rmse: 0.00114916    valid's RMSPE: 0.316471
Early stopping, best iteration is:
[90]    train's rmse: 0.00107092    train's RMSPE: 0.288776 valid's rmse: 0.00114808    valid's RMSPE: 0.316172
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00111825    train's RMSPE: 0.301841 valid's rmse: 0.00117597    valid's RMSPE: 0.322609
[100]   train's rmse: 0.00107019    train's RMSPE: 0.288869 valid's rmse: 0.00116224    valid's RMSPE: 0.318843
[150]   train's rmse: 0.00104097    train's RMSPE: 0.28098  valid's rmse: 0.00115831    valid's RMSPE: 0.317766
[200]   train's rmse: 0.00101926    train's RMSPE: 0.275121 valid's rmse: 0.0011551 valid's RMSPE: 0.316884
[250]   train's rmse: 0.000998883   train's RMSPE: 0.269621 valid's rmse: 0.00115509    valid's RMSPE: 0.316881
Early stopping, best iteration is:
[223]   train's rmse: 0.00100991    train's RMSPE: 0.272597 valid's rmse: 0.00115152    valid's RMSPE: 0.315901
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00112694    train's RMSPE: 0.304311 valid's rmse: 0.0011462 valid's RMSPE: 0.313946
[100]   train's rmse: 0.00107574    train's RMSPE: 0.290483 valid's rmse: 0.00113937    valid's RMSPE: 0.312078
Early stopping, best iteration is:
[95]    train's rmse: 0.00107892    train's RMSPE: 0.291342 valid's rmse: 0.00113781    valid's RMSPE: 0.311648
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00112118    train's RMSPE: 0.304964 valid's rmse: 0.00114648    valid's RMSPE: 0.304953
[100]   train's rmse: 0.00107201    train's RMSPE: 0.291589 valid's rmse: 0.001127  valid's RMSPE: 0.299773
[150]   train's rmse: 0.00104226    train's RMSPE: 0.283497 valid's rmse: 0.00112456    valid's RMSPE: 0.299123
[200]   train's rmse: 0.00102055    train's RMSPE: 0.277591 valid's rmse: 0.00112023    valid's RMSPE: 0.297972
[250]   train's rmse: 0.00100086    train's RMSPE: 0.272235 valid's rmse: 0.00112191    valid's RMSPE: 0.298418
[300]   train's rmse: 0.000985378   train's RMSPE: 0.268025 valid's rmse: 0.00111947    valid's RMSPE: 0.297769
Early stopping, best iteration is:
[281]   train's rmse: 0.000990801   train's RMSPE: 0.2695   valid's rmse: 0.00111786    valid's RMSPE: 0.297341

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.308, compared to 0.2867198191034136, giving gain 0.021280180896586398
Our cv fold scores are [0.301, 0.316, 0.316, 0.312, 0.297]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000417942   train's RMSPE: 0.242059 valid's rmse: 0.000445869   valid's RMSPE: 0.257241
[100]   train's rmse: 0.00040016    train's RMSPE: 0.23176  valid's rmse: 0.000431344   valid's RMSPE: 0.248861
[150]   train's rmse: 0.000391543   train's RMSPE: 0.226769 valid's rmse: 0.000427086   valid's RMSPE: 0.246405
[200]   train's rmse: 0.000384166   train's RMSPE: 0.222497 valid's rmse: 0.000424822   valid's RMSPE: 0.245099
[250]   train's rmse: 0.000377458   train's RMSPE: 0.218612 valid's rmse: 0.000422495   valid's RMSPE: 0.243756
[300]   train's rmse: 0.000372235   train's RMSPE: 0.215587 valid's rmse: 0.00042037    valid's RMSPE: 0.24253
[350]   train's rmse: 0.00036736    train's RMSPE: 0.212763 valid's rmse: 0.000418507   valid's RMSPE: 0.241455
[400]   train's rmse: 0.000363575   train's RMSPE: 0.210571 valid's rmse: 0.000417663   valid's RMSPE: 0.240968
[450]   train's rmse: 0.000358973   train's RMSPE: 0.207906 valid's rmse: 0.000416549   valid's RMSPE: 0.240325
[500]   train's rmse: 0.000354936   train's RMSPE: 0.205568 valid's rmse: 0.00041519    valid's RMSPE: 0.239541
[550]   train's rmse: 0.000351251   train's RMSPE: 0.203434 valid's rmse: 0.000414363   valid's RMSPE: 0.239064
[600]   train's rmse: 0.000347262   train's RMSPE: 0.201123 valid's rmse: 0.000413474   valid's RMSPE: 0.238551
[650]   train's rmse: 0.000343936   train's RMSPE: 0.199197 valid's rmse: 0.000412601   valid's RMSPE: 0.238047
[700]   train's rmse: 0.000340524   train's RMSPE: 0.197221 valid's rmse: 0.000412269   valid's RMSPE: 0.237856
[750]   train's rmse: 0.00033753    train's RMSPE: 0.195487 valid's rmse: 0.000411725   valid's RMSPE: 0.237542
[800]   train's rmse: 0.00033443    train's RMSPE: 0.193692 valid's rmse: 0.000411257   valid's RMSPE: 0.237272
Early stopping, best iteration is:
[799]   train's rmse: 0.000334482   train's RMSPE: 0.193722 valid's rmse: 0.000411179   valid's RMSPE: 0.237227
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000416788   train's RMSPE: 0.241073 valid's rmse: 0.000445878   valid's RMSPE: 0.258606
[100]   train's rmse: 0.000398077   train's RMSPE: 0.230251 valid's rmse: 0.000434007   valid's RMSPE: 0.251721
[150]   train's rmse: 0.000388474   train's RMSPE: 0.224696 valid's rmse: 0.000430368   valid's RMSPE: 0.24961
[200]   train's rmse: 0.000381385   train's RMSPE: 0.220596 valid's rmse: 0.000428261   valid's RMSPE: 0.248388
[250]   train's rmse: 0.000375034   train's RMSPE: 0.216922 valid's rmse: 0.000426926   valid's RMSPE: 0.247614
[300]   train's rmse: 0.00036994    train's RMSPE: 0.213976 valid's rmse: 0.000426173   valid's RMSPE: 0.247177
[350]   train's rmse: 0.000364936   train's RMSPE: 0.211081 valid's rmse: 0.000424211   valid's RMSPE: 0.246039
[400]   train's rmse: 0.000360471   train's RMSPE: 0.208499 valid's rmse: 0.000423409   valid's RMSPE: 0.245574
[450]   train's rmse: 0.00035672    train's RMSPE: 0.20633  valid's rmse: 0.000422035   valid's RMSPE: 0.244777
[500]   train's rmse: 0.000352741   train's RMSPE: 0.204028 valid's rmse: 0.000421127   valid's RMSPE: 0.24425
[550]   train's rmse: 0.000348868   train's RMSPE: 0.201788 valid's rmse: 0.000420472   valid's RMSPE: 0.243871
[600]   train's rmse: 0.000345328   train's RMSPE: 0.19974  valid's rmse: 0.000420137   valid's RMSPE: 0.243676
[650]   train's rmse: 0.000341883   train's RMSPE: 0.197747 valid's rmse: 0.000418918   valid's RMSPE: 0.24297
[700]   train's rmse: 0.000338069   train's RMSPE: 0.195541 valid's rmse: 0.000417985   valid's RMSPE: 0.242428
[750]   train's rmse: 0.000335019   train's RMSPE: 0.193777 valid's rmse: 0.000417463   valid's RMSPE: 0.242125
[800]   train's rmse: 0.000332661   train's RMSPE: 0.192413 valid's rmse: 0.000416665   valid's RMSPE: 0.241663
[850]   train's rmse: 0.00033022    train's RMSPE: 0.191002 valid's rmse: 0.000415868   valid's RMSPE: 0.241201
[900]   train's rmse: 0.000327855   train's RMSPE: 0.189634 valid's rmse: 0.000415387   valid's RMSPE: 0.240921
Early stopping, best iteration is:
[886]   train's rmse: 0.000328412   train's RMSPE: 0.189956 valid's rmse: 0.000415224   valid's RMSPE: 0.240827
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00042349    train's RMSPE: 0.244181 valid's rmse: 0.000411397   valid's RMSPE: 0.241563
[100]   train's rmse: 0.000404399   train's RMSPE: 0.233173 valid's rmse: 0.000399507   valid's RMSPE: 0.234582
[150]   train's rmse: 0.00039646    train's RMSPE: 0.228596 valid's rmse: 0.000399502   valid's RMSPE: 0.234579
[200]   train's rmse: 0.000388574   train's RMSPE: 0.224049 valid's rmse: 0.000398456   valid's RMSPE: 0.233964
[250]   train's rmse: 0.00038214    train's RMSPE: 0.220339 valid's rmse: 0.000399302   valid's RMSPE: 0.234461
Early stopping, best iteration is:
[236]   train's rmse: 0.000383407   train's RMSPE: 0.221069 valid's rmse: 0.000397808   valid's RMSPE: 0.233584
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000426107   train's RMSPE: 0.246845 valid's rmse: 0.000406535   valid's RMSPE: 0.234329
[100]   train's rmse: 0.000407051   train's RMSPE: 0.235806 valid's rmse: 0.000396857   valid's RMSPE: 0.228751
[150]   train's rmse: 0.000397979   train's RMSPE: 0.23055  valid's rmse: 0.000396006   valid's RMSPE: 0.228261
[200]   train's rmse: 0.000390013   train's RMSPE: 0.225936 valid's rmse: 0.000393931   valid's RMSPE: 0.227065
[250]   train's rmse: 0.000383496   train's RMSPE: 0.22216  valid's rmse: 0.000392614   valid's RMSPE: 0.226305
[300]   train's rmse: 0.000377878   train's RMSPE: 0.218906 valid's rmse: 0.000392372   valid's RMSPE: 0.226166
[350]   train's rmse: 0.000373446   train's RMSPE: 0.216338 valid's rmse: 0.000392922   valid's RMSPE: 0.226483
Early stopping, best iteration is:
[303]   train's rmse: 0.000377604   train's RMSPE: 0.218747 valid's rmse: 0.000392058   valid's RMSPE: 0.225985
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00042068    train's RMSPE: 0.244056 valid's rmse: 0.000433792   valid's RMSPE: 0.248564
[100]   train's rmse: 0.000402341   train's RMSPE: 0.233417 valid's rmse: 0.000421603   valid's RMSPE: 0.24158
[150]   train's rmse: 0.000394481   train's RMSPE: 0.228856 valid's rmse: 0.000419888   valid's RMSPE: 0.240597
[200]   train's rmse: 0.000386905   train's RMSPE: 0.224461 valid's rmse: 0.000417157   valid's RMSPE: 0.239032
[250]   train's rmse: 0.000381181   train's RMSPE: 0.221141 valid's rmse: 0.000415899   valid's RMSPE: 0.238312
[300]   train's rmse: 0.000375316   train's RMSPE: 0.217738 valid's rmse: 0.000414409   valid's RMSPE: 0.237458
[350]   train's rmse: 0.00037034    train's RMSPE: 0.214851 valid's rmse: 0.000412873   valid's RMSPE: 0.236578
[400]   train's rmse: 0.000365805   train's RMSPE: 0.21222  valid's rmse: 0.000411412   valid's RMSPE: 0.23574
[450]   train's rmse: 0.000361803   train's RMSPE: 0.209899 valid's rmse: 0.000412677   valid's RMSPE: 0.236465
Early stopping, best iteration is:
[409]   train's rmse: 0.000364936   train's RMSPE: 0.211716 valid's rmse: 0.000410972   valid's RMSPE: 0.235488
Our out of folds RMSPE is 0.235, compared to 0.18658038014822179, giving gain 0.0484196198517782
Our cv fold scores are [0.237, 0.241, 0.234, 0.226, 0.235]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000446344   train's RMSPE: 0.218843 valid's rmse: 0.000476402   valid's RMSPE: 0.234381
[100]   train's rmse: 0.000417835   train's RMSPE: 0.204865 valid's rmse: 0.000453947   valid's RMSPE: 0.223334
[150]   train's rmse: 0.000406005   train's RMSPE: 0.199065 valid's rmse: 0.000447468   valid's RMSPE: 0.220146
[200]   train's rmse: 0.000396539   train's RMSPE: 0.194424 valid's rmse: 0.000443771   valid's RMSPE: 0.218327
[250]   train's rmse: 0.000389794   train's RMSPE: 0.191117 valid's rmse: 0.000441542   valid's RMSPE: 0.217231
[300]   train's rmse: 0.000383058   train's RMSPE: 0.187814 valid's rmse: 0.000439806   valid's RMSPE: 0.216376
[350]   train's rmse: 0.000376519   train's RMSPE: 0.184608 valid's rmse: 0.000436031   valid's RMSPE: 0.214519
[400]   train's rmse: 0.000371895   train's RMSPE: 0.182341 valid's rmse: 0.000433654   valid's RMSPE: 0.213349
[450]   train's rmse: 0.000366016   train's RMSPE: 0.179458 valid's rmse: 0.000433619   valid's RMSPE: 0.213333
[500]   train's rmse: 0.000361721   train's RMSPE: 0.177352 valid's rmse: 0.000431966   valid's RMSPE: 0.21252
[550]   train's rmse: 0.000357701   train's RMSPE: 0.175381 valid's rmse: 0.000430264   valid's RMSPE: 0.211682
[600]   train's rmse: 0.00035355    train's RMSPE: 0.173346 valid's rmse: 0.000428168   valid's RMSPE: 0.210651
[650]   train's rmse: 0.000349589   train's RMSPE: 0.171404 valid's rmse: 0.000427023   valid's RMSPE: 0.210088
[700]   train's rmse: 0.000346183   train's RMSPE: 0.169734 valid's rmse: 0.000425777   valid's RMSPE: 0.209475
[750]   train's rmse: 0.000342831   train's RMSPE: 0.16809  valid's rmse: 0.000425205   valid's RMSPE: 0.209193
[800]   train's rmse: 0.000339932   train's RMSPE: 0.166669 valid's rmse: 0.000424385   valid's RMSPE: 0.208789
[850]   train's rmse: 0.000336988   train's RMSPE: 0.165226 valid's rmse: 0.000424013   valid's RMSPE: 0.208606
[900]   train's rmse: 0.000334425   train's RMSPE: 0.163969 valid's rmse: 0.000424357   valid's RMSPE: 0.208776
Early stopping, best iteration is:
[873]   train's rmse: 0.000335731   train's RMSPE: 0.16461  valid's rmse: 0.000423619   valid's RMSPE: 0.208413
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000442589   train's RMSPE: 0.216954 valid's rmse: 0.000481789   valid's RMSPE: 0.237241
[100]   train's rmse: 0.000414509   train's RMSPE: 0.203189 valid's rmse: 0.000463327   valid's RMSPE: 0.228151
[150]   train's rmse: 0.000403039   train's RMSPE: 0.197567 valid's rmse: 0.000456519   valid's RMSPE: 0.224798
[200]   train's rmse: 0.000394344   train's RMSPE: 0.193304 valid's rmse: 0.000451963   valid's RMSPE: 0.222554
[250]   train's rmse: 0.00038743    train's RMSPE: 0.189915 valid's rmse: 0.000449032   valid's RMSPE: 0.221111
[300]   train's rmse: 0.0003807 train's RMSPE: 0.186616 valid's rmse: 0.000445178   valid's RMSPE: 0.219213
[350]   train's rmse: 0.000374966   train's RMSPE: 0.183806 valid's rmse: 0.000443249   valid's RMSPE: 0.218263
[400]   train's rmse: 0.000369799   train's RMSPE: 0.181273 valid's rmse: 0.000441243   valid's RMSPE: 0.217276
[450]   train's rmse: 0.000365105   train's RMSPE: 0.178971 valid's rmse: 0.000439255   valid's RMSPE: 0.216297
[500]   train's rmse: 0.000360607   train's RMSPE: 0.176767 valid's rmse: 0.000437633   valid's RMSPE: 0.215498
[550]   train's rmse: 0.000356986   train's RMSPE: 0.174992 valid's rmse: 0.000436627   valid's RMSPE: 0.215003
[600]   train's rmse: 0.000352849   train's RMSPE: 0.172964 valid's rmse: 0.000434361   valid's RMSPE: 0.213887
[650]   train's rmse: 0.000349375   train's RMSPE: 0.171261 valid's rmse: 0.000433866   valid's RMSPE: 0.213643
[700]   train's rmse: 0.000346109   train's RMSPE: 0.16966  valid's rmse: 0.000433359   valid's RMSPE: 0.213394
[750]   train's rmse: 0.000342729   train's RMSPE: 0.168003 valid's rmse: 0.000431179   valid's RMSPE: 0.21232
[800]   train's rmse: 0.000339584   train's RMSPE: 0.166462 valid's rmse: 0.000430392   valid's RMSPE: 0.211933
Early stopping, best iteration is:
[778]   train's rmse: 0.000340892   train's RMSPE: 0.167102 valid's rmse: 0.00043006    valid's RMSPE: 0.211769
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000451291   train's RMSPE: 0.221319 valid's rmse: 0.000435711   valid's RMSPE: 0.214168
[100]   train's rmse: 0.000421945   train's RMSPE: 0.206927 valid's rmse: 0.00041421    valid's RMSPE: 0.203599
[150]   train's rmse: 0.00040955    train's RMSPE: 0.200849 valid's rmse: 0.000408239   valid's RMSPE: 0.200664
[200]   train's rmse: 0.000399516   train's RMSPE: 0.195928 valid's rmse: 0.00040293    valid's RMSPE: 0.198055
[250]   train's rmse: 0.000391941   train's RMSPE: 0.192213 valid's rmse: 0.000400605   valid's RMSPE: 0.196912
[300]   train's rmse: 0.000385581   train's RMSPE: 0.189094 valid's rmse: 0.000399063   valid's RMSPE: 0.196154
[350]   train's rmse: 0.000380271   train's RMSPE: 0.18649  valid's rmse: 0.000398977   valid's RMSPE: 0.196112
[400]   train's rmse: 0.000374882   train's RMSPE: 0.183847 valid's rmse: 0.000398129   valid's RMSPE: 0.195695
[450]   train's rmse: 0.000369822   train's RMSPE: 0.181366 valid's rmse: 0.000397197   valid's RMSPE: 0.195237
[500]   train's rmse: 0.000365765   train's RMSPE: 0.179376 valid's rmse: 0.000396422   valid's RMSPE: 0.194856
[550]   train's rmse: 0.000361797   train's RMSPE: 0.17743  valid's rmse: 0.000396363   valid's RMSPE: 0.194827
Early stopping, best iteration is:
[508]   train's rmse: 0.000365096   train's RMSPE: 0.179048 valid's rmse: 0.00039628    valid's RMSPE: 0.194786
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000455365   train's RMSPE: 0.223326 valid's rmse: 0.000433018   valid's RMSPE: 0.21281
[100]   train's rmse: 0.000426077   train's RMSPE: 0.208962 valid's rmse: 0.000410157   valid's RMSPE: 0.201575
[150]   train's rmse: 0.000413755   train's RMSPE: 0.202919 valid's rmse: 0.000403949   valid's RMSPE: 0.198524
[200]   train's rmse: 0.000405443   train's RMSPE: 0.198843 valid's rmse: 0.000401512   valid's RMSPE: 0.197327
[250]   train's rmse: 0.000398068   train's RMSPE: 0.195226 valid's rmse: 0.000398827   valid's RMSPE: 0.196007
[300]   train's rmse: 0.000391417   train's RMSPE: 0.191964 valid's rmse: 0.00039782    valid's RMSPE: 0.195512
[350]   train's rmse: 0.000386055   train's RMSPE: 0.189334 valid's rmse: 0.000396077   valid's RMSPE: 0.194656
[400]   train's rmse: 0.000380402   train's RMSPE: 0.186561 valid's rmse: 0.000392823   valid's RMSPE: 0.193057
[450]   train's rmse: 0.000375122   train's RMSPE: 0.183972 valid's rmse: 0.000391623   valid's RMSPE: 0.192467
[500]   train's rmse: 0.000371157   train's RMSPE: 0.182028 valid's rmse: 0.000391563   valid's RMSPE: 0.192437
Early stopping, best iteration is:
[490]   train's rmse: 0.000371854   train's RMSPE: 0.182369 valid's rmse: 0.000391245   valid's RMSPE: 0.192281
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000450345   train's RMSPE: 0.221502 valid's rmse: 0.000466906   valid's RMSPE: 0.226806
[100]   train's rmse: 0.000422108   train's RMSPE: 0.207613 valid's rmse: 0.000442206   valid's RMSPE: 0.214808
[150]   train's rmse: 0.000410842   train's RMSPE: 0.202072 valid's rmse: 0.00043617    valid's RMSPE: 0.211876
[200]   train's rmse: 0.00040189    train's RMSPE: 0.197669 valid's rmse: 0.000431385   valid's RMSPE: 0.209552
[250]   train's rmse: 0.00039435    train's RMSPE: 0.193961 valid's rmse: 0.000428592   valid's RMSPE: 0.208195
[300]   train's rmse: 0.000388214   train's RMSPE: 0.190942 valid's rmse: 0.000427098   valid's RMSPE: 0.207469
[350]   train's rmse: 0.000382148   train's RMSPE: 0.187959 valid's rmse: 0.000425249   valid's RMSPE: 0.206571
[400]   train's rmse: 0.000377317   train's RMSPE: 0.185583 valid's rmse: 0.000424829   valid's RMSPE: 0.206367
[450]   train's rmse: 0.000372493   train's RMSPE: 0.18321  valid's rmse: 0.000424056   valid's RMSPE: 0.205991
[500]   train's rmse: 0.00036765    train's RMSPE: 0.180828 valid's rmse: 0.000422307   valid's RMSPE: 0.205141
[550]   train's rmse: 0.000363568   train's RMSPE: 0.178821 valid's rmse: 0.000421682   valid's RMSPE: 0.204838
[600]   train's rmse: 0.000359786   train's RMSPE: 0.17696  valid's rmse: 0.00042086    valid's RMSPE: 0.204439
Early stopping, best iteration is:
[585]   train's rmse: 0.000360964   train's RMSPE: 0.177539 valid's rmse: 0.000420713   valid's RMSPE: 0.204367
Our out of folds RMSPE is 0.202, compared to 0.16995311675322403, giving gain 0.03204688324677599
Our cv fold scores are [0.208, 0.212, 0.195, 0.192, 0.204]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000510245   train's RMSPE: 0.208969 valid's rmse: 0.000554907   valid's RMSPE: 0.226135
[100]   train's rmse: 0.000485833   train's RMSPE: 0.198971 valid's rmse: 0.000533288   valid's RMSPE: 0.217325
[150]   train's rmse: 0.000474737   train's RMSPE: 0.194427 valid's rmse: 0.000529576   valid's RMSPE: 0.215813
[200]   train's rmse: 0.000465351   train's RMSPE: 0.190583 valid's rmse: 0.000526653   valid's RMSPE: 0.214621
[250]   train's rmse: 0.000457885   train's RMSPE: 0.187525 valid's rmse: 0.000524208   valid's RMSPE: 0.213625
[300]   train's rmse: 0.000450833   train's RMSPE: 0.184637 valid's rmse: 0.000524202   valid's RMSPE: 0.213622
[350]   train's rmse: 0.000444406   train's RMSPE: 0.182005 valid's rmse: 0.000524495   valid's RMSPE: 0.213742
Early stopping, best iteration is:
[306]   train's rmse: 0.000449979   train's RMSPE: 0.184287 valid's rmse: 0.000523719   valid's RMSPE: 0.213425
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000509038   train's RMSPE: 0.207649 valid's rmse: 0.000551159   valid's RMSPE: 0.228167
[100]   train's rmse: 0.00048366    train's RMSPE: 0.197296 valid's rmse: 0.000527558   valid's RMSPE: 0.218397
[150]   train's rmse: 0.000472599   train's RMSPE: 0.192784 valid's rmse: 0.000522067   valid's RMSPE: 0.216124
[200]   train's rmse: 0.000463758   train's RMSPE: 0.189178 valid's rmse: 0.000520127   valid's RMSPE: 0.21532
[250]   train's rmse: 0.000456442   train's RMSPE: 0.186193 valid's rmse: 0.000518663   valid's RMSPE: 0.214715
[300]   train's rmse: 0.000449743   train's RMSPE: 0.183461 valid's rmse: 0.000517252   valid's RMSPE: 0.21413
[350]   train's rmse: 0.000443263   train's RMSPE: 0.180817 valid's rmse: 0.000516569   valid's RMSPE: 0.213848
[400]   train's rmse: 0.000438167   train's RMSPE: 0.178738 valid's rmse: 0.000515116   valid's RMSPE: 0.213246
[450]   train's rmse: 0.000433207   train's RMSPE: 0.176715 valid's rmse: 0.000515223   valid's RMSPE: 0.21329
[500]   train's rmse: 0.000428689   train's RMSPE: 0.174872 valid's rmse: 0.000514596   valid's RMSPE: 0.213031
Early stopping, best iteration is:
[492]   train's rmse: 0.000429376   train's RMSPE: 0.175153 valid's rmse: 0.000514194   valid's RMSPE: 0.212864
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000517757   train's RMSPE: 0.21227  valid's rmse: 0.000497619   valid's RMSPE: 0.201919
[100]   train's rmse: 0.000490622   train's RMSPE: 0.201145 valid's rmse: 0.000482214   valid's RMSPE: 0.195668
[150]   train's rmse: 0.000479304   train's RMSPE: 0.196505 valid's rmse: 0.000480532   valid's RMSPE: 0.194986
[200]   train's rmse: 0.000470325   train's RMSPE: 0.192824 valid's rmse: 0.000480268   valid's RMSPE: 0.194879
[250]   train's rmse: 0.00046261    train's RMSPE: 0.189661 valid's rmse: 0.000480065   valid's RMSPE: 0.194796
[300]   train's rmse: 0.000455978   train's RMSPE: 0.186942 valid's rmse: 0.00047842    valid's RMSPE: 0.194129
[350]   train's rmse: 0.000450297   train's RMSPE: 0.184613 valid's rmse: 0.00047778    valid's RMSPE: 0.193869
Early stopping, best iteration is:
[347]   train's rmse: 0.000450675   train's RMSPE: 0.184768 valid's rmse: 0.000477496   valid's RMSPE: 0.193754
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000517935   train's RMSPE: 0.211657 valid's rmse: 0.000500252   valid's RMSPE: 0.205644
[100]   train's rmse: 0.000491913   train's RMSPE: 0.201023 valid's rmse: 0.000487488   valid's RMSPE: 0.200397
[150]   train's rmse: 0.000481308   train's RMSPE: 0.196689 valid's rmse: 0.000485263   valid's RMSPE: 0.199482
[200]   train's rmse: 0.000471858   train's RMSPE: 0.192827 valid's rmse: 0.000484973   valid's RMSPE: 0.199363
Early stopping, best iteration is:
[169]   train's rmse: 0.000477301   train's RMSPE: 0.195052 valid's rmse: 0.000484481   valid's RMSPE: 0.199161
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000513214   train's RMSPE: 0.210212 valid's rmse: 0.000524501   valid's RMSPE: 0.213632
[100]   train's rmse: 0.000485449   train's RMSPE: 0.198839 valid's rmse: 0.000516254   valid's RMSPE: 0.210273
[150]   train's rmse: 0.000473543   train's RMSPE: 0.193963 valid's rmse: 0.0005196 valid's RMSPE: 0.211636
Early stopping, best iteration is:
[120]   train's rmse: 0.000480258   train's RMSPE: 0.196713 valid's rmse: 0.0005154 valid's RMSPE: 0.209926
Our out of folds RMSPE is 0.206, compared to 0.18527433835243848, giving gain 0.02072566164756151
Our cv fold scores are [0.213, 0.213, 0.194, 0.199, 0.21]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00132234    train's RMSPE: 0.303079 valid's rmse: 0.00136657    valid's RMSPE: 0.320983
[100]   train's rmse: 0.00126374    train's RMSPE: 0.289647 valid's rmse: 0.00135468    valid's RMSPE: 0.318192
[150]   train's rmse: 0.0012242 train's RMSPE: 0.280585 valid's rmse: 0.00134842    valid's RMSPE: 0.316719
Early stopping, best iteration is:
[129]   train's rmse: 0.00124003    train's RMSPE: 0.284213 valid's rmse: 0.00134588    valid's RMSPE: 0.316123
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00131211    train's RMSPE: 0.301431 valid's rmse: 0.00144151    valid's RMSPE: 0.335587
[100]   train's rmse: 0.00125864    train's RMSPE: 0.289145 valid's rmse: 0.00141549    valid's RMSPE: 0.329529
[150]   train's rmse: 0.00122255    train's RMSPE: 0.280854 valid's rmse: 0.00140551    valid's RMSPE: 0.327206
[200]   train's rmse: 0.00119214    train's RMSPE: 0.273869 valid's rmse: 0.00140189    valid's RMSPE: 0.326364
[250]   train's rmse: 0.0011656 train's RMSPE: 0.267772 valid's rmse: 0.0014019 valid's RMSPE: 0.326366
Early stopping, best iteration is:
[205]   train's rmse: 0.0011893 train's RMSPE: 0.273216 valid's rmse: 0.00139994    valid's RMSPE: 0.325908
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00131533    train's RMSPE: 0.303011 valid's rmse: 0.00137633    valid's RMSPE: 0.316911
[100]   train's rmse: 0.00125301    train's RMSPE: 0.288655 valid's rmse: 0.00136538    valid's RMSPE: 0.31439
[150]   train's rmse: 0.00121009    train's RMSPE: 0.278767 valid's rmse: 0.00137057    valid's RMSPE: 0.315584
Early stopping, best iteration is:
[105]   train's rmse: 0.00124759    train's RMSPE: 0.287406 valid's rmse: 0.00136383    valid's RMSPE: 0.314034
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0013307 train's RMSPE: 0.305694 valid's rmse: 0.00134207    valid's RMSPE: 0.31246
[100]   train's rmse: 0.00127379    train's RMSPE: 0.292622 valid's rmse: 0.00131196    valid's RMSPE: 0.305451
[150]   train's rmse: 0.00123701    train's RMSPE: 0.284171 valid's rmse: 0.0013074 valid's RMSPE: 0.304389
[200]   train's rmse: 0.00120723    train's RMSPE: 0.27733  valid's rmse: 0.00131055    valid's RMSPE: 0.305122
Early stopping, best iteration is:
[162]   train's rmse: 0.00122894    train's RMSPE: 0.282318 valid's rmse: 0.00130525    valid's RMSPE: 0.303888
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00132626    train's RMSPE: 0.308617 valid's rmse: 0.00134424    valid's RMSPE: 0.296673
[100]   train's rmse: 0.00126559    train's RMSPE: 0.294498 valid's rmse: 0.00134179    valid's RMSPE: 0.296131
Early stopping, best iteration is:
[71]    train's rmse: 0.00129631    train's RMSPE: 0.301647 valid's rmse: 0.00133786    valid's RMSPE: 0.295264
Our out of folds RMSPE is 0.311, compared to 0.29956908631074286, giving gain 0.011430913689257138
Our cv fold scores are [0.316, 0.326, 0.314, 0.304, 0.295]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000827845   train's RMSPE: 0.263586 valid's rmse: 0.000876612   valid's RMSPE: 0.279166
[100]   train's rmse: 0.000788547   train's RMSPE: 0.251073 valid's rmse: 0.000859402   valid's RMSPE: 0.273686
[150]   train's rmse: 0.000766807   train's RMSPE: 0.244151 valid's rmse: 0.00085265    valid's RMSPE: 0.271535
[200]   train's rmse: 0.0007509 train's RMSPE: 0.239086 valid's rmse: 0.000851763   valid's RMSPE: 0.271253
[250]   train's rmse: 0.000736839   train's RMSPE: 0.234609 valid's rmse: 0.000851255   valid's RMSPE: 0.271091
[300]   train's rmse: 0.000723966   train's RMSPE: 0.23051  valid's rmse: 0.000849551   valid's RMSPE: 0.270549
[350]   train's rmse: 0.000712837   train's RMSPE: 0.226967 valid's rmse: 0.000849703   valid's RMSPE: 0.270597
Early stopping, best iteration is:
[314]   train's rmse: 0.00072093    train's RMSPE: 0.229544 valid's rmse: 0.00084856    valid's RMSPE: 0.270233
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000832168   train's RMSPE: 0.264929 valid's rmse: 0.000870116   valid's RMSPE: 0.277235
[100]   train's rmse: 0.000793867   train's RMSPE: 0.252736 valid's rmse: 0.00084072    valid's RMSPE: 0.267869
[150]   train's rmse: 0.000773728   train's RMSPE: 0.246324 valid's rmse: 0.000833545   valid's RMSPE: 0.265583
[200]   train's rmse: 0.000757768   train's RMSPE: 0.241243 valid's rmse: 0.000833455   valid's RMSPE: 0.265554
Early stopping, best iteration is:
[155]   train's rmse: 0.000772052   train's RMSPE: 0.24579  valid's rmse: 0.00083201    valid's RMSPE: 0.265094
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000831619   train's RMSPE: 0.264806 valid's rmse: 0.00086984    valid's RMSPE: 0.276931
[100]   train's rmse: 0.000794497   train's RMSPE: 0.252986 valid's rmse: 0.00085114    valid's RMSPE: 0.270977
[150]   train's rmse: 0.000773853   train's RMSPE: 0.246412 valid's rmse: 0.000844718   valid's RMSPE: 0.268933
[200]   train's rmse: 0.000759539   train's RMSPE: 0.241854 valid's rmse: 0.000844053   valid's RMSPE: 0.268721
Early stopping, best iteration is:
[158]   train's rmse: 0.000771377   train's RMSPE: 0.245624 valid's rmse: 0.000842612   valid's RMSPE: 0.268262
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000845721   train's RMSPE: 0.269481 valid's rmse: 0.000811102   valid's RMSPE: 0.257523
[100]   train's rmse: 0.000808158   train's RMSPE: 0.257512 valid's rmse: 0.000789324   valid's RMSPE: 0.250608
[150]   train's rmse: 0.000787964   train's RMSPE: 0.251077 valid's rmse: 0.000783138   valid's RMSPE: 0.248644
[200]   train's rmse: 0.000771519   train's RMSPE: 0.245837 valid's rmse: 0.000782716   valid's RMSPE: 0.24851
[250]   train's rmse: 0.000758956   train's RMSPE: 0.241834 valid's rmse: 0.000779229   valid's RMSPE: 0.247403
[300]   train's rmse: 0.000746787   train's RMSPE: 0.237956 valid's rmse: 0.00077704    valid's RMSPE: 0.246708
[350]   train's rmse: 0.000736501   train's RMSPE: 0.234679 valid's rmse: 0.000776995   valid's RMSPE: 0.246693
[400]   train's rmse: 0.000725673   train's RMSPE: 0.231229 valid's rmse: 0.000773691   valid's RMSPE: 0.245645
[450]   train's rmse: 0.00071665    train's RMSPE: 0.228353 valid's rmse: 0.000772458   valid's RMSPE: 0.245253
[500]   train's rmse: 0.00070696    train's RMSPE: 0.225266 valid's rmse: 0.000771267   valid's RMSPE: 0.244875
[550]   train's rmse: 0.000699386   train's RMSPE: 0.222852 valid's rmse: 0.000772531   valid's RMSPE: 0.245276
Early stopping, best iteration is:
[540]   train's rmse: 0.000700813   train's RMSPE: 0.223307 valid's rmse: 0.000771021   valid's RMSPE: 0.244797
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000834304   train's RMSPE: 0.265506 valid's rmse: 0.000854708   valid's RMSPE: 0.272747
[100]   train's rmse: 0.000795167   train's RMSPE: 0.253051 valid's rmse: 0.000838232   valid's RMSPE: 0.26749
[150]   train's rmse: 0.000775309   train's RMSPE: 0.246732 valid's rmse: 0.000831368   valid's RMSPE: 0.265299
[200]   train's rmse: 0.000760209   train's RMSPE: 0.241927 valid's rmse: 0.000825205   valid's RMSPE: 0.263332
Early stopping, best iteration is:
[195]   train's rmse: 0.000761323   train's RMSPE: 0.242281 valid's rmse: 0.000824975   valid's RMSPE: 0.263259
Our out of folds RMSPE is 0.262, compared to 0.22961166109765224, giving gain 0.03238833890234777
Our cv fold scores are [0.27, 0.265, 0.268, 0.245, 0.263]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000445318   train's RMSPE: 0.254122 valid's rmse: 0.000474526   valid's RMSPE: 0.26794
[100]   train's rmse: 0.00042452    train's RMSPE: 0.242254 valid's rmse: 0.000457837   valid's RMSPE: 0.258517
[150]   train's rmse: 0.000414165   train's RMSPE: 0.236345 valid's rmse: 0.000453177   valid's RMSPE: 0.255885
[200]   train's rmse: 0.000406225   train's RMSPE: 0.231814 valid's rmse: 0.000450738   valid's RMSPE: 0.254508
[250]   train's rmse: 0.000399319   train's RMSPE: 0.227873 valid's rmse: 0.000448452   valid's RMSPE: 0.253218
[300]   train's rmse: 0.000393581   train's RMSPE: 0.224598 valid's rmse: 0.000447552   valid's RMSPE: 0.252709
[350]   train's rmse: 0.000387729   train's RMSPE: 0.221259 valid's rmse: 0.000446529   valid's RMSPE: 0.252132
[400]   train's rmse: 0.000382469   train's RMSPE: 0.218257 valid's rmse: 0.000445165   valid's RMSPE: 0.251362
[450]   train's rmse: 0.000378311   train's RMSPE: 0.215885 valid's rmse: 0.000444213   valid's RMSPE: 0.250824
[500]   train's rmse: 0.000374862   train's RMSPE: 0.213916 valid's rmse: 0.00044391    valid's RMSPE: 0.250653
[550]   train's rmse: 0.000371347   train's RMSPE: 0.211911 valid's rmse: 0.000444085   valid's RMSPE: 0.250752
Early stopping, best iteration is:
[524]   train's rmse: 0.000373035   train's RMSPE: 0.212874 valid's rmse: 0.000443615   valid's RMSPE: 0.250487
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000440059   train's RMSPE: 0.251215 valid's rmse: 0.00049702    valid's RMSPE: 0.280215
[100]   train's rmse: 0.000418972   train's RMSPE: 0.239177 valid's rmse: 0.000481284   valid's RMSPE: 0.271343
[150]   train's rmse: 0.000409427   train's RMSPE: 0.233728 valid's rmse: 0.000477581   valid's RMSPE: 0.269255
[200]   train's rmse: 0.00040184    train's RMSPE: 0.229397 valid's rmse: 0.000473508   valid's RMSPE: 0.266959
[250]   train's rmse: 0.000395786   train's RMSPE: 0.225941 valid's rmse: 0.000471367   valid's RMSPE: 0.265752
[300]   train's rmse: 0.00038991    train's RMSPE: 0.222586 valid's rmse: 0.000468859   valid's RMSPE: 0.264338
[350]   train's rmse: 0.000384979   train's RMSPE: 0.219772 valid's rmse: 0.000466715   valid's RMSPE: 0.263129
[400]   train's rmse: 0.000380609   train's RMSPE: 0.217277 valid's rmse: 0.000466011   valid's RMSPE: 0.262733
[450]   train's rmse: 0.000376364   train's RMSPE: 0.214854 valid's rmse: 0.000464658   valid's RMSPE: 0.26197
[500]   train's rmse: 0.000372561   train's RMSPE: 0.212682 valid's rmse: 0.000464072   valid's RMSPE: 0.261639
[550]   train's rmse: 0.00036923    train's RMSPE: 0.210781 valid's rmse: 0.000463387   valid's RMSPE: 0.261253
[600]   train's rmse: 0.000366032   train's RMSPE: 0.208955 valid's rmse: 0.000462787   valid's RMSPE: 0.260915
[650]   train's rmse: 0.000363053   train's RMSPE: 0.207255 valid's rmse: 0.000462628   valid's RMSPE: 0.260825
Early stopping, best iteration is:
[647]   train's rmse: 0.000363219   train's RMSPE: 0.207349 valid's rmse: 0.000462533   valid's RMSPE: 0.260771
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000455239   train's RMSPE: 0.258592 valid's rmse: 0.000431958   valid's RMSPE: 0.248422
[100]   train's rmse: 0.000433125   train's RMSPE: 0.246031 valid's rmse: 0.000416842   valid's RMSPE: 0.239729
[150]   train's rmse: 0.00042274    train's RMSPE: 0.240132 valid's rmse: 0.000413246   valid's RMSPE: 0.23766
[200]   train's rmse: 0.000415304   train's RMSPE: 0.235908 valid's rmse: 0.000411767   valid's RMSPE: 0.23681
[250]   train's rmse: 0.000408692   train's RMSPE: 0.232152 valid's rmse: 0.000410179   valid's RMSPE: 0.235897
[300]   train's rmse: 0.000403591   train's RMSPE: 0.229255 valid's rmse: 0.000410619   valid's RMSPE: 0.23615
Early stopping, best iteration is:
[270]   train's rmse: 0.000406552   train's RMSPE: 0.230936 valid's rmse: 0.000409589   valid's RMSPE: 0.235557
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000454973   train's RMSPE: 0.258381 valid's rmse: 0.00042869    valid's RMSPE: 0.246766
[100]   train's rmse: 0.000433664   train's RMSPE: 0.24628  valid's rmse: 0.00041544    valid's RMSPE: 0.239139
[150]   train's rmse: 0.000422919   train's RMSPE: 0.240178 valid's rmse: 0.00041149    valid's RMSPE: 0.236865
[200]   train's rmse: 0.000414858   train's RMSPE: 0.2356   valid's rmse: 0.000409728   valid's RMSPE: 0.23585
[250]   train's rmse: 0.000408481   train's RMSPE: 0.231978 valid's rmse: 0.000408087   valid's RMSPE: 0.234906
[300]   train's rmse: 0.000402323   train's RMSPE: 0.228481 valid's rmse: 0.000406253   valid's RMSPE: 0.233851
[350]   train's rmse: 0.000396528   train's RMSPE: 0.22519  valid's rmse: 0.00040588    valid's RMSPE: 0.233635
[400]   train's rmse: 0.000391941   train's RMSPE: 0.222585 valid's rmse: 0.00040514    valid's RMSPE: 0.23321
[450]   train's rmse: 0.000387435   train's RMSPE: 0.220026 valid's rmse: 0.000404976   valid's RMSPE: 0.233115
[500]   train's rmse: 0.000383757   train's RMSPE: 0.217937 valid's rmse: 0.000404198   valid's RMSPE: 0.232668
[550]   train's rmse: 0.000379997   train's RMSPE: 0.215802 valid's rmse: 0.000403736   valid's RMSPE: 0.232402
[600]   train's rmse: 0.00037609    train's RMSPE: 0.213583 valid's rmse: 0.000403634   valid's RMSPE: 0.232343
Early stopping, best iteration is:
[594]   train's rmse: 0.000376526   train's RMSPE: 0.213831 valid's rmse: 0.000403421   valid's RMSPE: 0.23222
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00044848    train's RMSPE: 0.255553 valid's rmse: 0.000458319   valid's RMSPE: 0.260327
[100]   train's rmse: 0.000427756   train's RMSPE: 0.243744 valid's rmse: 0.000445529   valid's RMSPE: 0.253062
[150]   train's rmse: 0.000417468   train's RMSPE: 0.237882 valid's rmse: 0.000440054   valid's RMSPE: 0.249952
[200]   train's rmse: 0.000409979   train's RMSPE: 0.233614 valid's rmse: 0.000436806   valid's RMSPE: 0.248108
[250]   train's rmse: 0.000403363   train's RMSPE: 0.229844 valid's rmse: 0.00043526    valid's RMSPE: 0.247229
[300]   train's rmse: 0.000397797   train's RMSPE: 0.226673 valid's rmse: 0.000434675   valid's RMSPE: 0.246897
[350]   train's rmse: 0.000392743   train's RMSPE: 0.223793 valid's rmse: 0.000432361   valid's RMSPE: 0.245583
[400]   train's rmse: 0.000388726   train's RMSPE: 0.221504 valid's rmse: 0.000432577   valid's RMSPE: 0.245706
Early stopping, best iteration is:
[376]   train's rmse: 0.000390759   train's RMSPE: 0.222662 valid's rmse: 0.000431648   valid's RMSPE: 0.245178

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.245, compared to 0.19686216926245892, giving gain 0.048137830737541076
Our cv fold scores are [0.25, 0.261, 0.236, 0.232, 0.245]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00101107    train's RMSPE: 0.250236 valid's rmse: 0.00107613    valid's RMSPE: 0.268845
[100]   train's rmse: 0.000966506   train's RMSPE: 0.239207 valid's rmse: 0.00104335    valid's RMSPE: 0.260654
[150]   train's rmse: 0.000942252   train's RMSPE: 0.233204 valid's rmse: 0.00104109    valid's RMSPE: 0.26009
[200]   train's rmse: 0.000922865   train's RMSPE: 0.228406 valid's rmse: 0.00104024    valid's RMSPE: 0.259878
[250]   train's rmse: 0.000904178   train's RMSPE: 0.223781 valid's rmse: 0.00103993    valid's RMSPE: 0.259801
Early stopping, best iteration is:
[207]   train's rmse: 0.000919993   train's RMSPE: 0.227695 valid's rmse: 0.00103967    valid's RMSPE: 0.259736
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00100147    train's RMSPE: 0.248071 valid's rmse: 0.00110652    valid's RMSPE: 0.275515
[100]   train's rmse: 0.000957784   train's RMSPE: 0.237249 valid's rmse: 0.00109035    valid's RMSPE: 0.27149
[150]   train's rmse: 0.000934431   train's RMSPE: 0.231464 valid's rmse: 0.00108571    valid's RMSPE: 0.270334
[200]   train's rmse: 0.000914579   train's RMSPE: 0.226547 valid's rmse: 0.0010807 valid's RMSPE: 0.269086
[250]   train's rmse: 0.000898498   train's RMSPE: 0.222563 valid's rmse: 0.0010783 valid's RMSPE: 0.268487
[300]   train's rmse: 0.000884614   train's RMSPE: 0.219124 valid's rmse: 0.00107654    valid's RMSPE: 0.268051
[350]   train's rmse: 0.000872229   train's RMSPE: 0.216056 valid's rmse: 0.00107571    valid's RMSPE: 0.267844
[400]   train's rmse: 0.000859814   train's RMSPE: 0.212981 valid's rmse: 0.00107581    valid's RMSPE: 0.267868
Early stopping, best iteration is:
[387]   train's rmse: 0.00086329    train's RMSPE: 0.213842 valid's rmse: 0.00107444    valid's RMSPE: 0.267526
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00102391    train's RMSPE: 0.253814 valid's rmse: 0.00100884    valid's RMSPE: 0.250467
[100]   train's rmse: 0.000978457   train's RMSPE: 0.242546 valid's rmse: 0.000985337   valid's RMSPE: 0.244631
[150]   train's rmse: 0.00095459    train's RMSPE: 0.23663  valid's rmse: 0.000984817   valid's RMSPE: 0.244502
Early stopping, best iteration is:
[119]   train's rmse: 0.000968235   train's RMSPE: 0.240013 valid's rmse: 0.000983361   valid's RMSPE: 0.244141
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00101828    train's RMSPE: 0.252819 valid's rmse: 0.00104528    valid's RMSPE: 0.257867
[100]   train's rmse: 0.000975984   train's RMSPE: 0.242317 valid's rmse: 0.00103286    valid's RMSPE: 0.254804
[150]   train's rmse: 0.000952175   train's RMSPE: 0.236405 valid's rmse: 0.00102694    valid's RMSPE: 0.253343
[200]   train's rmse: 0.000933601   train's RMSPE: 0.231794 valid's rmse: 0.00102596    valid's RMSPE: 0.253101
[250]   train's rmse: 0.000916656   train's RMSPE: 0.227587 valid's rmse: 0.00102167    valid's RMSPE: 0.252042
[300]   train's rmse: 0.000902172   train's RMSPE: 0.223991 valid's rmse: 0.00102092    valid's RMSPE: 0.251857
[350]   train's rmse: 0.000888484   train's RMSPE: 0.220592 valid's rmse: 0.00101872    valid's RMSPE: 0.251315
[400]   train's rmse: 0.00087517    train's RMSPE: 0.217287 valid's rmse: 0.00101795    valid's RMSPE: 0.251125
Early stopping, best iteration is:
[372]   train's rmse: 0.000882191   train's RMSPE: 0.21903  valid's rmse: 0.0010165 valid's RMSPE: 0.250768
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00102898    train's RMSPE: 0.25565  valid's rmse: 0.00100078    valid's RMSPE: 0.246204
[100]   train's rmse: 0.000986292   train's RMSPE: 0.245043 valid's rmse: 0.000984709   valid's RMSPE: 0.242251
[150]   train's rmse: 0.000962004   train's RMSPE: 0.239009 valid's rmse: 0.000986939   valid's RMSPE: 0.2428
Early stopping, best iteration is:
[108]   train's rmse: 0.000981779   train's RMSPE: 0.243922 valid's rmse: 0.000984386   valid's RMSPE: 0.242171
Our out of folds RMSPE is 0.253, compared to 0.23432519394342552, giving gain 0.018674806056574483
Our cv fold scores are [0.26, 0.268, 0.244, 0.251, 0.242]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000357238   train's RMSPE: 0.254097 valid's rmse: 0.000384688   valid's RMSPE: 0.273498
[100]   train's rmse: 0.000337361   train's RMSPE: 0.239959 valid's rmse: 0.000369382   valid's RMSPE: 0.262617
[150]   train's rmse: 0.000327938   train's RMSPE: 0.233256 valid's rmse: 0.000365195   valid's RMSPE: 0.25964
[200]   train's rmse: 0.000319582   train's RMSPE: 0.227313 valid's rmse: 0.00036185    valid's RMSPE: 0.257262
[250]   train's rmse: 0.000313052   train's RMSPE: 0.222668 valid's rmse: 0.000360033   valid's RMSPE: 0.25597
[300]   train's rmse: 0.000307643   train's RMSPE: 0.218821 valid's rmse: 0.000359535   valid's RMSPE: 0.255616
Early stopping, best iteration is:
[280]   train's rmse: 0.00030954    train's RMSPE: 0.220171 valid's rmse: 0.00035903    valid's RMSPE: 0.255257
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00035321    train's RMSPE: 0.251435 valid's rmse: 0.000392498   valid's RMSPE: 0.278148
[100]   train's rmse: 0.000334016   train's RMSPE: 0.237771 valid's rmse: 0.000379872   valid's RMSPE: 0.2692
[150]   train's rmse: 0.000324559   train's RMSPE: 0.23104  valid's rmse: 0.000375806   valid's RMSPE: 0.266319
[200]   train's rmse: 0.000317186   train's RMSPE: 0.225791 valid's rmse: 0.000373094   valid's RMSPE: 0.264397
[250]   train's rmse: 0.000310625   train's RMSPE: 0.22112  valid's rmse: 0.000370354   valid's RMSPE: 0.262455
[300]   train's rmse: 0.000304505   train's RMSPE: 0.216764 valid's rmse: 0.000368869   valid's RMSPE: 0.261403
[350]   train's rmse: 0.000299483   train's RMSPE: 0.213189 valid's rmse: 0.000367303   valid's RMSPE: 0.260293
[400]   train's rmse: 0.000295337   train's RMSPE: 0.210237 valid's rmse: 0.000366037   valid's RMSPE: 0.259396
[450]   train's rmse: 0.000291547   train's RMSPE: 0.207539 valid's rmse: 0.000365553   valid's RMSPE: 0.259053
[500]   train's rmse: 0.000287886   train's RMSPE: 0.204933 valid's rmse: 0.00036559    valid's RMSPE: 0.259079
[550]   train's rmse: 0.000284667   train's RMSPE: 0.202642 valid's rmse: 0.000364225   valid's RMSPE: 0.258111
[600]   train's rmse: 0.000281504   train's RMSPE: 0.20039  valid's rmse: 0.000363407   valid's RMSPE: 0.257532
[650]   train's rmse: 0.000278511   train's RMSPE: 0.19826  valid's rmse: 0.00036249    valid's RMSPE: 0.256882
[700]   train's rmse: 0.000276046   train's RMSPE: 0.196505 valid's rmse: 0.00036357    valid's RMSPE: 0.257648
Early stopping, best iteration is:
[657]   train's rmse: 0.000278202   train's RMSPE: 0.19804  valid's rmse: 0.000362269   valid's RMSPE: 0.256726
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000363584   train's RMSPE: 0.258912 valid's rmse: 0.000349481   valid's RMSPE: 0.247303
[100]   train's rmse: 0.000345215   train's RMSPE: 0.245832 valid's rmse: 0.000338054   valid's RMSPE: 0.239217
[150]   train's rmse: 0.00033506    train's RMSPE: 0.2386   valid's rmse: 0.000333135   valid's RMSPE: 0.235736
[200]   train's rmse: 0.000326276   train's RMSPE: 0.232345 valid's rmse: 0.000330098   valid's RMSPE: 0.233587
[250]   train's rmse: 0.000319797   train's RMSPE: 0.227731 valid's rmse: 0.000329029   valid's RMSPE: 0.232831
[300]   train's rmse: 0.000313914   train's RMSPE: 0.223542 valid's rmse: 0.000327633   valid's RMSPE: 0.231843
[350]   train's rmse: 0.000309422   train's RMSPE: 0.220343 valid's rmse: 0.000326564   valid's RMSPE: 0.231086
[400]   train's rmse: 0.000305009   train's RMSPE: 0.2172   valid's rmse: 0.000325949   valid's RMSPE: 0.230651
[450]   train's rmse: 0.000300934   train's RMSPE: 0.214299 valid's rmse: 0.000325575   valid's RMSPE: 0.230386
[500]   train's rmse: 0.000297533   train's RMSPE: 0.211877 valid's rmse: 0.000325337   valid's RMSPE: 0.230218
[550]   train's rmse: 0.000294102   train's RMSPE: 0.209433 valid's rmse: 0.000324481   valid's RMSPE: 0.229612
[600]   train's rmse: 0.000290955   train's RMSPE: 0.207193 valid's rmse: 0.000324077   valid's RMSPE: 0.229326
Early stopping, best iteration is:
[595]   train's rmse: 0.000291319   train's RMSPE: 0.207452 valid's rmse: 0.000323826   valid's RMSPE: 0.229149
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00036526    train's RMSPE: 0.258029 valid's rmse: 0.000355526   valid's RMSPE: 0.259558
[100]   train's rmse: 0.000345908   train's RMSPE: 0.244359 valid's rmse: 0.000342721   valid's RMSPE: 0.25021
[150]   train's rmse: 0.000336162   train's RMSPE: 0.237474 valid's rmse: 0.000339554   valid's RMSPE: 0.247898
[200]   train's rmse: 0.000327581   train's RMSPE: 0.231412 valid's rmse: 0.000335932   valid's RMSPE: 0.245254
[250]   train's rmse: 0.000321555   train's RMSPE: 0.227155 valid's rmse: 0.000333887   valid's RMSPE: 0.24376
[300]   train's rmse: 0.000316458   train's RMSPE: 0.223554 valid's rmse: 0.000333168   valid's RMSPE: 0.243236
[350]   train's rmse: 0.000311846   train's RMSPE: 0.220297 valid's rmse: 0.000332817   valid's RMSPE: 0.242979
Early stopping, best iteration is:
[317]   train's rmse: 0.000314898   train's RMSPE: 0.222453 valid's rmse: 0.000332285   valid's RMSPE: 0.242591
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000360087   train's RMSPE: 0.257242 valid's rmse: 0.000362287   valid's RMSPE: 0.253018
[100]   train's rmse: 0.000340509   train's RMSPE: 0.243256 valid's rmse: 0.000354536   valid's RMSPE: 0.247605
[150]   train's rmse: 0.000330642   train's RMSPE: 0.236207 valid's rmse: 0.000351794   valid's RMSPE: 0.245689
[200]   train's rmse: 0.000322808   train's RMSPE: 0.230611 valid's rmse: 0.000349359   valid's RMSPE: 0.243989
[250]   train's rmse: 0.000315747   train's RMSPE: 0.225566 valid's rmse: 0.000347542   valid's RMSPE: 0.24272
[300]   train's rmse: 0.000310269   train's RMSPE: 0.221653 valid's rmse: 0.000346377   valid's RMSPE: 0.241906
[350]   train's rmse: 0.000305358   train's RMSPE: 0.218144 valid's rmse: 0.000345788   valid's RMSPE: 0.241495
[400]   train's rmse: 0.000300882   train's RMSPE: 0.214947 valid's rmse: 0.000345217   valid's RMSPE: 0.241096
[450]   train's rmse: 0.00029697    train's RMSPE: 0.212152 valid's rmse: 0.000345328   valid's RMSPE: 0.241174
Early stopping, best iteration is:
[417]   train's rmse: 0.000299258   train's RMSPE: 0.213787 valid's rmse: 0.000344867   valid's RMSPE: 0.240852
Our out of folds RMSPE is 0.245, compared to 0.21141539873479068, giving gain 0.03358460126520932
Our cv fold scores are [0.255, 0.257, 0.229, 0.243, 0.241]

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000528673   train's RMSPE: 0.247051 valid's rmse: 0.000575775   valid's RMSPE: 0.267044
[100]   train's rmse: 0.000502562   train's RMSPE: 0.23485  valid's rmse: 0.000561608   valid's RMSPE: 0.260474
[150]   train's rmse: 0.000490681   train's RMSPE: 0.229298 valid's rmse: 0.000558188   valid's RMSPE: 0.258888
[200]   train's rmse: 0.000479732   train's RMSPE: 0.224181 valid's rmse: 0.000555477   valid's RMSPE: 0.25763
[250]   train's rmse: 0.000471442   train's RMSPE: 0.220307 valid's rmse: 0.000555481   valid's RMSPE: 0.257632
[300]   train's rmse: 0.000463977   train's RMSPE: 0.216819 valid's rmse: 0.000554474   valid's RMSPE: 0.257165
[350]   train's rmse: 0.000457563   train's RMSPE: 0.213821 valid's rmse: 0.000556091   valid's RMSPE: 0.257915
Early stopping, best iteration is:
[311]   train's rmse: 0.000462471   train's RMSPE: 0.216115 valid's rmse: 0.00055348    valid's RMSPE: 0.256704
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000529195   train's RMSPE: 0.246414 valid's rmse: 0.000561202   valid's RMSPE: 0.264021
[100]   train's rmse: 0.000503151   train's RMSPE: 0.234286 valid's rmse: 0.000542479   valid's RMSPE: 0.255213
[150]   train's rmse: 0.000490372   train's RMSPE: 0.228336 valid's rmse: 0.00053968    valid's RMSPE: 0.253896
[200]   train's rmse: 0.000480185   train's RMSPE: 0.223593 valid's rmse: 0.000537065   valid's RMSPE: 0.252665
[250]   train's rmse: 0.000471456   train's RMSPE: 0.219528 valid's rmse: 0.000535621   valid's RMSPE: 0.251986
[300]   train's rmse: 0.000464925   train's RMSPE: 0.216487 valid's rmse: 0.000534693   valid's RMSPE: 0.25155
[350]   train's rmse: 0.00045862    train's RMSPE: 0.213551 valid's rmse: 0.000533267   valid's RMSPE: 0.250879
[400]   train's rmse: 0.000452665   train's RMSPE: 0.210778 valid's rmse: 0.000531345   valid's RMSPE: 0.249975
[450]   train's rmse: 0.000446723   train's RMSPE: 0.208011 valid's rmse: 0.000531415   valid's RMSPE: 0.250007
Early stopping, best iteration is:
[411]   train's rmse: 0.000450935   train's RMSPE: 0.209973 valid's rmse: 0.000530846   valid's RMSPE: 0.24974
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000536132   train's RMSPE: 0.249863 valid's rmse: 0.000522643   valid's RMSPE: 0.24503
[100]   train's rmse: 0.000507821   train's RMSPE: 0.236669 valid's rmse: 0.000507766   valid's RMSPE: 0.238056
[150]   train's rmse: 0.000496047   train's RMSPE: 0.231182 valid's rmse: 0.000506372   valid's RMSPE: 0.237402
Early stopping, best iteration is:
[137]   train's rmse: 0.000498826   train's RMSPE: 0.232477 valid's rmse: 0.000505791   valid's RMSPE: 0.23713
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00053751    train's RMSPE: 0.250463 valid's rmse: 0.000529292   valid's RMSPE: 0.248314
[100]   train's rmse: 0.000510597   train's RMSPE: 0.237923 valid's rmse: 0.000513893   valid's RMSPE: 0.24109
[150]   train's rmse: 0.000498514   train's RMSPE: 0.232292 valid's rmse: 0.000511303   valid's RMSPE: 0.239875
[200]   train's rmse: 0.000488599   train's RMSPE: 0.227672 valid's rmse: 0.000507853   valid's RMSPE: 0.238257
[250]   train's rmse: 0.000480904   train's RMSPE: 0.224086 valid's rmse: 0.000508522   valid's RMSPE: 0.23857
Early stopping, best iteration is:
[222]   train's rmse: 0.000485196   train's RMSPE: 0.226086 valid's rmse: 0.000507136   valid's RMSPE: 0.23792
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000532208   train's RMSPE: 0.249107 valid's rmse: 0.000542083   valid's RMSPE: 0.249752
[100]   train's rmse: 0.000505832   train's RMSPE: 0.236762 valid's rmse: 0.000531399   valid's RMSPE: 0.244829
[150]   train's rmse: 0.000494768   train's RMSPE: 0.231583 valid's rmse: 0.000529038   valid's RMSPE: 0.243741
[200]   train's rmse: 0.00048465    train's RMSPE: 0.226848 valid's rmse: 0.00052727    valid's RMSPE: 0.242927
Early stopping, best iteration is:
[195]   train's rmse: 0.000485445   train's RMSPE: 0.227219 valid's rmse: 0.000526979   valid's RMSPE: 0.242793
Our out of folds RMSPE is 0.245, compared to 0.21009390571045697, giving gain 0.03490609428954303
Our cv fold scores are [0.257, 0.25, 0.237, 0.238, 0.243]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000207856   train's RMSPE: 0.249234 valid's rmse: 0.000226576   valid's RMSPE: 0.275623
[100]   train's rmse: 0.000196259   train's RMSPE: 0.235329 valid's rmse: 0.000216983   valid's RMSPE: 0.263954
[150]   train's rmse: 0.000191205   train's RMSPE: 0.229268 valid's rmse: 0.000212909   valid's RMSPE: 0.258998
[200]   train's rmse: 0.00018783    train's RMSPE: 0.225221 valid's rmse: 0.000211122   valid's RMSPE: 0.256825
[250]   train's rmse: 0.000185138   train's RMSPE: 0.221993 valid's rmse: 0.000210381   valid's RMSPE: 0.255923
[300]   train's rmse: 0.00018286    train's RMSPE: 0.219263 valid's rmse: 0.000209388   valid's RMSPE: 0.254715
[350]   train's rmse: 0.000180207   train's RMSPE: 0.216081 valid's rmse: 0.000208446   valid's RMSPE: 0.253568
[400]   train's rmse: 0.000178237   train's RMSPE: 0.213719 valid's rmse: 0.000208019   valid's RMSPE: 0.253049
[450]   train's rmse: 0.000176425   train's RMSPE: 0.211546 valid's rmse: 0.000207339   valid's RMSPE: 0.252222
[500]   train's rmse: 0.000174746   train's RMSPE: 0.209533 valid's rmse: 0.000207387   valid's RMSPE: 0.25228
[550]   train's rmse: 0.000172926   train's RMSPE: 0.207351 valid's rmse: 0.000206668   valid's RMSPE: 0.251407
[600]   train's rmse: 0.000171358   train's RMSPE: 0.20547  valid's rmse: 0.000205817   valid's RMSPE: 0.250371
[650]   train's rmse: 0.000169943   train's RMSPE: 0.203774 valid's rmse: 0.000205312   valid's RMSPE: 0.249756
[700]   train's rmse: 0.000168507   train's RMSPE: 0.202052 valid's rmse: 0.000204852   valid's RMSPE: 0.249197
[750]   train's rmse: 0.000167085   train's RMSPE: 0.200347 valid's rmse: 0.000204797   valid's RMSPE: 0.24913
Early stopping, best iteration is:
[722]   train's rmse: 0.000167823   train's RMSPE: 0.201232 valid's rmse: 0.000204636   valid's RMSPE: 0.248935
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000206558   train's RMSPE: 0.248581 valid's rmse: 0.000224257   valid's RMSPE: 0.268901
[100]   train's rmse: 0.00019425    train's RMSPE: 0.233769 valid's rmse: 0.00021552    valid's RMSPE: 0.258424
[150]   train's rmse: 0.00018957    train's RMSPE: 0.228137 valid's rmse: 0.000213084   valid's RMSPE: 0.255503
[200]   train's rmse: 0.000186133   train's RMSPE: 0.224001 valid's rmse: 0.000211647   valid's RMSPE: 0.25378
[250]   train's rmse: 0.000183135   train's RMSPE: 0.220393 valid's rmse: 0.000210559   valid's RMSPE: 0.252475
[300]   train's rmse: 0.000180573   train's RMSPE: 0.217309 valid's rmse: 0.000209737   valid's RMSPE: 0.25149
[350]   train's rmse: 0.000178382   train's RMSPE: 0.214673 valid's rmse: 0.000209248   valid's RMSPE: 0.250904
[400]   train's rmse: 0.000176236   train's RMSPE: 0.21209  valid's rmse: 0.000208851   valid's RMSPE: 0.250427
[450]   train's rmse: 0.000174278   train's RMSPE: 0.209734 valid's rmse: 0.000208275   valid's RMSPE: 0.249737
[500]   train's rmse: 0.000172301   train's RMSPE: 0.207355 valid's rmse: 0.000208147   valid's RMSPE: 0.249584
[550]   train's rmse: 0.000170595   train's RMSPE: 0.205302 valid's rmse: 0.000208261   valid's RMSPE: 0.24972
Early stopping, best iteration is:
[517]   train's rmse: 0.000171752   train's RMSPE: 0.206694 valid's rmse: 0.00020794    valid's RMSPE: 0.249335
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000212087   train's RMSPE: 0.255342 valid's rmse: 0.000203957   valid's RMSPE: 0.244146
[100]   train's rmse: 0.000199873   train's RMSPE: 0.240637 valid's rmse: 0.000194753   valid's RMSPE: 0.233128
[150]   train's rmse: 0.000194983   train's RMSPE: 0.23475  valid's rmse: 0.000192682   valid's RMSPE: 0.230649
[200]   train's rmse: 0.000191384   train's RMSPE: 0.230417 valid's rmse: 0.000191504   valid's RMSPE: 0.229239
[250]   train's rmse: 0.000188445   train's RMSPE: 0.226879 valid's rmse: 0.000191163   valid's RMSPE: 0.228831
[300]   train's rmse: 0.000186049   train's RMSPE: 0.223993 valid's rmse: 0.000190781   valid's RMSPE: 0.228374
[350]   train's rmse: 0.000183909   train's RMSPE: 0.221417 valid's rmse: 0.000190512   valid's RMSPE: 0.228051
[400]   train's rmse: 0.000181896   train's RMSPE: 0.218994 valid's rmse: 0.000190133   valid's RMSPE: 0.227599
[450]   train's rmse: 0.000179614   train's RMSPE: 0.216246 valid's rmse: 0.000189613   valid's RMSPE: 0.226975
Early stopping, best iteration is:
[447]   train's rmse: 0.00017973    train's RMSPE: 0.216386 valid's rmse: 0.000189597   valid's RMSPE: 0.226956
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000212571   train's RMSPE: 0.255494 valid's rmse: 0.000203092   valid's RMSPE: 0.244759
[100]   train's rmse: 0.000200116   train's RMSPE: 0.240524 valid's rmse: 0.000194825   valid's RMSPE: 0.234796
[150]   train's rmse: 0.000195126   train's RMSPE: 0.234526 valid's rmse: 0.000191687   valid's RMSPE: 0.231014
[200]   train's rmse: 0.000192053   train's RMSPE: 0.230833 valid's rmse: 0.000190212   valid's RMSPE: 0.229236
[250]   train's rmse: 0.000189438   train's RMSPE: 0.22769  valid's rmse: 0.000189839   valid's RMSPE: 0.228786
[300]   train's rmse: 0.000186582   train's RMSPE: 0.224257 valid's rmse: 0.000188701   valid's RMSPE: 0.227415
[350]   train's rmse: 0.000184331   train's RMSPE: 0.221552 valid's rmse: 0.000188493   valid's RMSPE: 0.227165
[400]   train's rmse: 0.00018249    train's RMSPE: 0.21934  valid's rmse: 0.000188142   valid's RMSPE: 0.226741
[450]   train's rmse: 0.00018092    train's RMSPE: 0.217452 valid's rmse: 0.000187794   valid's RMSPE: 0.226322
[500]   train's rmse: 0.000179214   train's RMSPE: 0.215401 valid's rmse: 0.000187496   valid's RMSPE: 0.225962
[550]   train's rmse: 0.000177454   train's RMSPE: 0.213286 valid's rmse: 0.000186942   valid's RMSPE: 0.225296
[600]   train's rmse: 0.000175711   train's RMSPE: 0.211191 valid's rmse: 0.000186878   valid's RMSPE: 0.225218
Early stopping, best iteration is:
[593]   train's rmse: 0.000175956   train's RMSPE: 0.211486 valid's rmse: 0.000186767   valid's RMSPE: 0.225084
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000211984   train's RMSPE: 0.255327 valid's rmse: 0.000214151   valid's RMSPE: 0.255907
[100]   train's rmse: 0.000199611   train's RMSPE: 0.240424 valid's rmse: 0.000204682   valid's RMSPE: 0.244592
[150]   train's rmse: 0.000194992   train's RMSPE: 0.234861 valid's rmse: 0.000202769   valid's RMSPE: 0.242305
[200]   train's rmse: 0.000191808   train's RMSPE: 0.231026 valid's rmse: 0.0002025 valid's RMSPE: 0.241984
[250]   train's rmse: 0.000189207   train's RMSPE: 0.227893 valid's rmse: 0.000201831   valid's RMSPE: 0.241185
[300]   train's rmse: 0.000186913   train's RMSPE: 0.22513  valid's rmse: 0.000201447   valid's RMSPE: 0.240726
Early stopping, best iteration is:
[298]   train's rmse: 0.000186986   train's RMSPE: 0.225218 valid's rmse: 0.000201428   valid's RMSPE: 0.240703
Our out of folds RMSPE is 0.238, compared to 0.19746750811566835, giving gain 0.04053249188433164
Our cv fold scores are [0.249, 0.249, 0.227, 0.225, 0.241]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000487826   train's RMSPE: 0.210579 valid's rmse: 0.000513017   valid's RMSPE: 0.223738
[100]   train's rmse: 0.000457666   train's RMSPE: 0.19756  valid's rmse: 0.000492531   valid's RMSPE: 0.214804
[150]   train's rmse: 0.000446484   train's RMSPE: 0.192733 valid's rmse: 0.000491333   valid's RMSPE: 0.214281
[200]   train's rmse: 0.000437977   train's RMSPE: 0.189061 valid's rmse: 0.000489263   valid's RMSPE: 0.213378
[250]   train's rmse: 0.00043041    train's RMSPE: 0.185794 valid's rmse: 0.000487646   valid's RMSPE: 0.212673
[300]   train's rmse: 0.000423437   train's RMSPE: 0.182785 valid's rmse: 0.000486085   valid's RMSPE: 0.211992
Early stopping, best iteration is:
[298]   train's rmse: 0.000423683   train's RMSPE: 0.182891 valid's rmse: 0.000485906   valid's RMSPE: 0.211914
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000484335   train's RMSPE: 0.209958 valid's rmse: 0.000519667   valid's RMSPE: 0.222838
[100]   train's rmse: 0.000457423   train's RMSPE: 0.198291 valid's rmse: 0.000502326   valid's RMSPE: 0.215402
[150]   train's rmse: 0.000445395   train's RMSPE: 0.193077 valid's rmse: 0.000496559   valid's RMSPE: 0.212929
[200]   train's rmse: 0.000436642   train's RMSPE: 0.189283 valid's rmse: 0.000493628   valid's RMSPE: 0.211672
[250]   train's rmse: 0.00042952    train's RMSPE: 0.186195 valid's rmse: 0.000491101   valid's RMSPE: 0.210588
[300]   train's rmse: 0.000423284   train's RMSPE: 0.183492 valid's rmse: 0.000489907   valid's RMSPE: 0.210076
[350]   train's rmse: 0.000417306   train's RMSPE: 0.180901 valid's rmse: 0.000488921   valid's RMSPE: 0.209653
[400]   train's rmse: 0.000412608   train's RMSPE: 0.178864 valid's rmse: 0.000488176   valid's RMSPE: 0.209334
[450]   train's rmse: 0.000407552   train's RMSPE: 0.176673 valid's rmse: 0.000486736   valid's RMSPE: 0.208716
[500]   train's rmse: 0.00040296    train's RMSPE: 0.174682 valid's rmse: 0.000486316   valid's RMSPE: 0.208536
[550]   train's rmse: 0.000398449   train's RMSPE: 0.172726 valid's rmse: 0.000486232   valid's RMSPE: 0.2085
Early stopping, best iteration is:
[539]   train's rmse: 0.00039947    train's RMSPE: 0.173169 valid's rmse: 0.000485647   valid's RMSPE: 0.20825
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000495218   train's RMSPE: 0.213962 valid's rmse: 0.000477298   valid's RMSPE: 0.207426
[100]   train's rmse: 0.000468042   train's RMSPE: 0.202221 valid's rmse: 0.000455117   valid's RMSPE: 0.197786
[150]   train's rmse: 0.00045695    train's RMSPE: 0.197428 valid's rmse: 0.00045118    valid's RMSPE: 0.196075
[200]   train's rmse: 0.000447797   train's RMSPE: 0.193473 valid's rmse: 0.0004486 valid's RMSPE: 0.194954
[250]   train's rmse: 0.000440307   train's RMSPE: 0.190238 valid's rmse: 0.000447741   valid's RMSPE: 0.194581
[300]   train's rmse: 0.000433915   train's RMSPE: 0.187476 valid's rmse: 0.000448023   valid's RMSPE: 0.194703
Early stopping, best iteration is:
[263]   train's rmse: 0.000438415   train's RMSPE: 0.18942  valid's rmse: 0.000447324   valid's RMSPE: 0.194399
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000492713   train's RMSPE: 0.213112 valid's rmse: 0.00048918    valid's RMSPE: 0.211671
[100]   train's rmse: 0.000464528   train's RMSPE: 0.200921 valid's rmse: 0.000470561   valid's RMSPE: 0.203615
[150]   train's rmse: 0.000453428   train's RMSPE: 0.19612  valid's rmse: 0.000467062   valid's RMSPE: 0.202101
[200]   train's rmse: 0.000444643   train's RMSPE: 0.19232  valid's rmse: 0.000466716   valid's RMSPE: 0.201951
[250]   train's rmse: 0.000437644   train's RMSPE: 0.189293 valid's rmse: 0.000465663   valid's RMSPE: 0.201495
[300]   train's rmse: 0.000430873   train's RMSPE: 0.186364 valid's rmse: 0.000464515   valid's RMSPE: 0.200999
Early stopping, best iteration is:
[297]   train's rmse: 0.00043122    train's RMSPE: 0.186514 valid's rmse: 0.000464207   valid's RMSPE: 0.200865
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000487819   train's RMSPE: 0.211257 valid's rmse: 0.000499965   valid's RMSPE: 0.215262
[100]   train's rmse: 0.000460513   train's RMSPE: 0.199432 valid's rmse: 0.000481653   valid's RMSPE: 0.207378
[150]   train's rmse: 0.000449961   train's RMSPE: 0.194862 valid's rmse: 0.000479238   valid's RMSPE: 0.206338
[200]   train's rmse: 0.000441545   train's RMSPE: 0.191217 valid's rmse: 0.000478122   valid's RMSPE: 0.205858
[250]   train's rmse: 0.000433334   train's RMSPE: 0.187661 valid's rmse: 0.00047918    valid's RMSPE: 0.206313
Early stopping, best iteration is:
[224]   train's rmse: 0.000437733   train's RMSPE: 0.189566 valid's rmse: 0.000478094   valid's RMSPE: 0.205845
Our out of folds RMSPE is 0.204, compared to 0.17670245970948012, giving gain 0.027297540290519867
Our cv fold scores are [0.212, 0.208, 0.194, 0.201, 0.206]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000311052   train's RMSPE: 0.245669 valid's rmse: 0.000343938   valid's RMSPE: 0.279705
[100]   train's rmse: 0.000293155   train's RMSPE: 0.231533 valid's rmse: 0.000330066   valid's RMSPE: 0.268424
[150]   train's rmse: 0.000285595   train's RMSPE: 0.225562 valid's rmse: 0.000327164   valid's RMSPE: 0.266064
[200]   train's rmse: 0.000279848   train's RMSPE: 0.221024 valid's rmse: 0.000324661   valid's RMSPE: 0.264028
[250]   train's rmse: 0.000274633   train's RMSPE: 0.216905 valid's rmse: 0.000323892   valid's RMSPE: 0.263403
[300]   train's rmse: 0.000270234   train's RMSPE: 0.21343  valid's rmse: 0.000323294   valid's RMSPE: 0.262917
[350]   train's rmse: 0.000266457   train's RMSPE: 0.210447 valid's rmse: 0.000324046   valid's RMSPE: 0.263528
Early stopping, best iteration is:
[305]   train's rmse: 0.000269835   train's RMSPE: 0.213115 valid's rmse: 0.000323  valid's RMSPE: 0.262678
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0003101 train's RMSPE: 0.246224 valid's rmse: 0.000344949   valid's RMSPE: 0.274806
[100]   train's rmse: 0.000291655   train's RMSPE: 0.231578 valid's rmse: 0.00033136    valid's RMSPE: 0.26398
[150]   train's rmse: 0.000283954   train's RMSPE: 0.225463 valid's rmse: 0.000328797   valid's RMSPE: 0.261938
[200]   train's rmse: 0.000277788   train's RMSPE: 0.220567 valid's rmse: 0.000327206   valid's RMSPE: 0.260671
[250]   train's rmse: 0.000272774   train's RMSPE: 0.216587 valid's rmse: 0.000325341   valid's RMSPE: 0.259185
[300]   train's rmse: 0.000268413   train's RMSPE: 0.213124 valid's rmse: 0.000324024   valid's RMSPE: 0.258136
[350]   train's rmse: 0.000264394   train's RMSPE: 0.209932 valid's rmse: 0.000323247   valid's RMSPE: 0.257517
[400]   train's rmse: 0.00026098    train's RMSPE: 0.207222 valid's rmse: 0.000322175   valid's RMSPE: 0.256662
[450]   train's rmse: 0.000257669   train's RMSPE: 0.204593 valid's rmse: 0.000320736   valid's RMSPE: 0.255516
[500]   train's rmse: 0.000254957   train's RMSPE: 0.202439 valid's rmse: 0.00031975    valid's RMSPE: 0.254731
[550]   train's rmse: 0.000252178   train's RMSPE: 0.200233 valid's rmse: 0.000319254   valid's RMSPE: 0.254336
[600]   train's rmse: 0.000249494   train's RMSPE: 0.198102 valid's rmse: 0.000318825   valid's RMSPE: 0.253993
[650]   train's rmse: 0.000247003   train's RMSPE: 0.196124 valid's rmse: 0.000319142   valid's RMSPE: 0.254246
Early stopping, best iteration is:
[617]   train's rmse: 0.000248694   train's RMSPE: 0.197467 valid's rmse: 0.000318521   valid's RMSPE: 0.253752
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000319108   train's RMSPE: 0.254332 valid's rmse: 0.000301476   valid's RMSPE: 0.236541
[100]   train's rmse: 0.000301055   train's RMSPE: 0.239943 valid's rmse: 0.000291706   valid's RMSPE: 0.228875
[150]   train's rmse: 0.000293015   train's RMSPE: 0.233535 valid's rmse: 0.000289897   valid's RMSPE: 0.227456
[200]   train's rmse: 0.000286795   train's RMSPE: 0.228578 valid's rmse: 0.00028804    valid's RMSPE: 0.225999
[250]   train's rmse: 0.000281336   train's RMSPE: 0.224226 valid's rmse: 0.000286724   valid's RMSPE: 0.224967
[300]   train's rmse: 0.000276778   train's RMSPE: 0.220594 valid's rmse: 0.000286299   valid's RMSPE: 0.224633
[350]   train's rmse: 0.000272845   train's RMSPE: 0.217459 valid's rmse: 0.000285775   valid's RMSPE: 0.224222
[400]   train's rmse: 0.000269137   train's RMSPE: 0.214504 valid's rmse: 0.00028505    valid's RMSPE: 0.223653
[450]   train's rmse: 0.000265774   train's RMSPE: 0.211824 valid's rmse: 0.00028411    valid's RMSPE: 0.222915
[500]   train's rmse: 0.000263211   train's RMSPE: 0.209781 valid's rmse: 0.000283748   valid's RMSPE: 0.222631
[550]   train's rmse: 0.000260092   train's RMSPE: 0.207295 valid's rmse: 0.000283167   valid's RMSPE: 0.222175
[600]   train's rmse: 0.000257437   train's RMSPE: 0.205179 valid's rmse: 0.000283076   valid's RMSPE: 0.222104
Early stopping, best iteration is:
[565]   train's rmse: 0.000259183   train's RMSPE: 0.20657  valid's rmse: 0.000282968   valid's RMSPE: 0.222019
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000319041   train's RMSPE: 0.254593 valid's rmse: 0.000309009   valid's RMSPE: 0.241206
[100]   train's rmse: 0.000300518   train's RMSPE: 0.239812 valid's rmse: 0.000299949   valid's RMSPE: 0.234133
[150]   train's rmse: 0.000292576   train's RMSPE: 0.233475 valid's rmse: 0.000297006   valid's RMSPE: 0.231836
[200]   train's rmse: 0.000286591   train's RMSPE: 0.228698 valid's rmse: 0.00029499    valid's RMSPE: 0.230262
[250]   train's rmse: 0.000281553   train's RMSPE: 0.224678 valid's rmse: 0.000293532   valid's RMSPE: 0.229124
[300]   train's rmse: 0.000277274   train's RMSPE: 0.221263 valid's rmse: 0.00029363    valid's RMSPE: 0.229201
Early stopping, best iteration is:
[265]   train's rmse: 0.00028027    train's RMSPE: 0.223655 valid's rmse: 0.000293216   valid's RMSPE: 0.228878
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000316654   train's RMSPE: 0.251383 valid's rmse: 0.000315892   valid's RMSPE: 0.251834
[100]   train's rmse: 0.000299015   train's RMSPE: 0.23738  valid's rmse: 0.000303629   valid's RMSPE: 0.242058
[150]   train's rmse: 0.000290875   train's RMSPE: 0.230918 valid's rmse: 0.000299872   valid's RMSPE: 0.239063
[200]   train's rmse: 0.000284784   train's RMSPE: 0.226082 valid's rmse: 0.000298604   valid's RMSPE: 0.238052
[250]   train's rmse: 0.000279846   train's RMSPE: 0.222162 valid's rmse: 0.000296943   valid's RMSPE: 0.236728
[300]   train's rmse: 0.00027546    train's RMSPE: 0.218681 valid's rmse: 0.000296113   valid's RMSPE: 0.236066
[350]   train's rmse: 0.00027145    train's RMSPE: 0.215497 valid's rmse: 0.000295307   valid's RMSPE: 0.235424
[400]   train's rmse: 0.000267885   train's RMSPE: 0.212667 valid's rmse: 0.000294812   valid's RMSPE: 0.235029
Early stopping, best iteration is:
[390]   train's rmse: 0.000268552   train's RMSPE: 0.213196 valid's rmse: 0.000294551   valid's RMSPE: 0.234821

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.241, compared to 0.20578409495577155, giving gain 0.035215905044228446
Our cv fold scores are [0.263, 0.254, 0.222, 0.229, 0.235]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00036893    train's RMSPE: 0.250754 valid's rmse: 0.000413822   valid's RMSPE: 0.275053
[100]   train's rmse: 0.000346207   train's RMSPE: 0.235309 valid's rmse: 0.000396428   valid's RMSPE: 0.263492
[150]   train's rmse: 0.000335774   train's RMSPE: 0.228218 valid's rmse: 0.000392301   valid's RMSPE: 0.260749
[200]   train's rmse: 0.000327032   train's RMSPE: 0.222277 valid's rmse: 0.000389626   valid's RMSPE: 0.258971
[250]   train's rmse: 0.000320412   train's RMSPE: 0.217777 valid's rmse: 0.000387041   valid's RMSPE: 0.257253
[300]   train's rmse: 0.000314604   train's RMSPE: 0.213829 valid's rmse: 0.000386047   valid's RMSPE: 0.256592
[350]   train's rmse: 0.000309093   train's RMSPE: 0.210083 valid's rmse: 0.000384019   valid's RMSPE: 0.255244
[400]   train's rmse: 0.000304119   train's RMSPE: 0.206703 valid's rmse: 0.000382534   valid's RMSPE: 0.254257
[450]   train's rmse: 0.000300032   train's RMSPE: 0.203925 valid's rmse: 0.00038207    valid's RMSPE: 0.253949
[500]   train's rmse: 0.000296556   train's RMSPE: 0.201562 valid's rmse: 0.000380845   valid's RMSPE: 0.253135
[550]   train's rmse: 0.000293121   train's RMSPE: 0.199228 valid's rmse: 0.000379746   valid's RMSPE: 0.252404
Early stopping, best iteration is:
[530]   train's rmse: 0.000294452   train's RMSPE: 0.200132 valid's rmse: 0.000379578   valid's RMSPE: 0.252293
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000367987   train's RMSPE: 0.248132 valid's rmse: 0.000409113   valid's RMSPE: 0.280749
[100]   train's rmse: 0.000347729   train's RMSPE: 0.234473 valid's rmse: 0.000393478   valid's RMSPE: 0.27002
[150]   train's rmse: 0.000337667   train's RMSPE: 0.227687 valid's rmse: 0.000387287   valid's RMSPE: 0.265772
[200]   train's rmse: 0.00032986    train's RMSPE: 0.222423 valid's rmse: 0.00038334    valid's RMSPE: 0.263063
[250]   train's rmse: 0.000323682   train's RMSPE: 0.218257 valid's rmse: 0.00037968    valid's RMSPE: 0.260551
[300]   train's rmse: 0.000318076   train's RMSPE: 0.214478 valid's rmse: 0.000378511   valid's RMSPE: 0.259749
[350]   train's rmse: 0.00031321    train's RMSPE: 0.211196 valid's rmse: 0.000377033   valid's RMSPE: 0.258735
[400]   train's rmse: 0.000308648   train's RMSPE: 0.20812  valid's rmse: 0.000375108   valid's RMSPE: 0.257414
[450]   train's rmse: 0.000304837   train's RMSPE: 0.205551 valid's rmse: 0.000374366   valid's RMSPE: 0.256905
[500]   train's rmse: 0.00030154    train's RMSPE: 0.203328 valid's rmse: 0.000373061   valid's RMSPE: 0.256009
[550]   train's rmse: 0.000298067   train's RMSPE: 0.200985 valid's rmse: 0.000372295   valid's RMSPE: 0.255484
[600]   train's rmse: 0.000295169   train's RMSPE: 0.199031 valid's rmse: 0.00037132    valid's RMSPE: 0.254814
Early stopping, best iteration is:
[594]   train's rmse: 0.000295592   train's RMSPE: 0.199317 valid's rmse: 0.000371119   valid's RMSPE: 0.254676
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000379817   train's RMSPE: 0.256542 valid's rmse: 0.000353676   valid's RMSPE: 0.241115
[100]   train's rmse: 0.000357694   train's RMSPE: 0.2416   valid's rmse: 0.000340457   valid's RMSPE: 0.232102
[150]   train's rmse: 0.000346926   train's RMSPE: 0.234327 valid's rmse: 0.000337557   valid's RMSPE: 0.230125
[200]   train's rmse: 0.00033818    train's RMSPE: 0.228419 valid's rmse: 0.000336782   valid's RMSPE: 0.229597
[250]   train's rmse: 0.000331294   train's RMSPE: 0.223768 valid's rmse: 0.000335524   valid's RMSPE: 0.22874
[300]   train's rmse: 0.000324978   train's RMSPE: 0.219502 valid's rmse: 0.000335106   valid's RMSPE: 0.228455
Early stopping, best iteration is:
[265]   train's rmse: 0.000329054   train's RMSPE: 0.222255 valid's rmse: 0.000334742   valid's RMSPE: 0.228207
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000379041   train's RMSPE: 0.25543  valid's rmse: 0.00036966    valid's RMSPE: 0.25427
[100]   train's rmse: 0.000355171   train's RMSPE: 0.239345 valid's rmse: 0.000356939   valid's RMSPE: 0.245519
[150]   train's rmse: 0.000343889   train's RMSPE: 0.231742 valid's rmse: 0.000355341   valid's RMSPE: 0.24442
[200]   train's rmse: 0.00033528    train's RMSPE: 0.22594  valid's rmse: 0.000353375   valid's RMSPE: 0.243068
[250]   train's rmse: 0.000328688   train's RMSPE: 0.221498 valid's rmse: 0.000352191   valid's RMSPE: 0.242253
[300]   train's rmse: 0.000322819   train's RMSPE: 0.217543 valid's rmse: 0.000351935   valid's RMSPE: 0.242077
[350]   train's rmse: 0.000317116   train's RMSPE: 0.2137   valid's rmse: 0.000350678   valid's RMSPE: 0.241213
[400]   train's rmse: 0.000312358   train's RMSPE: 0.210494 valid's rmse: 0.000353542   valid's RMSPE: 0.243183
Early stopping, best iteration is:
[357]   train's rmse: 0.000316294   train's RMSPE: 0.213147 valid's rmse: 0.000350625   valid's RMSPE: 0.241176
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000374489   train's RMSPE: 0.254722 valid's rmse: 0.000376241   valid's RMSPE: 0.249292
[100]   train's rmse: 0.000351665   train's RMSPE: 0.239197 valid's rmse: 0.000368132   valid's RMSPE: 0.243919
[150]   train's rmse: 0.000340192   train's RMSPE: 0.231393 valid's rmse: 0.000366087   valid's RMSPE: 0.242564
[200]   train's rmse: 0.000331251   train's RMSPE: 0.225312 valid's rmse: 0.000366179   valid's RMSPE: 0.242625
Early stopping, best iteration is:
[152]   train's rmse: 0.000339807   train's RMSPE: 0.231132 valid's rmse: 0.000365774   valid's RMSPE: 0.242356
Our out of folds RMSPE is 0.244, compared to 0.21423163704470313, giving gain 0.029768362955296862
Our cv fold scores are [0.252, 0.255, 0.228, 0.241, 0.242]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000518921   train's RMSPE: 0.228128 valid's rmse: 0.000553911   valid's RMSPE: 0.2421
[100]   train's rmse: 0.000492685   train's RMSPE: 0.216595 valid's rmse: 0.000531333   valid's RMSPE: 0.232232
[150]   train's rmse: 0.000482513   train's RMSPE: 0.212123 valid's rmse: 0.000528038   valid's RMSPE: 0.230792
[200]   train's rmse: 0.000473695   train's RMSPE: 0.208246 valid's rmse: 0.000525256   valid's RMSPE: 0.229576
[250]   train's rmse: 0.000464846   train's RMSPE: 0.204356 valid's rmse: 0.000524295   valid's RMSPE: 0.229156
[300]   train's rmse: 0.000458344   train's RMSPE: 0.201498 valid's rmse: 0.000522757   valid's RMSPE: 0.228484
[350]   train's rmse: 0.000452011   train's RMSPE: 0.198713 valid's rmse: 0.000520959   valid's RMSPE: 0.227698
Early stopping, best iteration is:
[336]   train's rmse: 0.000453873   train's RMSPE: 0.199532 valid's rmse: 0.000520481   valid's RMSPE: 0.227489
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000518506   train's RMSPE: 0.226821 valid's rmse: 0.000549291   valid's RMSPE: 0.244817
[100]   train's rmse: 0.000492229   train's RMSPE: 0.215326 valid's rmse: 0.000531408   valid's RMSPE: 0.236847
[150]   train's rmse: 0.000480461   train's RMSPE: 0.210179 valid's rmse: 0.000529729   valid's RMSPE: 0.236099
[200]   train's rmse: 0.000470785   train's RMSPE: 0.205945 valid's rmse: 0.000526797   valid's RMSPE: 0.234792
[250]   train's rmse: 0.000463199   train's RMSPE: 0.202627 valid's rmse: 0.000527352   valid's RMSPE: 0.235039
Early stopping, best iteration is:
[214]   train's rmse: 0.000468637   train's RMSPE: 0.205006 valid's rmse: 0.000526436   valid's RMSPE: 0.234631
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000524928   train's RMSPE: 0.229497 valid's rmse: 0.000512947   valid's RMSPE: 0.22913
[100]   train's rmse: 0.000498676   train's RMSPE: 0.21802  valid's rmse: 0.000503047   valid's RMSPE: 0.224708
[150]   train's rmse: 0.000487918   train's RMSPE: 0.213316 valid's rmse: 0.000500814   valid's RMSPE: 0.22371
Early stopping, best iteration is:
[149]   train's rmse: 0.000488036   train's RMSPE: 0.213368 valid's rmse: 0.000500625   valid's RMSPE: 0.223626
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000525394   train's RMSPE: 0.230881 valid's rmse: 0.000530216   valid's RMSPE: 0.232122
[100]   train's rmse: 0.000499654   train's RMSPE: 0.21957  valid's rmse: 0.000515259   valid's RMSPE: 0.225574
[150]   train's rmse: 0.000487737   train's RMSPE: 0.214333 valid's rmse: 0.00051355    valid's RMSPE: 0.224826
[200]   train's rmse: 0.000478557   train's RMSPE: 0.210299 valid's rmse: 0.000512954   valid's RMSPE: 0.224565
Early stopping, best iteration is:
[185]   train's rmse: 0.00048105    train's RMSPE: 0.211394 valid's rmse: 0.000512226   valid's RMSPE: 0.224247
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000521727   train's RMSPE: 0.230517 valid's rmse: 0.000530557   valid's RMSPE: 0.227105
[100]   train's rmse: 0.00049672    train's RMSPE: 0.219468 valid's rmse: 0.000518427   valid's RMSPE: 0.221913
[150]   train's rmse: 0.000485823   train's RMSPE: 0.214653 valid's rmse: 0.0005161 valid's RMSPE: 0.220917
[200]   train's rmse: 0.000476768   train's RMSPE: 0.210653 valid's rmse: 0.000514112   valid's RMSPE: 0.220066
[250]   train's rmse: 0.000468212   train's RMSPE: 0.206872 valid's rmse: 0.000511887   valid's RMSPE: 0.219114
[300]   train's rmse: 0.000461538   train's RMSPE: 0.203924 valid's rmse: 0.000511118   valid's RMSPE: 0.218785
[350]   train's rmse: 0.000455047   train's RMSPE: 0.201055 valid's rmse: 0.000511768   valid's RMSPE: 0.219063
Early stopping, best iteration is:
[316]   train's rmse: 0.000459405   train's RMSPE: 0.202981 valid's rmse: 0.000510663   valid's RMSPE: 0.21859
Our out of folds RMSPE is 0.226, compared to 0.20040336277301074, giving gain 0.025596637226989266
Our cv fold scores are [0.227, 0.235, 0.224, 0.224, 0.219]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000433189   train's RMSPE: 0.192845 valid's rmse: 0.000478944   valid's RMSPE: 0.214132
[100]   train's rmse: 0.000407693   train's RMSPE: 0.181495 valid's rmse: 0.000461463   valid's RMSPE: 0.206317
[150]   train's rmse: 0.000397858   train's RMSPE: 0.177117 valid's rmse: 0.000457334   valid's RMSPE: 0.204471
[200]   train's rmse: 0.000389874   train's RMSPE: 0.173563 valid's rmse: 0.000456115   valid's RMSPE: 0.203926
[250]   train's rmse: 0.000383429   train's RMSPE: 0.170693 valid's rmse: 0.000454218   valid's RMSPE: 0.203077
[300]   train's rmse: 0.000377816   train's RMSPE: 0.168194 valid's rmse: 0.00045248    valid's RMSPE: 0.2023
[350]   train's rmse: 0.000372742   train's RMSPE: 0.165936 valid's rmse: 0.000451808   valid's RMSPE: 0.202
[400]   train's rmse: 0.000368173   train's RMSPE: 0.163902 valid's rmse: 0.00045178    valid's RMSPE: 0.201988
Early stopping, best iteration is:
[379]   train's rmse: 0.000369885   train's RMSPE: 0.164664 valid's rmse: 0.000451161   valid's RMSPE: 0.201711
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000432943   train's RMSPE: 0.192394 valid's rmse: 0.00046348    valid's RMSPE: 0.208671
[100]   train's rmse: 0.000407794   train's RMSPE: 0.181218 valid's rmse: 0.000450354   valid's RMSPE: 0.202761
[150]   train's rmse: 0.000397779   train's RMSPE: 0.176767 valid's rmse: 0.000448407   valid's RMSPE: 0.201884
[200]   train's rmse: 0.000389511   train's RMSPE: 0.173093 valid's rmse: 0.000446317   valid's RMSPE: 0.200943
[250]   train's rmse: 0.000382848   train's RMSPE: 0.170132 valid's rmse: 0.000444879   valid's RMSPE: 0.200296
[300]   train's rmse: 0.000377465   train's RMSPE: 0.16774  valid's rmse: 0.000442946   valid's RMSPE: 0.199426
[350]   train's rmse: 0.000371738   train's RMSPE: 0.165195 valid's rmse: 0.000441549   valid's RMSPE: 0.198797
[400]   train's rmse: 0.000367062   train's RMSPE: 0.163117 valid's rmse: 0.000440566   valid's RMSPE: 0.198354
[450]   train's rmse: 0.000362614   train's RMSPE: 0.16114  valid's rmse: 0.000439655   valid's RMSPE: 0.197944
[500]   train's rmse: 0.000358581   train's RMSPE: 0.159348 valid's rmse: 0.00043999    valid's RMSPE: 0.198095
Early stopping, best iteration is:
[486]   train's rmse: 0.000359724   train's RMSPE: 0.159856 valid's rmse: 0.000439535   valid's RMSPE: 0.19789
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000444898   train's RMSPE: 0.19725  valid's rmse: 0.000413365   valid's RMSPE: 0.18777
[100]   train's rmse: 0.000420029   train's RMSPE: 0.186225 valid's rmse: 0.000397322   valid's RMSPE: 0.180483
[150]   train's rmse: 0.000409487   train's RMSPE: 0.181551 valid's rmse: 0.000396058   valid's RMSPE: 0.179909
[200]   train's rmse: 0.000401497   train's RMSPE: 0.178008 valid's rmse: 0.000395737   valid's RMSPE: 0.179763
[250]   train's rmse: 0.000394792   train's RMSPE: 0.175035 valid's rmse: 0.000393723   valid's RMSPE: 0.178848
[300]   train's rmse: 0.000388874   train's RMSPE: 0.172411 valid's rmse: 0.000393443   valid's RMSPE: 0.178721
[350]   train's rmse: 0.000383262   train's RMSPE: 0.169923 valid's rmse: 0.000393503   valid's RMSPE: 0.178748
Early stopping, best iteration is:
[327]   train's rmse: 0.000385684   train's RMSPE: 0.170997 valid's rmse: 0.000392937   valid's RMSPE: 0.178491
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000441435   train's RMSPE: 0.197068 valid's rmse: 0.000444912   valid's RMSPE: 0.196685
[100]   train's rmse: 0.000415582   train's RMSPE: 0.185527 valid's rmse: 0.000427652   valid's RMSPE: 0.189055
[150]   train's rmse: 0.000405661   train's RMSPE: 0.181098 valid's rmse: 0.000425473   valid's RMSPE: 0.188091
[200]   train's rmse: 0.00039786    train's RMSPE: 0.177615 valid's rmse: 0.000424109   valid's RMSPE: 0.187489
[250]   train's rmse: 0.000391521   train's RMSPE: 0.174785 valid's rmse: 0.000422262   valid's RMSPE: 0.186672
[300]   train's rmse: 0.000385754   train's RMSPE: 0.172211 valid's rmse: 0.000421269   valid's RMSPE: 0.186233
Early stopping, best iteration is:
[288]   train's rmse: 0.000387177   train's RMSPE: 0.172846 valid's rmse: 0.000420809   valid's RMSPE: 0.18603
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00043869    train's RMSPE: 0.196723 valid's rmse: 0.000453379   valid's RMSPE: 0.196711
[100]   train's rmse: 0.000413437   train's RMSPE: 0.185399 valid's rmse: 0.000434064   valid's RMSPE: 0.18833
[150]   train's rmse: 0.000403443   train's RMSPE: 0.180917 valid's rmse: 0.000431563   valid's RMSPE: 0.187245
[200]   train's rmse: 0.000395527   train's RMSPE: 0.177367 valid's rmse: 0.000429264   valid's RMSPE: 0.186248
[250]   train's rmse: 0.000389031   train's RMSPE: 0.174454 valid's rmse: 0.000428326   valid's RMSPE: 0.185841
[300]   train's rmse: 0.000383514   train's RMSPE: 0.17198  valid's rmse: 0.000427696   valid's RMSPE: 0.185568
[350]   train's rmse: 0.000378363   train's RMSPE: 0.169671 valid's rmse: 0.00042843    valid's RMSPE: 0.185886
Early stopping, best iteration is:
[304]   train's rmse: 0.000383086   train's RMSPE: 0.171788 valid's rmse: 0.000427423   valid's RMSPE: 0.185449
Our out of folds RMSPE is 0.19, compared to 0.1716341490656949, giving gain 0.018365850934305095
Our cv fold scores are [0.202, 0.198, 0.178, 0.186, 0.185]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000404051   train's RMSPE: 0.232355 valid's rmse: 0.000434802   valid's RMSPE: 0.247156
[100]   train's rmse: 0.000382754   train's RMSPE: 0.220108 valid's rmse: 0.000415534   valid's RMSPE: 0.236203
[150]   train's rmse: 0.000373717   train's RMSPE: 0.214911 valid's rmse: 0.000411464   valid's RMSPE: 0.233889
[200]   train's rmse: 0.000365743   train's RMSPE: 0.210326 valid's rmse: 0.000407746   valid's RMSPE: 0.231776
[250]   train's rmse: 0.000359713   train's RMSPE: 0.206858 valid's rmse: 0.000406331   valid's RMSPE: 0.230972
[300]   train's rmse: 0.000354397   train's RMSPE: 0.203801 valid's rmse: 0.000404264   valid's RMSPE: 0.229797
[350]   train's rmse: 0.000349901   train's RMSPE: 0.201216 valid's rmse: 0.000403529   valid's RMSPE: 0.229379
[400]   train's rmse: 0.000345342   train's RMSPE: 0.198594 valid's rmse: 0.000402146   valid's RMSPE: 0.228593
[450]   train's rmse: 0.000341279   train's RMSPE: 0.196257 valid's rmse: 0.000401563   valid's RMSPE: 0.228262
[500]   train's rmse: 0.000337948   train's RMSPE: 0.194342 valid's rmse: 0.000401939   valid's RMSPE: 0.228475
Early stopping, best iteration is:
[454]   train's rmse: 0.000340983   train's RMSPE: 0.196087 valid's rmse: 0.000401335   valid's RMSPE: 0.228132
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000405912   train's RMSPE: 0.232367 valid's rmse: 0.000427819   valid's RMSPE: 0.247649
[100]   train's rmse: 0.00038456    train's RMSPE: 0.220144 valid's rmse: 0.000408604   valid's RMSPE: 0.236526
[150]   train's rmse: 0.000375432   train's RMSPE: 0.214919 valid's rmse: 0.000403445   valid's RMSPE: 0.233539
[200]   train's rmse: 0.000368753   train's RMSPE: 0.211095 valid's rmse: 0.000399153   valid's RMSPE: 0.231055
[250]   train's rmse: 0.000363059   train's RMSPE: 0.207836 valid's rmse: 0.000396217   valid's RMSPE: 0.229355
[300]   train's rmse: 0.000358084   train's RMSPE: 0.204988 valid's rmse: 0.000393707   valid's RMSPE: 0.227902
[350]   train's rmse: 0.000354042   train's RMSPE: 0.202674 valid's rmse: 0.000391848   valid's RMSPE: 0.226827
[400]   train's rmse: 0.000350475   train's RMSPE: 0.200632 valid's rmse: 0.000390511   valid's RMSPE: 0.226052
[450]   train's rmse: 0.000347116   train's RMSPE: 0.198709 valid's rmse: 0.000389586   valid's RMSPE: 0.225517
[500]   train's rmse: 0.000343917   train's RMSPE: 0.196878 valid's rmse: 0.000388703   valid's RMSPE: 0.225006
[550]   train's rmse: 0.000340863   train's RMSPE: 0.19513  valid's rmse: 0.000388083   valid's RMSPE: 0.224647
[600]   train's rmse: 0.000337905   train's RMSPE: 0.193436 valid's rmse: 0.000387192   valid's RMSPE: 0.224131
[650]   train's rmse: 0.000335076   train's RMSPE: 0.191817 valid's rmse: 0.000386627   valid's RMSPE: 0.223804
[700]   train's rmse: 0.000332262   train's RMSPE: 0.190206 valid's rmse: 0.00038645    valid's RMSPE: 0.223702
[750]   train's rmse: 0.000329628   train's RMSPE: 0.188698 valid's rmse: 0.00038572    valid's RMSPE: 0.223279
[800]   train's rmse: 0.000327232   train's RMSPE: 0.187326 valid's rmse: 0.000385353   valid's RMSPE: 0.223067
Early stopping, best iteration is:
[781]   train's rmse: 0.000327971   train's RMSPE: 0.187749 valid's rmse: 0.0003852 valid's RMSPE: 0.222978
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000408327   train's RMSPE: 0.234056 valid's rmse: 0.000415989   valid's RMSPE: 0.239562
[100]   train's rmse: 0.000386992   train's RMSPE: 0.221826 valid's rmse: 0.000401929   valid's RMSPE: 0.231465
[150]   train's rmse: 0.000377042   train's RMSPE: 0.216123 valid's rmse: 0.000397917   valid's RMSPE: 0.229155
[200]   train's rmse: 0.0003689 train's RMSPE: 0.211456 valid's rmse: 0.000395072   valid's RMSPE: 0.227517
[250]   train's rmse: 0.000363144   train's RMSPE: 0.208157 valid's rmse: 0.000393398   valid's RMSPE: 0.226552
[300]   train's rmse: 0.000358014   train's RMSPE: 0.205216 valid's rmse: 0.000391945   valid's RMSPE: 0.225716
[350]   train's rmse: 0.000353009   train's RMSPE: 0.202347 valid's rmse: 0.000390648   valid's RMSPE: 0.224969
[400]   train's rmse: 0.000349111   train's RMSPE: 0.200113 valid's rmse: 0.000391155   valid's RMSPE: 0.225261
Early stopping, best iteration is:
[367]   train's rmse: 0.000351635   train's RMSPE: 0.20156  valid's rmse: 0.000390199   valid's RMSPE: 0.224711
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000411013   train's RMSPE: 0.235809 valid's rmse: 0.000399902   valid's RMSPE: 0.229468
[100]   train's rmse: 0.000389888   train's RMSPE: 0.22369  valid's rmse: 0.000386833   valid's RMSPE: 0.221969
[150]   train's rmse: 0.000380972   train's RMSPE: 0.218574 valid's rmse: 0.000382896   valid's RMSPE: 0.21971
[200]   train's rmse: 0.000374245   train's RMSPE: 0.214715 valid's rmse: 0.000380018   valid's RMSPE: 0.218058
[250]   train's rmse: 0.000368265   train's RMSPE: 0.211284 valid's rmse: 0.000377296   valid's RMSPE: 0.216496
[300]   train's rmse: 0.000363414   train's RMSPE: 0.2085   valid's rmse: 0.000376207   valid's RMSPE: 0.215872
[350]   train's rmse: 0.000358632   train's RMSPE: 0.205757 valid's rmse: 0.000375153   valid's RMSPE: 0.215267
[400]   train's rmse: 0.00035442    train's RMSPE: 0.20334  valid's rmse: 0.000374973   valid's RMSPE: 0.215163
[450]   train's rmse: 0.000350739   train's RMSPE: 0.201229 valid's rmse: 0.000374934   valid's RMSPE: 0.215141
[500]   train's rmse: 0.000347392   train's RMSPE: 0.199308 valid's rmse: 0.000374268   valid's RMSPE: 0.214759
[550]   train's rmse: 0.000344122   train's RMSPE: 0.197432 valid's rmse: 0.000374304   valid's RMSPE: 0.21478
Early stopping, best iteration is:
[516]   train's rmse: 0.000346313   train's RMSPE: 0.198689 valid's rmse: 0.000374007   valid's RMSPE: 0.214609
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000412607   train's RMSPE: 0.236944 valid's rmse: 0.000402675   valid's RMSPE: 0.230198
[100]   train's rmse: 0.000391933   train's RMSPE: 0.225072 valid's rmse: 0.000389888   valid's RMSPE: 0.222888
[150]   train's rmse: 0.000382524   train's RMSPE: 0.219668 valid's rmse: 0.000385454   valid's RMSPE: 0.220353
[200]   train's rmse: 0.000375209   train's RMSPE: 0.215468 valid's rmse: 0.000382009   valid's RMSPE: 0.218384
[250]   train's rmse: 0.000369039   train's RMSPE: 0.211925 valid's rmse: 0.000380712   valid's RMSPE: 0.217642
[300]   train's rmse: 0.000363679   train's RMSPE: 0.208846 valid's rmse: 0.000379597   valid's RMSPE: 0.217005
[350]   train's rmse: 0.000359604   train's RMSPE: 0.206506 valid's rmse: 0.000378535   valid's RMSPE: 0.216398
[400]   train's rmse: 0.000355003   train's RMSPE: 0.203865 valid's rmse: 0.000378447   valid's RMSPE: 0.216348
[450]   train's rmse: 0.000351286   train's RMSPE: 0.20173  valid's rmse: 0.000377493   valid's RMSPE: 0.215802
[500]   train's rmse: 0.000348219   train's RMSPE: 0.199969 valid's rmse: 0.00037743    valid's RMSPE: 0.215766
Early stopping, best iteration is:
[472]   train's rmse: 0.000349937   train's RMSPE: 0.200955 valid's rmse: 0.000377105   valid's RMSPE: 0.21558
Our out of folds RMSPE is 0.221, compared to 0.17552957994839127, giving gain 0.04547042005160873
Our cv fold scores are [0.228, 0.223, 0.225, 0.215, 0.216]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000482897   train's RMSPE: 0.238967 valid's rmse: 0.000519933   valid's RMSPE: 0.255886
[100]   train's rmse: 0.00045544    train's RMSPE: 0.22538  valid's rmse: 0.000498117   valid's RMSPE: 0.245149
[150]   train's rmse: 0.000443839   train's RMSPE: 0.219639 valid's rmse: 0.000493753   valid's RMSPE: 0.243002
[200]   train's rmse: 0.000435175   train's RMSPE: 0.215352 valid's rmse: 0.000491886   valid's RMSPE: 0.242083
[250]   train's rmse: 0.00042811    train's RMSPE: 0.211855 valid's rmse: 0.000490361   valid's RMSPE: 0.241332
Early stopping, best iteration is:
[241]   train's rmse: 0.000429225   train's RMSPE: 0.212407 valid's rmse: 0.000489946   valid's RMSPE: 0.241128
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000479038   train's RMSPE: 0.23643  valid's rmse: 0.000516401   valid's RMSPE: 0.256852
[100]   train's rmse: 0.000454994   train's RMSPE: 0.224563 valid's rmse: 0.000499308   valid's RMSPE: 0.248349
[150]   train's rmse: 0.000444677   train's RMSPE: 0.219471 valid's rmse: 0.000497334   valid's RMSPE: 0.247368
[200]   train's rmse: 0.000436866   train's RMSPE: 0.215616 valid's rmse: 0.000496933   valid's RMSPE: 0.247168
[250]   train's rmse: 0.000428719   train's RMSPE: 0.211595 valid's rmse: 0.00049422    valid's RMSPE: 0.245819
Early stopping, best iteration is:
[246]   train's rmse: 0.000429248   train's RMSPE: 0.211856 valid's rmse: 0.000493708   valid's RMSPE: 0.245564
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00048693    train's RMSPE: 0.240517 valid's rmse: 0.000493618   valid's RMSPE: 0.244745
[100]   train's rmse: 0.000462006   train's RMSPE: 0.228206 valid's rmse: 0.000476138   valid's RMSPE: 0.236078
[150]   train's rmse: 0.000450937   train's RMSPE: 0.222739 valid's rmse: 0.000471592   valid's RMSPE: 0.233824
[200]   train's rmse: 0.000442292   train's RMSPE: 0.218468 valid's rmse: 0.000471026   valid's RMSPE: 0.233544
[250]   train's rmse: 0.000435488   train's RMSPE: 0.215107 valid's rmse: 0.000470153   valid's RMSPE: 0.23311
[300]   train's rmse: 0.000429508   train's RMSPE: 0.212154 valid's rmse: 0.000469168   valid's RMSPE: 0.232622
Early stopping, best iteration is:
[293]   train's rmse: 0.000430163   train's RMSPE: 0.212477 valid's rmse: 0.000468832   valid's RMSPE: 0.232456
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000494743   train's RMSPE: 0.244663 valid's rmse: 0.000459296   valid's RMSPE: 0.226664
[100]   train's rmse: 0.000469041   train's RMSPE: 0.231952 valid's rmse: 0.000443093   valid's RMSPE: 0.218668
[150]   train's rmse: 0.000458311   train's RMSPE: 0.226646 valid's rmse: 0.000440706   valid's RMSPE: 0.21749
[200]   train's rmse: 0.00044926    train's RMSPE: 0.222171 valid's rmse: 0.000439178   valid's RMSPE: 0.216736
[250]   train's rmse: 0.000442394   train's RMSPE: 0.218775 valid's rmse: 0.000438838   valid's RMSPE: 0.216568
[300]   train's rmse: 0.000435531   train's RMSPE: 0.215381 valid's rmse: 0.000437719   valid's RMSPE: 0.216016
Early stopping, best iteration is:
[263]   train's rmse: 0.000440876   train's RMSPE: 0.218024 valid's rmse: 0.000437308   valid's RMSPE: 0.215813
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000484861   train's RMSPE: 0.23987  valid's rmse: 0.000493303   valid's RMSPE: 0.243062
[100]   train's rmse: 0.000460185   train's RMSPE: 0.227663 valid's rmse: 0.000480031   valid's RMSPE: 0.236523
[150]   train's rmse: 0.000448945   train's RMSPE: 0.222102 valid's rmse: 0.000476134   valid's RMSPE: 0.234602
[200]   train's rmse: 0.000440498   train's RMSPE: 0.217923 valid's rmse: 0.000475142   valid's RMSPE: 0.234113
[250]   train's rmse: 0.000433026   train's RMSPE: 0.214226 valid's rmse: 0.000474694   valid's RMSPE: 0.233893
Early stopping, best iteration is:
[225]   train's rmse: 0.000436639   train's RMSPE: 0.216014 valid's rmse: 0.000474356   valid's RMSPE: 0.233726

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.234, compared to 0.19115105952198225, giving gain 0.042848940478017766
Our cv fold scores are [0.241, 0.246, 0.232, 0.216, 0.234]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000457988   train's RMSPE: 0.2435   valid's rmse: 0.000493914   valid's RMSPE: 0.257165
[100]   train's rmse: 0.000437976   train's RMSPE: 0.23286  valid's rmse: 0.000479809   valid's RMSPE: 0.249821
[150]   train's rmse: 0.000428478   train's RMSPE: 0.227811 valid's rmse: 0.000476945   valid's RMSPE: 0.24833
[200]   train's rmse: 0.000419562   train's RMSPE: 0.22307  valid's rmse: 0.000473035   valid's RMSPE: 0.246294
[250]   train's rmse: 0.00041283    train's RMSPE: 0.219491 valid's rmse: 0.00047206    valid's RMSPE: 0.245787
[300]   train's rmse: 0.000406502   train's RMSPE: 0.216126 valid's rmse: 0.000471996   valid's RMSPE: 0.245754
[350]   train's rmse: 0.00040105    train's RMSPE: 0.213228 valid's rmse: 0.000469674   valid's RMSPE: 0.244545
[400]   train's rmse: 0.00039595    train's RMSPE: 0.210516 valid's rmse: 0.000467856   valid's RMSPE: 0.243598
[450]   train's rmse: 0.000390724   train's RMSPE: 0.207738 valid's rmse: 0.000466949   valid's RMSPE: 0.243125
[500]   train's rmse: 0.000386568   train's RMSPE: 0.205528 valid's rmse: 0.000466829   valid's RMSPE: 0.243063
Early stopping, best iteration is:
[464]   train's rmse: 0.000389597   train's RMSPE: 0.207139 valid's rmse: 0.000466048   valid's RMSPE: 0.242657
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000456083   train's RMSPE: 0.240487 valid's rmse: 0.000499657   valid's RMSPE: 0.268923
[100]   train's rmse: 0.000434162   train's RMSPE: 0.228928 valid's rmse: 0.000485322   valid's RMSPE: 0.261208
[150]   train's rmse: 0.000424924   train's RMSPE: 0.224057 valid's rmse: 0.000482623   valid's RMSPE: 0.259756
[200]   train's rmse: 0.000417131   train's RMSPE: 0.219948 valid's rmse: 0.000479623   valid's RMSPE: 0.258141
[250]   train's rmse: 0.000411123   train's RMSPE: 0.21678  valid's rmse: 0.00047793    valid's RMSPE: 0.25723
[300]   train's rmse: 0.00040528    train's RMSPE: 0.213699 valid's rmse: 0.000477153   valid's RMSPE: 0.256811
[350]   train's rmse: 0.000399708   train's RMSPE: 0.210761 valid's rmse: 0.000475235   valid's RMSPE: 0.255779
[400]   train's rmse: 0.000395149   train's RMSPE: 0.208357 valid's rmse: 0.000474903   valid's RMSPE: 0.2556
[450]   train's rmse: 0.000390486   train's RMSPE: 0.205899 valid's rmse: 0.000473561   valid's RMSPE: 0.254878
[500]   train's rmse: 0.000386514   train's RMSPE: 0.203804 valid's rmse: 0.000473374   valid's RMSPE: 0.254777
Early stopping, best iteration is:
[475]   train's rmse: 0.000388297   train's RMSPE: 0.204744 valid's rmse: 0.000472769   valid's RMSPE: 0.254452
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000469695   train's RMSPE: 0.247912 valid's rmse: 0.0004357 valid's RMSPE: 0.233597
[100]   train's rmse: 0.000448543   train's RMSPE: 0.236748 valid's rmse: 0.000429  valid's RMSPE: 0.230005
Early stopping, best iteration is:
[90]    train's rmse: 0.000451159   train's RMSPE: 0.238129 valid's rmse: 0.000428174   valid's RMSPE: 0.229562
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000466601   train's RMSPE: 0.246927 valid's rmse: 0.000460628   valid's RMSPE: 0.244428
[100]   train's rmse: 0.000445622   train's RMSPE: 0.235825 valid's rmse: 0.000448304   valid's RMSPE: 0.237888
[150]   train's rmse: 0.000436219   train's RMSPE: 0.230849 valid's rmse: 0.000445323   valid's RMSPE: 0.236306
[200]   train's rmse: 0.000426812   train's RMSPE: 0.225871 valid's rmse: 0.000443304   valid's RMSPE: 0.235235
Early stopping, best iteration is:
[199]   train's rmse: 0.000426915   train's RMSPE: 0.225925 valid's rmse: 0.000443244   valid's RMSPE: 0.235203
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000461482   train's RMSPE: 0.24526  valid's rmse: 0.000474622   valid's RMSPE: 0.247535
[100]   train's rmse: 0.000441185   train's RMSPE: 0.234472 valid's rmse: 0.000466305   valid's RMSPE: 0.243198
[150]   train's rmse: 0.00043124    train's RMSPE: 0.229187 valid's rmse: 0.000466249   valid's RMSPE: 0.243169
Early stopping, best iteration is:
[132]   train's rmse: 0.000434167   train's RMSPE: 0.230743 valid's rmse: 0.000464804   valid's RMSPE: 0.242415
Our out of folds RMSPE is 0.241, compared to 0.1976775402077671, giving gain 0.0433224597922329
Our cv fold scores are [0.243, 0.254, 0.23, 0.235, 0.242]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000791794   train's RMSPE: 0.239436 valid's rmse: 0.000846052   valid's RMSPE: 0.250488
[100]   train's rmse: 0.000744666   train's RMSPE: 0.225184 valid's rmse: 0.000822681   valid's RMSPE: 0.243569
[150]   train's rmse: 0.000723299   train's RMSPE: 0.218723 valid's rmse: 0.000818577   valid's RMSPE: 0.242354
Early stopping, best iteration is:
[145]   train's rmse: 0.000725098   train's RMSPE: 0.219267 valid's rmse: 0.000817545   valid's RMSPE: 0.242048
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00080158    train's RMSPE: 0.239645 valid's rmse: 0.000833736   valid's RMSPE: 0.258199
[100]   train's rmse: 0.000754277   train's RMSPE: 0.225503 valid's rmse: 0.000799332   valid's RMSPE: 0.247544
[150]   train's rmse: 0.00073459    train's RMSPE: 0.219617 valid's rmse: 0.000799653   valid's RMSPE: 0.247643
Early stopping, best iteration is:
[124]   train's rmse: 0.000742947   train's RMSPE: 0.222116 valid's rmse: 0.000797544   valid's RMSPE: 0.24699
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000793593   train's RMSPE: 0.238448 valid's rmse: 0.000834368   valid's RMSPE: 0.253501
[100]   train's rmse: 0.000748077   train's RMSPE: 0.224772 valid's rmse: 0.000817881   valid's RMSPE: 0.248492
[150]   train's rmse: 0.000729303   train's RMSPE: 0.219131 valid's rmse: 0.000814916   valid's RMSPE: 0.247591
[200]   train's rmse: 0.000714793   train's RMSPE: 0.214772 valid's rmse: 0.000814032   valid's RMSPE: 0.247323
[250]   train's rmse: 0.000701329   train's RMSPE: 0.210726 valid's rmse: 0.000810996   valid's RMSPE: 0.2464
Early stopping, best iteration is:
[245]   train's rmse: 0.000702554   train's RMSPE: 0.211094 valid's rmse: 0.000810648   valid's RMSPE: 0.246294
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000801343   train's RMSPE: 0.240706 valid's rmse: 0.000831118   valid's RMSPE: 0.252806
[100]   train's rmse: 0.00075551    train's RMSPE: 0.226938 valid's rmse: 0.000787973   valid's RMSPE: 0.239682
[150]   train's rmse: 0.000736424   train's RMSPE: 0.221205 valid's rmse: 0.000784355   valid's RMSPE: 0.238582
[200]   train's rmse: 0.000721422   train's RMSPE: 0.216699 valid's rmse: 0.000783625   valid's RMSPE: 0.23836
[250]   train's rmse: 0.000709516   train's RMSPE: 0.213123 valid's rmse: 0.000782045   valid's RMSPE: 0.237879
Early stopping, best iteration is:
[243]   train's rmse: 0.000711039   train's RMSPE: 0.21358  valid's rmse: 0.000781064   valid's RMSPE: 0.237581
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000802203   train's RMSPE: 0.243451 valid's rmse: 0.000810728   valid's RMSPE: 0.236414
[100]   train's rmse: 0.000756068   train's RMSPE: 0.22945  valid's rmse: 0.000799085   valid's RMSPE: 0.233019
[150]   train's rmse: 0.000736431   train's RMSPE: 0.22349  valid's rmse: 0.000796241   valid's RMSPE: 0.23219
[200]   train's rmse: 0.000720297   train's RMSPE: 0.218594 valid's rmse: 0.000799286   valid's RMSPE: 0.233078
Early stopping, best iteration is:
[150]   train's rmse: 0.000736431   train's RMSPE: 0.22349  valid's rmse: 0.000796241   valid's RMSPE: 0.23219
Our out of folds RMSPE is 0.241, compared to 0.21694871689478218, giving gain 0.024051283105217813
Our cv fold scores are [0.242, 0.247, 0.246, 0.238, 0.232]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000615269   train's RMSPE: 0.186362 valid's rmse: 0.000641006   valid's RMSPE: 0.196004
[100]   train's rmse: 0.000580305   train's RMSPE: 0.175771 valid's rmse: 0.0006102 valid's RMSPE: 0.186584
[150]   train's rmse: 0.000566782   train's RMSPE: 0.171675 valid's rmse: 0.00060373    valid's RMSPE: 0.184606
[200]   train's rmse: 0.00055788    train's RMSPE: 0.168979 valid's rmse: 0.000602189   valid's RMSPE: 0.184134
[250]   train's rmse: 0.000549013   train's RMSPE: 0.166293 valid's rmse: 0.000598017   valid's RMSPE: 0.182859
[300]   train's rmse: 0.000541119   train's RMSPE: 0.163902 valid's rmse: 0.000595226   valid's RMSPE: 0.182005
[350]   train's rmse: 0.000534016   train's RMSPE: 0.161751 valid's rmse: 0.000594943   valid's RMSPE: 0.181919
Early stopping, best iteration is:
[328]   train's rmse: 0.000536995   train's RMSPE: 0.162653 valid's rmse: 0.000593733   valid's RMSPE: 0.181549
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000606196   train's RMSPE: 0.183558 valid's rmse: 0.00064853    valid's RMSPE: 0.198542
[100]   train's rmse: 0.000569989   train's RMSPE: 0.172594 valid's rmse: 0.000628047   valid's RMSPE: 0.192271
[150]   train's rmse: 0.000557647   train's RMSPE: 0.168857 valid's rmse: 0.00062342    valid's RMSPE: 0.190854
[200]   train's rmse: 0.000547873   train's RMSPE: 0.165898 valid's rmse: 0.00062116    valid's RMSPE: 0.190162
[250]   train's rmse: 0.000539335   train's RMSPE: 0.163312 valid's rmse: 0.000618729   valid's RMSPE: 0.189418
[300]   train's rmse: 0.000532515   train's RMSPE: 0.161247 valid's rmse: 0.000617852   valid's RMSPE: 0.18915
[350]   train's rmse: 0.000525297   train's RMSPE: 0.159062 valid's rmse: 0.000616304   valid's RMSPE: 0.188676
[400]   train's rmse: 0.00051941    train's RMSPE: 0.157279 valid's rmse: 0.000614427   valid's RMSPE: 0.188101
[450]   train's rmse: 0.0005135 train's RMSPE: 0.155489 valid's rmse: 0.000612414   valid's RMSPE: 0.187485
[500]   train's rmse: 0.000508403   train's RMSPE: 0.153946 valid's rmse: 0.000614133   valid's RMSPE: 0.188011
Early stopping, best iteration is:
[460]   train's rmse: 0.000512414   train's RMSPE: 0.15516  valid's rmse: 0.00061223    valid's RMSPE: 0.187429
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000614314   train's RMSPE: 0.186293 valid's rmse: 0.000615201   valid's RMSPE: 0.187236
[100]   train's rmse: 0.000578689   train's RMSPE: 0.17549  valid's rmse: 0.00059317    valid's RMSPE: 0.180531
[150]   train's rmse: 0.000564913   train's RMSPE: 0.171312 valid's rmse: 0.000589397   valid's RMSPE: 0.179383
[200]   train's rmse: 0.000554796   train's RMSPE: 0.168244 valid's rmse: 0.00058796    valid's RMSPE: 0.178945
[250]   train's rmse: 0.000546414   train's RMSPE: 0.165702 valid's rmse: 0.000586651   valid's RMSPE: 0.178547
[300]   train's rmse: 0.000538896   train's RMSPE: 0.163422 valid's rmse: 0.000585551   valid's RMSPE: 0.178212
[350]   train's rmse: 0.000532578   train's RMSPE: 0.161506 valid's rmse: 0.000585597   valid's RMSPE: 0.178226
[400]   train's rmse: 0.000525952   train's RMSPE: 0.159497 valid's rmse: 0.000584727   valid's RMSPE: 0.177961
[450]   train's rmse: 0.000519912   train's RMSPE: 0.157665 valid's rmse: 0.000584623   valid's RMSPE: 0.17793
Early stopping, best iteration is:
[434]   train's rmse: 0.000522265   train's RMSPE: 0.158379 valid's rmse: 0.000584008   valid's RMSPE: 0.177743
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000611281   train's RMSPE: 0.18588  valid's rmse: 0.000633623   valid's RMSPE: 0.190736
[100]   train's rmse: 0.000573544   train's RMSPE: 0.174405 valid's rmse: 0.00061131    valid's RMSPE: 0.184019
[150]   train's rmse: 0.000560159   train's RMSPE: 0.170335 valid's rmse: 0.00060869    valid's RMSPE: 0.18323
[200]   train's rmse: 0.000549555   train's RMSPE: 0.16711  valid's rmse: 0.000608894   valid's RMSPE: 0.183292
Early stopping, best iteration is:
[167]   train's rmse: 0.000556357   train's RMSPE: 0.169178 valid's rmse: 0.000608108   valid's RMSPE: 0.183055
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000618542   train's RMSPE: 0.188241 valid's rmse: 0.000601608   valid's RMSPE: 0.180498
[100]   train's rmse: 0.000582901   train's RMSPE: 0.177394 valid's rmse: 0.000577334   valid's RMSPE: 0.173215
[150]   train's rmse: 0.000569689   train's RMSPE: 0.173373 valid's rmse: 0.000574581   valid's RMSPE: 0.172389
[200]   train's rmse: 0.000559663   train's RMSPE: 0.170322 valid's rmse: 0.000573911   valid's RMSPE: 0.172188
[250]   train's rmse: 0.000552088   train's RMSPE: 0.168017 valid's rmse: 0.000573715   valid's RMSPE: 0.172129
Early stopping, best iteration is:
[228]   train's rmse: 0.000555191   train's RMSPE: 0.168961 valid's rmse: 0.00057269    valid's RMSPE: 0.171821

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.18, compared to 0.1547566259930346, giving gain 0.025243374006965386
Our cv fold scores are [0.182, 0.187, 0.178, 0.183, 0.172]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000653347   train's RMSPE: 0.284463 valid's rmse: 0.00067398    valid's RMSPE: 0.296359
[100]   train's rmse: 0.000615357   train's RMSPE: 0.267922 valid's rmse: 0.000649711   valid's RMSPE: 0.285688
[150]   train's rmse: 0.000596022   train's RMSPE: 0.259504 valid's rmse: 0.000644953   valid's RMSPE: 0.283596
[200]   train's rmse: 0.000579897   train's RMSPE: 0.252483 valid's rmse: 0.000640869   valid's RMSPE: 0.2818
[250]   train's rmse: 0.000568857   train's RMSPE: 0.247677 valid's rmse: 0.000639326   valid's RMSPE: 0.281121
[300]   train's rmse: 0.000558628   train's RMSPE: 0.243223 valid's rmse: 0.000639336   valid's RMSPE: 0.281125
[350]   train's rmse: 0.000548593   train's RMSPE: 0.238854 valid's rmse: 0.000637894   valid's RMSPE: 0.280491
[400]   train's rmse: 0.000540247   train's RMSPE: 0.23522  valid's rmse: 0.000635785   valid's RMSPE: 0.279564
[450]   train's rmse: 0.00053369    train's RMSPE: 0.232365 valid's rmse: 0.000634087   valid's RMSPE: 0.278818
[500]   train's rmse: 0.000526832   train's RMSPE: 0.229379 valid's rmse: 0.000634704   valid's RMSPE: 0.279089
Early stopping, best iteration is:
[454]   train's rmse: 0.000533049   train's RMSPE: 0.232086 valid's rmse: 0.00063376    valid's RMSPE: 0.278674
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000643142   train's RMSPE: 0.282491 valid's rmse: 0.00071781    valid's RMSPE: 0.304466
[100]   train's rmse: 0.000603254   train's RMSPE: 0.26497  valid's rmse: 0.000710149   valid's RMSPE: 0.301216
[150]   train's rmse: 0.00058535    train's RMSPE: 0.257106 valid's rmse: 0.000704136   valid's RMSPE: 0.298665
[200]   train's rmse: 0.000570741   train's RMSPE: 0.250689 valid's rmse: 0.00070202    valid's RMSPE: 0.297768
[250]   train's rmse: 0.000558947   train's RMSPE: 0.245509 valid's rmse: 0.000698846   valid's RMSPE: 0.296421
[300]   train's rmse: 0.000548396   train's RMSPE: 0.240875 valid's rmse: 0.000694201   valid's RMSPE: 0.294451
[350]   train's rmse: 0.000540736   train's RMSPE: 0.23751  valid's rmse: 0.000693636   valid's RMSPE: 0.294212
Early stopping, best iteration is:
[333]   train's rmse: 0.000543294   train's RMSPE: 0.238634 valid's rmse: 0.000692972   valid's RMSPE: 0.29393
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000658388   train's RMSPE: 0.28523  valid's rmse: 0.000650381   valid's RMSPE: 0.291501
[100]   train's rmse: 0.000618793   train's RMSPE: 0.268076 valid's rmse: 0.000624014   valid's RMSPE: 0.279684
[150]   train's rmse: 0.000600976   train's RMSPE: 0.260358 valid's rmse: 0.000617916   valid's RMSPE: 0.276951
[200]   train's rmse: 0.000588288   train's RMSPE: 0.254861 valid's rmse: 0.000615701   valid's RMSPE: 0.275958
[250]   train's rmse: 0.000576903   train's RMSPE: 0.249929 valid's rmse: 0.000613306   valid's RMSPE: 0.274885
[300]   train's rmse: 0.000567887   train's RMSPE: 0.246023 valid's rmse: 0.000611892   valid's RMSPE: 0.274251
[350]   train's rmse: 0.000558752   train's RMSPE: 0.242065 valid's rmse: 0.000610563   valid's RMSPE: 0.273655
Early stopping, best iteration is:
[339]   train's rmse: 0.000560427   train's RMSPE: 0.242791 valid's rmse: 0.000609634   valid's RMSPE: 0.273239
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000657689   train's RMSPE: 0.286063 valid's rmse: 0.000653216   valid's RMSPE: 0.288372
[100]   train's rmse: 0.000616121   train's RMSPE: 0.267982 valid's rmse: 0.000631939   valid's RMSPE: 0.278978
[150]   train's rmse: 0.000598411   train's RMSPE: 0.260279 valid's rmse: 0.000627939   valid's RMSPE: 0.277212
[200]   train's rmse: 0.000586262   train's RMSPE: 0.254995 valid's rmse: 0.000623271   valid's RMSPE: 0.275152
[250]   train's rmse: 0.00057668    train's RMSPE: 0.250827 valid's rmse: 0.000620468   valid's RMSPE: 0.273914
[300]   train's rmse: 0.000567081   train's RMSPE: 0.246653 valid's rmse: 0.000621924   valid's RMSPE: 0.274557
Early stopping, best iteration is:
[253]   train's rmse: 0.000575936   train's RMSPE: 0.250504 valid's rmse: 0.000620043   valid's RMSPE: 0.273727
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000658365   train's RMSPE: 0.288675 valid's rmse: 0.000701107   valid's RMSPE: 0.299583
[100]   train's rmse: 0.000620059   train's RMSPE: 0.271879 valid's rmse: 0.000671588   valid's RMSPE: 0.286969
[150]   train's rmse: 0.000600608   train's RMSPE: 0.26335  valid's rmse: 0.000665254   valid's RMSPE: 0.284263
[200]   train's rmse: 0.000586583   train's RMSPE: 0.257201 valid's rmse: 0.000661195   valid's RMSPE: 0.282528
[250]   train's rmse: 0.00057478    train's RMSPE: 0.252026 valid's rmse: 0.00065954    valid's RMSPE: 0.281821
[300]   train's rmse: 0.000564957   train's RMSPE: 0.247719 valid's rmse: 0.000656489   valid's RMSPE: 0.280518
Early stopping, best iteration is:
[291]   train's rmse: 0.000566392   train's RMSPE: 0.248348 valid's rmse: 0.000653194   valid's RMSPE: 0.27911
Our out of folds RMSPE is 0.28, compared to 0.2571888420300794, giving gain 0.022811157969920615
Our cv fold scores are [0.279, 0.294, 0.273, 0.274, 0.279]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000592643   train's RMSPE: 0.239815 valid's rmse: 0.000649744   valid's RMSPE: 0.261161
[100]   train's rmse: 0.000564777   train's RMSPE: 0.228539 valid's rmse: 0.000631439   valid's RMSPE: 0.253803
[150]   train's rmse: 0.0005525 train's RMSPE: 0.223571 valid's rmse: 0.000627468   valid's RMSPE: 0.252207
[200]   train's rmse: 0.000542016   train's RMSPE: 0.219329 valid's rmse: 0.000624393   valid's RMSPE: 0.250971
[250]   train's rmse: 0.000533366   train's RMSPE: 0.215828 valid's rmse: 0.00062263    valid's RMSPE: 0.250263
[300]   train's rmse: 0.000525572   train's RMSPE: 0.212674 valid's rmse: 0.000620122   valid's RMSPE: 0.249255
[350]   train's rmse: 0.000519037   train's RMSPE: 0.21003  valid's rmse: 0.000619169   valid's RMSPE: 0.248872
[400]   train's rmse: 0.0005129 train's RMSPE: 0.207547 valid's rmse: 0.000619507   valid's RMSPE: 0.249007
Early stopping, best iteration is:
[355]   train's rmse: 0.000518265   train's RMSPE: 0.209718 valid's rmse: 0.000618675   valid's RMSPE: 0.248673
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000588115   train's RMSPE: 0.235936 valid's rmse: 0.000659847   valid's RMSPE: 0.274274
[100]   train's rmse: 0.00056167    train's RMSPE: 0.225327 valid's rmse: 0.000639841   valid's RMSPE: 0.265958
[150]   train's rmse: 0.000549552   train's RMSPE: 0.220465 valid's rmse: 0.000633935   valid's RMSPE: 0.263504
[200]   train's rmse: 0.000538994   train's RMSPE: 0.21623  valid's rmse: 0.000630732   valid's RMSPE: 0.262172
[250]   train's rmse: 0.000529825   train's RMSPE: 0.212552 valid's rmse: 0.000628428   valid's RMSPE: 0.261214
[300]   train's rmse: 0.000522013   train's RMSPE: 0.209418 valid's rmse: 0.000627584   valid's RMSPE: 0.260863
[350]   train's rmse: 0.000515379   train's RMSPE: 0.206756 valid's rmse: 0.000626335   valid's RMSPE: 0.260345
[400]   train's rmse: 0.000509091   train's RMSPE: 0.204233 valid's rmse: 0.000626693   valid's RMSPE: 0.260493
Early stopping, best iteration is:
[360]   train's rmse: 0.000514039   train's RMSPE: 0.206218 valid's rmse: 0.000626034   valid's RMSPE: 0.260219
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000604796   train's RMSPE: 0.244743 valid's rmse: 0.000578225   valid's RMSPE: 0.232372
[100]   train's rmse: 0.000577325   train's RMSPE: 0.233627 valid's rmse: 0.000566194   valid's RMSPE: 0.227537
[150]   train's rmse: 0.000564589   train's RMSPE: 0.228473 valid's rmse: 0.00056717    valid's RMSPE: 0.22793
Early stopping, best iteration is:
[125]   train's rmse: 0.000570275   train's RMSPE: 0.230774 valid's rmse: 0.000565499   valid's RMSPE: 0.227258
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000610668   train's RMSPE: 0.246852 valid's rmse: 0.00056888    valid's RMSPE: 0.229618
[100]   train's rmse: 0.000582751   train's RMSPE: 0.235567 valid's rmse: 0.000554773   valid's RMSPE: 0.223924
Early stopping, best iteration is:
[98]    train's rmse: 0.000583262   train's RMSPE: 0.235774 valid's rmse: 0.000554434   valid's RMSPE: 0.223787
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000603144   train's RMSPE: 0.244767 valid's rmse: 0.000611424   valid's RMSPE: 0.242867
[100]   train's rmse: 0.000575411   train's RMSPE: 0.233512 valid's rmse: 0.000593664   valid's RMSPE: 0.235813
[150]   train's rmse: 0.00056308    train's RMSPE: 0.228508 valid's rmse: 0.000591702   valid's RMSPE: 0.235033
[200]   train's rmse: 0.000553042   train's RMSPE: 0.224435 valid's rmse: 0.000591264   valid's RMSPE: 0.234859
[250]   train's rmse: 0.000544438   train's RMSPE: 0.220943 valid's rmse: 0.000589866   valid's RMSPE: 0.234304
[300]   train's rmse: 0.000536829   train's RMSPE: 0.217855 valid's rmse: 0.000588097   valid's RMSPE: 0.233601
[350]   train's rmse: 0.000528788   train's RMSPE: 0.214592 valid's rmse: 0.000587547   valid's RMSPE: 0.233383
Early stopping, best iteration is:
[314]   train's rmse: 0.000533983   train's RMSPE: 0.2167   valid's rmse: 0.000586689   valid's RMSPE: 0.233042
Our out of folds RMSPE is 0.239, compared to 0.2034623871895212, giving gain 0.03553761281047879
Our cv fold scores are [0.249, 0.26, 0.227, 0.224, 0.233]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000647078   train's RMSPE: 0.293665 valid's rmse: 0.000685696   valid's RMSPE: 0.311645
[100]   train's rmse: 0.000619927   train's RMSPE: 0.281343 valid's rmse: 0.000679838   valid's RMSPE: 0.308982
[150]   train's rmse: 0.000603621   train's RMSPE: 0.273942 valid's rmse: 0.000678228   valid's RMSPE: 0.308251
Early stopping, best iteration is:
[124]   train's rmse: 0.000611371   train's RMSPE: 0.27746  valid's rmse: 0.000677764   valid's RMSPE: 0.30804
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000647619   train's RMSPE: 0.294631 valid's rmse: 0.000692665   valid's RMSPE: 0.311713
[100]   train's rmse: 0.000619039   train's RMSPE: 0.281629 valid's rmse: 0.000677564   valid's RMSPE: 0.304918
[150]   train's rmse: 0.000602966   train's RMSPE: 0.274316 valid's rmse: 0.000676745   valid's RMSPE: 0.304549
Early stopping, best iteration is:
[141]   train's rmse: 0.000605885   train's RMSPE: 0.275645 valid's rmse: 0.000676201   valid's RMSPE: 0.304304
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000650616   train's RMSPE: 0.294064 valid's rmse: 0.000669726   valid's RMSPE: 0.309296
[100]   train's rmse: 0.000623311   train's RMSPE: 0.281723 valid's rmse: 0.000659633   valid's RMSPE: 0.304635
Early stopping, best iteration is:
[98]    train's rmse: 0.000624403   train's RMSPE: 0.282216 valid's rmse: 0.000659542   valid's RMSPE: 0.304593
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000659495   train's RMSPE: 0.298752 valid's rmse: 0.000645272   valid's RMSPE: 0.295403
[100]   train's rmse: 0.00063007    train's RMSPE: 0.285423 valid's rmse: 0.000649741   valid's RMSPE: 0.297449
Early stopping, best iteration is:
[52]    train's rmse: 0.000656908   train's RMSPE: 0.29758  valid's rmse: 0.000644148   valid's RMSPE: 0.294888
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000651301   train's RMSPE: 0.297029 valid's rmse: 0.000678911   valid's RMSPE: 0.302459
[100]   train's rmse: 0.000624452   train's RMSPE: 0.284784 valid's rmse: 0.00066566    valid's RMSPE: 0.296556
[150]   train's rmse: 0.000609248   train's RMSPE: 0.27785  valid's rmse: 0.00066511    valid's RMSPE: 0.296311
[200]   train's rmse: 0.000596537   train's RMSPE: 0.272053 valid's rmse: 0.000665137   valid's RMSPE: 0.296323
[250]   train's rmse: 0.000585274   train's RMSPE: 0.266917 valid's rmse: 0.000664061   valid's RMSPE: 0.295844
Early stopping, best iteration is:
[227]   train's rmse: 0.000590226   train's RMSPE: 0.269175 valid's rmse: 0.000661964   valid's RMSPE: 0.294909

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.301, compared to 0.2733105310684778, giving gain 0.027689468931522188
Our cv fold scores are [0.308, 0.304, 0.305, 0.295, 0.295]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000554553   train's RMSPE: 0.192769 valid's rmse: 0.000581179   valid's RMSPE: 0.202781
[100]   train's rmse: 0.000526003   train's RMSPE: 0.182845 valid's rmse: 0.000560138   valid's RMSPE: 0.195439
[150]   train's rmse: 0.000513714   train's RMSPE: 0.178573 valid's rmse: 0.000556266   valid's RMSPE: 0.194088
[200]   train's rmse: 0.000503673   train's RMSPE: 0.175082 valid's rmse: 0.000554582   valid's RMSPE: 0.193501
[250]   train's rmse: 0.000494841   train's RMSPE: 0.172012 valid's rmse: 0.000551328   valid's RMSPE: 0.192365
[300]   train's rmse: 0.000487175   train's RMSPE: 0.169348 valid's rmse: 0.000549686   valid's RMSPE: 0.191793
[350]   train's rmse: 0.000480328   train's RMSPE: 0.166967 valid's rmse: 0.000549545   valid's RMSPE: 0.191743
[400]   train's rmse: 0.000474671   train's RMSPE: 0.165001 valid's rmse: 0.000548515   valid's RMSPE: 0.191384
[450]   train's rmse: 0.000468371   train's RMSPE: 0.162811 valid's rmse: 0.000548118   valid's RMSPE: 0.191245
[500]   train's rmse: 0.000463146   train's RMSPE: 0.160995 valid's rmse: 0.000547567   valid's RMSPE: 0.191053
[550]   train's rmse: 0.000458006   train's RMSPE: 0.159208 valid's rmse: 0.000546866   valid's RMSPE: 0.190809
Early stopping, best iteration is:
[540]   train's rmse: 0.000459026   train's RMSPE: 0.159563 valid's rmse: 0.000546802   valid's RMSPE: 0.190786
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000555916   train's RMSPE: 0.192921 valid's rmse: 0.00057217    valid's RMSPE: 0.200951
[100]   train's rmse: 0.000525743   train's RMSPE: 0.18245  valid's rmse: 0.000563328   valid's RMSPE: 0.197846
[150]   train's rmse: 0.000511696   train's RMSPE: 0.177575 valid's rmse: 0.000562272   valid's RMSPE: 0.197475
Early stopping, best iteration is:
[135]   train's rmse: 0.000515395   train's RMSPE: 0.178859 valid's rmse: 0.000561119   valid's RMSPE: 0.19707
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000563361   train's RMSPE: 0.195209 valid's rmse: 0.000550007   valid's RMSPE: 0.194303
[100]   train's rmse: 0.000533299   train's RMSPE: 0.184792 valid's rmse: 0.0005275 valid's RMSPE: 0.186352
[150]   train's rmse: 0.000521538   train's RMSPE: 0.180717 valid's rmse: 0.000524835   valid's RMSPE: 0.185411
Early stopping, best iteration is:
[118]   train's rmse: 0.000528483   train's RMSPE: 0.183124 valid's rmse: 0.000524685   valid's RMSPE: 0.185357
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000552832   train's RMSPE: 0.192807 valid's rmse: 0.000586216   valid's RMSPE: 0.201827
[100]   train's rmse: 0.000521447   train's RMSPE: 0.181861 valid's rmse: 0.000568689   valid's RMSPE: 0.195792
[150]   train's rmse: 0.000508269   train's RMSPE: 0.177265 valid's rmse: 0.000565374   valid's RMSPE: 0.194651
[200]   train's rmse: 0.000497927   train's RMSPE: 0.173658 valid's rmse: 0.000563661   valid's RMSPE: 0.194061
[250]   train's rmse: 0.000490015   train's RMSPE: 0.170899 valid's rmse: 0.000561812   valid's RMSPE: 0.193424
[300]   train's rmse: 0.000483104   train's RMSPE: 0.168488 valid's rmse: 0.000560362   valid's RMSPE: 0.192925
[350]   train's rmse: 0.000475974   train's RMSPE: 0.166002 valid's rmse: 0.000559414   valid's RMSPE: 0.192599
[400]   train's rmse: 0.000469909   train's RMSPE: 0.163886 valid's rmse: 0.0005591 valid's RMSPE: 0.192491
[450]   train's rmse: 0.000464384   train's RMSPE: 0.16196  valid's rmse: 0.000557918   valid's RMSPE: 0.192084
[500]   train's rmse: 0.0004591 train's RMSPE: 0.160117 valid's rmse: 0.000557501   valid's RMSPE: 0.191941
[550]   train's rmse: 0.000453454   train's RMSPE: 0.158148 valid's rmse: 0.00055683    valid's RMSPE: 0.191709
Early stopping, best iteration is:
[544]   train's rmse: 0.000454245   train's RMSPE: 0.158424 valid's rmse: 0.000556531   valid's RMSPE: 0.191606
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000559425   train's RMSPE: 0.195484 valid's rmse: 0.00056871    valid's RMSPE: 0.194238
[100]   train's rmse: 0.000528071   train's RMSPE: 0.184527 valid's rmse: 0.000552926   valid's RMSPE: 0.188847
[150]   train's rmse: 0.000515257   train's RMSPE: 0.18005  valid's rmse: 0.000551374   valid's RMSPE: 0.188317
[200]   train's rmse: 0.000505178   train's RMSPE: 0.176528 valid's rmse: 0.000550552   valid's RMSPE: 0.188036
[250]   train's rmse: 0.00049654    train's RMSPE: 0.173509 valid's rmse: 0.000550167   valid's RMSPE: 0.187904
Early stopping, best iteration is:
[219]   train's rmse: 0.000501717   train's RMSPE: 0.175318 valid's rmse: 0.000548835   valid's RMSPE: 0.18745
Our out of folds RMSPE is 0.19, compared to 0.1692650099200244, giving gain 0.02073499007997559
Our cv fold scores are [0.191, 0.197, 0.185, 0.192, 0.187]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000851682   train's RMSPE: 0.226868 valid's rmse: 0.000896929   valid's RMSPE: 0.239833
[100]   train's rmse: 0.000811057   train's RMSPE: 0.216046 valid's rmse: 0.000868569   valid's RMSPE: 0.23225
[150]   train's rmse: 0.000791799   train's RMSPE: 0.210916 valid's rmse: 0.000862799   valid's RMSPE: 0.230707
[200]   train's rmse: 0.000777334   train's RMSPE: 0.207063 valid's rmse: 0.000860982   valid's RMSPE: 0.230221
Early stopping, best iteration is:
[186]   train's rmse: 0.000781278   train's RMSPE: 0.208114 valid's rmse: 0.000860174   valid's RMSPE: 0.230005
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000857015   train's RMSPE: 0.227693 valid's rmse: 0.000875075   valid's RMSPE: 0.236397
[100]   train's rmse: 0.000815757   train's RMSPE: 0.216731 valid's rmse: 0.000854086   valid's RMSPE: 0.230727
[150]   train's rmse: 0.000798119   train's RMSPE: 0.212045 valid's rmse: 0.000852753   valid's RMSPE: 0.230367
[200]   train's rmse: 0.000783392   train's RMSPE: 0.208132 valid's rmse: 0.000848316   valid's RMSPE: 0.229168
[250]   train's rmse: 0.000771322   train's RMSPE: 0.204925 valid's rmse: 0.00084747    valid's RMSPE: 0.22894
[300]   train's rmse: 0.000759969   train's RMSPE: 0.201909 valid's rmse: 0.000846513   valid's RMSPE: 0.228681
[350]   train's rmse: 0.000749992   train's RMSPE: 0.199259 valid's rmse: 0.000846192   valid's RMSPE: 0.228594
Early stopping, best iteration is:
[328]   train's rmse: 0.000754583   train's RMSPE: 0.200478 valid's rmse: 0.000845535   valid's RMSPE: 0.228417
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000850433   train's RMSPE: 0.226681 valid's rmse: 0.000863004   valid's RMSPE: 0.23017
[100]   train's rmse: 0.000811659   train's RMSPE: 0.216346 valid's rmse: 0.000833426   valid's RMSPE: 0.222281
[150]   train's rmse: 0.000795082   train's RMSPE: 0.211927 valid's rmse: 0.000832319   valid's RMSPE: 0.221986
Early stopping, best iteration is:
[144]   train's rmse: 0.0007969 train's RMSPE: 0.212412 valid's rmse: 0.000831595   valid's RMSPE: 0.221793
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000844683   train's RMSPE: 0.225866 valid's rmse: 0.000898947   valid's RMSPE: 0.236678
[100]   train's rmse: 0.000804166   train's RMSPE: 0.215032 valid's rmse: 0.000881621   valid's RMSPE: 0.232116
[150]   train's rmse: 0.000786661   train's RMSPE: 0.210351 valid's rmse: 0.000878811   valid's RMSPE: 0.231376
[200]   train's rmse: 0.00077232    train's RMSPE: 0.206516 valid's rmse: 0.000876985   valid's RMSPE: 0.230896
[250]   train's rmse: 0.00075904    train's RMSPE: 0.202965 valid's rmse: 0.000878257   valid's RMSPE: 0.231231
Early stopping, best iteration is:
[204]   train's rmse: 0.000771091   train's RMSPE: 0.206188 valid's rmse: 0.000876752   valid's RMSPE: 0.230834
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00085827    train's RMSPE: 0.229067 valid's rmse: 0.000843225   valid's RMSPE: 0.223725
[100]   train's rmse: 0.000817196   train's RMSPE: 0.218105 valid's rmse: 0.000821287   valid's RMSPE: 0.217904
[150]   train's rmse: 0.000798899   train's RMSPE: 0.213221 valid's rmse: 0.000819659   valid's RMSPE: 0.217472
[200]   train's rmse: 0.000785274   train's RMSPE: 0.209585 valid's rmse: 0.000817562   valid's RMSPE: 0.216916
[250]   train's rmse: 0.000772876   train's RMSPE: 0.206276 valid's rmse: 0.000819399   valid's RMSPE: 0.217403
Early stopping, best iteration is:
[210]   train's rmse: 0.000782296   train's RMSPE: 0.20879  valid's rmse: 0.000816871   valid's RMSPE: 0.216732
Our out of folds RMSPE is 0.226, compared to 0.20024889662073747, giving gain 0.025751103379262535
Our cv fold scores are [0.23, 0.228, 0.222, 0.231, 0.217]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000507026   train's RMSPE: 0.20618  valid's rmse: 0.000547288   valid's RMSPE: 0.225075
[100]   train's rmse: 0.000477092   train's RMSPE: 0.194007 valid's rmse: 0.000526681   valid's RMSPE: 0.2166
[150]   train's rmse: 0.000466161   train's RMSPE: 0.189563 valid's rmse: 0.000522931   valid's RMSPE: 0.215058
[200]   train's rmse: 0.000457379   train's RMSPE: 0.185991 valid's rmse: 0.000521497   valid's RMSPE: 0.214468
[250]   train's rmse: 0.000449717   train's RMSPE: 0.182875 valid's rmse: 0.000519175   valid's RMSPE: 0.213513
[300]   train's rmse: 0.000442827   train's RMSPE: 0.180074 valid's rmse: 0.000518729   valid's RMSPE: 0.21333
[350]   train's rmse: 0.000436482   train's RMSPE: 0.177493 valid's rmse: 0.000517745   valid's RMSPE: 0.212925
[400]   train's rmse: 0.00043069    train's RMSPE: 0.175138 valid's rmse: 0.000516889   valid's RMSPE: 0.212573
[450]   train's rmse: 0.000425096   train's RMSPE: 0.172864 valid's rmse: 0.00051669    valid's RMSPE: 0.212492
[500]   train's rmse: 0.0004206 train's RMSPE: 0.171035 valid's rmse: 0.000515644   valid's RMSPE: 0.212061
Early stopping, best iteration is:
[490]   train's rmse: 0.000421336   train's RMSPE: 0.171335 valid's rmse: 0.000515409   valid's RMSPE: 0.211965
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000504681   train's RMSPE: 0.205312 valid's rmse: 0.00055277    valid's RMSPE: 0.226957
[100]   train's rmse: 0.000475629   train's RMSPE: 0.193493 valid's rmse: 0.000529467   valid's RMSPE: 0.21739
[150]   train's rmse: 0.000463096   train's RMSPE: 0.188395 valid's rmse: 0.000527179   valid's RMSPE: 0.21645
[200]   train's rmse: 0.000453806   train's RMSPE: 0.184616 valid's rmse: 0.000527686   valid's RMSPE: 0.216659
[250]   train's rmse: 0.000445677   train's RMSPE: 0.181309 valid's rmse: 0.000526267   valid's RMSPE: 0.216076
Early stopping, best iteration is:
[249]   train's rmse: 0.000445737   train's RMSPE: 0.181333 valid's rmse: 0.000526168   valid's RMSPE: 0.216035
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000518078   train's RMSPE: 0.210202 valid's rmse: 0.000496007   valid's RMSPE: 0.205764
[100]   train's rmse: 0.000486903   train's RMSPE: 0.197553 valid's rmse: 0.000477075   valid's RMSPE: 0.19791
[150]   train's rmse: 0.000474265   train's RMSPE: 0.192426 valid's rmse: 0.000476173   valid's RMSPE: 0.197536
[200]   train's rmse: 0.000464431   train's RMSPE: 0.188436 valid's rmse: 0.000476087   valid's RMSPE: 0.1975
Early stopping, best iteration is:
[176]   train's rmse: 0.000468662   train's RMSPE: 0.190153 valid's rmse: 0.000475617   valid's RMSPE: 0.197305
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000514363   train's RMSPE: 0.210361 valid's rmse: 0.000516239   valid's RMSPE: 0.207485
[100]   train's rmse: 0.000483773   train's RMSPE: 0.197851 valid's rmse: 0.00050172    valid's RMSPE: 0.201649
[150]   train's rmse: 0.000472607   train's RMSPE: 0.193284 valid's rmse: 0.000499953   valid's RMSPE: 0.200939
[200]   train's rmse: 0.00046278    train's RMSPE: 0.189265 valid's rmse: 0.000499481   valid's RMSPE: 0.200749
[250]   train's rmse: 0.000454348   train's RMSPE: 0.185817 valid's rmse: 0.000497751   valid's RMSPE: 0.200054
[300]   train's rmse: 0.000447286   train's RMSPE: 0.182928 valid's rmse: 0.000496789   valid's RMSPE: 0.199667
[350]   train's rmse: 0.000440984   train's RMSPE: 0.180351 valid's rmse: 0.000495637   valid's RMSPE: 0.199204
[400]   train's rmse: 0.0004353 train's RMSPE: 0.178026 valid's rmse: 0.000495336   valid's RMSPE: 0.199083
[450]   train's rmse: 0.000430339   train's RMSPE: 0.175997 valid's rmse: 0.000495352   valid's RMSPE: 0.19909
Early stopping, best iteration is:
[414]   train's rmse: 0.000433944   train's RMSPE: 0.177472 valid's rmse: 0.000494468   valid's RMSPE: 0.198734
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000513289   train's RMSPE: 0.210282 valid's rmse: 0.000519671   valid's RMSPE: 0.207373
[100]   train's rmse: 0.000482986   train's RMSPE: 0.197867 valid's rmse: 0.000508594   valid's RMSPE: 0.202953
[150]   train's rmse: 0.000470847   train's RMSPE: 0.192895 valid's rmse: 0.000506309   valid's RMSPE: 0.202041
[200]   train's rmse: 0.000460839   train's RMSPE: 0.188795 valid's rmse: 0.000504388   valid's RMSPE: 0.201274
[250]   train's rmse: 0.000451799   train's RMSPE: 0.185091 valid's rmse: 0.000503017   valid's RMSPE: 0.200727
[300]   train's rmse: 0.000444348   train's RMSPE: 0.182038 valid's rmse: 0.000503367   valid's RMSPE: 0.200867
[350]   train's rmse: 0.000437803   train's RMSPE: 0.179357 valid's rmse: 0.000502146   valid's RMSPE: 0.200379
[400]   train's rmse: 0.000431879   train's RMSPE: 0.17693  valid's rmse: 0.000502718   valid's RMSPE: 0.200608
Early stopping, best iteration is:
[362]   train's rmse: 0.000436314   train's RMSPE: 0.178747 valid's rmse: 0.000501424   valid's RMSPE: 0.200091
Our out of folds RMSPE is 0.205, compared to 0.19076291548509186, giving gain 0.014237084514908127
Our cv fold scores are [0.212, 0.216, 0.197, 0.199, 0.2]

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000376799   train's RMSPE: 0.231146 valid's rmse: 0.000404137   valid's RMSPE: 0.247565
[100]   train's rmse: 0.000359359   train's RMSPE: 0.220448 valid's rmse: 0.000391165   valid's RMSPE: 0.239618
[150]   train's rmse: 0.000351228   train's RMSPE: 0.21546  valid's rmse: 0.000389375   valid's RMSPE: 0.238522
[200]   train's rmse: 0.00034423    train's RMSPE: 0.211167 valid's rmse: 0.000387454   valid's RMSPE: 0.237345
[250]   train's rmse: 0.000338112   train's RMSPE: 0.207414 valid's rmse: 0.000385162   valid's RMSPE: 0.235941
[300]   train's rmse: 0.000333216   train's RMSPE: 0.20441  valid's rmse: 0.00038362    valid's RMSPE: 0.234997
[350]   train's rmse: 0.000328844   train's RMSPE: 0.201728 valid's rmse: 0.00038301    valid's RMSPE: 0.234623
[400]   train's rmse: 0.000324751   train's RMSPE: 0.199218 valid's rmse: 0.000380855   valid's RMSPE: 0.233303
[450]   train's rmse: 0.000321097   train's RMSPE: 0.196976 valid's rmse: 0.000380074   valid's RMSPE: 0.232825
[500]   train's rmse: 0.000317557   train's RMSPE: 0.194804 valid's rmse: 0.000379689   valid's RMSPE: 0.232588
[550]   train's rmse: 0.000313988   train's RMSPE: 0.192615 valid's rmse: 0.00037842    valid's RMSPE: 0.231811
[600]   train's rmse: 0.000310924   train's RMSPE: 0.190736 valid's rmse: 0.000378195   valid's RMSPE: 0.231674
[650]   train's rmse: 0.000308302   train's RMSPE: 0.189127 valid's rmse: 0.000378574   valid's RMSPE: 0.231905
Early stopping, best iteration is:
[619]   train's rmse: 0.000309937   train's RMSPE: 0.19013  valid's rmse: 0.000378007   valid's RMSPE: 0.231558
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000374348   train's RMSPE: 0.229207 valid's rmse: 0.000404253   valid's RMSPE: 0.249513
[100]   train's rmse: 0.000356271   train's RMSPE: 0.218138 valid's rmse: 0.000391955   valid's RMSPE: 0.241923
[150]   train's rmse: 0.000347931   train's RMSPE: 0.213032 valid's rmse: 0.000389802   valid's RMSPE: 0.240594
[200]   train's rmse: 0.000340845   train's RMSPE: 0.208694 valid's rmse: 0.000387349   valid's RMSPE: 0.239079
[250]   train's rmse: 0.000335069   train's RMSPE: 0.205157 valid's rmse: 0.000384978   valid's RMSPE: 0.237616
[300]   train's rmse: 0.000329524   train's RMSPE: 0.201762 valid's rmse: 0.000383088   valid's RMSPE: 0.236449
[350]   train's rmse: 0.000324627   train's RMSPE: 0.198763 valid's rmse: 0.000382422   valid's RMSPE: 0.236039
[400]   train's rmse: 0.0003208 train's RMSPE: 0.19642  valid's rmse: 0.000381492   valid's RMSPE: 0.235464
Early stopping, best iteration is:
[383]   train's rmse: 0.000322099   train's RMSPE: 0.197215 valid's rmse: 0.000380985   valid's RMSPE: 0.235152
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000382683   train's RMSPE: 0.232931 valid's rmse: 0.000362272   valid's RMSPE: 0.228708
[100]   train's rmse: 0.000363483   train's RMSPE: 0.221244 valid's rmse: 0.000354905   valid's RMSPE: 0.224057
[150]   train's rmse: 0.000354712   train's RMSPE: 0.215906 valid's rmse: 0.000354732   valid's RMSPE: 0.223948
Early stopping, best iteration is:
[122]   train's rmse: 0.000359394   train's RMSPE: 0.218755 valid's rmse: 0.000353647   valid's RMSPE: 0.223263
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000381485   train's RMSPE: 0.234822 valid's rmse: 0.000376463   valid's RMSPE: 0.227417
[100]   train's rmse: 0.000362999   train's RMSPE: 0.223443 valid's rmse: 0.000364272   valid's RMSPE: 0.220052
[150]   train's rmse: 0.000354037   train's RMSPE: 0.217927 valid's rmse: 0.000361229   valid's RMSPE: 0.218214
[200]   train's rmse: 0.000346385   train's RMSPE: 0.213217 valid's rmse: 0.00035976    valid's RMSPE: 0.217327
[250]   train's rmse: 0.00034007    train's RMSPE: 0.20933  valid's rmse: 0.000358037   valid's RMSPE: 0.216286
[300]   train's rmse: 0.000334479   train's RMSPE: 0.205888 valid's rmse: 0.000357263   valid's RMSPE: 0.215818
[350]   train's rmse: 0.000329483   train's RMSPE: 0.202812 valid's rmse: 0.000356789   valid's RMSPE: 0.215532
[400]   train's rmse: 0.000325092   train's RMSPE: 0.20011  valid's rmse: 0.000355783   valid's RMSPE: 0.214924
[450]   train's rmse: 0.000321273   train's RMSPE: 0.197759 valid's rmse: 0.000355189   valid's RMSPE: 0.214565
Early stopping, best iteration is:
[446]   train's rmse: 0.000321593   train's RMSPE: 0.197956 valid's rmse: 0.000354924   valid's RMSPE: 0.214405
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000377732   train's RMSPE: 0.232826 valid's rmse: 0.000386763   valid's RMSPE: 0.232324
[100]   train's rmse: 0.000359772   train's RMSPE: 0.221756 valid's rmse: 0.000376073   valid's RMSPE: 0.225902
[150]   train's rmse: 0.000351702   train's RMSPE: 0.216782 valid's rmse: 0.000373365   valid's RMSPE: 0.224275
[200]   train's rmse: 0.000344799   train's RMSPE: 0.212527 valid's rmse: 0.000371322   valid's RMSPE: 0.223049
[250]   train's rmse: 0.000339426   train's RMSPE: 0.209215 valid's rmse: 0.000370377   valid's RMSPE: 0.222481
[300]   train's rmse: 0.000334211   train's RMSPE: 0.206001 valid's rmse: 0.00036862    valid's RMSPE: 0.221425
[350]   train's rmse: 0.000329497   train's RMSPE: 0.203095 valid's rmse: 0.000367918   valid's RMSPE: 0.221004
[400]   train's rmse: 0.000325246   train's RMSPE: 0.200475 valid's rmse: 0.000367886   valid's RMSPE: 0.220985
[450]   train's rmse: 0.000321717   train's RMSPE: 0.198299 valid's rmse: 0.000367854   valid's RMSPE: 0.220966
[500]   train's rmse: 0.000318381   train's RMSPE: 0.196244 valid's rmse: 0.000367419   valid's RMSPE: 0.220704
[550]   train's rmse: 0.000315203   train's RMSPE: 0.194285 valid's rmse: 0.000366688   valid's RMSPE: 0.220265
[600]   train's rmse: 0.000312036   train's RMSPE: 0.192332 valid's rmse: 0.000367127   valid's RMSPE: 0.220528
Early stopping, best iteration is:
[565]   train's rmse: 0.000314317   train's RMSPE: 0.193738 valid's rmse: 0.000366406   valid's RMSPE: 0.220095
Our out of folds RMSPE is 0.225, compared to 0.19287498737385145, giving gain 0.03212501262614856
Our cv fold scores are [0.232, 0.235, 0.223, 0.214, 0.22]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000602067   train's RMSPE: 0.265293 valid's rmse: 0.000660112   valid's RMSPE: 0.287981
[100]   train's rmse: 0.000571319   train's RMSPE: 0.251744 valid's rmse: 0.000648742   valid's RMSPE: 0.28302
[150]   train's rmse: 0.000558184   train's RMSPE: 0.245956 valid's rmse: 0.000646453   valid's RMSPE: 0.282022
[200]   train's rmse: 0.000546798   train's RMSPE: 0.240939 valid's rmse: 0.000644711   valid's RMSPE: 0.281261
[250]   train's rmse: 0.000535684   train's RMSPE: 0.236042 valid's rmse: 0.000640624   valid's RMSPE: 0.279479
[300]   train's rmse: 0.000526119   train's RMSPE: 0.231827 valid's rmse: 0.000641294   valid's RMSPE: 0.279771
Early stopping, best iteration is:
[259]   train's rmse: 0.000533687   train's RMSPE: 0.235162 valid's rmse: 0.000640328   valid's RMSPE: 0.279349
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000610385   train's RMSPE: 0.268811 valid's rmse: 0.000622238   valid's RMSPE: 0.272062
[100]   train's rmse: 0.000581086   train's RMSPE: 0.255907 valid's rmse: 0.000604524   valid's RMSPE: 0.264318
[150]   train's rmse: 0.000565682   train's RMSPE: 0.249123 valid's rmse: 0.000598973   valid's RMSPE: 0.26189
[200]   train's rmse: 0.000553326   train's RMSPE: 0.243682 valid's rmse: 0.00059689    valid's RMSPE: 0.26098
[250]   train's rmse: 0.000542278   train's RMSPE: 0.238817 valid's rmse: 0.000594252   valid's RMSPE: 0.259826
[300]   train's rmse: 0.000533347   train's RMSPE: 0.234884 valid's rmse: 0.000592494   valid's RMSPE: 0.259058
[350]   train's rmse: 0.00052429    train's RMSPE: 0.230895 valid's rmse: 0.000590833   valid's RMSPE: 0.258331
[400]   train's rmse: 0.000517358   train's RMSPE: 0.227842 valid's rmse: 0.000588829   valid's RMSPE: 0.257455
[450]   train's rmse: 0.000511204   train's RMSPE: 0.225132 valid's rmse: 0.0005875 valid's RMSPE: 0.256874
[500]   train's rmse: 0.000504956   train's RMSPE: 0.22238  valid's rmse: 0.000586521   valid's RMSPE: 0.256446
[550]   train's rmse: 0.00049934    train's RMSPE: 0.219907 valid's rmse: 0.000586646   valid's RMSPE: 0.256501
Early stopping, best iteration is:
[517]   train's rmse: 0.000503068   train's RMSPE: 0.221549 valid's rmse: 0.000586244   valid's RMSPE: 0.256325
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000613962   train's RMSPE: 0.268909 valid's rmse: 0.000611982   valid's RMSPE: 0.27343
[100]   train's rmse: 0.000583663   train's RMSPE: 0.255638 valid's rmse: 0.000593652   valid's RMSPE: 0.26524
[150]   train's rmse: 0.000569927   train's RMSPE: 0.249622 valid's rmse: 0.000589956   valid's RMSPE: 0.263589
[200]   train's rmse: 0.000557645   train's RMSPE: 0.244243 valid's rmse: 0.000586722   valid's RMSPE: 0.262143
[250]   train's rmse: 0.000548078   train's RMSPE: 0.240053 valid's rmse: 0.000585518   valid's RMSPE: 0.261606
[300]   train's rmse: 0.000539311   train's RMSPE: 0.236212 valid's rmse: 0.000586058   valid's RMSPE: 0.261847
Early stopping, best iteration is:
[255]   train's rmse: 0.000547306   train's RMSPE: 0.239715 valid's rmse: 0.000585187   valid's RMSPE: 0.261458
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000612043   train's RMSPE: 0.269036 valid's rmse: 0.000612464   valid's RMSPE: 0.269814
[100]   train's rmse: 0.000580475   train's RMSPE: 0.25516  valid's rmse: 0.000607419   valid's RMSPE: 0.267591
Early stopping, best iteration is:
[75]    train's rmse: 0.000592006   train's RMSPE: 0.260229 valid's rmse: 0.000606608   valid's RMSPE: 0.267234
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000611141   train's RMSPE: 0.26904  valid's rmse: 0.000624944   valid's RMSPE: 0.273673
[100]   train's rmse: 0.000583239   train's RMSPE: 0.256757 valid's rmse: 0.000602051   valid's RMSPE: 0.263648
[150]   train's rmse: 0.000568708   train's RMSPE: 0.25036  valid's rmse: 0.000598554   valid's RMSPE: 0.262117
[200]   train's rmse: 0.000556861   train's RMSPE: 0.245144 valid's rmse: 0.000597655   valid's RMSPE: 0.261723
[250]   train's rmse: 0.000546608   train's RMSPE: 0.240631 valid's rmse: 0.000595421   valid's RMSPE: 0.260745
[300]   train's rmse: 0.000538043   train's RMSPE: 0.23686  valid's rmse: 0.000593699   valid's RMSPE: 0.259991
[350]   train's rmse: 0.000530558   train's RMSPE: 0.233565 valid's rmse: 0.00059338    valid's RMSPE: 0.259851
[400]   train's rmse: 0.000523236   train's RMSPE: 0.230342 valid's rmse: 0.000592491   valid's RMSPE: 0.259462
Early stopping, best iteration is:
[364]   train's rmse: 0.000528334   train's RMSPE: 0.232586 valid's rmse: 0.000592319   valid's RMSPE: 0.259387
Our out of folds RMSPE is 0.265, compared to 0.2352463789322846, giving gain 0.029753621067715424
Our cv fold scores are [0.279, 0.256, 0.261, 0.267, 0.259]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000430111   train's RMSPE: 0.233273 valid's rmse: 0.000467896   valid's RMSPE: 0.253851
[100]   train's rmse: 0.000410974   train's RMSPE: 0.222894 valid's rmse: 0.000450308   valid's RMSPE: 0.244308
[150]   train's rmse: 0.000401482   train's RMSPE: 0.217746 valid's rmse: 0.000444669   valid's RMSPE: 0.241249
[200]   train's rmse: 0.000394701   train's RMSPE: 0.214068 valid's rmse: 0.000442663   valid's RMSPE: 0.24016
[250]   train's rmse: 0.000388104   train's RMSPE: 0.21049  valid's rmse: 0.000440236   valid's RMSPE: 0.238844
[300]   train's rmse: 0.00038251    train's RMSPE: 0.207456 valid's rmse: 0.000438881   valid's RMSPE: 0.238109
[350]   train's rmse: 0.000377628   train's RMSPE: 0.204809 valid's rmse: 0.00043739    valid's RMSPE: 0.2373
[400]   train's rmse: 0.000373084   train's RMSPE: 0.202344 valid's rmse: 0.000436172   valid's RMSPE: 0.236639
[450]   train's rmse: 0.000369202   train's RMSPE: 0.200239 valid's rmse: 0.000435833   valid's RMSPE: 0.236455
[500]   train's rmse: 0.000365306   train's RMSPE: 0.198126 valid's rmse: 0.00043491    valid's RMSPE: 0.235954
[550]   train's rmse: 0.00036204    train's RMSPE: 0.196355 valid's rmse: 0.00043444    valid's RMSPE: 0.235699
Early stopping, best iteration is:
[545]   train's rmse: 0.000362381   train's RMSPE: 0.19654  valid's rmse: 0.000434307   valid's RMSPE: 0.235627
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000429461   train's RMSPE: 0.232837 valid's rmse: 0.000460611   valid's RMSPE: 0.250255
[100]   train's rmse: 0.000409081   train's RMSPE: 0.221788 valid's rmse: 0.000448583   valid's RMSPE: 0.24372
[150]   train's rmse: 0.000400761   train's RMSPE: 0.217277 valid's rmse: 0.000445531   valid's RMSPE: 0.242062
[200]   train's rmse: 0.000394394   train's RMSPE: 0.213825 valid's rmse: 0.000444651   valid's RMSPE: 0.241583
[250]   train's rmse: 0.000388222   train's RMSPE: 0.210479 valid's rmse: 0.000443192   valid's RMSPE: 0.240791
[300]   train's rmse: 0.000382206   train's RMSPE: 0.207217 valid's rmse: 0.000442357   valid's RMSPE: 0.240337
[350]   train's rmse: 0.000377042   train's RMSPE: 0.204418 valid's rmse: 0.000440543   valid's RMSPE: 0.239352
[400]   train's rmse: 0.000372448   train's RMSPE: 0.201927 valid's rmse: 0.000440375   valid's RMSPE: 0.23926
[450]   train's rmse: 0.000368545   train's RMSPE: 0.199811 valid's rmse: 0.00044114    valid's RMSPE: 0.239676
Early stopping, best iteration is:
[401]   train's rmse: 0.000372397   train's RMSPE: 0.201899 valid's rmse: 0.000440222   valid's RMSPE: 0.239177
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000432589   train's RMSPE: 0.234403 valid's rmse: 0.000451375   valid's RMSPE: 0.245779
[100]   train's rmse: 0.000413906   train's RMSPE: 0.224279 valid's rmse: 0.000440484   valid's RMSPE: 0.239849
[150]   train's rmse: 0.000404958   train's RMSPE: 0.219431 valid's rmse: 0.000437762   valid's RMSPE: 0.238367
[200]   train's rmse: 0.000397351   train's RMSPE: 0.215309 valid's rmse: 0.000437105   valid's RMSPE: 0.238009
[250]   train's rmse: 0.000390896   train's RMSPE: 0.211811 valid's rmse: 0.000435226   valid's RMSPE: 0.236986
[300]   train's rmse: 0.000385333   train's RMSPE: 0.208797 valid's rmse: 0.000434606   valid's RMSPE: 0.236649
[350]   train's rmse: 0.000380894   train's RMSPE: 0.206391 valid's rmse: 0.000435175   valid's RMSPE: 0.236958
Early stopping, best iteration is:
[309]   train's rmse: 0.000384462   train's RMSPE: 0.208324 valid's rmse: 0.000434274   valid's RMSPE: 0.236468
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000440703   train's RMSPE: 0.238942 valid's rmse: 0.000417088   valid's RMSPE: 0.226571
[100]   train's rmse: 0.000419876   train's RMSPE: 0.22765  valid's rmse: 0.000408072   valid's RMSPE: 0.221674
[150]   train's rmse: 0.000410697   train's RMSPE: 0.222673 valid's rmse: 0.000406021   valid's RMSPE: 0.220559
[200]   train's rmse: 0.000403617   train's RMSPE: 0.218835 valid's rmse: 0.00040581    valid's RMSPE: 0.220445
[250]   train's rmse: 0.000397828   train's RMSPE: 0.215696 valid's rmse: 0.000404016   valid's RMSPE: 0.21947
[300]   train's rmse: 0.000392225   train's RMSPE: 0.212658 valid's rmse: 0.000402878   valid's RMSPE: 0.218852
[350]   train's rmse: 0.000386813   train's RMSPE: 0.209724 valid's rmse: 0.000401766   valid's RMSPE: 0.218248
[400]   train's rmse: 0.000381422   train's RMSPE: 0.206801 valid's rmse: 0.000400845   valid's RMSPE: 0.217748
Early stopping, best iteration is:
[362]   train's rmse: 0.000385311   train's RMSPE: 0.208909 valid's rmse: 0.00040054    valid's RMSPE: 0.217582
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000437956   train's RMSPE: 0.237983 valid's rmse: 0.000422216   valid's RMSPE: 0.227303
[100]   train's rmse: 0.000419281   train's RMSPE: 0.227836 valid's rmse: 0.000412983   valid's RMSPE: 0.222332
[150]   train's rmse: 0.00041007    train's RMSPE: 0.22283  valid's rmse: 0.000409831   valid's RMSPE: 0.220635
[200]   train's rmse: 0.000402202   train's RMSPE: 0.218555 valid's rmse: 0.000407584   valid's RMSPE: 0.219426
[250]   train's rmse: 0.000395445   train's RMSPE: 0.214883 valid's rmse: 0.000406958   valid's RMSPE: 0.219088
[300]   train's rmse: 0.000390022   train's RMSPE: 0.211936 valid's rmse: 0.000406037   valid's RMSPE: 0.218593
Early stopping, best iteration is:
[288]   train's rmse: 0.000391207   train's RMSPE: 0.21258  valid's rmse: 0.000405673   valid's RMSPE: 0.218397

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.23, compared to 0.18588084352573406, giving gain 0.04411915647426595
Our cv fold scores are [0.236, 0.239, 0.236, 0.218, 0.218]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000509811   train's RMSPE: 0.259309 valid's rmse: 0.00053327    valid's RMSPE: 0.272249
[100]   train's rmse: 0.000487741   train's RMSPE: 0.248083 valid's rmse: 0.000517672   valid's RMSPE: 0.264286
[150]   train's rmse: 0.000476966   train's RMSPE: 0.242603 valid's rmse: 0.000514161   valid's RMSPE: 0.262493
[200]   train's rmse: 0.000467961   train's RMSPE: 0.238023 valid's rmse: 0.000510741   valid's RMSPE: 0.260747
[250]   train's rmse: 0.00046002    train's RMSPE: 0.233983 valid's rmse: 0.000509485   valid's RMSPE: 0.260106
[300]   train's rmse: 0.00045357    train's RMSPE: 0.230703 valid's rmse: 0.000506574   valid's RMSPE: 0.25862
[350]   train's rmse: 0.000448121   train's RMSPE: 0.227931 valid's rmse: 0.000505014   valid's RMSPE: 0.257824
[400]   train's rmse: 0.000442681   train's RMSPE: 0.225164 valid's rmse: 0.000503781   valid's RMSPE: 0.257194
[450]   train's rmse: 0.000438041   train's RMSPE: 0.222804 valid's rmse: 0.000503567   valid's RMSPE: 0.257085
[500]   train's rmse: 0.000433547   train's RMSPE: 0.220518 valid's rmse: 0.000502695   valid's RMSPE: 0.25664
[550]   train's rmse: 0.000428989   train's RMSPE: 0.2182   valid's rmse: 0.000501747   valid's RMSPE: 0.256156
[600]   train's rmse: 0.000424781   train's RMSPE: 0.21606  valid's rmse: 0.000500041   valid's RMSPE: 0.255285
[650]   train's rmse: 0.000421061   train's RMSPE: 0.214168 valid's rmse: 0.00049895    valid's RMSPE: 0.254728
[700]   train's rmse: 0.000417705   train's RMSPE: 0.21246  valid's rmse: 0.000499018   valid's RMSPE: 0.254762
Early stopping, best iteration is:
[669]   train's rmse: 0.000419842   train's RMSPE: 0.213547 valid's rmse: 0.000498301   valid's RMSPE: 0.254396
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000504709   train's RMSPE: 0.256708 valid's rmse: 0.000548306   valid's RMSPE: 0.279952
[100]   train's rmse: 0.000484521   train's RMSPE: 0.24644  valid's rmse: 0.0005377 valid's RMSPE: 0.274537
[150]   train's rmse: 0.000474146   train's RMSPE: 0.241163 valid's rmse: 0.00053526    valid's RMSPE: 0.273291
[200]   train's rmse: 0.000465469   train's RMSPE: 0.236749 valid's rmse: 0.000534155   valid's RMSPE: 0.272726
[250]   train's rmse: 0.00045797    train's RMSPE: 0.232935 valid's rmse: 0.000531676   valid's RMSPE: 0.271461
[300]   train's rmse: 0.000451602   train's RMSPE: 0.229696 valid's rmse: 0.000530185   valid's RMSPE: 0.2707
[350]   train's rmse: 0.000446173   train's RMSPE: 0.226935 valid's rmse: 0.000529539   valid's RMSPE: 0.27037
[400]   train's rmse: 0.00044069    train's RMSPE: 0.224146 valid's rmse: 0.000529249   valid's RMSPE: 0.270222
[450]   train's rmse: 0.000435914   train's RMSPE: 0.221717 valid's rmse: 0.000528702   valid's RMSPE: 0.269943
Early stopping, best iteration is:
[428]   train's rmse: 0.000438044   train's RMSPE: 0.2228   valid's rmse: 0.000528397   valid's RMSPE: 0.269786
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000512479   train's RMSPE: 0.260254 valid's rmse: 0.000509737   valid's RMSPE: 0.26186
[100]   train's rmse: 0.000491841   train's RMSPE: 0.249774 valid's rmse: 0.000498237   valid's RMSPE: 0.255953
[150]   train's rmse: 0.00048211    train's RMSPE: 0.244832 valid's rmse: 0.000496918   valid's RMSPE: 0.255275
[200]   train's rmse: 0.000472649   train's RMSPE: 0.240027 valid's rmse: 0.000493745   valid's RMSPE: 0.253645
[250]   train's rmse: 0.000465577   train's RMSPE: 0.236436 valid's rmse: 0.00049128    valid's RMSPE: 0.252379
Early stopping, best iteration is:
[243]   train's rmse: 0.00046635    train's RMSPE: 0.236828 valid's rmse: 0.000490901   valid's RMSPE: 0.252184
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000515909   train's RMSPE: 0.263375 valid's rmse: 0.000492388   valid's RMSPE: 0.247676
[100]   train's rmse: 0.000493681   train's RMSPE: 0.252027 valid's rmse: 0.000480763   valid's RMSPE: 0.241828
[150]   train's rmse: 0.000482455   train's RMSPE: 0.246296 valid's rmse: 0.000478192   valid's RMSPE: 0.240535
[200]   train's rmse: 0.0004737 train's RMSPE: 0.241827 valid's rmse: 0.000477539   valid's RMSPE: 0.240207
[250]   train's rmse: 0.000466209   train's RMSPE: 0.238003 valid's rmse: 0.000475623   valid's RMSPE: 0.239243
[300]   train's rmse: 0.000459745   train's RMSPE: 0.234703 valid's rmse: 0.000475869   valid's RMSPE: 0.239367
Early stopping, best iteration is:
[262]   train's rmse: 0.00046453    train's RMSPE: 0.237146 valid's rmse: 0.000475278   valid's RMSPE: 0.23907
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000512518   train's RMSPE: 0.261114 valid's rmse: 0.000525835   valid's RMSPE: 0.266696
[100]   train's rmse: 0.000492654   train's RMSPE: 0.250994 valid's rmse: 0.000515652   valid's RMSPE: 0.261531
[150]   train's rmse: 0.000480921   train's RMSPE: 0.245016 valid's rmse: 0.000516212   valid's RMSPE: 0.261815
Early stopping, best iteration is:
[118]   train's rmse: 0.000487667   train's RMSPE: 0.248453 valid's rmse: 0.000513919   valid's RMSPE: 0.260652
Our out of folds RMSPE is 0.255, compared to 0.2073331029306695, giving gain 0.0476668970693305
Our cv fold scores are [0.254, 0.27, 0.252, 0.239, 0.261]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000356952   train's RMSPE: 0.228317 valid's rmse: 0.000396208   valid's RMSPE: 0.257044
[100]   train's rmse: 0.000339253   train's RMSPE: 0.216996 valid's rmse: 0.000380672   valid's RMSPE: 0.246965
[150]   train's rmse: 0.000332083   train's RMSPE: 0.21241  valid's rmse: 0.00037613    valid's RMSPE: 0.244018
[200]   train's rmse: 0.000325732   train's RMSPE: 0.208348 valid's rmse: 0.000372461   valid's RMSPE: 0.241638
[250]   train's rmse: 0.000320849   train's RMSPE: 0.205225 valid's rmse: 0.000370056   valid's RMSPE: 0.240078
[300]   train's rmse: 0.00031711    train's RMSPE: 0.202833 valid's rmse: 0.000369607   valid's RMSPE: 0.239787
[350]   train's rmse: 0.000313452   train's RMSPE: 0.200493 valid's rmse: 0.000368784   valid's RMSPE: 0.239252
[400]   train's rmse: 0.000310177   train's RMSPE: 0.198398 valid's rmse: 0.000368282   valid's RMSPE: 0.238927
Early stopping, best iteration is:
[375]   train's rmse: 0.000311695   train's RMSPE: 0.199369 valid's rmse: 0.00036801    valid's RMSPE: 0.23875
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000359292   train's RMSPE: 0.229248 valid's rmse: 0.000391718   valid's RMSPE: 0.25655
[100]   train's rmse: 0.000340377   train's RMSPE: 0.21718  valid's rmse: 0.000376653   valid's RMSPE: 0.246683
[150]   train's rmse: 0.000332985   train's RMSPE: 0.212463 valid's rmse: 0.000373564   valid's RMSPE: 0.24466
[200]   train's rmse: 0.000327402   train's RMSPE: 0.208901 valid's rmse: 0.000370881   valid's RMSPE: 0.242903
[250]   train's rmse: 0.000323206   train's RMSPE: 0.206223 valid's rmse: 0.000371028   valid's RMSPE: 0.242999
Early stopping, best iteration is:
[203]   train's rmse: 0.000327177   train's RMSPE: 0.208757 valid's rmse: 0.000370624   valid's RMSPE: 0.242735
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000366005   train's RMSPE: 0.234963 valid's rmse: 0.000342851   valid's RMSPE: 0.21924
[100]   train's rmse: 0.00034619    train's RMSPE: 0.222243 valid's rmse: 0.000333747   valid's RMSPE: 0.213418
[150]   train's rmse: 0.000338544   train's RMSPE: 0.217334 valid's rmse: 0.000331713   valid's RMSPE: 0.212118
[200]   train's rmse: 0.000333376   train's RMSPE: 0.214016 valid's rmse: 0.000330358   valid's RMSPE: 0.211252
[250]   train's rmse: 0.000328936   train's RMSPE: 0.211166 valid's rmse: 0.000330063   valid's RMSPE: 0.211063
Early stopping, best iteration is:
[205]   train's rmse: 0.000332942   train's RMSPE: 0.213738 valid's rmse: 0.000329908   valid's RMSPE: 0.210964
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000365702   train's RMSPE: 0.234822 valid's rmse: 0.000348875   valid's RMSPE: 0.222889
[100]   train's rmse: 0.000346211   train's RMSPE: 0.222306 valid's rmse: 0.000336785   valid's RMSPE: 0.215165
[150]   train's rmse: 0.00033929    train's RMSPE: 0.217862 valid's rmse: 0.000335356   valid's RMSPE: 0.214252
[200]   train's rmse: 0.000333739   train's RMSPE: 0.214298 valid's rmse: 0.000334544   valid's RMSPE: 0.213733
[250]   train's rmse: 0.000329286   train's RMSPE: 0.211438 valid's rmse: 0.000334314   valid's RMSPE: 0.213587
[300]   train's rmse: 0.000325013   train's RMSPE: 0.208695 valid's rmse: 0.000333161   valid's RMSPE: 0.21285
[350]   train's rmse: 0.000321041   train's RMSPE: 0.206144 valid's rmse: 0.00033263    valid's RMSPE: 0.21251
[400]   train's rmse: 0.000317733   train's RMSPE: 0.20402  valid's rmse: 0.000332152   valid's RMSPE: 0.212205
Early stopping, best iteration is:
[389]   train's rmse: 0.000318515   train's RMSPE: 0.204523 valid's rmse: 0.000332065   valid's RMSPE: 0.21215
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000364635   train's RMSPE: 0.235388 valid's rmse: 0.00036751    valid's RMSPE: 0.229653
[100]   train's rmse: 0.000344059   train's RMSPE: 0.222105 valid's rmse: 0.000355329   valid's RMSPE: 0.222041
[150]   train's rmse: 0.000336592   train's RMSPE: 0.217285 valid's rmse: 0.000353523   valid's RMSPE: 0.220913
[200]   train's rmse: 0.000331448   train's RMSPE: 0.213965 valid's rmse: 0.000352891   valid's RMSPE: 0.220518
[250]   train's rmse: 0.000326893   train's RMSPE: 0.211024 valid's rmse: 0.000351551   valid's RMSPE: 0.219681
[300]   train's rmse: 0.000322709   train's RMSPE: 0.208323 valid's rmse: 0.000350918   valid's RMSPE: 0.219285
[350]   train's rmse: 0.000319018   train's RMSPE: 0.20594  valid's rmse: 0.000350714   valid's RMSPE: 0.219157
[400]   train's rmse: 0.000315422   train's RMSPE: 0.203619 valid's rmse: 0.000349857   valid's RMSPE: 0.218622
[450]   train's rmse: 0.00031208    train's RMSPE: 0.201461 valid's rmse: 0.000349336   valid's RMSPE: 0.218296
[500]   train's rmse: 0.000309241   train's RMSPE: 0.199629 valid's rmse: 0.000349146   valid's RMSPE: 0.218178
[550]   train's rmse: 0.000306617   train's RMSPE: 0.197935 valid's rmse: 0.000349325   valid's RMSPE: 0.21829
Early stopping, best iteration is:
[505]   train's rmse: 0.000309018   train's RMSPE: 0.199485 valid's rmse: 0.000348939   valid's RMSPE: 0.218048
Our out of folds RMSPE is 0.225, compared to 0.1820360318807276, giving gain 0.04296396811927242
Our cv fold scores are [0.239, 0.243, 0.211, 0.212, 0.218]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000458083   train's RMSPE: 0.257949 valid's rmse: 0.000507298   valid's RMSPE: 0.274822
[100]   train's rmse: 0.000434666   train's RMSPE: 0.244763 valid's rmse: 0.000494276   valid's RMSPE: 0.267767
[150]   train's rmse: 0.000423453   train's RMSPE: 0.238449 valid's rmse: 0.000489306   valid's RMSPE: 0.265075
[200]   train's rmse: 0.000414848   train's RMSPE: 0.233604 valid's rmse: 0.000488022   valid's RMSPE: 0.264379
[250]   train's rmse: 0.000407502   train's RMSPE: 0.229467 valid's rmse: 0.000483455   valid's RMSPE: 0.261905
[300]   train's rmse: 0.000401289   train's RMSPE: 0.225968 valid's rmse: 0.000482082   valid's RMSPE: 0.261161
[350]   train's rmse: 0.000395343   train's RMSPE: 0.22262  valid's rmse: 0.000480966   valid's RMSPE: 0.260557
[400]   train's rmse: 0.000389761   train's RMSPE: 0.219477 valid's rmse: 0.000479409   valid's RMSPE: 0.259713
[450]   train's rmse: 0.00038554    train's RMSPE: 0.2171   valid's rmse: 0.000479033   valid's RMSPE: 0.259509
Early stopping, best iteration is:
[438]   train's rmse: 0.000386348   train's RMSPE: 0.217555 valid's rmse: 0.000478095   valid's RMSPE: 0.259001
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000460466   train's RMSPE: 0.25632  valid's rmse: 0.00049442    valid's RMSPE: 0.280722
[100]   train's rmse: 0.0004369 train's RMSPE: 0.243202 valid's rmse: 0.000478539   valid's RMSPE: 0.271705
[150]   train's rmse: 0.000426932   train's RMSPE: 0.237654 valid's rmse: 0.000474134   valid's RMSPE: 0.269204
[200]   train's rmse: 0.000417218   train's RMSPE: 0.232246 valid's rmse: 0.000471553   valid's RMSPE: 0.267738
[250]   train's rmse: 0.000410552   train's RMSPE: 0.228536 valid's rmse: 0.00047086    valid's RMSPE: 0.267345
[300]   train's rmse: 0.000404709   train's RMSPE: 0.225283 valid's rmse: 0.000469996   valid's RMSPE: 0.266854
[350]   train's rmse: 0.000399001   train's RMSPE: 0.222106 valid's rmse: 0.000469865   valid's RMSPE: 0.26678
[400]   train's rmse: 0.000394071   train's RMSPE: 0.219361 valid's rmse: 0.00046796    valid's RMSPE: 0.265699
[450]   train's rmse: 0.000389323   train's RMSPE: 0.216718 valid's rmse: 0.000467973   valid's RMSPE: 0.265706
Early stopping, best iteration is:
[413]   train's rmse: 0.000392552   train's RMSPE: 0.218516 valid's rmse: 0.000467405   valid's RMSPE: 0.265383
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000470174   train's RMSPE: 0.26157  valid's rmse: 0.000445851   valid's RMSPE: 0.253719
[100]   train's rmse: 0.000445797   train's RMSPE: 0.248008 valid's rmse: 0.000432949   valid's RMSPE: 0.246377
[150]   train's rmse: 0.000434793   train's RMSPE: 0.241887 valid's rmse: 0.000429427   valid's RMSPE: 0.244373
Early stopping, best iteration is:
[147]   train's rmse: 0.000435194   train's RMSPE: 0.24211  valid's rmse: 0.000429332   valid's RMSPE: 0.244319
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000471379   train's RMSPE: 0.262959 valid's rmse: 0.000446624   valid's RMSPE: 0.251479
[100]   train's rmse: 0.000447099   train's RMSPE: 0.249415 valid's rmse: 0.000430975   valid's RMSPE: 0.242667
[150]   train's rmse: 0.000437577   train's RMSPE: 0.244102 valid's rmse: 0.000428383   valid's RMSPE: 0.241208
[200]   train's rmse: 0.000428928   train's RMSPE: 0.239278 valid's rmse: 0.000428957   valid's RMSPE: 0.241531
Early stopping, best iteration is:
[152]   train's rmse: 0.00043715    train's RMSPE: 0.243864 valid's rmse: 0.000428028   valid's RMSPE: 0.241008
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000463149   train's RMSPE: 0.259605 valid's rmse: 0.000483699   valid's RMSPE: 0.267175
[100]   train's rmse: 0.000440592   train's RMSPE: 0.246961 valid's rmse: 0.000469752   valid's RMSPE: 0.259471
[150]   train's rmse: 0.000429377   train's RMSPE: 0.240675 valid's rmse: 0.000464743   valid's RMSPE: 0.256704
[200]   train's rmse: 0.000421435   train's RMSPE: 0.236223 valid's rmse: 0.00046295    valid's RMSPE: 0.255714
[250]   train's rmse: 0.000413722   train's RMSPE: 0.231899 valid's rmse: 0.000461052   valid's RMSPE: 0.254666
[300]   train's rmse: 0.000407993   train's RMSPE: 0.228689 valid's rmse: 0.00046142    valid's RMSPE: 0.254869
Early stopping, best iteration is:
[268]   train's rmse: 0.000411479   train's RMSPE: 0.230642 valid's rmse: 0.000460379   valid's RMSPE: 0.254294
Our out of folds RMSPE is 0.253, compared to 0.21683397615845648, giving gain 0.03616602384154352
Our cv fold scores are [0.259, 0.265, 0.244, 0.241, 0.254]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000537405   train's RMSPE: 0.260913 valid's rmse: 0.000576788   valid's RMSPE: 0.275982
[100]   train's rmse: 0.000512389   train's RMSPE: 0.248767 valid's rmse: 0.000563651   valid's RMSPE: 0.269697
[150]   train's rmse: 0.000499026   train's RMSPE: 0.24228  valid's rmse: 0.000559439   valid's RMSPE: 0.267681
[200]   train's rmse: 0.000487799   train's RMSPE: 0.236829 valid's rmse: 0.000554911   valid's RMSPE: 0.265515
[250]   train's rmse: 0.000478858   train's RMSPE: 0.232488 valid's rmse: 0.000551539   valid's RMSPE: 0.263902
[300]   train's rmse: 0.00047041    train's RMSPE: 0.228387 valid's rmse: 0.000549146   valid's RMSPE: 0.262756
[350]   train's rmse: 0.000463087   train's RMSPE: 0.224831 valid's rmse: 0.000547998   valid's RMSPE: 0.262207
[400]   train's rmse: 0.000457036   train's RMSPE: 0.221893 valid's rmse: 0.000548984   valid's RMSPE: 0.262679
Early stopping, best iteration is:
[356]   train's rmse: 0.000462374   train's RMSPE: 0.224485 valid's rmse: 0.000547744   valid's RMSPE: 0.262085
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000538024   train's RMSPE: 0.259672 valid's rmse: 0.000579501   valid's RMSPE: 0.283921
[100]   train's rmse: 0.00051363    train's RMSPE: 0.247899 valid's rmse: 0.000569232   valid's RMSPE: 0.27889
[150]   train's rmse: 0.000500677   train's RMSPE: 0.241647 valid's rmse: 0.00056456    valid's RMSPE: 0.276601
[200]   train's rmse: 0.000490488   train's RMSPE: 0.236729 valid's rmse: 0.000560659   valid's RMSPE: 0.27469
[250]   train's rmse: 0.0004826 train's RMSPE: 0.232922 valid's rmse: 0.000559461   valid's RMSPE: 0.274103
[300]   train's rmse: 0.000474694   train's RMSPE: 0.229106 valid's rmse: 0.000555702   valid's RMSPE: 0.272261
[350]   train's rmse: 0.000467811   train's RMSPE: 0.225784 valid's rmse: 0.000554188   valid's RMSPE: 0.271519
[400]   train's rmse: 0.00046211    train's RMSPE: 0.223033 valid's rmse: 0.000555074   valid's RMSPE: 0.271953
Early stopping, best iteration is:
[360]   train's rmse: 0.000466723   train's RMSPE: 0.225259 valid's rmse: 0.000553756   valid's RMSPE: 0.271307
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000549544   train's RMSPE: 0.265739 valid's rmse: 0.000528384   valid's RMSPE: 0.256946
[100]   train's rmse: 0.000522551   train's RMSPE: 0.252686 valid's rmse: 0.000511119   valid's RMSPE: 0.248551
[150]   train's rmse: 0.000508981   train's RMSPE: 0.246124 valid's rmse: 0.000505203   valid's RMSPE: 0.245674
[200]   train's rmse: 0.000497616   train's RMSPE: 0.240628 valid's rmse: 0.000501399   valid's RMSPE: 0.243824
[250]   train's rmse: 0.000489522   train's RMSPE: 0.236715 valid's rmse: 0.000500122   valid's RMSPE: 0.243203
[300]   train's rmse: 0.000481379   train's RMSPE: 0.232777 valid's rmse: 0.000499631   valid's RMSPE: 0.242964
[350]   train's rmse: 0.00047501    train's RMSPE: 0.229697 valid's rmse: 0.000497232   valid's RMSPE: 0.241798
[400]   train's rmse: 0.000468754   train's RMSPE: 0.226672 valid's rmse: 0.000496987   valid's RMSPE: 0.241678
[450]   train's rmse: 0.000462387   train's RMSPE: 0.223593 valid's rmse: 0.000496672   valid's RMSPE: 0.241525
Early stopping, best iteration is:
[419]   train's rmse: 0.000466349   train's RMSPE: 0.225509 valid's rmse: 0.000495897   valid's RMSPE: 0.241148
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000548101   train's RMSPE: 0.264629 valid's rmse: 0.00054142    valid's RMSPE: 0.2649
[100]   train's rmse: 0.000522338   train's RMSPE: 0.25219  valid's rmse: 0.000520595   valid's RMSPE: 0.254711
[150]   train's rmse: 0.000508051   train's RMSPE: 0.245292 valid's rmse: 0.000513643   valid's RMSPE: 0.251309
[200]   train's rmse: 0.000498183   train's RMSPE: 0.240528 valid's rmse: 0.000513183   valid's RMSPE: 0.251084
Early stopping, best iteration is:
[161]   train's rmse: 0.000505488   train's RMSPE: 0.244055 valid's rmse: 0.000512341   valid's RMSPE: 0.250672
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000544548   train's RMSPE: 0.264659 valid's rmse: 0.000552976   valid's RMSPE: 0.26344
[100]   train's rmse: 0.000518581   train's RMSPE: 0.252039 valid's rmse: 0.000541886   valid's RMSPE: 0.258157
[150]   train's rmse: 0.00050572    train's RMSPE: 0.245788 valid's rmse: 0.000538184   valid's RMSPE: 0.256393
[200]   train's rmse: 0.000494255   train's RMSPE: 0.240215 valid's rmse: 0.000536072   valid's RMSPE: 0.255387
[250]   train's rmse: 0.000485442   train's RMSPE: 0.235933 valid's rmse: 0.000535036   valid's RMSPE: 0.254894
[300]   train's rmse: 0.000477326   train's RMSPE: 0.231988 valid's rmse: 0.000534276   valid's RMSPE: 0.254531
[350]   train's rmse: 0.000470796   train's RMSPE: 0.228814 valid's rmse: 0.000533135   valid's RMSPE: 0.253988
[400]   train's rmse: 0.000465008   train's RMSPE: 0.226001 valid's rmse: 0.000531293   valid's RMSPE: 0.25311
Early stopping, best iteration is:
[398]   train's rmse: 0.000465215   train's RMSPE: 0.226102 valid's rmse: 0.000531027   valid's RMSPE: 0.252984
Our out of folds RMSPE is 0.256, compared to 0.21855958211975457, giving gain 0.037440417880245436
Our cv fold scores are [0.262, 0.271, 0.241, 0.251, 0.253]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000581782   train's RMSPE: 0.2095   valid's rmse: 0.000617178   valid's RMSPE: 0.219567
[100]   train's rmse: 0.000542805   train's RMSPE: 0.195464 valid's rmse: 0.000597274   valid's RMSPE: 0.212486
[150]   train's rmse: 0.000528932   train's RMSPE: 0.190468 valid's rmse: 0.000592981   valid's RMSPE: 0.210959
[200]   train's rmse: 0.000518597   train's RMSPE: 0.186747 valid's rmse: 0.000590925   valid's RMSPE: 0.210227
[250]   train's rmse: 0.000509795   train's RMSPE: 0.183577 valid's rmse: 0.000590227   valid's RMSPE: 0.209979
[300]   train's rmse: 0.000501791   train's RMSPE: 0.180695 valid's rmse: 0.000589193   valid's RMSPE: 0.209611
[350]   train's rmse: 0.000494907   train's RMSPE: 0.178216 valid's rmse: 0.000588486   valid's RMSPE: 0.20936
[400]   train's rmse: 0.000488443   train's RMSPE: 0.175889 valid's rmse: 0.000588449   valid's RMSPE: 0.209346
[450]   train's rmse: 0.000481905   train's RMSPE: 0.173534 valid's rmse: 0.000587807   valid's RMSPE: 0.209118
Early stopping, best iteration is:
[429]   train's rmse: 0.000484464   train's RMSPE: 0.174456 valid's rmse: 0.00058741    valid's RMSPE: 0.208977
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000582123   train's RMSPE: 0.207613 valid's rmse: 0.000626697   valid's RMSPE: 0.231505
[100]   train's rmse: 0.000547738   train's RMSPE: 0.19535  valid's rmse: 0.000590935   valid's RMSPE: 0.218294
[150]   train's rmse: 0.000534341   train's RMSPE: 0.190572 valid's rmse: 0.000586729   valid's RMSPE: 0.216741
[200]   train's rmse: 0.000524247   train's RMSPE: 0.186972 valid's rmse: 0.000585769   valid's RMSPE: 0.216386
[250]   train's rmse: 0.000514615   train's RMSPE: 0.183537 valid's rmse: 0.000582179   valid's RMSPE: 0.21506
[300]   train's rmse: 0.000506749   train's RMSPE: 0.180731 valid's rmse: 0.000579139   valid's RMSPE: 0.213937
[350]   train's rmse: 0.000499312   train's RMSPE: 0.178079 valid's rmse: 0.000576535   valid's RMSPE: 0.212975
[400]   train's rmse: 0.000492604   train's RMSPE: 0.175687 valid's rmse: 0.000575242   valid's RMSPE: 0.212497
[450]   train's rmse: 0.0004861 train's RMSPE: 0.173367 valid's rmse: 0.000573467   valid's RMSPE: 0.211842
[500]   train's rmse: 0.000480444   train's RMSPE: 0.17135  valid's rmse: 0.00057312    valid's RMSPE: 0.211714
[550]   train's rmse: 0.000474642   train's RMSPE: 0.16928  valid's rmse: 0.000571291   valid's RMSPE: 0.211038
[600]   train's rmse: 0.000469865   train's RMSPE: 0.167577 valid's rmse: 0.000569594   valid's RMSPE: 0.210411
[650]   train's rmse: 0.000465071   train's RMSPE: 0.165867 valid's rmse: 0.000568846   valid's RMSPE: 0.210135
[700]   train's rmse: 0.000460382   train's RMSPE: 0.164194 valid's rmse: 0.000568263   valid's RMSPE: 0.20992
[750]   train's rmse: 0.000455852   train's RMSPE: 0.162579 valid's rmse: 0.000568594   valid's RMSPE: 0.210042
Early stopping, best iteration is:
[703]   train's rmse: 0.000460029   train's RMSPE: 0.164069 valid's rmse: 0.000568012   valid's RMSPE: 0.209827
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000587149   train's RMSPE: 0.21058  valid's rmse: 0.000581324   valid's RMSPE: 0.210195
[100]   train's rmse: 0.000550563   train's RMSPE: 0.197458 valid's rmse: 0.000561208   valid's RMSPE: 0.202922
[150]   train's rmse: 0.000537571   train's RMSPE: 0.192799 valid's rmse: 0.000557619   valid's RMSPE: 0.201624
[200]   train's rmse: 0.000526811   train's RMSPE: 0.18894  valid's rmse: 0.000558387   valid's RMSPE: 0.201902
Early stopping, best iteration is:
[187]   train's rmse: 0.000529224   train's RMSPE: 0.189805 valid's rmse: 0.000556606   valid's RMSPE: 0.201258
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000584502   train's RMSPE: 0.209694 valid's rmse: 0.000596987   valid's RMSPE: 0.215603
[100]   train's rmse: 0.000545788   train's RMSPE: 0.195804 valid's rmse: 0.00057097    valid's RMSPE: 0.206207
[150]   train's rmse: 0.000532721   train's RMSPE: 0.191117 valid's rmse: 0.000570264   valid's RMSPE: 0.205952
Early stopping, best iteration is:
[133]   train's rmse: 0.000536551   train's RMSPE: 0.192491 valid's rmse: 0.000569798   valid's RMSPE: 0.205784
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000586306   train's RMSPE: 0.212247 valid's rmse: 0.000580796   valid's RMSPE: 0.202078
[100]   train's rmse: 0.000550904   train's RMSPE: 0.199431 valid's rmse: 0.000566277   valid's RMSPE: 0.197026
Early stopping, best iteration is:
[97]    train's rmse: 0.000551888   train's RMSPE: 0.199787 valid's rmse: 0.000565992   valid's RMSPE: 0.196927
Our out of folds RMSPE is 0.205, compared to 0.1819471504420077, giving gain 0.023052849557992278
Our cv fold scores are [0.209, 0.21, 0.201, 0.206, 0.197]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000566734   train's RMSPE: 0.242855 valid's rmse: 0.00060816    valid's RMSPE: 0.264038
[100]   train's rmse: 0.00053628    train's RMSPE: 0.229805 valid's rmse: 0.000586685   valid's RMSPE: 0.254714
[150]   train's rmse: 0.000521165   train's RMSPE: 0.223328 valid's rmse: 0.000581427   valid's RMSPE: 0.252432
[200]   train's rmse: 0.000509479   train's RMSPE: 0.21832  valid's rmse: 0.000579331   valid's RMSPE: 0.251522
[250]   train's rmse: 0.000500124   train's RMSPE: 0.214311 valid's rmse: 0.000578194   valid's RMSPE: 0.251028
[300]   train's rmse: 0.00049039    train's RMSPE: 0.21014  valid's rmse: 0.000575329   valid's RMSPE: 0.249784
[350]   train's rmse: 0.000482867   train's RMSPE: 0.206916 valid's rmse: 0.000575986   valid's RMSPE: 0.250069
Early stopping, best iteration is:
[304]   train's rmse: 0.000489577   train's RMSPE: 0.209791 valid's rmse: 0.000574966   valid's RMSPE: 0.249627
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00056408    train's RMSPE: 0.242116 valid's rmse: 0.000602345   valid's RMSPE: 0.259827
[100]   train's rmse: 0.000532044   train's RMSPE: 0.228365 valid's rmse: 0.000590182   valid's RMSPE: 0.254581
Early stopping, best iteration is:
[92]    train's rmse: 0.000534361   train's RMSPE: 0.22936  valid's rmse: 0.000588919   valid's RMSPE: 0.254036
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00057247    train's RMSPE: 0.245755 valid's rmse: 0.000564963   valid's RMSPE: 0.243552
[100]   train's rmse: 0.000539921   train's RMSPE: 0.231782 valid's rmse: 0.000547116   valid's RMSPE: 0.235858
[150]   train's rmse: 0.000524914   train's RMSPE: 0.22534  valid's rmse: 0.000545541   valid's RMSPE: 0.235179
[200]   train's rmse: 0.00051326    train's RMSPE: 0.220337 valid's rmse: 0.000545363   valid's RMSPE: 0.235102
[250]   train's rmse: 0.000504157   train's RMSPE: 0.216429 valid's rmse: 0.000545537   valid's RMSPE: 0.235177
Early stopping, best iteration is:
[225]   train's rmse: 0.000508623   train's RMSPE: 0.218346 valid's rmse: 0.00054477    valid's RMSPE: 0.234847
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000572534   train's RMSPE: 0.246019 valid's rmse: 0.000586165   valid's RMSPE: 0.251726
[100]   train's rmse: 0.000541195   train's RMSPE: 0.232552 valid's rmse: 0.00056543    valid's RMSPE: 0.242822
[150]   train's rmse: 0.000528371   train's RMSPE: 0.227042 valid's rmse: 0.000564979   valid's RMSPE: 0.242628
Early stopping, best iteration is:
[131]   train's rmse: 0.000532645   train's RMSPE: 0.228878 valid's rmse: 0.000563772   valid's RMSPE: 0.24211
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000572878   train's RMSPE: 0.247207 valid's rmse: 0.000571769   valid's RMSPE: 0.241343
[100]   train's rmse: 0.000543083   train's RMSPE: 0.23435  valid's rmse: 0.000555619   valid's RMSPE: 0.234526
[150]   train's rmse: 0.000528723   train's RMSPE: 0.228153 valid's rmse: 0.000551832   valid's RMSPE: 0.232927
[200]   train's rmse: 0.000517586   train's RMSPE: 0.223348 valid's rmse: 0.000552707   valid's RMSPE: 0.233297
Early stopping, best iteration is:
[161]   train's rmse: 0.000526152   train's RMSPE: 0.227044 valid's rmse: 0.000551391   valid's RMSPE: 0.232741
Our out of folds RMSPE is 0.243, compared to 0.21239050356685427, giving gain 0.030609496433145728
Our cv fold scores are [0.25, 0.254, 0.235, 0.242, 0.233]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00125597    train's RMSPE: 0.25398  valid's rmse: 0.00133036    valid's RMSPE: 0.269266
[100]   train's rmse: 0.00120359    train's RMSPE: 0.243388 valid's rmse: 0.00131837    valid's RMSPE: 0.266839
Early stopping, best iteration is:
[82]    train's rmse: 0.0012181 train's RMSPE: 0.246322 valid's rmse: 0.0013169 valid's RMSPE: 0.266541
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00125382    train's RMSPE: 0.253216 valid's rmse: 0.00131614    valid's RMSPE: 0.267762
[100]   train's rmse: 0.00120414    train's RMSPE: 0.243184 valid's rmse: 0.00130231    valid's RMSPE: 0.264947
[150]   train's rmse: 0.00116878    train's RMSPE: 0.236042 valid's rmse: 0.00130049    valid's RMSPE: 0.264578
[200]   train's rmse: 0.00114183    train's RMSPE: 0.230599 valid's rmse: 0.00130328    valid's RMSPE: 0.265147
Early stopping, best iteration is:
[165]   train's rmse: 0.00115989    train's RMSPE: 0.234248 valid's rmse: 0.00129964    valid's RMSPE: 0.264405
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00124894    train's RMSPE: 0.251835 valid's rmse: 0.00136328    valid's RMSPE: 0.279056
[100]   train's rmse: 0.00120059    train's RMSPE: 0.242087 valid's rmse: 0.00135766    valid's RMSPE: 0.277904
[150]   train's rmse: 0.00117056    train's RMSPE: 0.236031 valid's rmse: 0.00135283    valid's RMSPE: 0.276916
[200]   train's rmse: 0.00114545    train's RMSPE: 0.230968 valid's rmse: 0.00135801    valid's RMSPE: 0.277976
Early stopping, best iteration is:
[158]   train's rmse: 0.00116658    train's RMSPE: 0.235228 valid's rmse: 0.0013512 valid's RMSPE: 0.276582
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00127376    train's RMSPE: 0.25714  valid's rmse: 0.00126053    valid's RMSPE: 0.256853
[100]   train's rmse: 0.00122271    train's RMSPE: 0.246834 valid's rmse: 0.00125068    valid's RMSPE: 0.254844
[150]   train's rmse: 0.00119322    train's RMSPE: 0.240881 valid's rmse: 0.00124779    valid's RMSPE: 0.254256
Early stopping, best iteration is:
[124]   train's rmse: 0.00120686    train's RMSPE: 0.243635 valid's rmse: 0.00124683    valid's RMSPE: 0.25406
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00127917    train's RMSPE: 0.260412 valid's rmse: 0.00123468    valid's RMSPE: 0.243075
[100]   train's rmse: 0.00123   train's RMSPE: 0.250401 valid's rmse: 0.00121841    valid's RMSPE: 0.239872
Early stopping, best iteration is:
[89]    train's rmse: 0.00123779    train's RMSPE: 0.251987 valid's rmse: 0.00121725    valid's RMSPE: 0.239644
Our out of folds RMSPE is 0.261, compared to 0.24476881619667987, giving gain 0.016231183803320143
Our cv fold scores are [0.267, 0.264, 0.277, 0.254, 0.24]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000603909   train's RMSPE: 0.209152 valid's rmse: 0.000662158   valid's RMSPE: 0.227311
[100]   train's rmse: 0.000573538   train's RMSPE: 0.198633 valid's rmse: 0.000641397   valid's RMSPE: 0.220184
[150]   train's rmse: 0.00056009    train's RMSPE: 0.193976 valid's rmse: 0.000636365   valid's RMSPE: 0.218456
[200]   train's rmse: 0.000548184   train's RMSPE: 0.189853 valid's rmse: 0.000634728   valid's RMSPE: 0.217895
[250]   train's rmse: 0.000538035   train's RMSPE: 0.186338 valid's rmse: 0.000633519   valid's RMSPE: 0.217479
[300]   train's rmse: 0.000530174   train's RMSPE: 0.183615 valid's rmse: 0.000632618   valid's RMSPE: 0.21717
Early stopping, best iteration is:
[273]   train's rmse: 0.000533994   train's RMSPE: 0.184938 valid's rmse: 0.000632236   valid's RMSPE: 0.217039
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000603613   train's RMSPE: 0.208383 valid's rmse: 0.000674021   valid's RMSPE: 0.234363
[100]   train's rmse: 0.000568819   train's RMSPE: 0.196371 valid's rmse: 0.000657435   valid's RMSPE: 0.228596
[150]   train's rmse: 0.000555085   train's RMSPE: 0.19163  valid's rmse: 0.000652756   valid's RMSPE: 0.226969
[200]   train's rmse: 0.000544363   train's RMSPE: 0.187928 valid's rmse: 0.000652122   valid's RMSPE: 0.226748
[250]   train's rmse: 0.000535419   train's RMSPE: 0.184841 valid's rmse: 0.000649036   valid's RMSPE: 0.225675
[300]   train's rmse: 0.000526509   train's RMSPE: 0.181765 valid's rmse: 0.000647956   valid's RMSPE: 0.2253
[350]   train's rmse: 0.000519234   train's RMSPE: 0.179253 valid's rmse: 0.000646768   valid's RMSPE: 0.224886
[400]   train's rmse: 0.000512983   train's RMSPE: 0.177095 valid's rmse: 0.000646535   valid's RMSPE: 0.224805
Early stopping, best iteration is:
[368]   train's rmse: 0.000516776   train's RMSPE: 0.178404 valid's rmse: 0.000646272   valid's RMSPE: 0.224714
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0006206 train's RMSPE: 0.213192 valid's rmse: 0.000599062   valid's RMSPE: 0.212295
[100]   train's rmse: 0.000585169   train's RMSPE: 0.201021 valid's rmse: 0.000581892   valid's RMSPE: 0.20621
[150]   train's rmse: 0.000571238   train's RMSPE: 0.196235 valid's rmse: 0.000581256   valid's RMSPE: 0.205985
Early stopping, best iteration is:
[147]   train's rmse: 0.000571927   train's RMSPE: 0.196472 valid's rmse: 0.000580817   valid's RMSPE: 0.205829
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000622636   train's RMSPE: 0.215935 valid's rmse: 0.000607949   valid's RMSPE: 0.207524
[100]   train's rmse: 0.000588277   train's RMSPE: 0.204019 valid's rmse: 0.000581424   valid's RMSPE: 0.19847
[150]   train's rmse: 0.00057586    train's RMSPE: 0.199713 valid's rmse: 0.0005766 valid's RMSPE: 0.196823
[200]   train's rmse: 0.000565277   train's RMSPE: 0.196043 valid's rmse: 0.000573429   valid's RMSPE: 0.195741
[250]   train's rmse: 0.000557087   train's RMSPE: 0.193203 valid's rmse: 0.000573556   valid's RMSPE: 0.195784
[300]   train's rmse: 0.000548231   train's RMSPE: 0.190131 valid's rmse: 0.000573053   valid's RMSPE: 0.195612
Early stopping, best iteration is:
[288]   train's rmse: 0.000550124   train's RMSPE: 0.190788 valid's rmse: 0.000572659   valid's RMSPE: 0.195478
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000620666   train's RMSPE: 0.215196 valid's rmse: 0.000613031   valid's RMSPE: 0.209484
[100]   train's rmse: 0.000588667   train's RMSPE: 0.204101 valid's rmse: 0.000592272   valid's RMSPE: 0.20239
[150]   train's rmse: 0.000574429   train's RMSPE: 0.199165 valid's rmse: 0.000588093   valid's RMSPE: 0.200963
[200]   train's rmse: 0.000563771   train's RMSPE: 0.19547  valid's rmse: 0.000587783   valid's RMSPE: 0.200856
[250]   train's rmse: 0.000553603   train's RMSPE: 0.191944 valid's rmse: 0.000585326   valid's RMSPE: 0.200017
[300]   train's rmse: 0.000544726   train's RMSPE: 0.188866 valid's rmse: 0.0005851 valid's RMSPE: 0.19994
[350]   train's rmse: 0.000536625   train's RMSPE: 0.186058 valid's rmse: 0.000583672   valid's RMSPE: 0.199452
[400]   train's rmse: 0.00052957    train's RMSPE: 0.183611 valid's rmse: 0.000582484   valid's RMSPE: 0.199046
[450]   train's rmse: 0.000522447   train's RMSPE: 0.181142 valid's rmse: 0.000582583   valid's RMSPE: 0.19908
Early stopping, best iteration is:
[411]   train's rmse: 0.00052804    train's RMSPE: 0.183081 valid's rmse: 0.000581992   valid's RMSPE: 0.198877
Our out of folds RMSPE is 0.209, compared to 0.18132182041428943, giving gain 0.02767817958571056
Our cv fold scores are [0.217, 0.225, 0.206, 0.195, 0.199]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000434626   train's RMSPE: 0.199601 valid's rmse: 0.000448672   valid's RMSPE: 0.210609
[100]   train's rmse: 0.000408326   train's RMSPE: 0.187523 valid's rmse: 0.000427868   valid's RMSPE: 0.200844
[150]   train's rmse: 0.000395985   train's RMSPE: 0.181855 valid's rmse: 0.000423469   valid's RMSPE: 0.198779
[200]   train's rmse: 0.000386962   train's RMSPE: 0.177711 valid's rmse: 0.000420436   valid's RMSPE: 0.197355
[250]   train's rmse: 0.000379633   train's RMSPE: 0.174346 valid's rmse: 0.000417507   valid's RMSPE: 0.19598
[300]   train's rmse: 0.000373548   train's RMSPE: 0.171551 valid's rmse: 0.000415828   valid's RMSPE: 0.195192
[350]   train's rmse: 0.000367841   train's RMSPE: 0.16893  valid's rmse: 0.000414159   valid's RMSPE: 0.194408
[400]   train's rmse: 0.00036363    train's RMSPE: 0.166996 valid's rmse: 0.000413005   valid's RMSPE: 0.193867
[450]   train's rmse: 0.000359603   train's RMSPE: 0.165147 valid's rmse: 0.000412338   valid's RMSPE: 0.193553
[500]   train's rmse: 0.00035538    train's RMSPE: 0.163208 valid's rmse: 0.000411421   valid's RMSPE: 0.193123
[550]   train's rmse: 0.000351951   train's RMSPE: 0.161633 valid's rmse: 0.000410939   valid's RMSPE: 0.192897
[600]   train's rmse: 0.000348475   train's RMSPE: 0.160037 valid's rmse: 0.000409907   valid's RMSPE: 0.192412
[650]   train's rmse: 0.000345451   train's RMSPE: 0.158648 valid's rmse: 0.00040896    valid's RMSPE: 0.191968
[700]   train's rmse: 0.000342216   train's RMSPE: 0.157162 valid's rmse: 0.000408175   valid's RMSPE: 0.191599
[750]   train's rmse: 0.000339268   train's RMSPE: 0.155808 valid's rmse: 0.000407909   valid's RMSPE: 0.191475
[800]   train's rmse: 0.000336482   train's RMSPE: 0.154529 valid's rmse: 0.000407547   valid's RMSPE: 0.191305
[850]   train's rmse: 0.000333892   train's RMSPE: 0.153339 valid's rmse: 0.000407609   valid's RMSPE: 0.191334
[900]   train's rmse: 0.000331416   train's RMSPE: 0.152202 valid's rmse: 0.000406987   valid's RMSPE: 0.191042
[950]   train's rmse: 0.000329114   train's RMSPE: 0.151145 valid's rmse: 0.000406987   valid's RMSPE: 0.191042
[1000]  train's rmse: 0.000326795   train's RMSPE: 0.15008  valid's rmse: 0.000406473   valid's RMSPE: 0.1908
[1050]  train's rmse: 0.000324639   train's RMSPE: 0.14909  valid's rmse: 0.000407127   valid's RMSPE: 0.191108
Early stopping, best iteration is:
[1000]  train's rmse: 0.000326795   train's RMSPE: 0.15008  valid's rmse: 0.000406473   valid's RMSPE: 0.1908
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000430379   train's RMSPE: 0.198042 valid's rmse: 0.000455708   valid's RMSPE: 0.212281
[100]   train's rmse: 0.000404225   train's RMSPE: 0.186007 valid's rmse: 0.000439262   valid's RMSPE: 0.20462
[150]   train's rmse: 0.000393438   train's RMSPE: 0.181044 valid's rmse: 0.00043463    valid's RMSPE: 0.202462
[200]   train's rmse: 0.000384794   train's RMSPE: 0.177066 valid's rmse: 0.000430094   valid's RMSPE: 0.200349
[250]   train's rmse: 0.00037891    train's RMSPE: 0.174358 valid's rmse: 0.000428851   valid's RMSPE: 0.19977
[300]   train's rmse: 0.000373039   train's RMSPE: 0.171657 valid's rmse: 0.000427345   valid's RMSPE: 0.199069
[350]   train's rmse: 0.00036772    train's RMSPE: 0.16921  valid's rmse: 0.000425388   valid's RMSPE: 0.198157
[400]   train's rmse: 0.000363685   train's RMSPE: 0.167352 valid's rmse: 0.000425753   valid's RMSPE: 0.198327
Early stopping, best iteration is:
[350]   train's rmse: 0.00036772    train's RMSPE: 0.16921  valid's rmse: 0.000425388   valid's RMSPE: 0.198157
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000438649   train's RMSPE: 0.202519 valid's rmse: 0.000417162   valid's RMSPE: 0.191782
[100]   train's rmse: 0.000412518   train's RMSPE: 0.190455 valid's rmse: 0.000394813   valid's RMSPE: 0.181508
[150]   train's rmse: 0.000400528   train's RMSPE: 0.184919 valid's rmse: 0.000389328   valid's RMSPE: 0.178986
[200]   train's rmse: 0.000391958   train's RMSPE: 0.180962 valid's rmse: 0.000385922   valid's RMSPE: 0.17742
[250]   train's rmse: 0.000384957   train's RMSPE: 0.17773  valid's rmse: 0.000384351   valid's RMSPE: 0.176698
[300]   train's rmse: 0.000378689   train's RMSPE: 0.174836 valid's rmse: 0.000382984   valid's RMSPE: 0.17607
[350]   train's rmse: 0.000373352   train's RMSPE: 0.172372 valid's rmse: 0.000381891   valid's RMSPE: 0.175567
[400]   train's rmse: 0.00036856    train's RMSPE: 0.17016  valid's rmse: 0.000381326   valid's RMSPE: 0.175307
[450]   train's rmse: 0.00036427    train's RMSPE: 0.168179 valid's rmse: 0.000381296   valid's RMSPE: 0.175294
Early stopping, best iteration is:
[420]   train's rmse: 0.000366623   train's RMSPE: 0.169265 valid's rmse: 0.000380741   valid's RMSPE: 0.175039
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000438696   train's RMSPE: 0.202768 valid's rmse: 0.000436621   valid's RMSPE: 0.199817
[100]   train's rmse: 0.000411649   train's RMSPE: 0.190267 valid's rmse: 0.000418449   valid's RMSPE: 0.191501
[150]   train's rmse: 0.000400344   train's RMSPE: 0.185041 valid's rmse: 0.000415545   valid's RMSPE: 0.190172
[200]   train's rmse: 0.000392005   train's RMSPE: 0.181187 valid's rmse: 0.0004121 valid's RMSPE: 0.188595
[250]   train's rmse: 0.000385988   train's RMSPE: 0.178406 valid's rmse: 0.000411558   valid's RMSPE: 0.188347
[300]   train's rmse: 0.000380046   train's RMSPE: 0.17566  valid's rmse: 0.000410723   valid's RMSPE: 0.187965
[350]   train's rmse: 0.000374946   train's RMSPE: 0.173302 valid's rmse: 0.000409852   valid's RMSPE: 0.187566
Early stopping, best iteration is:
[328]   train's rmse: 0.000377009   train's RMSPE: 0.174256 valid's rmse: 0.000409452   valid's RMSPE: 0.187383
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00043188    train's RMSPE: 0.200036 valid's rmse: 0.000455327   valid's RMSPE: 0.206585
[100]   train's rmse: 0.000406364   train's RMSPE: 0.188218 valid's rmse: 0.000438422   valid's RMSPE: 0.198914
[150]   train's rmse: 0.000395214   train's RMSPE: 0.183054 valid's rmse: 0.000434234   valid's RMSPE: 0.197014
[200]   train's rmse: 0.000386618   train's RMSPE: 0.179072 valid's rmse: 0.000431955   valid's RMSPE: 0.19598
[250]   train's rmse: 0.00038063    train's RMSPE: 0.176298 valid's rmse: 0.000431111   valid's RMSPE: 0.195598
Early stopping, best iteration is:
[223]   train's rmse: 0.000383825   train's RMSPE: 0.177778 valid's rmse: 0.000430824   valid's RMSPE: 0.195467

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.19, compared to 0.16990778241496282, giving gain 0.02009221758503718
Our cv fold scores are [0.191, 0.198, 0.175, 0.187, 0.195]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000776612   train's RMSPE: 0.228189 valid's rmse: 0.000813077   valid's RMSPE: 0.239094
[100]   train's rmse: 0.00074033    train's RMSPE: 0.217528 valid's rmse: 0.000798015   valid's RMSPE: 0.234665
[150]   train's rmse: 0.000721395   train's RMSPE: 0.211964 valid's rmse: 0.00079684    valid's RMSPE: 0.234319
[200]   train's rmse: 0.000705656   train's RMSPE: 0.20734  valid's rmse: 0.000793142   valid's RMSPE: 0.233232
[250]   train's rmse: 0.000692778   train's RMSPE: 0.203556 valid's rmse: 0.000793585   valid's RMSPE: 0.233362
Early stopping, best iteration is:
[218]   train's rmse: 0.000700864   train's RMSPE: 0.205932 valid's rmse: 0.00079232    valid's RMSPE: 0.23299
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000776122   train's RMSPE: 0.226793 valid's rmse: 0.000816951   valid's RMSPE: 0.245429
[100]   train's rmse: 0.000741536   train's RMSPE: 0.216686 valid's rmse: 0.000790972   valid's RMSPE: 0.237625
[150]   train's rmse: 0.000722092   train's RMSPE: 0.211004 valid's rmse: 0.000786204   valid's RMSPE: 0.236192
Early stopping, best iteration is:
[149]   train's rmse: 0.000722261   train's RMSPE: 0.211054 valid's rmse: 0.000785833   valid's RMSPE: 0.236081
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000784108   train's RMSPE: 0.229698 valid's rmse: 0.000778834   valid's RMSPE: 0.231757
[100]   train's rmse: 0.000748653   train's RMSPE: 0.219311 valid's rmse: 0.000765088   valid's RMSPE: 0.227667
[150]   train's rmse: 0.000728915   train's RMSPE: 0.213529 valid's rmse: 0.000762636   valid's RMSPE: 0.226937
[200]   train's rmse: 0.000713382   train's RMSPE: 0.208979 valid's rmse: 0.000765094   valid's RMSPE: 0.227668
Early stopping, best iteration is:
[153]   train's rmse: 0.000727953   train's RMSPE: 0.213247 valid's rmse: 0.000762123   valid's RMSPE: 0.226784
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000771106   train's RMSPE: 0.227306 valid's rmse: 0.000840125   valid's RMSPE: 0.24382
[100]   train's rmse: 0.000733315   train's RMSPE: 0.216166 valid's rmse: 0.000831469   valid's RMSPE: 0.241308
Early stopping, best iteration is:
[76]    train's rmse: 0.000744735   train's RMSPE: 0.219532 valid's rmse: 0.000830039   valid's RMSPE: 0.240893
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000783469   train's RMSPE: 0.231587 valid's rmse: 0.000771185   valid's RMSPE: 0.221247
[100]   train's rmse: 0.0007497 train's RMSPE: 0.221605 valid's rmse: 0.000753054   valid's RMSPE: 0.216045
[150]   train's rmse: 0.00073066    train's RMSPE: 0.215977 valid's rmse: 0.000753189   valid's RMSPE: 0.216084
Early stopping, best iteration is:
[102]   train's rmse: 0.000748536   train's RMSPE: 0.221261 valid's rmse: 0.000752458   valid's RMSPE: 0.215874
Our out of folds RMSPE is 0.231, compared to 0.21143836908517027, giving gain 0.019561630914829736
Our cv fold scores are [0.233, 0.236, 0.227, 0.241, 0.216]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00125607    train's RMSPE: 0.2637   valid's rmse: 0.00139675    valid's RMSPE: 0.289916
[100]   train's rmse: 0.00118274    train's RMSPE: 0.248305 valid's rmse: 0.00137234    valid's RMSPE: 0.28485
[150]   train's rmse: 0.00114729    train's RMSPE: 0.240863 valid's rmse: 0.00136479    valid's RMSPE: 0.283282
[200]   train's rmse: 0.00111579    train's RMSPE: 0.234249 valid's rmse: 0.00135467    valid's RMSPE: 0.281182
[250]   train's rmse: 0.00109024    train's RMSPE: 0.228886 valid's rmse: 0.00134986    valid's RMSPE: 0.280182
Early stopping, best iteration is:
[239]   train's rmse: 0.00109565    train's RMSPE: 0.23002  valid's rmse: 0.00134837    valid's RMSPE: 0.279873
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00127376    train's RMSPE: 0.26183  valid's rmse: 0.00132568    valid's RMSPE: 0.297465
[100]   train's rmse: 0.00120218    train's RMSPE: 0.247115 valid's rmse: 0.00129404    valid's RMSPE: 0.290364
[150]   train's rmse: 0.00116562    train's RMSPE: 0.239601 valid's rmse: 0.00130074    valid's RMSPE: 0.291868
Early stopping, best iteration is:
[106]   train's rmse: 0.00119694    train's RMSPE: 0.246038 valid's rmse: 0.00128865    valid's RMSPE: 0.289154
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00127096    train's RMSPE: 0.266979 valid's rmse: 0.00124417    valid's RMSPE: 0.25764
[100]   train's rmse: 0.00119751    train's RMSPE: 0.251551 valid's rmse: 0.00122135    valid's RMSPE: 0.252916
[150]   train's rmse: 0.00116139    train's RMSPE: 0.243962 valid's rmse: 0.00121982    valid's RMSPE: 0.252598
[200]   train's rmse: 0.00113061    train's RMSPE: 0.237498 valid's rmse: 0.00121861    valid's RMSPE: 0.252348
[250]   train's rmse: 0.00110579    train's RMSPE: 0.232284 valid's rmse: 0.00121411    valid's RMSPE: 0.251416
Early stopping, best iteration is:
[221]   train's rmse: 0.00111995    train's RMSPE: 0.235257 valid's rmse: 0.00121307    valid's RMSPE: 0.251201
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0012717 train's RMSPE: 0.269703 valid's rmse: 0.00128828    valid's RMSPE: 0.255924
[100]   train's rmse: 0.00119866    train's RMSPE: 0.254212 valid's rmse: 0.00127095    valid's RMSPE: 0.252481
[150]   train's rmse: 0.00115793    train's RMSPE: 0.245574 valid's rmse: 0.00126277    valid's RMSPE: 0.250856
[200]   train's rmse: 0.00112844    train's RMSPE: 0.239319 valid's rmse: 0.00126049    valid's RMSPE: 0.250402
[250]   train's rmse: 0.00110468    train's RMSPE: 0.234281 valid's rmse: 0.0012556 valid's RMSPE: 0.24943
[300]   train's rmse: 0.00108457    train's RMSPE: 0.230016 valid's rmse: 0.00125441    valid's RMSPE: 0.249194
[350]   train's rmse: 0.00106721    train's RMSPE: 0.226335 valid's rmse: 0.00125149    valid's RMSPE: 0.248614
[400]   train's rmse: 0.00105233    train's RMSPE: 0.223178 valid's rmse: 0.00124787    valid's RMSPE: 0.247895
[450]   train's rmse: 0.00103853    train's RMSPE: 0.220252 valid's rmse: 0.00124706    valid's RMSPE: 0.247734
[500]   train's rmse: 0.00102493    train's RMSPE: 0.217368 valid's rmse: 0.00125171    valid's RMSPE: 0.248658
Early stopping, best iteration is:
[466]   train's rmse: 0.00103396    train's RMSPE: 0.219283 valid's rmse: 0.00124496    valid's RMSPE: 0.247318
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0012565 train's RMSPE: 0.263417 valid's rmse: 0.00137292    valid's RMSPE: 0.286616
[100]   train's rmse: 0.00118308    train's RMSPE: 0.248024 valid's rmse: 0.00133543    valid's RMSPE: 0.27879
Early stopping, best iteration is:
[92]    train's rmse: 0.00119128    train's RMSPE: 0.249744 valid's rmse: 0.00133472    valid's RMSPE: 0.278642
Our out of folds RMSPE is 0.27, compared to 0.252659555011557, giving gain 0.017340444988443005
Our cv fold scores are [0.28, 0.289, 0.251, 0.247, 0.279]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000859374   train's RMSPE: 0.238913 valid's rmse: 0.00111969    valid's RMSPE: 0.324016
Early stopping, best iteration is:
[30]    train's rmse: 0.000940611   train's RMSPE: 0.261498 valid's rmse: 0.00110555    valid's RMSPE: 0.319925
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000883416   train's RMSPE: 0.248503 valid's rmse: 0.000924472   valid's RMSPE: 0.255497
[100]   train's rmse: 0.000824528   train's RMSPE: 0.231938 valid's rmse: 0.000907675   valid's RMSPE: 0.250855
[150]   train's rmse: 0.00079671    train's RMSPE: 0.224113 valid's rmse: 0.000907227   valid's RMSPE: 0.250731
Early stopping, best iteration is:
[142]   train's rmse: 0.000800431   train's RMSPE: 0.22516  valid's rmse: 0.00090506    valid's RMSPE: 0.250132
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000887387   train's RMSPE: 0.247454 valid's rmse: 0.000899849   valid's RMSPE: 0.257444
[100]   train's rmse: 0.000830622   train's RMSPE: 0.231624 valid's rmse: 0.00089047    valid's RMSPE: 0.25476
Early stopping, best iteration is:
[66]    train's rmse: 0.000866151   train's RMSPE: 0.241532 valid's rmse: 0.000880169   valid's RMSPE: 0.251813
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000902314   train's RMSPE: 0.253216 valid's rmse: 0.000857521   valid's RMSPE: 0.239314
[100]   train's rmse: 0.000840801   train's RMSPE: 0.235953 valid's rmse: 0.000854473   valid's RMSPE: 0.238463
Early stopping, best iteration is:
[68]    train's rmse: 0.000870068   train's RMSPE: 0.244167 valid's rmse: 0.000844345   valid's RMSPE: 0.235637
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000890606   train's RMSPE: 0.251845 valid's rmse: 0.000884328   valid's RMSPE: 0.238995
[100]   train's rmse: 0.000832472   train's RMSPE: 0.235406 valid's rmse: 0.000873685   valid's RMSPE: 0.236119
[150]   train's rmse: 0.000798956   train's RMSPE: 0.225928 valid's rmse: 0.000868872   valid's RMSPE: 0.234818
Early stopping, best iteration is:
[139]   train's rmse: 0.00080607    train's RMSPE: 0.22794  valid's rmse: 0.000867499   valid's RMSPE: 0.234447
Our out of folds RMSPE is 0.26, compared to 0.25720950158996386, giving gain 0.0027904984100361463
Our cv fold scores are [0.32, 0.25, 0.252, 0.236, 0.234]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0005912 train's RMSPE: 0.256216 valid's rmse: 0.000595862   valid's RMSPE: 0.262508
[100]   train's rmse: 0.000567636   train's RMSPE: 0.246003 valid's rmse: 0.000580197   valid's RMSPE: 0.255606
[150]   train's rmse: 0.000555138   train's RMSPE: 0.240587 valid's rmse: 0.000577446   valid's RMSPE: 0.254394
[200]   train's rmse: 0.000544827   train's RMSPE: 0.236119 valid's rmse: 0.000576202   valid's RMSPE: 0.253846
[250]   train's rmse: 0.000536114   train's RMSPE: 0.232342 valid's rmse: 0.000577043   valid's RMSPE: 0.254217
Early stopping, best iteration is:
[209]   train's rmse: 0.000543098   train's RMSPE: 0.235369 valid's rmse: 0.000575467   valid's RMSPE: 0.253523
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000587992   train's RMSPE: 0.256752 valid's rmse: 0.000605714   valid's RMSPE: 0.258891
[100]   train's rmse: 0.000563195   train's RMSPE: 0.245924 valid's rmse: 0.000591377   valid's RMSPE: 0.252763
[150]   train's rmse: 0.000549547   train's RMSPE: 0.239965 valid's rmse: 0.000590004   valid's RMSPE: 0.252176
[200]   train's rmse: 0.000538666   train's RMSPE: 0.235213 valid's rmse: 0.000588343   valid's RMSPE: 0.251466
[250]   train's rmse: 0.000529653   train's RMSPE: 0.231278 valid's rmse: 0.000585925   valid's RMSPE: 0.250433
[300]   train's rmse: 0.000522218   train's RMSPE: 0.228031 valid's rmse: 0.000585774   valid's RMSPE: 0.250368
[350]   train's rmse: 0.000515565   train's RMSPE: 0.225126 valid's rmse: 0.000584894   valid's RMSPE: 0.249992
Early stopping, best iteration is:
[349]   train's rmse: 0.000515645   train's RMSPE: 0.225161 valid's rmse: 0.000584665   valid's RMSPE: 0.249894
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000587395   train's RMSPE: 0.254243 valid's rmse: 0.000604562   valid's RMSPE: 0.267649
[100]   train's rmse: 0.000563248   train's RMSPE: 0.243791 valid's rmse: 0.000587464   valid's RMSPE: 0.26008
[150]   train's rmse: 0.000553038   train's RMSPE: 0.239372 valid's rmse: 0.00058266    valid's RMSPE: 0.257953
[200]   train's rmse: 0.000542611   train's RMSPE: 0.234859 valid's rmse: 0.000579881   valid's RMSPE: 0.256722
[250]   train's rmse: 0.000533871   train's RMSPE: 0.231076 valid's rmse: 0.000577869   valid's RMSPE: 0.255832
[300]   train's rmse: 0.000526284   train's RMSPE: 0.227792 valid's rmse: 0.0005764 valid's RMSPE: 0.255181
[350]   train's rmse: 0.000520033   train's RMSPE: 0.225086 valid's rmse: 0.000576727   valid's RMSPE: 0.255326
Early stopping, best iteration is:
[317]   train's rmse: 0.000524219   train's RMSPE: 0.226898 valid's rmse: 0.000575308   valid's RMSPE: 0.254698
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000592784   train's RMSPE: 0.258697 valid's rmse: 0.000574199   valid's RMSPE: 0.246003
[100]   train's rmse: 0.000568757   train's RMSPE: 0.248212 valid's rmse: 0.000565677   valid's RMSPE: 0.242352
[150]   train's rmse: 0.000556475   train's RMSPE: 0.242852 valid's rmse: 0.000564477   valid's RMSPE: 0.241838
Early stopping, best iteration is:
[144]   train's rmse: 0.000557731   train's RMSPE: 0.243399 valid's rmse: 0.000564057   valid's RMSPE: 0.241658
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000580131   train's RMSPE: 0.25226  valid's rmse: 0.000631366   valid's RMSPE: 0.274518
[100]   train's rmse: 0.000556472   train's RMSPE: 0.241972 valid's rmse: 0.000623675   valid's RMSPE: 0.271174
[150]   train's rmse: 0.000543833   train's RMSPE: 0.236476 valid's rmse: 0.000620774   valid's RMSPE: 0.269912
[200]   train's rmse: 0.000533269   train's RMSPE: 0.231883 valid's rmse: 0.000621072   valid's RMSPE: 0.270042
Early stopping, best iteration is:
[151]   train's rmse: 0.000543389   train's RMSPE: 0.236283 valid's rmse: 0.000620303   valid's RMSPE: 0.269708
Our out of folds RMSPE is 0.254, compared to 0.22477318010808972, giving gain 0.029226819891910283
Our cv fold scores are [0.254, 0.25, 0.255, 0.242, 0.27]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000786756   train's RMSPE: 0.247637 valid's rmse: 0.000852991   valid's RMSPE: 0.271178
[100]   train's rmse: 0.000752664   train's RMSPE: 0.236907 valid's rmse: 0.000836619   valid's RMSPE: 0.265973
[150]   train's rmse: 0.000734339   train's RMSPE: 0.231139 valid's rmse: 0.000830671   valid's RMSPE: 0.264082
[200]   train's rmse: 0.000720413   train's RMSPE: 0.226756 valid's rmse: 0.0008279 valid's RMSPE: 0.263201
[250]   train's rmse: 0.000708212   train's RMSPE: 0.222915 valid's rmse: 0.000825797   valid's RMSPE: 0.262532
[300]   train's rmse: 0.000697318   train's RMSPE: 0.219486 valid's rmse: 0.000826573   valid's RMSPE: 0.262779
Early stopping, best iteration is:
[252]   train's rmse: 0.000707835   train's RMSPE: 0.222797 valid's rmse: 0.000825425   valid's RMSPE: 0.262414
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000787808   train's RMSPE: 0.248668 valid's rmse: 0.000849822   valid's RMSPE: 0.26716
[100]   train's rmse: 0.000751861   train's RMSPE: 0.237322 valid's rmse: 0.000829397   valid's RMSPE: 0.260739
[150]   train's rmse: 0.000733492   train's RMSPE: 0.231524 valid's rmse: 0.000826519   valid's RMSPE: 0.259834
[200]   train's rmse: 0.000719201   train's RMSPE: 0.227013 valid's rmse: 0.000824125   valid's RMSPE: 0.259081
[250]   train's rmse: 0.000704544   train's RMSPE: 0.222387 valid's rmse: 0.000823403   valid's RMSPE: 0.258854
[300]   train's rmse: 0.00069285    train's RMSPE: 0.218695 valid's rmse: 0.000823599   valid's RMSPE: 0.258916
Early stopping, best iteration is:
[280]   train's rmse: 0.000697257   train's RMSPE: 0.220086 valid's rmse: 0.00082196    valid's RMSPE: 0.258401
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000801363   train's RMSPE: 0.253708 valid's rmse: 0.000776594   valid's RMSPE: 0.241156
[100]   train's rmse: 0.00076904    train's RMSPE: 0.243474 valid's rmse: 0.00076251    valid's RMSPE: 0.236783
[150]   train's rmse: 0.000748897   train's RMSPE: 0.237097 valid's rmse: 0.000762682   valid's RMSPE: 0.236836
Early stopping, best iteration is:
[128]   train's rmse: 0.000756703   train's RMSPE: 0.239568 valid's rmse: 0.00076045    valid's RMSPE: 0.236143
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000798816   train's RMSPE: 0.252005 valid's rmse: 0.000815938   valid's RMSPE: 0.257073
[100]   train's rmse: 0.000764994   train's RMSPE: 0.241335 valid's rmse: 0.000797392   valid's RMSPE: 0.25123
[150]   train's rmse: 0.000746452   train's RMSPE: 0.235486 valid's rmse: 0.000794203   valid's RMSPE: 0.250225
[200]   train's rmse: 0.000730356   train's RMSPE: 0.230408 valid's rmse: 0.000790642   valid's RMSPE: 0.249103
[250]   train's rmse: 0.000717452   train's RMSPE: 0.226337 valid's rmse: 0.000789336   valid's RMSPE: 0.248692
Early stopping, best iteration is:
[242]   train's rmse: 0.000719158   train's RMSPE: 0.226875 valid's rmse: 0.000788282   valid's RMSPE: 0.24836
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000802989   train's RMSPE: 0.252525 valid's rmse: 0.000796074   valid's RMSPE: 0.253953
[100]   train's rmse: 0.000767627   train's RMSPE: 0.241404 valid's rmse: 0.000775393   valid's RMSPE: 0.247355
[150]   train's rmse: 0.000748761   train's RMSPE: 0.235471 valid's rmse: 0.000773228   valid's RMSPE: 0.246665
[200]   train's rmse: 0.000733276   train's RMSPE: 0.230602 valid's rmse: 0.000771356   valid's RMSPE: 0.246068
[250]   train's rmse: 0.000720612   train's RMSPE: 0.226619 valid's rmse: 0.000768289   valid's RMSPE: 0.245089
Early stopping, best iteration is:
[240]   train's rmse: 0.000723026   train's RMSPE: 0.227378 valid's rmse: 0.000767688   valid's RMSPE: 0.244898
Our out of folds RMSPE is 0.25, compared to 0.23302903670661276, giving gain 0.016970963293387237
Our cv fold scores are [0.262, 0.258, 0.236, 0.248, 0.245]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00050701    train's RMSPE: 0.197631 valid's rmse: 0.000555618   valid's RMSPE: 0.21158
[100]   train's rmse: 0.000470677   train's RMSPE: 0.183468 valid's rmse: 0.000536578   valid's RMSPE: 0.20433
[150]   train's rmse: 0.000459419   train's RMSPE: 0.17908  valid's rmse: 0.000534323   valid's RMSPE: 0.203471
[200]   train's rmse: 0.000450343   train's RMSPE: 0.175543 valid's rmse: 0.000532011   valid's RMSPE: 0.202591
[250]   train's rmse: 0.000442545   train's RMSPE: 0.172503 valid's rmse: 0.000529697   valid's RMSPE: 0.20171
[300]   train's rmse: 0.000435716   train's RMSPE: 0.169841 valid's rmse: 0.000528681   valid's RMSPE: 0.201323
[350]   train's rmse: 0.000429951   train's RMSPE: 0.167594 valid's rmse: 0.000528788   valid's RMSPE: 0.201363
[400]   train's rmse: 0.00042367    train's RMSPE: 0.165145 valid's rmse: 0.000527062   valid's RMSPE: 0.200706
[450]   train's rmse: 0.000419098   train's RMSPE: 0.163363 valid's rmse: 0.000527251   valid's RMSPE: 0.200778
[500]   train's rmse: 0.000414687   train's RMSPE: 0.161644 valid's rmse: 0.000526738   valid's RMSPE: 0.200583
[550]   train's rmse: 0.000409968   train's RMSPE: 0.159804 valid's rmse: 0.000526032   valid's RMSPE: 0.200314
[600]   train's rmse: 0.000406169   train's RMSPE: 0.158324 valid's rmse: 0.000525791   valid's RMSPE: 0.200222
Early stopping, best iteration is:
[577]   train's rmse: 0.000407878   train's RMSPE: 0.15899  valid's rmse: 0.000525707   valid's RMSPE: 0.200191
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000520336   train's RMSPE: 0.200131 valid's rmse: 0.000535971   valid's RMSPE: 0.215092
[100]   train's rmse: 0.000486072   train's RMSPE: 0.186952 valid's rmse: 0.00050119    valid's RMSPE: 0.201134
[150]   train's rmse: 0.000474641   train's RMSPE: 0.182556 valid's rmse: 0.00049606    valid's RMSPE: 0.199075
[200]   train's rmse: 0.000464454   train's RMSPE: 0.178638 valid's rmse: 0.000492751   valid's RMSPE: 0.197747
[250]   train's rmse: 0.000456125   train's RMSPE: 0.175434 valid's rmse: 0.000491353   valid's RMSPE: 0.197186
[300]   train's rmse: 0.000448917   train's RMSPE: 0.172662 valid's rmse: 0.000489143   valid's RMSPE: 0.196299
[350]   train's rmse: 0.000442296   train's RMSPE: 0.170115 valid's rmse: 0.000485563   valid's RMSPE: 0.194862
[400]   train's rmse: 0.000436606   train's RMSPE: 0.167927 valid's rmse: 0.000484759   valid's RMSPE: 0.19454
[450]   train's rmse: 0.000431388   train's RMSPE: 0.16592  valid's rmse: 0.000483853   valid's RMSPE: 0.194176
[500]   train's rmse: 0.000426854   train's RMSPE: 0.164176 valid's rmse: 0.000483305   valid's RMSPE: 0.193956
[550]   train's rmse: 0.00042213    train's RMSPE: 0.162359 valid's rmse: 0.000482412   valid's RMSPE: 0.193598
[600]   train's rmse: 0.000417739   train's RMSPE: 0.16067  valid's rmse: 0.000483401   valid's RMSPE: 0.193995
Early stopping, best iteration is:
[564]   train's rmse: 0.000421168   train's RMSPE: 0.161989 valid's rmse: 0.000481899   valid's RMSPE: 0.193392
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000521355   train's RMSPE: 0.202161 valid's rmse: 0.00051672    valid's RMSPE: 0.201021
[100]   train's rmse: 0.000482038   train's RMSPE: 0.186915 valid's rmse: 0.000495474   valid's RMSPE: 0.192755
[150]   train's rmse: 0.000470308   train's RMSPE: 0.182367 valid's rmse: 0.000493342   valid's RMSPE: 0.191926
[200]   train's rmse: 0.000461697   train's RMSPE: 0.179028 valid's rmse: 0.000493651   valid's RMSPE: 0.192046
Early stopping, best iteration is:
[184]   train's rmse: 0.00046442    train's RMSPE: 0.180084 valid's rmse: 0.000491873   valid's RMSPE: 0.191355
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00051638    train's RMSPE: 0.200637 valid's rmse: 0.000533314   valid's RMSPE: 0.205799
[100]   train's rmse: 0.000480431   train's RMSPE: 0.186669 valid's rmse: 0.000509478   valid's RMSPE: 0.196601
[150]   train's rmse: 0.000467963   train's RMSPE: 0.181825 valid's rmse: 0.000507839   valid's RMSPE: 0.195969
Early stopping, best iteration is:
[148]   train's rmse: 0.000468317   train's RMSPE: 0.181962 valid's rmse: 0.000507081   valid's RMSPE: 0.195676
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000523437   train's RMSPE: 0.203791 valid's rmse: 0.000517142   valid's RMSPE: 0.197911
[100]   train's rmse: 0.000487977   train's RMSPE: 0.189985 valid's rmse: 0.000487325   valid's RMSPE: 0.1865
[150]   train's rmse: 0.000475391   train's RMSPE: 0.185085 valid's rmse: 0.000483722   valid's RMSPE: 0.185121
[200]   train's rmse: 0.000466086   train's RMSPE: 0.181462 valid's rmse: 0.000482079   valid's RMSPE: 0.184492
[250]   train's rmse: 0.000457584   train's RMSPE: 0.178152 valid's rmse: 0.000480619   valid's RMSPE: 0.183934
[300]   train's rmse: 0.000450316   train's RMSPE: 0.175322 valid's rmse: 0.000480031   valid's RMSPE: 0.183709
Early stopping, best iteration is:
[285]   train's rmse: 0.000452458   train's RMSPE: 0.176156 valid's rmse: 0.00047931    valid's RMSPE: 0.183433
Our out of folds RMSPE is 0.193, compared to 0.16946425653881092, giving gain 0.023535743461189085
Our cv fold scores are [0.2, 0.193, 0.191, 0.196, 0.183]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000476716   train's RMSPE: 0.240945 valid's rmse: 0.000489848   valid's RMSPE: 0.24539
[100]   train's rmse: 0.000448124   train's RMSPE: 0.226494 valid's rmse: 0.000477045   valid's RMSPE: 0.238976
[150]   train's rmse: 0.000432803   train's RMSPE: 0.21875  valid's rmse: 0.000472804   valid's RMSPE: 0.236851
[200]   train's rmse: 0.00042161    train's RMSPE: 0.213093 valid's rmse: 0.000471003   valid's RMSPE: 0.235949
[250]   train's rmse: 0.000412061   train's RMSPE: 0.208267 valid's rmse: 0.000468224   valid's RMSPE: 0.234557
[300]   train's rmse: 0.000403522   train's RMSPE: 0.203951 valid's rmse: 0.000467577   valid's RMSPE: 0.234233
[350]   train's rmse: 0.00039677    train's RMSPE: 0.200538 valid's rmse: 0.000466959   valid's RMSPE: 0.233923
[400]   train's rmse: 0.000390883   train's RMSPE: 0.197563 valid's rmse: 0.000466683   valid's RMSPE: 0.233785
[450]   train's rmse: 0.000385647   train's RMSPE: 0.194916 valid's rmse: 0.000464296   valid's RMSPE: 0.232589
[500]   train's rmse: 0.000380804   train's RMSPE: 0.192469 valid's rmse: 0.000463508   valid's RMSPE: 0.232194
[550]   train's rmse: 0.000376428   train's RMSPE: 0.190257 valid's rmse: 0.000462203   valid's RMSPE: 0.231541
Early stopping, best iteration is:
[535]   train's rmse: 0.000377852   train's RMSPE: 0.190977 valid's rmse: 0.000461978   valid's RMSPE: 0.231428
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000472473   train's RMSPE: 0.239542 valid's rmse: 0.00050551    valid's RMSPE: 0.25001
[100]   train's rmse: 0.000442399   train's RMSPE: 0.224294 valid's rmse: 0.000498573   valid's RMSPE: 0.24658
[150]   train's rmse: 0.000426059   train's RMSPE: 0.21601  valid's rmse: 0.000496963   valid's RMSPE: 0.245783
[200]   train's rmse: 0.000414683   train's RMSPE: 0.210243 valid's rmse: 0.000497637   valid's RMSPE: 0.246116
Early stopping, best iteration is:
[180]   train's rmse: 0.000418357   train's RMSPE: 0.212105 valid's rmse: 0.000496646   valid's RMSPE: 0.245626
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00047743    train's RMSPE: 0.239616 valid's rmse: 0.000503229   valid's RMSPE: 0.25916
[100]   train's rmse: 0.00044837    train's RMSPE: 0.225031 valid's rmse: 0.000490546   valid's RMSPE: 0.252628
[150]   train's rmse: 0.000432772   train's RMSPE: 0.217202 valid's rmse: 0.000489657   valid's RMSPE: 0.25217
[200]   train's rmse: 0.000422214   train's RMSPE: 0.211904 valid's rmse: 0.000488483   valid's RMSPE: 0.251566
Early stopping, best iteration is:
[169]   train's rmse: 0.000428729   train's RMSPE: 0.215173 valid's rmse: 0.000487816   valid's RMSPE: 0.251222
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000480426   train's RMSPE: 0.243369 valid's rmse: 0.000459814   valid's RMSPE: 0.228212
[100]   train's rmse: 0.000451539   train's RMSPE: 0.228736 valid's rmse: 0.000451499   valid's RMSPE: 0.224085
[150]   train's rmse: 0.000436649   train's RMSPE: 0.221193 valid's rmse: 0.000449818   valid's RMSPE: 0.223251
Early stopping, best iteration is:
[123]   train's rmse: 0.000443509   train's RMSPE: 0.224668 valid's rmse: 0.000449082   valid's RMSPE: 0.222886
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000477687   train's RMSPE: 0.23969  valid's rmse: 0.000532458   valid's RMSPE: 0.274452
[100]   train's rmse: 0.000451544   train's RMSPE: 0.226572 valid's rmse: 0.000511332   valid's RMSPE: 0.263562
[150]   train's rmse: 0.000438294   train's RMSPE: 0.219923 valid's rmse: 0.000515922   valid's RMSPE: 0.265929
Early stopping, best iteration is:
[124]   train's rmse: 0.000445054   train's RMSPE: 0.223315 valid's rmse: 0.000508233   valid's RMSPE: 0.261965
Our out of folds RMSPE is 0.243, compared to 0.21071689415292186, giving gain 0.032283105847078136
Our cv fold scores are [0.231, 0.246, 0.251, 0.223, 0.262]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000440131   train's RMSPE: 0.232742 valid's rmse: 0.000474224   valid's RMSPE: 0.240135
[100]   train's rmse: 0.000409977   train's RMSPE: 0.216797 valid's rmse: 0.000458973   valid's RMSPE: 0.232412
[150]   train's rmse: 0.000396138   train's RMSPE: 0.209479 valid's rmse: 0.000454192   valid's RMSPE: 0.229992
[200]   train's rmse: 0.000387063   train's RMSPE: 0.20468  valid's rmse: 0.000451409   valid's RMSPE: 0.228582
[250]   train's rmse: 0.000379487   train's RMSPE: 0.200674 valid's rmse: 0.000449586   valid's RMSPE: 0.227659
[300]   train's rmse: 0.000372728   train's RMSPE: 0.1971   valid's rmse: 0.000446829   valid's RMSPE: 0.226263
[350]   train's rmse: 0.000367199   train's RMSPE: 0.194176 valid's rmse: 0.000445359   valid's RMSPE: 0.225519
[400]   train's rmse: 0.000362131   train's RMSPE: 0.191496 valid's rmse: 0.000444039   valid's RMSPE: 0.224851
[450]   train's rmse: 0.000357329   train's RMSPE: 0.188957 valid's rmse: 0.000442055   valid's RMSPE: 0.223846
[500]   train's rmse: 0.000353518   train's RMSPE: 0.186941 valid's rmse: 0.000441399   valid's RMSPE: 0.223513
[550]   train's rmse: 0.000349406   train's RMSPE: 0.184767 valid's rmse: 0.000440564   valid's RMSPE: 0.223091
[600]   train's rmse: 0.000345404   train's RMSPE: 0.182651 valid's rmse: 0.00044061    valid's RMSPE: 0.223114
Early stopping, best iteration is:
[557]   train's rmse: 0.000348964   train's RMSPE: 0.184533 valid's rmse: 0.000440233   valid's RMSPE: 0.222923
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000440085   train's RMSPE: 0.228433 valid's rmse: 0.000478733   valid's RMSPE: 0.261
[100]   train's rmse: 0.00041191    train's RMSPE: 0.213808 valid's rmse: 0.000457349   valid's RMSPE: 0.249342
[150]   train's rmse: 0.000399074   train's RMSPE: 0.207145 valid's rmse: 0.000452255   valid's RMSPE: 0.246565
[200]   train's rmse: 0.000389438   train's RMSPE: 0.202144 valid's rmse: 0.000450564   valid's RMSPE: 0.245643
[250]   train's rmse: 0.000381928   train's RMSPE: 0.198245 valid's rmse: 0.000449387   valid's RMSPE: 0.245002
Early stopping, best iteration is:
[240]   train's rmse: 0.000383133   train's RMSPE: 0.198871 valid's rmse: 0.000448765   valid's RMSPE: 0.244662
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000451626   train's RMSPE: 0.235513 valid's rmse: 0.000430134   valid's RMSPE: 0.23051
[100]   train's rmse: 0.000421608   train's RMSPE: 0.219859 valid's rmse: 0.00040436    valid's RMSPE: 0.216698
[150]   train's rmse: 0.000408574   train's RMSPE: 0.213062 valid's rmse: 0.000400054   valid's RMSPE: 0.21439
[200]   train's rmse: 0.000398639   train's RMSPE: 0.207881 valid's rmse: 0.000398257   valid's RMSPE: 0.213427
[250]   train's rmse: 0.000390586   train's RMSPE: 0.203682 valid's rmse: 0.000397598   valid's RMSPE: 0.213074
Early stopping, best iteration is:
[227]   train's rmse: 0.000394036   train's RMSPE: 0.205481 valid's rmse: 0.000396942   valid's RMSPE: 0.212723
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000438908   train's RMSPE: 0.230845 valid's rmse: 0.000481403   valid's RMSPE: 0.249422
[100]   train's rmse: 0.000409979   train's RMSPE: 0.215629 valid's rmse: 0.000470739   valid's RMSPE: 0.243896
[150]   train's rmse: 0.000397344   train's RMSPE: 0.208984 valid's rmse: 0.000467545   valid's RMSPE: 0.242241
[200]   train's rmse: 0.000387965   train's RMSPE: 0.204051 valid's rmse: 0.000466083   valid's RMSPE: 0.241484
[250]   train's rmse: 0.000379791   train's RMSPE: 0.199752 valid's rmse: 0.000464886   valid's RMSPE: 0.240864
Early stopping, best iteration is:
[228]   train's rmse: 0.00038327    train's RMSPE: 0.201582 valid's rmse: 0.000464628   valid's RMSPE: 0.24073
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000448186   train's RMSPE: 0.23602  valid's rmse: 0.000434564   valid's RMSPE: 0.223985
[100]   train's rmse: 0.000419599   train's RMSPE: 0.220966 valid's rmse: 0.000415263   valid's RMSPE: 0.214037
[150]   train's rmse: 0.000405756   train's RMSPE: 0.213676 valid's rmse: 0.000409702   valid's RMSPE: 0.211171
[200]   train's rmse: 0.000395761   train's RMSPE: 0.208412 valid's rmse: 0.00041017    valid's RMSPE: 0.211412
Early stopping, best iteration is:
[162]   train's rmse: 0.000403185   train's RMSPE: 0.212322 valid's rmse: 0.000408971   valid's RMSPE: 0.210794
Our out of folds RMSPE is 0.227, compared to 0.2142372903386656, giving gain 0.012762709661334415
Our cv fold scores are [0.223, 0.245, 0.213, 0.241, 0.211]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000712395   train's RMSPE: 0.22981  valid's rmse: 0.000735326   valid's RMSPE: 0.235179
[100]   train's rmse: 0.000679388   train's RMSPE: 0.219162 valid's rmse: 0.000706437   valid's RMSPE: 0.225939
[150]   train's rmse: 0.000664594   train's RMSPE: 0.214389 valid's rmse: 0.000700107   valid's RMSPE: 0.223915
[200]   train's rmse: 0.000653316   train's RMSPE: 0.210751 valid's rmse: 0.000697839   valid's RMSPE: 0.223189
[250]   train's rmse: 0.000643487   train's RMSPE: 0.207581 valid's rmse: 0.000697106   valid's RMSPE: 0.222955
[300]   train's rmse: 0.000633841   train's RMSPE: 0.204469 valid's rmse: 0.000693588   valid's RMSPE: 0.22183
[350]   train's rmse: 0.000625418   train's RMSPE: 0.201752 valid's rmse: 0.000691788   valid's RMSPE: 0.221254
[400]   train's rmse: 0.000617754   train's RMSPE: 0.199279 valid's rmse: 0.000691129   valid's RMSPE: 0.221043
[450]   train's rmse: 0.000609212   train's RMSPE: 0.196524 valid's rmse: 0.000688109   valid's RMSPE: 0.220077
[500]   train's rmse: 0.0006027 train's RMSPE: 0.194423 valid's rmse: 0.000687326   valid's RMSPE: 0.219827
[550]   train's rmse: 0.000596618   train's RMSPE: 0.192461 valid's rmse: 0.000687566   valid's RMSPE: 0.219904
[600]   train's rmse: 0.000590649   train's RMSPE: 0.190536 valid's rmse: 0.000687086   valid's RMSPE: 0.21975
Early stopping, best iteration is:
[591]   train's rmse: 0.000591509   train's RMSPE: 0.190813 valid's rmse: 0.000686476   valid's RMSPE: 0.219555
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000709548   train's RMSPE: 0.2278   valid's rmse: 0.000749436   valid's RMSPE: 0.244283
[100]   train's rmse: 0.000674406   train's RMSPE: 0.216518 valid's rmse: 0.000729646   valid's RMSPE: 0.237832
[150]   train's rmse: 0.000660853   train's RMSPE: 0.212167 valid's rmse: 0.000726196   valid's RMSPE: 0.236708
[200]   train's rmse: 0.000647928   train's RMSPE: 0.208018 valid's rmse: 0.000726356   valid's RMSPE: 0.23676
Early stopping, best iteration is:
[164]   train's rmse: 0.000657033   train's RMSPE: 0.21094  valid's rmse: 0.000724836   valid's RMSPE: 0.236265
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000717705   train's RMSPE: 0.230558 valid's rmse: 0.000725756   valid's RMSPE: 0.236011
[100]   train's rmse: 0.000681213   train's RMSPE: 0.218835 valid's rmse: 0.000700727   valid's RMSPE: 0.227872
[150]   train's rmse: 0.000665687   train's RMSPE: 0.213847 valid's rmse: 0.000698775   valid's RMSPE: 0.227237
[200]   train's rmse: 0.000653588   train's RMSPE: 0.209961 valid's rmse: 0.000695587   valid's RMSPE: 0.226201
[250]   train's rmse: 0.000643308   train's RMSPE: 0.206659 valid's rmse: 0.000694996   valid's RMSPE: 0.226008
Early stopping, best iteration is:
[228]   train's rmse: 0.000647875   train's RMSPE: 0.208126 valid's rmse: 0.000694142   valid's RMSPE: 0.225731
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000714872   train's RMSPE: 0.230709 valid's rmse: 0.000727805   valid's RMSPE: 0.232361
[100]   train's rmse: 0.000680467   train's RMSPE: 0.219605 valid's rmse: 0.000710144   valid's RMSPE: 0.226722
[150]   train's rmse: 0.000665586   train's RMSPE: 0.214803 valid's rmse: 0.000708692   valid's RMSPE: 0.226259
[200]   train's rmse: 0.000653292   train's RMSPE: 0.210835 valid's rmse: 0.000707713   valid's RMSPE: 0.225946
[250]   train's rmse: 0.000641881   train's RMSPE: 0.207153 valid's rmse: 0.00070613    valid's RMSPE: 0.225441
[300]   train's rmse: 0.000632358   train's RMSPE: 0.204079 valid's rmse: 0.000705814   valid's RMSPE: 0.22534
Early stopping, best iteration is:
[297]   train's rmse: 0.000632894   train's RMSPE: 0.204252 valid's rmse: 0.000705039   valid's RMSPE: 0.225093
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00071502    train's RMSPE: 0.230647 valid's rmse: 0.000716721   valid's RMSPE: 0.229264
[100]   train's rmse: 0.000680058   train's RMSPE: 0.219369 valid's rmse: 0.000705658   valid's RMSPE: 0.225725
[150]   train's rmse: 0.000664748   train's RMSPE: 0.214431 valid's rmse: 0.000704343   valid's RMSPE: 0.225305
Early stopping, best iteration is:
[140]   train's rmse: 0.000668179   train's RMSPE: 0.215538 valid's rmse: 0.000703558   valid's RMSPE: 0.225053
Our out of folds RMSPE is 0.226, compared to 0.1966253016482257, giving gain 0.02937469835177431
Our cv fold scores are [0.22, 0.236, 0.226, 0.225, 0.225]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000964701   train's RMSPE: 0.301217 valid's rmse: 0.000994454   valid's RMSPE: 0.312071
[100]   train's rmse: 0.00091996    train's RMSPE: 0.287247 valid's rmse: 0.000979224   valid's RMSPE: 0.307292
[150]   train's rmse: 0.000894427   train's RMSPE: 0.279275 valid's rmse: 0.000975147   valid's RMSPE: 0.306012
[200]   train's rmse: 0.000873807   train's RMSPE: 0.272836 valid's rmse: 0.000972872   valid's RMSPE: 0.305299
[250]   train's rmse: 0.000856355   train's RMSPE: 0.267387 valid's rmse: 0.00097159    valid's RMSPE: 0.304896
[300]   train's rmse: 0.000840137   train's RMSPE: 0.262323 valid's rmse: 0.000968595   valid's RMSPE: 0.303956
[350]   train's rmse: 0.000826789   train's RMSPE: 0.258156 valid's rmse: 0.000968221   valid's RMSPE: 0.303839
Early stopping, best iteration is:
[344]   train's rmse: 0.000828218   train's RMSPE: 0.258602 valid's rmse: 0.000966917   valid's RMSPE: 0.30343
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000951501   train's RMSPE: 0.298494 valid's rmse: 0.00103469    valid's RMSPE: 0.31857
[100]   train's rmse: 0.000909125   train's RMSPE: 0.285201 valid's rmse: 0.00102522    valid's RMSPE: 0.315655
[150]   train's rmse: 0.000882433   train's RMSPE: 0.276827 valid's rmse: 0.00102132    valid's RMSPE: 0.314455
[200]   train's rmse: 0.000861598   train's RMSPE: 0.270291 valid's rmse: 0.00101556    valid's RMSPE: 0.31268
[250]   train's rmse: 0.000844232   train's RMSPE: 0.264843 valid's rmse: 0.00101101    valid's RMSPE: 0.31128
[300]   train's rmse: 0.000828937   train's RMSPE: 0.260045 valid's rmse: 0.00100942    valid's RMSPE: 0.310792
Early stopping, best iteration is:
[287]   train's rmse: 0.000832551   train's RMSPE: 0.261179 valid's rmse: 0.00100772    valid's RMSPE: 0.310268
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000966182   train's RMSPE: 0.299767 valid's rmse: 0.000988458   valid's RMSPE: 0.317856
[100]   train's rmse: 0.00092251    train's RMSPE: 0.286217 valid's rmse: 0.00097041    valid's RMSPE: 0.312053
Early stopping, best iteration is:
[82]    train's rmse: 0.000933965   train's RMSPE: 0.289771 valid's rmse: 0.000967095   valid's RMSPE: 0.310986
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000968632   train's RMSPE: 0.303557 valid's rmse: 0.000964182   valid's RMSPE: 0.298122
[100]   train's rmse: 0.000925969   train's RMSPE: 0.290187 valid's rmse: 0.000958094   valid's RMSPE: 0.296239
[150]   train's rmse: 0.000898408   train's RMSPE: 0.28155  valid's rmse: 0.000957749   valid's RMSPE: 0.296132
[200]   train's rmse: 0.000878659   train's RMSPE: 0.275361 valid's rmse: 0.000958066   valid's RMSPE: 0.296231
Early stopping, best iteration is:
[160]   train's rmse: 0.000894148   train's RMSPE: 0.280215 valid's rmse: 0.000955551   valid's RMSPE: 0.295453
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000965863   train's RMSPE: 0.302472 valid's rmse: 0.000988226   valid's RMSPE: 0.306457
[100]   train's rmse: 0.000921834   train's RMSPE: 0.288684 valid's rmse: 0.000971172   valid's RMSPE: 0.301168
[150]   train's rmse: 0.000893613   train's RMSPE: 0.279846 valid's rmse: 0.000968803   valid's RMSPE: 0.300434
[200]   train's rmse: 0.000872609   train's RMSPE: 0.273268 valid's rmse: 0.000967977   valid's RMSPE: 0.300178
[250]   train's rmse: 0.000854649   train's RMSPE: 0.267644 valid's rmse: 0.000966159   valid's RMSPE: 0.299614
[300]   train's rmse: 0.000838888   train's RMSPE: 0.262708 valid's rmse: 0.000965478   valid's RMSPE: 0.299403
[350]   train's rmse: 0.000825379   train's RMSPE: 0.258478 valid's rmse: 0.000967105   valid's RMSPE: 0.299907
Early stopping, best iteration is:
[314]   train's rmse: 0.000835205   train's RMSPE: 0.261555 valid's rmse: 0.00096449    valid's RMSPE: 0.299096
Our out of folds RMSPE is 0.304, compared to 0.283514033038324, giving gain 0.020485966961675983
Our cv fold scores are [0.303, 0.31, 0.311, 0.295, 0.299]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000588827   train's RMSPE: 0.25951  valid's rmse: 0.000619821   valid's RMSPE: 0.275645
[100]   train's rmse: 0.000554423   train's RMSPE: 0.244348 valid's rmse: 0.000598485   valid's RMSPE: 0.266157
[150]   train's rmse: 0.000534863   train's RMSPE: 0.235727 valid's rmse: 0.000598055   valid's RMSPE: 0.265966
Early stopping, best iteration is:
[115]   train's rmse: 0.000547916   train's RMSPE: 0.24148  valid's rmse: 0.000595775   valid's RMSPE: 0.264952
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000590436   train's RMSPE: 0.259291 valid's rmse: 0.000610662   valid's RMSPE: 0.275345
[100]   train's rmse: 0.000555297   train's RMSPE: 0.24386  valid's rmse: 0.000589978   valid's RMSPE: 0.266018
[150]   train's rmse: 0.00053713    train's RMSPE: 0.235882 valid's rmse: 0.000586955   valid's RMSPE: 0.264655
[200]   train's rmse: 0.000523068   train's RMSPE: 0.229706 valid's rmse: 0.000587153   valid's RMSPE: 0.264745
Early stopping, best iteration is:
[169]   train's rmse: 0.000531264   train's RMSPE: 0.233306 valid's rmse: 0.000584984   valid's RMSPE: 0.263767
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000590851   train's RMSPE: 0.26041  valid's rmse: 0.000605041   valid's RMSPE: 0.269042
[100]   train's rmse: 0.000558045   train's RMSPE: 0.245951 valid's rmse: 0.000588474   valid's RMSPE: 0.261675
Early stopping, best iteration is:
[76]    train's rmse: 0.000568652   train's RMSPE: 0.250626 valid's rmse: 0.000586894   valid's RMSPE: 0.260972
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000593166   train's RMSPE: 0.262589 valid's rmse: 0.0006077 valid's RMSPE: 0.265463
[100]   train's rmse: 0.000557276   train's RMSPE: 0.246701 valid's rmse: 0.000601953   valid's RMSPE: 0.262952
[150]   train's rmse: 0.000538577   train's RMSPE: 0.238423 valid's rmse: 0.000599199   valid's RMSPE: 0.261749
[200]   train's rmse: 0.00052404    train's RMSPE: 0.231988 valid's rmse: 0.000595127   valid's RMSPE: 0.259971
[250]   train's rmse: 0.000512541   train's RMSPE: 0.226897 valid's rmse: 0.000597854   valid's RMSPE: 0.261162
Early stopping, best iteration is:
[206]   train's rmse: 0.000522451   train's RMSPE: 0.231285 valid's rmse: 0.00059485    valid's RMSPE: 0.25985
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000593502   train's RMSPE: 0.263696 valid's rmse: 0.000604598   valid's RMSPE: 0.260113
[100]   train's rmse: 0.000560618   train's RMSPE: 0.249086 valid's rmse: 0.000583898   valid's RMSPE: 0.251207
[150]   train's rmse: 0.000543579   train's RMSPE: 0.241515 valid's rmse: 0.0005797 valid's RMSPE: 0.249401
[200]   train's rmse: 0.000529069   train's RMSPE: 0.235069 valid's rmse: 0.000576824   valid's RMSPE: 0.248164
[250]   train's rmse: 0.000517388   train's RMSPE: 0.229878 valid's rmse: 0.00057339    valid's RMSPE: 0.246687
[300]   train's rmse: 0.000507916   train's RMSPE: 0.22567  valid's rmse: 0.000573645   valid's RMSPE: 0.246796
[350]   train's rmse: 0.00049916    train's RMSPE: 0.22178  valid's rmse: 0.000571809   valid's RMSPE: 0.246006
[400]   train's rmse: 0.000490789   train's RMSPE: 0.21806  valid's rmse: 0.0005737 valid's RMSPE: 0.24682
Early stopping, best iteration is:
[354]   train's rmse: 0.000498504   train's RMSPE: 0.221489 valid's rmse: 0.000571522   valid's RMSPE: 0.245883
Our out of folds RMSPE is 0.259, compared to 0.24449780064112345, giving gain 0.01450219935887656
Our cv fold scores are [0.265, 0.264, 0.261, 0.26, 0.246]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000930657   train's RMSPE: 0.266921 valid's rmse: 0.000966669   valid's RMSPE: 0.267247
[100]   train's rmse: 0.000888957   train's RMSPE: 0.254961 valid's rmse: 0.000949897   valid's RMSPE: 0.26261
[150]   train's rmse: 0.000867355   train's RMSPE: 0.248766 valid's rmse: 0.000944181   valid's RMSPE: 0.26103
[200]   train's rmse: 0.000848453   train's RMSPE: 0.243344 valid's rmse: 0.000944353   valid's RMSPE: 0.261078
Early stopping, best iteration is:
[163]   train's rmse: 0.000862692   train's RMSPE: 0.247428 valid's rmse: 0.000943323   valid's RMSPE: 0.260793
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000928833   train's RMSPE: 0.262419 valid's rmse: 0.000978251   valid's RMSPE: 0.287191
[100]   train's rmse: 0.000885827   train's RMSPE: 0.250269 valid's rmse: 0.000962207   valid's RMSPE: 0.282481
[150]   train's rmse: 0.000863576   train's RMSPE: 0.243982 valid's rmse: 0.000958458   valid's RMSPE: 0.28138
[200]   train's rmse: 0.000846276   train's RMSPE: 0.239095 valid's rmse: 0.000954824   valid's RMSPE: 0.280314
[250]   train's rmse: 0.000830713   train's RMSPE: 0.234698 valid's rmse: 0.00095194    valid's RMSPE: 0.279467
Early stopping, best iteration is:
[243]   train's rmse: 0.0008327 train's RMSPE: 0.235259 valid's rmse: 0.000951439   valid's RMSPE: 0.27932
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000931639   train's RMSPE: 0.266363 valid's rmse: 0.000967899   valid's RMSPE: 0.271178
[100]   train's rmse: 0.000889222   train's RMSPE: 0.254236 valid's rmse: 0.000953028   valid's RMSPE: 0.267011
[150]   train's rmse: 0.000867002   train's RMSPE: 0.247883 valid's rmse: 0.000950891   valid's RMSPE: 0.266413
Early stopping, best iteration is:
[125]   train's rmse: 0.000877243   train's RMSPE: 0.250811 valid's rmse: 0.000948453   valid's RMSPE: 0.26573
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000936534   train's RMSPE: 0.26789  valid's rmse: 0.000948565   valid's RMSPE: 0.265233
[100]   train's rmse: 0.000894348   train's RMSPE: 0.255823 valid's rmse: 0.000938935   valid's RMSPE: 0.26254
[150]   train's rmse: 0.000872785   train's RMSPE: 0.249655 valid's rmse: 0.000937338   valid's RMSPE: 0.262094
[200]   train's rmse: 0.000853897   train's RMSPE: 0.244252 valid's rmse: 0.000930101   valid's RMSPE: 0.26007
Early stopping, best iteration is:
[188]   train's rmse: 0.000857532   train's RMSPE: 0.245292 valid's rmse: 0.000930081   valid's RMSPE: 0.260065
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000939579   train's RMSPE: 0.265462 valid's rmse: 0.000940261   valid's RMSPE: 0.276013
[100]   train's rmse: 0.000901656   train's RMSPE: 0.254747 valid's rmse: 0.000909632   valid's RMSPE: 0.267022
[150]   train's rmse: 0.000881668   train's RMSPE: 0.2491   valid's rmse: 0.000906189   valid's RMSPE: 0.266011
[200]   train's rmse: 0.000863597   train's RMSPE: 0.243994 valid's rmse: 0.000897487   valid's RMSPE: 0.263457
[250]   train's rmse: 0.000848374   train's RMSPE: 0.239693 valid's rmse: 0.000893715   valid's RMSPE: 0.262349
[300]   train's rmse: 0.000835552   train's RMSPE: 0.236071 valid's rmse: 0.000890168   valid's RMSPE: 0.261308
[350]   train's rmse: 0.000823274   train's RMSPE: 0.232602 valid's rmse: 0.000889818   valid's RMSPE: 0.261206
[400]   train's rmse: 0.000812412   train's RMSPE: 0.229533 valid's rmse: 0.000889914   valid's RMSPE: 0.261234
Early stopping, best iteration is:
[359]   train's rmse: 0.000821039   train's RMSPE: 0.23197  valid's rmse: 0.000888069   valid's RMSPE: 0.260692

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.265, compared to 0.23262856524594397, giving gain 0.032371434754056044
Our cv fold scores are [0.261, 0.279, 0.266, 0.26, 0.261]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000372408   train's RMSPE: 0.238591 valid's rmse: 0.000419037   valid's RMSPE: 0.268817
[100]   train's rmse: 0.000354431   train's RMSPE: 0.227074 valid's rmse: 0.000402946   valid's RMSPE: 0.258495
[150]   train's rmse: 0.000347043   train's RMSPE: 0.222341 valid's rmse: 0.000399094   valid's RMSPE: 0.256024
[200]   train's rmse: 0.000340927   train's RMSPE: 0.218422 valid's rmse: 0.000396471   valid's RMSPE: 0.254341
[250]   train's rmse: 0.000335971   train's RMSPE: 0.215247 valid's rmse: 0.000394518   valid's RMSPE: 0.253088
[300]   train's rmse: 0.000331465   train's RMSPE: 0.21236  valid's rmse: 0.00039235    valid's RMSPE: 0.251697
[350]   train's rmse: 0.000327886   train's RMSPE: 0.210067 valid's rmse: 0.000390887   valid's RMSPE: 0.250759
[400]   train's rmse: 0.000323925   train's RMSPE: 0.207529 valid's rmse: 0.000389455   valid's RMSPE: 0.24984
[450]   train's rmse: 0.000320218   train's RMSPE: 0.205154 valid's rmse: 0.000388436   valid's RMSPE: 0.249186
[500]   train's rmse: 0.000317056   train's RMSPE: 0.203129 valid's rmse: 0.000387889   valid's RMSPE: 0.248835
[550]   train's rmse: 0.000314214   train's RMSPE: 0.201308 valid's rmse: 0.000387163   valid's RMSPE: 0.24837
[600]   train's rmse: 0.000311268   train's RMSPE: 0.19942  valid's rmse: 0.000385804   valid's RMSPE: 0.247498
[650]   train's rmse: 0.000308721   train's RMSPE: 0.197788 valid's rmse: 0.000385289   valid's RMSPE: 0.247168
[700]   train's rmse: 0.000305828   train's RMSPE: 0.195935 valid's rmse: 0.00038494    valid's RMSPE: 0.246944
[750]   train's rmse: 0.000303355   train's RMSPE: 0.194351 valid's rmse: 0.000384664   valid's RMSPE: 0.246767
Early stopping, best iteration is:
[722]   train's rmse: 0.000304617   train's RMSPE: 0.195159 valid's rmse: 0.000384491   valid's RMSPE: 0.246655
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000374896   train's RMSPE: 0.240309 valid's rmse: 0.000403127   valid's RMSPE: 0.258079
[100]   train's rmse: 0.000356202   train's RMSPE: 0.228326 valid's rmse: 0.000389594   valid's RMSPE: 0.249415
[150]   train's rmse: 0.000347999   train's RMSPE: 0.223068 valid's rmse: 0.000385396   valid's RMSPE: 0.246728
[200]   train's rmse: 0.000341418   train's RMSPE: 0.218849 valid's rmse: 0.000382114   valid's RMSPE: 0.244627
[250]   train's rmse: 0.000336316   train's RMSPE: 0.215579 valid's rmse: 0.000380479   valid's RMSPE: 0.24358
[300]   train's rmse: 0.000331477   train's RMSPE: 0.212477 valid's rmse: 0.000379227   valid's RMSPE: 0.242778
[350]   train's rmse: 0.000327227   train's RMSPE: 0.209753 valid's rmse: 0.000378156   valid's RMSPE: 0.242093
[400]   train's rmse: 0.000323643   train's RMSPE: 0.207456 valid's rmse: 0.000377038   valid's RMSPE: 0.241377
[450]   train's rmse: 0.000320442   train's RMSPE: 0.205404 valid's rmse: 0.000376976   valid's RMSPE: 0.241337
[500]   train's rmse: 0.000317603   train's RMSPE: 0.203584 valid's rmse: 0.000376476   valid's RMSPE: 0.241017
[550]   train's rmse: 0.000314633   train's RMSPE: 0.20168  valid's rmse: 0.000375884   valid's RMSPE: 0.240638
Early stopping, best iteration is:
[541]   train's rmse: 0.000315087   train's RMSPE: 0.201971 valid's rmse: 0.000375791   valid's RMSPE: 0.240578
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000380779   train's RMSPE: 0.24353  valid's rmse: 0.00037262    valid's RMSPE: 0.240692
[100]   train's rmse: 0.000362122   train's RMSPE: 0.231598 valid's rmse: 0.000361063   valid's RMSPE: 0.233227
[150]   train's rmse: 0.000354808   train's RMSPE: 0.22692  valid's rmse: 0.000358387   valid's RMSPE: 0.231498
[200]   train's rmse: 0.000349344   train's RMSPE: 0.223425 valid's rmse: 0.000357334   valid's RMSPE: 0.230818
[250]   train's rmse: 0.000343091   train's RMSPE: 0.219426 valid's rmse: 0.000354386   valid's RMSPE: 0.228914
[300]   train's rmse: 0.000338293   train's RMSPE: 0.216358 valid's rmse: 0.000353533   valid's RMSPE: 0.228363
[350]   train's rmse: 0.000334335   train's RMSPE: 0.213826 valid's rmse: 0.000352119   valid's RMSPE: 0.22745
[400]   train's rmse: 0.000330296   train's RMSPE: 0.211243 valid's rmse: 0.000352119   valid's RMSPE: 0.22745
[450]   train's rmse: 0.000326637   train's RMSPE: 0.208903 valid's rmse: 0.000351718   valid's RMSPE: 0.227191
Early stopping, best iteration is:
[428]   train's rmse: 0.000328326   train's RMSPE: 0.209983 valid's rmse: 0.000351638   valid's RMSPE: 0.227139
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000386185   train's RMSPE: 0.248015 valid's rmse: 0.00035839    valid's RMSPE: 0.227683
[100]   train's rmse: 0.000367017   train's RMSPE: 0.235705 valid's rmse: 0.000346953   valid's RMSPE: 0.220417
[150]   train's rmse: 0.000359586   train's RMSPE: 0.230933 valid's rmse: 0.000346371   valid's RMSPE: 0.220048
[200]   train's rmse: 0.000353622   train's RMSPE: 0.227103 valid's rmse: 0.000345637   valid's RMSPE: 0.219581
[250]   train's rmse: 0.00034847    train's RMSPE: 0.223794 valid's rmse: 0.000345537   valid's RMSPE: 0.219517
Early stopping, best iteration is:
[228]   train's rmse: 0.000350555   train's RMSPE: 0.225133 valid's rmse: 0.000345089   valid's RMSPE: 0.219233
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00038105    train's RMSPE: 0.244157 valid's rmse: 0.000377149   valid's RMSPE: 0.241831
[100]   train's rmse: 0.000363108   train's RMSPE: 0.23266  valid's rmse: 0.00036647    valid's RMSPE: 0.234983
[150]   train's rmse: 0.000354585   train's RMSPE: 0.227199 valid's rmse: 0.000362696   valid's RMSPE: 0.232563
[200]   train's rmse: 0.000348785   train's RMSPE: 0.223483 valid's rmse: 0.0003623 valid's RMSPE: 0.23231
[250]   train's rmse: 0.000343742   train's RMSPE: 0.220252 valid's rmse: 0.00036111    valid's RMSPE: 0.231547
[300]   train's rmse: 0.00033953    train's RMSPE: 0.217553 valid's rmse: 0.000360479   valid's RMSPE: 0.231142
[350]   train's rmse: 0.000335194   train's RMSPE: 0.214774 valid's rmse: 0.000360726   valid's RMSPE: 0.2313
Early stopping, best iteration is:
[302]   train's rmse: 0.000339287   train's RMSPE: 0.217397 valid's rmse: 0.000360236   valid's RMSPE: 0.230986
Our out of folds RMSPE is 0.233, compared to 0.1891958234747661, giving gain 0.04380417652523391
Our cv fold scores are [0.247, 0.241, 0.227, 0.219, 0.231]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000645755   train's RMSPE: 0.242209 valid's rmse: 0.00065261    valid's RMSPE: 0.250796
[100]   train's rmse: 0.000608497   train's RMSPE: 0.228234 valid's rmse: 0.000628907   valid's RMSPE: 0.241687
[150]   train's rmse: 0.000591864   train's RMSPE: 0.221995 valid's rmse: 0.000622411   valid's RMSPE: 0.23919
[200]   train's rmse: 0.000578248   train's RMSPE: 0.216888 valid's rmse: 0.000618606   valid's RMSPE: 0.237728
[250]   train's rmse: 0.000567018   train's RMSPE: 0.212676 valid's rmse: 0.000616927   valid's RMSPE: 0.237083
[300]   train's rmse: 0.000558904   train's RMSPE: 0.209633 valid's rmse: 0.000617544   valid's RMSPE: 0.23732
Early stopping, best iteration is:
[276]   train's rmse: 0.0005624 train's RMSPE: 0.210944 valid's rmse: 0.00061633    valid's RMSPE: 0.236854
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000633331   train's RMSPE: 0.237505 valid's rmse: 0.000718038   valid's RMSPE: 0.276134
[100]   train's rmse: 0.000597305   train's RMSPE: 0.223995 valid's rmse: 0.000681815   valid's RMSPE: 0.262204
[150]   train's rmse: 0.000581477   train's RMSPE: 0.218059 valid's rmse: 0.000677228   valid's RMSPE: 0.26044
[200]   train's rmse: 0.000569638   train's RMSPE: 0.21362  valid's rmse: 0.000673823   valid's RMSPE: 0.25913
[250]   train's rmse: 0.000559143   train's RMSPE: 0.209684 valid's rmse: 0.000669266   valid's RMSPE: 0.257378
[300]   train's rmse: 0.000549509   train's RMSPE: 0.206071 valid's rmse: 0.000668243   valid's RMSPE: 0.256985
[350]   train's rmse: 0.000541409   train's RMSPE: 0.203033 valid's rmse: 0.000668205   valid's RMSPE: 0.25697
[400]   train's rmse: 0.000533405   train's RMSPE: 0.200032 valid's rmse: 0.000666126   valid's RMSPE: 0.25617
Early stopping, best iteration is:
[395]   train's rmse: 0.000534066   train's RMSPE: 0.200279 valid's rmse: 0.000665595   valid's RMSPE: 0.255966
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000641801   train's RMSPE: 0.241363 valid's rmse: 0.000619974   valid's RMSPE: 0.235836
[100]   train's rmse: 0.000607091   train's RMSPE: 0.228309 valid's rmse: 0.000606186   valid's RMSPE: 0.230591
[150]   train's rmse: 0.00059135    train's RMSPE: 0.22239  valid's rmse: 0.000605376   valid's RMSPE: 0.230283
Early stopping, best iteration is:
[120]   train's rmse: 0.000600299   train's RMSPE: 0.225755 valid's rmse: 0.00060408    valid's RMSPE: 0.22979
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00063781    train's RMSPE: 0.242013 valid's rmse: 0.000645532   valid's RMSPE: 0.236752
[100]   train's rmse: 0.000599782   train's RMSPE: 0.227584 valid's rmse: 0.000629068   valid's RMSPE: 0.230714
[150]   train's rmse: 0.000584285   train's RMSPE: 0.221703 valid's rmse: 0.000623699   valid's RMSPE: 0.228745
[200]   train's rmse: 0.000573169   train's RMSPE: 0.217485 valid's rmse: 0.000621597   valid's RMSPE: 0.227973
[250]   train's rmse: 0.00056404    train's RMSPE: 0.214021 valid's rmse: 0.000620947   valid's RMSPE: 0.227735
[300]   train's rmse: 0.00055548    train's RMSPE: 0.210773 valid's rmse: 0.000620034   valid's RMSPE: 0.2274
[350]   train's rmse: 0.000546966   train's RMSPE: 0.207543 valid's rmse: 0.000618955   valid's RMSPE: 0.227004
[400]   train's rmse: 0.000540498   train's RMSPE: 0.205089 valid's rmse: 0.0006189 valid's RMSPE: 0.226984
Early stopping, best iteration is:
[354]   train's rmse: 0.000546437   train's RMSPE: 0.207342 valid's rmse: 0.00061859    valid's RMSPE: 0.226871
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000639017   train's RMSPE: 0.242234 valid's rmse: 0.000665049   valid's RMSPE: 0.244927
[100]   train's rmse: 0.000603196   train's RMSPE: 0.228656 valid's rmse: 0.000650337   valid's RMSPE: 0.239508
[150]   train's rmse: 0.0005881 train's RMSPE: 0.222933 valid's rmse: 0.000648167   valid's RMSPE: 0.238709
[200]   train's rmse: 0.000576715   train's RMSPE: 0.218617 valid's rmse: 0.000647098   valid's RMSPE: 0.238315
[250]   train's rmse: 0.000565971   train's RMSPE: 0.214544 valid's rmse: 0.000644731   valid's RMSPE: 0.237444
[300]   train's rmse: 0.000557449   train's RMSPE: 0.211314 valid's rmse: 0.000645945   valid's RMSPE: 0.237891
Early stopping, best iteration is:
[250]   train's rmse: 0.000565971   train's RMSPE: 0.214544 valid's rmse: 0.000644731   valid's RMSPE: 0.237444
Our out of folds RMSPE is 0.238, compared to 0.210502884168752, giving gain 0.027497115831247976
Our cv fold scores are [0.237, 0.256, 0.23, 0.227, 0.237]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000475499   train's RMSPE: 0.222177 valid's rmse: 0.000501513   valid's RMSPE: 0.229924
[100]   train's rmse: 0.000451002   train's RMSPE: 0.21073  valid's rmse: 0.00048327    valid's RMSPE: 0.22156
[150]   train's rmse: 0.00044037    train's RMSPE: 0.205762 valid's rmse: 0.000479763   valid's RMSPE: 0.219952
[200]   train's rmse: 0.000432065   train's RMSPE: 0.201882 valid's rmse: 0.000475635   valid's RMSPE: 0.21806
[250]   train's rmse: 0.000424424   train's RMSPE: 0.198312 valid's rmse: 0.000474535   valid's RMSPE: 0.217555
[300]   train's rmse: 0.000417968   train's RMSPE: 0.195295 valid's rmse: 0.000473103   valid's RMSPE: 0.216899
[350]   train's rmse: 0.000413  train's RMSPE: 0.192974 valid's rmse: 0.000471231   valid's RMSPE: 0.21604
[400]   train's rmse: 0.000407639   train's RMSPE: 0.190469 valid's rmse: 0.000470086   valid's RMSPE: 0.215516
[450]   train's rmse: 0.00040316    train's RMSPE: 0.188376 valid's rmse: 0.000469052   valid's RMSPE: 0.215042
[500]   train's rmse: 0.000398583   train's RMSPE: 0.186238 valid's rmse: 0.000469878   valid's RMSPE: 0.21542
Early stopping, best iteration is:
[480]   train's rmse: 0.000400182   train's RMSPE: 0.186985 valid's rmse: 0.000468622   valid's RMSPE: 0.214844
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000474172   train's RMSPE: 0.219795 valid's rmse: 0.000507889   valid's RMSPE: 0.240389
[100]   train's rmse: 0.000449064   train's RMSPE: 0.208156 valid's rmse: 0.000490142   valid's RMSPE: 0.231989
[150]   train's rmse: 0.000439146   train's RMSPE: 0.203559 valid's rmse: 0.000486344   valid's RMSPE: 0.230191
[200]   train's rmse: 0.00043174    train's RMSPE: 0.200126 valid's rmse: 0.000486169   valid's RMSPE: 0.230109
[250]   train's rmse: 0.000424922   train's RMSPE: 0.196965 valid's rmse: 0.000483863   valid's RMSPE: 0.229017
[300]   train's rmse: 0.000418862   train's RMSPE: 0.194157 valid's rmse: 0.000483086   valid's RMSPE: 0.228649
[350]   train's rmse: 0.000413369   train's RMSPE: 0.19161  valid's rmse: 0.000481812   valid's RMSPE: 0.228046
Early stopping, best iteration is:
[345]   train's rmse: 0.000413719   train's RMSPE: 0.191772 valid's rmse: 0.000481538   valid's RMSPE: 0.227917
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00047938    train's RMSPE: 0.222543 valid's rmse: 0.000476849   valid's RMSPE: 0.224388
[100]   train's rmse: 0.000452882   train's RMSPE: 0.210242 valid's rmse: 0.000461836   valid's RMSPE: 0.217324
[150]   train's rmse: 0.000442147   train's RMSPE: 0.205258 valid's rmse: 0.000459855   valid's RMSPE: 0.216391
[200]   train's rmse: 0.000433321   train's RMSPE: 0.201161 valid's rmse: 0.000459516   valid's RMSPE: 0.216232
[250]   train's rmse: 0.00042606    train's RMSPE: 0.19779  valid's rmse: 0.000459866   valid's RMSPE: 0.216397
Early stopping, best iteration is:
[215]   train's rmse: 0.000430995   train's RMSPE: 0.200081 valid's rmse: 0.000458792   valid's RMSPE: 0.215892
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000483613   train's RMSPE: 0.22563  valid's rmse: 0.000470558   valid's RMSPE: 0.217064
[100]   train's rmse: 0.000456027   train's RMSPE: 0.21276  valid's rmse: 0.000456814   valid's RMSPE: 0.210724
[150]   train's rmse: 0.000444718   train's RMSPE: 0.207484 valid's rmse: 0.000455643   valid's RMSPE: 0.210184
[200]   train's rmse: 0.000436111   train's RMSPE: 0.203469 valid's rmse: 0.000454987   valid's RMSPE: 0.209882
Early stopping, best iteration is:
[174]   train's rmse: 0.000440069   train's RMSPE: 0.205315 valid's rmse: 0.000454293   valid's RMSPE: 0.209561
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000480531   train's RMSPE: 0.223902 valid's rmse: 0.000492141   valid's RMSPE: 0.22822
[100]   train's rmse: 0.000455391   train's RMSPE: 0.212188 valid's rmse: 0.000471626   valid's RMSPE: 0.218706
[150]   train's rmse: 0.000443968   train's RMSPE: 0.206866 valid's rmse: 0.000467748   valid's RMSPE: 0.216908
[200]   train's rmse: 0.000435913   train's RMSPE: 0.203113 valid's rmse: 0.0004665 valid's RMSPE: 0.216329
[250]   train's rmse: 0.000428089   train's RMSPE: 0.199467 valid's rmse: 0.000465846   valid's RMSPE: 0.216026
[300]   train's rmse: 0.000421871   train's RMSPE: 0.19657  valid's rmse: 0.000464787   valid's RMSPE: 0.215535
[350]   train's rmse: 0.000415913   train's RMSPE: 0.193794 valid's rmse: 0.000466711   valid's RMSPE: 0.216427
Early stopping, best iteration is:
[312]   train's rmse: 0.00042025    train's RMSPE: 0.195814 valid's rmse: 0.000464494   valid's RMSPE: 0.215399
Our out of folds RMSPE is 0.217, compared to 0.18540826325940205, giving gain 0.03159173674059795
Our cv fold scores are [0.215, 0.228, 0.216, 0.21, 0.215]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000631903   train's RMSPE: 0.188603 valid's rmse: 0.000687421   valid's RMSPE: 0.203406
[100]   train's rmse: 0.000596106   train's RMSPE: 0.177918 valid's rmse: 0.00066195    valid's RMSPE: 0.195869
[150]   train's rmse: 0.00058033    train's RMSPE: 0.17321  valid's rmse: 0.000656777   valid's RMSPE: 0.194338
[200]   train's rmse: 0.000569206   train's RMSPE: 0.169889 valid's rmse: 0.000654043   valid's RMSPE: 0.193529
[250]   train's rmse: 0.000559339   train's RMSPE: 0.166945 valid's rmse: 0.000653716   valid's RMSPE: 0.193432
[300]   train's rmse: 0.000551271   train's RMSPE: 0.164537 valid's rmse: 0.000651787   valid's RMSPE: 0.192862
[350]   train's rmse: 0.000544843   train's RMSPE: 0.162618 valid's rmse: 0.000653232   valid's RMSPE: 0.193289
Early stopping, best iteration is:
[307]   train's rmse: 0.000550205   train's RMSPE: 0.164218 valid's rmse: 0.000651735   valid's RMSPE: 0.192846
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000637895   train's RMSPE: 0.189179 valid's rmse: 0.000656914   valid's RMSPE: 0.199334
[100]   train's rmse: 0.000603699   train's RMSPE: 0.179038 valid's rmse: 0.000632781   valid's RMSPE: 0.192011
[150]   train's rmse: 0.000589424   train's RMSPE: 0.174804 valid's rmse: 0.000630173   valid's RMSPE: 0.191219
[200]   train's rmse: 0.000578063   train's RMSPE: 0.171435 valid's rmse: 0.000630073   valid's RMSPE: 0.191189
Early stopping, best iteration is:
[189]   train's rmse: 0.000580258   train's RMSPE: 0.172086 valid's rmse: 0.000628454   valid's RMSPE: 0.190698
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000643145   train's RMSPE: 0.190865 valid's rmse: 0.000624431   valid's RMSPE: 0.188988
[100]   train's rmse: 0.000608249   train's RMSPE: 0.180509 valid's rmse: 0.000601565   valid's RMSPE: 0.182067
[150]   train's rmse: 0.00059361    train's RMSPE: 0.176165 valid's rmse: 0.000596687   valid's RMSPE: 0.18059
[200]   train's rmse: 0.000582513   train's RMSPE: 0.172871 valid's rmse: 0.000593859   valid's RMSPE: 0.179735
[250]   train's rmse: 0.000572782   train's RMSPE: 0.169984 valid's rmse: 0.000592081   valid's RMSPE: 0.179197
[300]   train's rmse: 0.000564558   train's RMSPE: 0.167543 valid's rmse: 0.000590164   valid's RMSPE: 0.178616
Early stopping, best iteration is:
[298]   train's rmse: 0.000564822   train's RMSPE: 0.167621 valid's rmse: 0.000589934   valid's RMSPE: 0.178547
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000633938   train's RMSPE: 0.189575 valid's rmse: 0.000666448   valid's RMSPE: 0.195641
[100]   train's rmse: 0.000598395   train's RMSPE: 0.178947 valid's rmse: 0.000645333   valid's RMSPE: 0.189443
[150]   train's rmse: 0.000584202   train's RMSPE: 0.174702 valid's rmse: 0.000646056   valid's RMSPE: 0.189655
Early stopping, best iteration is:
[112]   train's rmse: 0.000594012   train's RMSPE: 0.177636 valid's rmse: 0.000645027   valid's RMSPE: 0.189353
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000641092   train's RMSPE: 0.191634 valid's rmse: 0.000636771   valid's RMSPE: 0.187258
[100]   train's rmse: 0.0006068 train's RMSPE: 0.181383 valid's rmse: 0.000613768   valid's RMSPE: 0.180493
[150]   train's rmse: 0.000592956   train's RMSPE: 0.177245 valid's rmse: 0.000609752   valid's RMSPE: 0.179312
[200]   train's rmse: 0.000581884   train's RMSPE: 0.173935 valid's rmse: 0.000608389   valid's RMSPE: 0.178911
[250]   train's rmse: 0.000571646   train's RMSPE: 0.170875 valid's rmse: 0.000608534   valid's RMSPE: 0.178954
[300]   train's rmse: 0.000563322   train's RMSPE: 0.168387 valid's rmse: 0.000606582   valid's RMSPE: 0.17838
Early stopping, best iteration is:
[293]   train's rmse: 0.000564443   train's RMSPE: 0.168722 valid's rmse: 0.000606037   valid's RMSPE: 0.17822

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.186, compared to 0.16624971333012886, giving gain 0.019750286669871137
Our cv fold scores are [0.193, 0.191, 0.179, 0.189, 0.178]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.0010901 train's RMSPE: 0.234976 valid's rmse: 0.00115226    valid's RMSPE: 0.254064
[100]   train's rmse: 0.00104095    train's RMSPE: 0.224381 valid's rmse: 0.00111374    valid's RMSPE: 0.24557
[150]   train's rmse: 0.00101633    train's RMSPE: 0.219074 valid's rmse: 0.00110823    valid's RMSPE: 0.244355
[200]   train's rmse: 0.000996925   train's RMSPE: 0.214892 valid's rmse: 0.0011045 valid's RMSPE: 0.243533
[250]   train's rmse: 0.000978353   train's RMSPE: 0.210888 valid's rmse: 0.00110115    valid's RMSPE: 0.242793
[300]   train's rmse: 0.000962653   train's RMSPE: 0.207504 valid's rmse: 0.00109685    valid's RMSPE: 0.241847
[350]   train's rmse: 0.000949257   train's RMSPE: 0.204616 valid's rmse: 0.0010996 valid's RMSPE: 0.242452
Early stopping, best iteration is:
[321]   train's rmse: 0.000956195   train's RMSPE: 0.206112 valid's rmse: 0.00109603    valid's RMSPE: 0.241665
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00109069    train's RMSPE: 0.235945 valid's rmse: 0.00112938    valid's RMSPE: 0.245579
[100]   train's rmse: 0.00104019    train's RMSPE: 0.225021 valid's rmse: 0.00110714    valid's RMSPE: 0.240743
[150]   train's rmse: 0.00101395    train's RMSPE: 0.219344 valid's rmse: 0.00110402    valid's RMSPE: 0.240065
[200]   train's rmse: 0.000991802   train's RMSPE: 0.214553 valid's rmse: 0.00110216    valid's RMSPE: 0.239661
Early stopping, best iteration is:
[192]   train's rmse: 0.00099442    train's RMSPE: 0.215119 valid's rmse: 0.00110065    valid's RMSPE: 0.239332
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00108747    train's RMSPE: 0.235801 valid's rmse: 0.00112887    valid's RMSPE: 0.243175
[100]   train's rmse: 0.00103964    train's RMSPE: 0.225429 valid's rmse: 0.00110068    valid's RMSPE: 0.237102
[150]   train's rmse: 0.00101408    train's RMSPE: 0.219887 valid's rmse: 0.00109691    valid's RMSPE: 0.236291
[200]   train's rmse: 0.000991947   train's RMSPE: 0.215088 valid's rmse: 0.00109517    valid's RMSPE: 0.235916
[250]   train's rmse: 0.000972401   train's RMSPE: 0.21085  valid's rmse: 0.00109207    valid's RMSPE: 0.235247
Early stopping, best iteration is:
[249]   train's rmse: 0.000972731   train's RMSPE: 0.210921 valid's rmse: 0.0010919 valid's RMSPE: 0.235211
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00109323    train's RMSPE: 0.237758 valid's rmse: 0.00110546    valid's RMSPE: 0.235221
[100]   train's rmse: 0.00103946    train's RMSPE: 0.226065 valid's rmse: 0.00109399    valid's RMSPE: 0.232782
Early stopping, best iteration is:
[83]    train's rmse: 0.00105139    train's RMSPE: 0.228658 valid's rmse: 0.00109384    valid's RMSPE: 0.232748
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00108962    train's RMSPE: 0.235959 valid's rmse: 0.00112831    valid's RMSPE: 0.244333
[100]   train's rmse: 0.00103838    train's RMSPE: 0.224864 valid's rmse: 0.00111471    valid's RMSPE: 0.241389
Early stopping, best iteration is:
[85]    train's rmse: 0.00104923    train's RMSPE: 0.227213 valid's rmse: 0.00111112    valid's RMSPE: 0.240612

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.238, compared to 0.22397550035495178, giving gain 0.014024499645048205
Our cv fold scores are [0.242, 0.239, 0.235, 0.233, 0.241]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000840623   train's RMSPE: 0.284471 valid's rmse: 0.000850233   valid's RMSPE: 0.282354
[100]   train's rmse: 0.000806727   train's RMSPE: 0.273001 valid's rmse: 0.000836277   valid's RMSPE: 0.277719
[150]   train's rmse: 0.000788228   train's RMSPE: 0.266741 valid's rmse: 0.000834146   valid's RMSPE: 0.277011
[200]   train's rmse: 0.000773021   train's RMSPE: 0.261595 valid's rmse: 0.000837931   valid's RMSPE: 0.278268
Early stopping, best iteration is:
[162]   train's rmse: 0.000783743   train's RMSPE: 0.265223 valid's rmse: 0.000832611   valid's RMSPE: 0.276502
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000830888   train's RMSPE: 0.281039 valid's rmse: 0.000879018   valid's RMSPE: 0.292508
[100]   train's rmse: 0.000796633   train's RMSPE: 0.269452 valid's rmse: 0.000862919   valid's RMSPE: 0.287151
[150]   train's rmse: 0.000777367   train's RMSPE: 0.262936 valid's rmse: 0.00086091    valid's RMSPE: 0.286483
[200]   train's rmse: 0.000761846   train's RMSPE: 0.257686 valid's rmse: 0.00085624    valid's RMSPE: 0.284929
[250]   train's rmse: 0.000748265   train's RMSPE: 0.253093 valid's rmse: 0.0008528 valid's RMSPE: 0.283784
Early stopping, best iteration is:
[236]   train's rmse: 0.000751954   train's RMSPE: 0.25434  valid's rmse: 0.000852084   valid's RMSPE: 0.283546
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000842273   train's RMSPE: 0.284163 valid's rmse: 0.000826538   valid's RMSPE: 0.277925
[100]   train's rmse: 0.000805362   train's RMSPE: 0.27171  valid's rmse: 0.00080714    valid's RMSPE: 0.271402
[150]   train's rmse: 0.000782963   train's RMSPE: 0.264153 valid's rmse: 0.000803339   valid's RMSPE: 0.270124
[200]   train's rmse: 0.00076708    train's RMSPE: 0.258795 valid's rmse: 0.000800319   valid's RMSPE: 0.269109
[250]   train's rmse: 0.000753035   train's RMSPE: 0.254056 valid's rmse: 0.000802097   valid's RMSPE: 0.269706
Early stopping, best iteration is:
[219]   train's rmse: 0.000761753   train's RMSPE: 0.256998 valid's rmse: 0.000800011   valid's RMSPE: 0.269005
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000840862   train's RMSPE: 0.282458 valid's rmse: 0.000862178   valid's RMSPE: 0.294914
[100]   train's rmse: 0.000805724   train's RMSPE: 0.270655 valid's rmse: 0.000857536   valid's RMSPE: 0.293326
Early stopping, best iteration is:
[86]    train's rmse: 0.000812213   train's RMSPE: 0.272834 valid's rmse: 0.000853652   valid's RMSPE: 0.291997
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000831609   train's RMSPE: 0.279266 valid's rmse: 0.000891345   valid's RMSPE: 0.30524
[100]   train's rmse: 0.000799893   train's RMSPE: 0.268616 valid's rmse: 0.000881743   valid's RMSPE: 0.301952
[150]   train's rmse: 0.000779396   train's RMSPE: 0.261732 valid's rmse: 0.000877975   valid's RMSPE: 0.300662
Early stopping, best iteration is:
[136]   train's rmse: 0.000783616   train's RMSPE: 0.26315  valid's rmse: 0.00087755    valid's RMSPE: 0.300516
Our out of folds RMSPE is 0.285, compared to 0.2524301885495816, giving gain 0.03256981145041837
Our cv fold scores are [0.277, 0.284, 0.269, 0.292, 0.301]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000400712   train's RMSPE: 0.24585  valid's rmse: 0.000427958   valid's RMSPE: 0.265441
[100]   train's rmse: 0.000378718   train's RMSPE: 0.232356 valid's rmse: 0.000415029   valid's RMSPE: 0.257422
[150]   train's rmse: 0.00036832    train's RMSPE: 0.225977 valid's rmse: 0.000410547   valid's RMSPE: 0.254642
[200]   train's rmse: 0.000360147   train's RMSPE: 0.220962 valid's rmse: 0.000407292   valid's RMSPE: 0.252623
[250]   train's rmse: 0.000353454   train's RMSPE: 0.216856 valid's rmse: 0.000405415   valid's RMSPE: 0.251459
[300]   train's rmse: 0.000347894   train's RMSPE: 0.213444 valid's rmse: 0.000403566   valid's RMSPE: 0.250312
[350]   train's rmse: 0.000342914   train's RMSPE: 0.210389 valid's rmse: 0.000401752   valid's RMSPE: 0.249186
[400]   train's rmse: 0.000338632   train's RMSPE: 0.207762 valid's rmse: 0.00040066    valid's RMSPE: 0.248509
[450]   train's rmse: 0.000334659   train's RMSPE: 0.205325 valid's rmse: 0.000400027   valid's RMSPE: 0.248117
[500]   train's rmse: 0.000331283   train's RMSPE: 0.203253 valid's rmse: 0.000398135   valid's RMSPE: 0.246943
[550]   train's rmse: 0.000327847   train's RMSPE: 0.201145 valid's rmse: 0.000397434   valid's RMSPE: 0.246509
[600]   train's rmse: 0.000324563   train's RMSPE: 0.19913  valid's rmse: 0.000397358   valid's RMSPE: 0.246461
[650]   train's rmse: 0.000321316   train's RMSPE: 0.197138 valid's rmse: 0.000396673   valid's RMSPE: 0.246036
[700]   train's rmse: 0.000318264   train's RMSPE: 0.195266 valid's rmse: 0.000396229   valid's RMSPE: 0.245761
Early stopping, best iteration is:
[689]   train's rmse: 0.000319036   train's RMSPE: 0.195739 valid's rmse: 0.000395991   valid's RMSPE: 0.245613
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000399741   train's RMSPE: 0.245487 valid's rmse: 0.000439764   valid's RMSPE: 0.271749
[100]   train's rmse: 0.000377872   train's RMSPE: 0.232057 valid's rmse: 0.000422058   valid's RMSPE: 0.260807
[150]   train's rmse: 0.000367287   train's RMSPE: 0.225557 valid's rmse: 0.00041924    valid's RMSPE: 0.259066
[200]   train's rmse: 0.000358995   train's RMSPE: 0.220465 valid's rmse: 0.000415242   valid's RMSPE: 0.256596
[250]   train's rmse: 0.000352268   train's RMSPE: 0.216333 valid's rmse: 0.000413227   valid's RMSPE: 0.25535
[300]   train's rmse: 0.000346818   train's RMSPE: 0.212986 valid's rmse: 0.000411867   valid's RMSPE: 0.254511
[350]   train's rmse: 0.00034182    train's RMSPE: 0.209917 valid's rmse: 0.000411945   valid's RMSPE: 0.254559
[400]   train's rmse: 0.000337473   train's RMSPE: 0.207247 valid's rmse: 0.000410745   valid's RMSPE: 0.253817
[450]   train's rmse: 0.000333515   train's RMSPE: 0.204817 valid's rmse: 0.000410609   valid's RMSPE: 0.253733
[500]   train's rmse: 0.000329872   train's RMSPE: 0.20258  valid's rmse: 0.000410059   valid's RMSPE: 0.253393
[550]   train's rmse: 0.00032654    train's RMSPE: 0.200533 valid's rmse: 0.000409749   valid's RMSPE: 0.253202
Early stopping, best iteration is:
[524]   train's rmse: 0.000328193   train's RMSPE: 0.201549 valid's rmse: 0.000409636   valid's RMSPE: 0.253132
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00040267    train's RMSPE: 0.247149 valid's rmse: 0.000418216   valid's RMSPE: 0.259
[100]   train's rmse: 0.00038126    train's RMSPE: 0.234008 valid's rmse: 0.000402406   valid's RMSPE: 0.249209
[150]   train's rmse: 0.000371162   train's RMSPE: 0.22781  valid's rmse: 0.000398028   valid's RMSPE: 0.246497
[200]   train's rmse: 0.000363261   train's RMSPE: 0.22296  valid's rmse: 0.000395038   valid's RMSPE: 0.244646
[250]   train's rmse: 0.000356632   train's RMSPE: 0.218892 valid's rmse: 0.000392601   valid's RMSPE: 0.243137
[300]   train's rmse: 0.000350541   train's RMSPE: 0.215153 valid's rmse: 0.000392403   valid's RMSPE: 0.243014
[350]   train's rmse: 0.000345627   train's RMSPE: 0.212137 valid's rmse: 0.000391867   valid's RMSPE: 0.242682
[400]   train's rmse: 0.00034119    train's RMSPE: 0.209414 valid's rmse: 0.000391828   valid's RMSPE: 0.242658
Early stopping, best iteration is:
[366]   train's rmse: 0.000343918   train's RMSPE: 0.211088 valid's rmse: 0.000391237   valid's RMSPE: 0.242292
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000411397   train's RMSPE: 0.253605 valid's rmse: 0.000378933   valid's RMSPE: 0.230612
[100]   train's rmse: 0.000387605   train's RMSPE: 0.238938 valid's rmse: 0.000373875   valid's RMSPE: 0.227534
[150]   train's rmse: 0.00037701    train's RMSPE: 0.232407 valid's rmse: 0.000373359   valid's RMSPE: 0.22722
[200]   train's rmse: 0.000368171   train's RMSPE: 0.226958 valid's rmse: 0.000373773   valid's RMSPE: 0.227472
Early stopping, best iteration is:
[156]   train's rmse: 0.000375767   train's RMSPE: 0.23164  valid's rmse: 0.000372855   valid's RMSPE: 0.226913
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000407674   train's RMSPE: 0.251346 valid's rmse: 0.000402054   valid's RMSPE: 0.24454
[100]   train's rmse: 0.000386915   train's RMSPE: 0.238547 valid's rmse: 0.000383499   valid's RMSPE: 0.233254
[150]   train's rmse: 0.000376889   train's RMSPE: 0.232366 valid's rmse: 0.000380031   valid's RMSPE: 0.231144
[200]   train's rmse: 0.000368858   train's RMSPE: 0.227414 valid's rmse: 0.000378267   valid's RMSPE: 0.230072
[250]   train's rmse: 0.000362341   train's RMSPE: 0.223396 valid's rmse: 0.000376732   valid's RMSPE: 0.229138
[300]   train's rmse: 0.000356255   train's RMSPE: 0.219644 valid's rmse: 0.000373997   valid's RMSPE: 0.227475
[350]   train's rmse: 0.000350736   train's RMSPE: 0.216241 valid's rmse: 0.000373381   valid's RMSPE: 0.2271
Early stopping, best iteration is:
[332]   train's rmse: 0.000352731   train's RMSPE: 0.217471 valid's rmse: 0.00037255    valid's RMSPE: 0.226595
Our out of folds RMSPE is 0.239, compared to 0.20861916092721863, giving gain 0.030380839072781357
Our cv fold scores are [0.246, 0.253, 0.242, 0.227, 0.227]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000716357   train's RMSPE: 0.240895 valid's rmse: 0.000737814   valid's RMSPE: 0.253702
[100]   train's rmse: 0.00067347    train's RMSPE: 0.226473 valid's rmse: 0.000697632   valid's RMSPE: 0.239885
[150]   train's rmse: 0.000657522   train's RMSPE: 0.22111  valid's rmse: 0.000688751   valid's RMSPE: 0.236831
[200]   train's rmse: 0.000646331   train's RMSPE: 0.217346 valid's rmse: 0.000686401   valid's RMSPE: 0.236024
[250]   train's rmse: 0.000635189   train's RMSPE: 0.2136   valid's rmse: 0.000684948   valid's RMSPE: 0.235524
Early stopping, best iteration is:
[236]   train's rmse: 0.00063785    train's RMSPE: 0.214495 valid's rmse: 0.000684195   valid's RMSPE: 0.235265
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000708432   train's RMSPE: 0.238696 valid's rmse: 0.000747498   valid's RMSPE: 0.255102
[100]   train's rmse: 0.000669011   train's RMSPE: 0.225414 valid's rmse: 0.000720727   valid's RMSPE: 0.245966
[150]   train's rmse: 0.000652866   train's RMSPE: 0.219974 valid's rmse: 0.000718765   valid's RMSPE: 0.245296
[200]   train's rmse: 0.000639666   train's RMSPE: 0.215527 valid's rmse: 0.000717656   valid's RMSPE: 0.244918
Early stopping, best iteration is:
[192]   train's rmse: 0.00064172    train's RMSPE: 0.216219 valid's rmse: 0.000717122   valid's RMSPE: 0.244736
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000707283   train's RMSPE: 0.23988  valid's rmse: 0.000729996   valid's RMSPE: 0.242619
[100]   train's rmse: 0.000666597   train's RMSPE: 0.226081 valid's rmse: 0.000713077   valid's RMSPE: 0.236996
[150]   train's rmse: 0.000650258   train's RMSPE: 0.220539 valid's rmse: 0.00071144    valid's RMSPE: 0.236452
[200]   train's rmse: 0.000638205   train's RMSPE: 0.216451 valid's rmse: 0.000711856   valid's RMSPE: 0.23659
[250]   train's rmse: 0.000627349   train's RMSPE: 0.21277  valid's rmse: 0.000709853   valid's RMSPE: 0.235924
[300]   train's rmse: 0.000618193   train's RMSPE: 0.209665 valid's rmse: 0.000708963   valid's RMSPE: 0.235629
[350]   train's rmse: 0.000610277   train's RMSPE: 0.20698  valid's rmse: 0.000707922   valid's RMSPE: 0.235282
[400]   train's rmse: 0.000602063   train's RMSPE: 0.204194 valid's rmse: 0.000708764   valid's RMSPE: 0.235562
Early stopping, best iteration is:
[367]   train's rmse: 0.000607575   train's RMSPE: 0.206063 valid's rmse: 0.000707144   valid's RMSPE: 0.235024
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000710562   train's RMSPE: 0.241335 valid's rmse: 0.000707376   valid's RMSPE: 0.233701
[100]   train's rmse: 0.000668272   train's RMSPE: 0.226972 valid's rmse: 0.000696656   valid's RMSPE: 0.23016
Early stopping, best iteration is:
[87]    train's rmse: 0.000673585   train's RMSPE: 0.228776 valid's rmse: 0.000696109   valid's RMSPE: 0.229979
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000715358   train's RMSPE: 0.241085 valid's rmse: 0.000738706   valid's RMSPE: 0.251871
[100]   train's rmse: 0.0006747 train's RMSPE: 0.227383 valid's rmse: 0.000701942   valid's RMSPE: 0.239336
[150]   train's rmse: 0.00065968    train's RMSPE: 0.222321 valid's rmse: 0.00069779    valid's RMSPE: 0.237921
[200]   train's rmse: 0.000648311   train's RMSPE: 0.21849  valid's rmse: 0.000694225   valid's RMSPE: 0.236705
[250]   train's rmse: 0.000637993   train's RMSPE: 0.215012 valid's rmse: 0.000693763   valid's RMSPE: 0.236548
Early stopping, best iteration is:
[228]   train's rmse: 0.000641917   train's RMSPE: 0.216335 valid's rmse: 0.000691087   valid's RMSPE: 0.235635
Our out of folds RMSPE is 0.236, compared to 0.20714316103379152, giving gain 0.02885683896620847
Our cv fold scores are [0.235, 0.245, 0.235, 0.23, 0.236]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000593939   train's RMSPE: 0.213721 valid's rmse: 0.000669988   valid's RMSPE: 0.236097
[100]   train's rmse: 0.000555796   train's RMSPE: 0.199996 valid's rmse: 0.000643857   valid's RMSPE: 0.226889
[150]   train's rmse: 0.000540744   train's RMSPE: 0.194579 valid's rmse: 0.000642392   valid's RMSPE: 0.226372
[200]   train's rmse: 0.000529582   train's RMSPE: 0.190563 valid's rmse: 0.000639788   valid's RMSPE: 0.225454
[250]   train's rmse: 0.000519909   train's RMSPE: 0.187082 valid's rmse: 0.000638735   valid's RMSPE: 0.225083
[300]   train's rmse: 0.000511137   train's RMSPE: 0.183926 valid's rmse: 0.000637264   valid's RMSPE: 0.224565
[350]   train's rmse: 0.000503998   train's RMSPE: 0.181357 valid's rmse: 0.000636047   valid's RMSPE: 0.224136
[400]   train's rmse: 0.000496857   train's RMSPE: 0.178787 valid's rmse: 0.000635718   valid's RMSPE: 0.22402
[450]   train's rmse: 0.000490648   train's RMSPE: 0.176553 valid's rmse: 0.000637089   valid's RMSPE: 0.224503
Early stopping, best iteration is:
[422]   train's rmse: 0.000493501   train's RMSPE: 0.17758  valid's rmse: 0.000635575   valid's RMSPE: 0.22397
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000607397   train's RMSPE: 0.216809 valid's rmse: 0.000612045   valid's RMSPE: 0.222757
[100]   train's rmse: 0.00057069    train's RMSPE: 0.203706 valid's rmse: 0.00058347    valid's RMSPE: 0.212357
[150]   train's rmse: 0.000556558   train's RMSPE: 0.198661 valid's rmse: 0.000582138   valid's RMSPE: 0.211872
Early stopping, best iteration is:
[138]   train's rmse: 0.000559052   train's RMSPE: 0.199552 valid's rmse: 0.000580588   valid's RMSPE: 0.211308
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000611964   train's RMSPE: 0.219147 valid's rmse: 0.000592289   valid's RMSPE: 0.212855
[100]   train's rmse: 0.000574899   train's RMSPE: 0.205874 valid's rmse: 0.000568249   valid's RMSPE: 0.204216
[150]   train's rmse: 0.000559635   train's RMSPE: 0.200408 valid's rmse: 0.000563457   valid's RMSPE: 0.202493
[200]   train's rmse: 0.000547846   train's RMSPE: 0.196186 valid's rmse: 0.000561507   valid's RMSPE: 0.201793
[250]   train's rmse: 0.000537984   train's RMSPE: 0.192655 valid's rmse: 0.0005609 valid's RMSPE: 0.201574
[300]   train's rmse: 0.000529667   train's RMSPE: 0.189676 valid's rmse: 0.000559578   valid's RMSPE: 0.2011
[350]   train's rmse: 0.000521028   train's RMSPE: 0.186583 valid's rmse: 0.000557493   valid's RMSPE: 0.20035
[400]   train's rmse: 0.000514583   train's RMSPE: 0.184275 valid's rmse: 0.000558709   valid's RMSPE: 0.200787
Early stopping, best iteration is:
[356]   train's rmse: 0.000520161   train's RMSPE: 0.186272 valid's rmse: 0.000557397   valid's RMSPE: 0.200316
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000607998   train's RMSPE: 0.217218 valid's rmse: 0.000619802   valid's RMSPE: 0.2248
[100]   train's rmse: 0.000570688   train's RMSPE: 0.203888 valid's rmse: 0.000599484   valid's RMSPE: 0.217431
[150]   train's rmse: 0.000556307   train's RMSPE: 0.19875  valid's rmse: 0.000595564   valid's RMSPE: 0.216009
[200]   train's rmse: 0.0005455 train's RMSPE: 0.194889 valid's rmse: 0.000594771   valid's RMSPE: 0.215721
[250]   train's rmse: 0.000535987   train's RMSPE: 0.191491 valid's rmse: 0.000594531   valid's RMSPE: 0.215634
Early stopping, best iteration is:
[220]   train's rmse: 0.000541338   train's RMSPE: 0.193402 valid's rmse: 0.000593448   valid's RMSPE: 0.215241
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000608833   train's RMSPE: 0.218956 valid's rmse: 0.000607616   valid's RMSPE: 0.214623
[100]   train's rmse: 0.000572655   train's RMSPE: 0.205945 valid's rmse: 0.000584793   valid's RMSPE: 0.206561
[150]   train's rmse: 0.00055727    train's RMSPE: 0.200413 valid's rmse: 0.000583536   valid's RMSPE: 0.206117
[200]   train's rmse: 0.000545508   train's RMSPE: 0.196182 valid's rmse: 0.000582752   valid's RMSPE: 0.205841
Early stopping, best iteration is:
[189]   train's rmse: 0.000547782   train's RMSPE: 0.197    valid's rmse: 0.000581961   valid's RMSPE: 0.205561
Our out of folds RMSPE is 0.211, compared to 0.19115350809715234, giving gain 0.01984649190284765
Our cv fold scores are [0.224, 0.211, 0.2, 0.215, 0.206]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000698212   train's RMSPE: 0.242413 valid's rmse: 0.000757357   valid's RMSPE: 0.262954
[100]   train's rmse: 0.00066275    train's RMSPE: 0.230102 valid's rmse: 0.00073952    valid's RMSPE: 0.256761
[150]   train's rmse: 0.000648143   train's RMSPE: 0.22503  valid's rmse: 0.00073914    valid's RMSPE: 0.256628
Early stopping, best iteration is:
[123]   train's rmse: 0.000656058   train's RMSPE: 0.227778 valid's rmse: 0.000738034   valid's RMSPE: 0.256245
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000701194   train's RMSPE: 0.242369 valid's rmse: 0.000743423   valid's RMSPE: 0.262646
[100]   train's rmse: 0.000667213   train's RMSPE: 0.230623 valid's rmse: 0.000720921   valid's RMSPE: 0.254696
[150]   train's rmse: 0.000651045   train's RMSPE: 0.225035 valid's rmse: 0.000718037   valid's RMSPE: 0.253678
[200]   train's rmse: 0.000637353   train's RMSPE: 0.220302 valid's rmse: 0.000715077   valid's RMSPE: 0.252632
[250]   train's rmse: 0.000626524   train's RMSPE: 0.216559 valid's rmse: 0.000714606   valid's RMSPE: 0.252465
[300]   train's rmse: 0.000616608   train's RMSPE: 0.213131 valid's rmse: 0.000711146   valid's RMSPE: 0.251243
[350]   train's rmse: 0.000608163   train's RMSPE: 0.210213 valid's rmse: 0.000709208   valid's RMSPE: 0.250559
[400]   train's rmse: 0.000600419   train's RMSPE: 0.207536 valid's rmse: 0.000709426   valid's RMSPE: 0.250635
Early stopping, best iteration is:
[372]   train's rmse: 0.000604543   train's RMSPE: 0.208961 valid's rmse: 0.000709119   valid's RMSPE: 0.250527
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000712942   train's RMSPE: 0.246397 valid's rmse: 0.000687011   valid's RMSPE: 0.24284
[100]   train's rmse: 0.00067728    train's RMSPE: 0.234072 valid's rmse: 0.000663251   valid's RMSPE: 0.234441
[150]   train's rmse: 0.000660955   train's RMSPE: 0.22843  valid's rmse: 0.000661055   valid's RMSPE: 0.233665
[200]   train's rmse: 0.000648296   train's RMSPE: 0.224055 valid's rmse: 0.000659698   valid's RMSPE: 0.233185
Early stopping, best iteration is:
[181]   train's rmse: 0.000652606   train's RMSPE: 0.225544 valid's rmse: 0.000658758   valid's RMSPE: 0.232853
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000712089   train's RMSPE: 0.247954 valid's rmse: 0.000711572   valid's RMSPE: 0.244148
[100]   train's rmse: 0.000675948   train's RMSPE: 0.235369 valid's rmse: 0.000695026   valid's RMSPE: 0.238471
[150]   train's rmse: 0.000659174   train's RMSPE: 0.229529 valid's rmse: 0.000689266   valid's RMSPE: 0.236495
[200]   train's rmse: 0.00064716    train's RMSPE: 0.225345 valid's rmse: 0.000686697   valid's RMSPE: 0.235613
[250]   train's rmse: 0.000636442   train's RMSPE: 0.221613 valid's rmse: 0.000686749   valid's RMSPE: 0.235631
[300]   train's rmse: 0.000626771   train's RMSPE: 0.218246 valid's rmse: 0.000685589   valid's RMSPE: 0.235233
[350]   train's rmse: 0.000618647   train's RMSPE: 0.215417 valid's rmse: 0.000686458   valid's RMSPE: 0.235531
Early stopping, best iteration is:
[307]   train's rmse: 0.000625588   train's RMSPE: 0.217834 valid's rmse: 0.000685232   valid's RMSPE: 0.235111
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000706779   train's RMSPE: 0.246875 valid's rmse: 0.000728727   valid's RMSPE: 0.246784
[100]   train's rmse: 0.000674472   train's RMSPE: 0.23559  valid's rmse: 0.000714592   valid's RMSPE: 0.241998
[150]   train's rmse: 0.000657375   train's RMSPE: 0.229619 valid's rmse: 0.000710653   valid's RMSPE: 0.240664
[200]   train's rmse: 0.000643781   train's RMSPE: 0.22487  valid's rmse: 0.000706376   valid's RMSPE: 0.239215
[250]   train's rmse: 0.000633789   train's RMSPE: 0.22138  valid's rmse: 0.000706215   valid's RMSPE: 0.239161
Early stopping, best iteration is:
[215]   train's rmse: 0.000640547   train's RMSPE: 0.223741 valid's rmse: 0.00070552    valid's RMSPE: 0.238925
Our out of folds RMSPE is 0.243, compared to 0.21093487209875394, giving gain 0.03206512790124605
Our cv fold scores are [0.256, 0.251, 0.233, 0.235, 0.239]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000923915   train's RMSPE: 0.285916 valid's rmse: 0.00101814    valid's RMSPE: 0.308634
[100]   train's rmse: 0.000883547   train's RMSPE: 0.273424 valid's rmse: 0.00100395    valid's RMSPE: 0.304333
[150]   train's rmse: 0.000860182   train's RMSPE: 0.266193 valid's rmse: 0.000992926   valid's RMSPE: 0.300991
[200]   train's rmse: 0.000841612   train's RMSPE: 0.260447 valid's rmse: 0.000988184   valid's RMSPE: 0.299554
[250]   train's rmse: 0.000825409   train's RMSPE: 0.255433 valid's rmse: 0.000987051   valid's RMSPE: 0.29921
[300]   train's rmse: 0.000812097   train's RMSPE: 0.251313 valid's rmse: 0.000982297   valid's RMSPE: 0.297769
[350]   train's rmse: 0.000801244   train's RMSPE: 0.247954 valid's rmse: 0.000982647   valid's RMSPE: 0.297875
Early stopping, best iteration is:
[327]   train's rmse: 0.000805928   train's RMSPE: 0.249404 valid's rmse: 0.000981133   valid's RMSPE: 0.297416
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000927434   train's RMSPE: 0.284639 valid's rmse: 0.000979401   valid's RMSPE: 0.306886
[100]   train's rmse: 0.000885899   train's RMSPE: 0.271891 valid's rmse: 0.000957289   valid's RMSPE: 0.299958
[150]   train's rmse: 0.000861436   train's RMSPE: 0.264383 valid's rmse: 0.000951414   valid's RMSPE: 0.298117
[200]   train's rmse: 0.000842509   train's RMSPE: 0.258574 valid's rmse: 0.000948574   valid's RMSPE: 0.297227
[250]   train's rmse: 0.000825816   train's RMSPE: 0.253451 valid's rmse: 0.000943476   valid's RMSPE: 0.29563
[300]   train's rmse: 0.000811479   train's RMSPE: 0.249051 valid's rmse: 0.000943714   valid's RMSPE: 0.295704
Early stopping, best iteration is:
[287]   train's rmse: 0.000814701   train's RMSPE: 0.25004  valid's rmse: 0.000942742   valid's RMSPE: 0.2954
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00093514    train's RMSPE: 0.288    valid's rmse: 0.000941571   valid's RMSPE: 0.291067
[100]   train's rmse: 0.000893008   train's RMSPE: 0.275025 valid's rmse: 0.000921712   valid's RMSPE: 0.284928
[150]   train's rmse: 0.000871125   train's RMSPE: 0.268286 valid's rmse: 0.000915274   valid's RMSPE: 0.282938
[200]   train's rmse: 0.000851891   train's RMSPE: 0.262362 valid's rmse: 0.000913994   valid's RMSPE: 0.282542
Early stopping, best iteration is:
[169]   train's rmse: 0.00086294    train's RMSPE: 0.265765 valid's rmse: 0.000912685   valid's RMSPE: 0.282138
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000944019   train's RMSPE: 0.28993  valid's rmse: 0.000907501   valid's RMSPE: 0.283597
[100]   train's rmse: 0.000903983   train's RMSPE: 0.277634 valid's rmse: 0.000893995   valid's RMSPE: 0.279377
[150]   train's rmse: 0.000879026   train's RMSPE: 0.269969 valid's rmse: 0.000890486   valid's RMSPE: 0.27828
[200]   train's rmse: 0.000860736   train's RMSPE: 0.264352 valid's rmse: 0.000890489   valid's RMSPE: 0.278281
[250]   train's rmse: 0.000843658   train's RMSPE: 0.259107 valid's rmse: 0.00088708    valid's RMSPE: 0.277216
[300]   train's rmse: 0.000827694   train's RMSPE: 0.254204 valid's rmse: 0.000885531   valid's RMSPE: 0.276731
[350]   train's rmse: 0.00081621    train's RMSPE: 0.250677 valid's rmse: 0.000887581   valid's RMSPE: 0.277372
Early stopping, best iteration is:
[309]   train's rmse: 0.000825792   train's RMSPE: 0.25362  valid's rmse: 0.00088507    valid's RMSPE: 0.276588
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000935128   train's RMSPE: 0.289472 valid's rmse: 0.000935255   valid's RMSPE: 0.283159
[100]   train's rmse: 0.000895432   train's RMSPE: 0.277184 valid's rmse: 0.000914353   valid's RMSPE: 0.27683
[150]   train's rmse: 0.000871796   train's RMSPE: 0.269867 valid's rmse: 0.000910514   valid's RMSPE: 0.275668
[200]   train's rmse: 0.000852381   train's RMSPE: 0.263857 valid's rmse: 0.000905493   valid's RMSPE: 0.274148
[250]   train's rmse: 0.000836551   train's RMSPE: 0.258957 valid's rmse: 0.000904224   valid's RMSPE: 0.273764
[300]   train's rmse: 0.000821907   train's RMSPE: 0.254424 valid's rmse: 0.000901649   valid's RMSPE: 0.272984
[350]   train's rmse: 0.000810509   train's RMSPE: 0.250896 valid's rmse: 0.000900796   valid's RMSPE: 0.272726
[400]   train's rmse: 0.000799737   train's RMSPE: 0.247561 valid's rmse: 0.000901383   valid's RMSPE: 0.272903
Early stopping, best iteration is:
[367]   train's rmse: 0.0008065 train's RMSPE: 0.249655 valid's rmse: 0.000900352   valid's RMSPE: 0.272591
Our out of folds RMSPE is 0.285, compared to 0.2536966485935066, giving gain 0.031303351406493374
Our cv fold scores are [0.297, 0.295, 0.282, 0.277, 0.273]

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000536062   train's RMSPE: 0.255047 valid's rmse: 0.000556521   valid's RMSPE: 0.264837
[100]   train's rmse: 0.000507308   train's RMSPE: 0.241367 valid's rmse: 0.000537848   valid's RMSPE: 0.255951
[150]   train's rmse: 0.000493843   train's RMSPE: 0.234961 valid's rmse: 0.000533952   valid's RMSPE: 0.254097
[200]   train's rmse: 0.000483153   train's RMSPE: 0.229874 valid's rmse: 0.000532044   valid's RMSPE: 0.253189
[250]   train's rmse: 0.000473997   train's RMSPE: 0.225518 valid's rmse: 0.000529497   valid's RMSPE: 0.251977
[300]   train's rmse: 0.000466121   train's RMSPE: 0.221771 valid's rmse: 0.00052777    valid's RMSPE: 0.251155
[350]   train's rmse: 0.00045898    train's RMSPE: 0.218373 valid's rmse: 0.000527188   valid's RMSPE: 0.250878
Early stopping, best iteration is:
[343]   train's rmse: 0.000459974   train's RMSPE: 0.218846 valid's rmse: 0.000526687   valid's RMSPE: 0.25064
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000531538   train's RMSPE: 0.251207 valid's rmse: 0.000576619   valid's RMSPE: 0.281606
[100]   train's rmse: 0.000503602   train's RMSPE: 0.238004 valid's rmse: 0.000553999   valid's RMSPE: 0.270559
[150]   train's rmse: 0.000491509   train's RMSPE: 0.232289 valid's rmse: 0.00054868    valid's RMSPE: 0.267962
[200]   train's rmse: 0.00048179    train's RMSPE: 0.227696 valid's rmse: 0.00054582    valid's RMSPE: 0.266565
[250]   train's rmse: 0.000473153   train's RMSPE: 0.223614 valid's rmse: 0.000543868   valid's RMSPE: 0.265611
[300]   train's rmse: 0.000465569   train's RMSPE: 0.220029 valid's rmse: 0.000543908   valid's RMSPE: 0.265631
[350]   train's rmse: 0.000459523   train's RMSPE: 0.217172 valid's rmse: 0.00054318    valid's RMSPE: 0.265275
Early stopping, best iteration is:
[318]   train's rmse: 0.000463382   train's RMSPE: 0.218996 valid's rmse: 0.000542336   valid's RMSPE: 0.264863
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000540811   train's RMSPE: 0.257667 valid's rmse: 0.000537245   valid's RMSPE: 0.254228
[100]   train's rmse: 0.000512059   train's RMSPE: 0.243968 valid's rmse: 0.000516289   valid's RMSPE: 0.244312
[150]   train's rmse: 0.000499235   train's RMSPE: 0.237858 valid's rmse: 0.000511427   valid's RMSPE: 0.242011
[200]   train's rmse: 0.000488967   train's RMSPE: 0.232967 valid's rmse: 0.000508634   valid's RMSPE: 0.240689
[250]   train's rmse: 0.000480632   train's RMSPE: 0.228995 valid's rmse: 0.000506873   valid's RMSPE: 0.239856
[300]   train's rmse: 0.000472937   train's RMSPE: 0.225329 valid's rmse: 0.00050559    valid's RMSPE: 0.239249
Early stopping, best iteration is:
[296]   train's rmse: 0.000473593   train's RMSPE: 0.225642 valid's rmse: 0.000505253   valid's RMSPE: 0.239089
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000542004   train's RMSPE: 0.25881  valid's rmse: 0.00052886    valid's RMSPE: 0.247988
[100]   train's rmse: 0.000511057   train's RMSPE: 0.244033 valid's rmse: 0.000516908   valid's RMSPE: 0.242384
[150]   train's rmse: 0.000498178   train's RMSPE: 0.237883 valid's rmse: 0.00051525    valid's RMSPE: 0.241606
[200]   train's rmse: 0.000487235   train's RMSPE: 0.232658 valid's rmse: 0.000512017   valid's RMSPE: 0.24009
[250]   train's rmse: 0.000477847   train's RMSPE: 0.228175 valid's rmse: 0.000511441   valid's RMSPE: 0.23982
[300]   train's rmse: 0.000469718   train's RMSPE: 0.224294 valid's rmse: 0.00051087    valid's RMSPE: 0.239553
Early stopping, best iteration is:
[277]   train's rmse: 0.000473253   train's RMSPE: 0.225981 valid's rmse: 0.00051038    valid's RMSPE: 0.239323
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000537171   train's RMSPE: 0.256042 valid's rmse: 0.000551229   valid's RMSPE: 0.260396
[100]   train's rmse: 0.000509653   train's RMSPE: 0.242925 valid's rmse: 0.00053575    valid's RMSPE: 0.253084
[150]   train's rmse: 0.000496625   train's RMSPE: 0.236716 valid's rmse: 0.00053219    valid's RMSPE: 0.251402
[200]   train's rmse: 0.000486308   train's RMSPE: 0.231798 valid's rmse: 0.000531072   valid's RMSPE: 0.250874
[250]   train's rmse: 0.000477364   train's RMSPE: 0.227534 valid's rmse: 0.000528655   valid's RMSPE: 0.249732
[300]   train's rmse: 0.000470305   train's RMSPE: 0.22417  valid's rmse: 0.000525923   valid's RMSPE: 0.248442
[350]   train's rmse: 0.000463917   train's RMSPE: 0.221125 valid's rmse: 0.000523626   valid's RMSPE: 0.247356
[400]   train's rmse: 0.000457398   train's RMSPE: 0.218018 valid's rmse: 0.000521483   valid's RMSPE: 0.246344
[450]   train's rmse: 0.000451882   train's RMSPE: 0.215389 valid's rmse: 0.000520633   valid's RMSPE: 0.245943
[500]   train's rmse: 0.000446936   train's RMSPE: 0.213031 valid's rmse: 0.000521024   valid's RMSPE: 0.246127
Early stopping, best iteration is:
[490]   train's rmse: 0.000447898   train's RMSPE: 0.21349  valid's rmse: 0.000519915   valid's RMSPE: 0.245604
Our out of folds RMSPE is 0.248, compared to 0.21260165312455714, giving gain 0.035398346875442854
Our cv fold scores are [0.251, 0.265, 0.239, 0.239, 0.246]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000439165   train's RMSPE: 0.252688 valid's rmse: 0.000479389   valid's RMSPE: 0.270325
[100]   train's rmse: 0.000414394   train's RMSPE: 0.238435 valid's rmse: 0.000464213   valid's RMSPE: 0.261768
[150]   train's rmse: 0.000401211   train's RMSPE: 0.23085  valid's rmse: 0.000458946   valid's RMSPE: 0.258797
[200]   train's rmse: 0.000390959   train's RMSPE: 0.224951 valid's rmse: 0.000455571   valid's RMSPE: 0.256895
[250]   train's rmse: 0.000383124   train's RMSPE: 0.220443 valid's rmse: 0.000452811   valid's RMSPE: 0.255338
[300]   train's rmse: 0.00037617    train's RMSPE: 0.216442 valid's rmse: 0.000451373   valid's RMSPE: 0.254527
[350]   train's rmse: 0.000369982   train's RMSPE: 0.212881 valid's rmse: 0.000448524   valid's RMSPE: 0.252921
[400]   train's rmse: 0.000364317   train's RMSPE: 0.209622 valid's rmse: 0.000447388   valid's RMSPE: 0.25228
[450]   train's rmse: 0.000359514   train's RMSPE: 0.206858 valid's rmse: 0.000445766   valid's RMSPE: 0.251365
[500]   train's rmse: 0.000355138   train's RMSPE: 0.20434  valid's rmse: 0.000444776   valid's RMSPE: 0.250807
Early stopping, best iteration is:
[478]   train's rmse: 0.000356887   train's RMSPE: 0.205347 valid's rmse: 0.000444446   valid's RMSPE: 0.250621
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.0004394 train's RMSPE: 0.25136  valid's rmse: 0.000491127   valid's RMSPE: 0.283523
[100]   train's rmse: 0.000415272   train's RMSPE: 0.237558 valid's rmse: 0.000474338   valid's RMSPE: 0.273831
[150]   train's rmse: 0.0004029 train's RMSPE: 0.23048  valid's rmse: 0.000468825   valid's RMSPE: 0.270648
[200]   train's rmse: 0.000394155   train's RMSPE: 0.225477 valid's rmse: 0.000467687   valid's RMSPE: 0.269991
[250]   train's rmse: 0.000386823   train's RMSPE: 0.221283 valid's rmse: 0.000464327   valid's RMSPE: 0.268052
[300]   train's rmse: 0.000379658   train's RMSPE: 0.217184 valid's rmse: 0.000462317   valid's RMSPE: 0.266891
[350]   train's rmse: 0.000374017   train's RMSPE: 0.213957 valid's rmse: 0.000464816   valid's RMSPE: 0.268334
Early stopping, best iteration is:
[300]   train's rmse: 0.000379658   train's RMSPE: 0.217184 valid's rmse: 0.000462317   valid's RMSPE: 0.266891
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000447261   train's RMSPE: 0.255867 valid's rmse: 0.000446094   valid's RMSPE: 0.257487
[100]   train's rmse: 0.000421214   train's RMSPE: 0.240966 valid's rmse: 0.000427561   valid's RMSPE: 0.24679
[150]   train's rmse: 0.000408875   train's RMSPE: 0.233907 valid's rmse: 0.000422775   valid's RMSPE: 0.244027
[200]   train's rmse: 0.000399247   train's RMSPE: 0.228399 valid's rmse: 0.000420908   valid's RMSPE: 0.242949
[250]   train's rmse: 0.000391346   train's RMSPE: 0.223879 valid's rmse: 0.000423089   valid's RMSPE: 0.244208
Early stopping, best iteration is:
[200]   train's rmse: 0.000399247   train's RMSPE: 0.228399 valid's rmse: 0.000420908   valid's RMSPE: 0.242949
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000452847   train's RMSPE: 0.259342 valid's rmse: 0.000425657   valid's RMSPE: 0.244645
[100]   train's rmse: 0.000426417   train's RMSPE: 0.244206 valid's rmse: 0.000415679   valid's RMSPE: 0.238911
[150]   train's rmse: 0.000412752   train's RMSPE: 0.23638  valid's rmse: 0.000413852   valid's RMSPE: 0.23786
[200]   train's rmse: 0.000401653   train's RMSPE: 0.230024 valid's rmse: 0.000413878   valid's RMSPE: 0.237875
Early stopping, best iteration is:
[186]   train's rmse: 0.000404755   train's RMSPE: 0.2318   valid's rmse: 0.000412875   valid's RMSPE: 0.237299
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000449411   train's RMSPE: 0.257652 valid's rmse: 0.000439338   valid's RMSPE: 0.251423
[100]   train's rmse: 0.000423167   train's RMSPE: 0.242606 valid's rmse: 0.000425349   valid's RMSPE: 0.243417
[150]   train's rmse: 0.000409754   train's RMSPE: 0.234917 valid's rmse: 0.000420392   valid's RMSPE: 0.240581
[200]   train's rmse: 0.000400763   train's RMSPE: 0.229762 valid's rmse: 0.000418058   valid's RMSPE: 0.239245
[250]   train's rmse: 0.000393165   train's RMSPE: 0.225406 valid's rmse: 0.000415751   valid's RMSPE: 0.237925
[300]   train's rmse: 0.000386051   train's RMSPE: 0.221327 valid's rmse: 0.000413183   valid's RMSPE: 0.236455
[350]   train's rmse: 0.000380191   train's RMSPE: 0.217967 valid's rmse: 0.0004126 valid's RMSPE: 0.236121
Early stopping, best iteration is:
[326]   train's rmse: 0.000382753   train's RMSPE: 0.219436 valid's rmse: 0.000411749   valid's RMSPE: 0.235634
Our out of folds RMSPE is 0.247, compared to 0.20796490181588065, giving gain 0.03903509818411935
Our cv fold scores are [0.251, 0.267, 0.243, 0.237, 0.236]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000503507   train's RMSPE: 0.260534 valid's rmse: 0.00054317    valid's RMSPE: 0.279398
[100]   train's rmse: 0.000478479   train's RMSPE: 0.247583 valid's rmse: 0.000528356   valid's RMSPE: 0.271778
[150]   train's rmse: 0.000466056   train's RMSPE: 0.241155 valid's rmse: 0.00052486    valid's RMSPE: 0.26998
[200]   train's rmse: 0.000457524   train's RMSPE: 0.236741 valid's rmse: 0.000522172   valid's RMSPE: 0.268597
[250]   train's rmse: 0.000448672   train's RMSPE: 0.23216  valid's rmse: 0.000520784   valid's RMSPE: 0.267883
[300]   train's rmse: 0.000442068   train's RMSPE: 0.228743 valid's rmse: 0.000520434   valid's RMSPE: 0.267703
[350]   train's rmse: 0.000435515   train's RMSPE: 0.225352 valid's rmse: 0.000518646   valid's RMSPE: 0.266783
[400]   train's rmse: 0.000430752   train's RMSPE: 0.222888 valid's rmse: 0.00051799    valid's RMSPE: 0.266446
[450]   train's rmse: 0.00042542    train's RMSPE: 0.220128 valid's rmse: 0.00051713    valid's RMSPE: 0.266004
[500]   train's rmse: 0.000421101   train's RMSPE: 0.217894 valid's rmse: 0.000517652   valid's RMSPE: 0.266272
Early stopping, best iteration is:
[454]   train's rmse: 0.000425145   train's RMSPE: 0.219986 valid's rmse: 0.000517097   valid's RMSPE: 0.265987
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000507374   train's RMSPE: 0.260832 valid's rmse: 0.000539737   valid's RMSPE: 0.284803
[100]   train's rmse: 0.000482809   train's RMSPE: 0.248204 valid's rmse: 0.000521399   valid's RMSPE: 0.275126
[150]   train's rmse: 0.000472751   train's RMSPE: 0.243033 valid's rmse: 0.000515243   valid's RMSPE: 0.271878
[200]   train's rmse: 0.000463779   train's RMSPE: 0.238421 valid's rmse: 0.000510281   valid's RMSPE: 0.26926
[250]   train's rmse: 0.00045605    train's RMSPE: 0.234447 valid's rmse: 0.000506411   valid's RMSPE: 0.267217
[300]   train's rmse: 0.00044974    train's RMSPE: 0.231204 valid's rmse: 0.000505239   valid's RMSPE: 0.266599
[350]   train's rmse: 0.000443647   train's RMSPE: 0.228071 valid's rmse: 0.000502559   valid's RMSPE: 0.265185
[400]   train's rmse: 0.000438234   train's RMSPE: 0.225289 valid's rmse: 0.00050105    valid's RMSPE: 0.264389
[450]   train's rmse: 0.00043303    train's RMSPE: 0.222613 valid's rmse: 0.000498849   valid's RMSPE: 0.263227
[500]   train's rmse: 0.000428717   train's RMSPE: 0.220396 valid's rmse: 0.000498641   valid's RMSPE: 0.263117
[550]   train's rmse: 0.00042445    train's RMSPE: 0.218202 valid's rmse: 0.000497775   valid's RMSPE: 0.262661
[600]   train's rmse: 0.000420381   train's RMSPE: 0.216111 valid's rmse: 0.00049831    valid's RMSPE: 0.262943
Early stopping, best iteration is:
[558]   train's rmse: 0.000423764   train's RMSPE: 0.21785  valid's rmse: 0.000497496   valid's RMSPE: 0.262514
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000510844   train's RMSPE: 0.264728 valid's rmse: 0.0005158 valid's RMSPE: 0.263699
[100]   train's rmse: 0.000485359   train's RMSPE: 0.251521 valid's rmse: 0.000499678   valid's RMSPE: 0.255458
[150]   train's rmse: 0.000473574   train's RMSPE: 0.245413 valid's rmse: 0.000497183   valid's RMSPE: 0.254182
[200]   train's rmse: 0.000464005   train's RMSPE: 0.240454 valid's rmse: 0.000495819   valid's RMSPE: 0.253485
[250]   train's rmse: 0.000457067   train's RMSPE: 0.236859 valid's rmse: 0.000494881   valid's RMSPE: 0.253005
[300]   train's rmse: 0.000450534   train's RMSPE: 0.233474 valid's rmse: 0.000493973   valid's RMSPE: 0.252541
[350]   train's rmse: 0.000444374   train's RMSPE: 0.230282 valid's rmse: 0.000492954   valid's RMSPE: 0.25202
Early stopping, best iteration is:
[340]   train's rmse: 0.000445446   train's RMSPE: 0.230837 valid's rmse: 0.000492659   valid's RMSPE: 0.251869
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000516952   train's RMSPE: 0.267016 valid's rmse: 0.000498668   valid's RMSPE: 0.25834
[100]   train's rmse: 0.000491179   train's RMSPE: 0.253704 valid's rmse: 0.000479976   valid's RMSPE: 0.248657
[150]   train's rmse: 0.000479503   train's RMSPE: 0.247673 valid's rmse: 0.000475231   valid's RMSPE: 0.246198
[200]   train's rmse: 0.000470622   train's RMSPE: 0.243086 valid's rmse: 0.000471913   valid's RMSPE: 0.24448
[250]   train's rmse: 0.000463052   train's RMSPE: 0.239176 valid's rmse: 0.000469848   valid's RMSPE: 0.24341
[300]   train's rmse: 0.000456563   train's RMSPE: 0.235824 valid's rmse: 0.000467462   valid's RMSPE: 0.242174
[350]   train's rmse: 0.000450605   train's RMSPE: 0.232746 valid's rmse: 0.000466948   valid's RMSPE: 0.241908
[400]   train's rmse: 0.000445338   train's RMSPE: 0.230026 valid's rmse: 0.000466753   valid's RMSPE: 0.241806
[450]   train's rmse: 0.000440628   train's RMSPE: 0.227593 valid's rmse: 0.000465722   valid's RMSPE: 0.241272
[500]   train's rmse: 0.000436514   train's RMSPE: 0.225468 valid's rmse: 0.000466065   valid's RMSPE: 0.24145
Early stopping, best iteration is:
[478]   train's rmse: 0.000438259   train's RMSPE: 0.22637  valid's rmse: 0.000465558   valid's RMSPE: 0.241187
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000513824   train's RMSPE: 0.266097 valid's rmse: 0.00050859    valid's RMSPE: 0.260712
[100]   train's rmse: 0.000488257   train's RMSPE: 0.252857 valid's rmse: 0.00049243    valid's RMSPE: 0.252428
[150]   train's rmse: 0.000476587   train's RMSPE: 0.246813 valid's rmse: 0.000490573   valid's RMSPE: 0.251476
[200]   train's rmse: 0.000467406   train's RMSPE: 0.242059 valid's rmse: 0.000490556   valid's RMSPE: 0.251467
[250]   train's rmse: 0.000459854   train's RMSPE: 0.238147 valid's rmse: 0.000490108   valid's RMSPE: 0.251238
[300]   train's rmse: 0.000453919   train's RMSPE: 0.235074 valid's rmse: 0.00048856    valid's RMSPE: 0.250444
Early stopping, best iteration is:
[283]   train's rmse: 0.000456043   train's RMSPE: 0.236174 valid's rmse: 0.00048849    valid's RMSPE: 0.250408
Our out of folds RMSPE is 0.255, compared to 0.21163535841016978, giving gain 0.04336464158983022
Our cv fold scores are [0.266, 0.263, 0.252, 0.241, 0.25]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000433375   train's RMSPE: 0.229555 valid's rmse: 0.00045624    valid's RMSPE: 0.241402
[100]   train's rmse: 0.000403805   train's RMSPE: 0.213892 valid's rmse: 0.00043702    valid's RMSPE: 0.231232
[150]   train's rmse: 0.000392654   train's RMSPE: 0.207986 valid's rmse: 0.000432894   valid's RMSPE: 0.229049
[200]   train's rmse: 0.000383119   train's RMSPE: 0.202935 valid's rmse: 0.0004298 valid's RMSPE: 0.227412
[250]   train's rmse: 0.000375938   train's RMSPE: 0.199131 valid's rmse: 0.000427535   valid's RMSPE: 0.226214
[300]   train's rmse: 0.000370307   train's RMSPE: 0.196149 valid's rmse: 0.000426193   valid's RMSPE: 0.225504
[350]   train's rmse: 0.000364909   train's RMSPE: 0.19329  valid's rmse: 0.000424804   valid's RMSPE: 0.224769
Early stopping, best iteration is:
[336]   train's rmse: 0.000366384   train's RMSPE: 0.194071 valid's rmse: 0.000424498   valid's RMSPE: 0.224607
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00042737    train's RMSPE: 0.22477  valid's rmse: 0.000475478   valid's RMSPE: 0.258607
[100]   train's rmse: 0.000399756   train's RMSPE: 0.210247 valid's rmse: 0.000453635   valid's RMSPE: 0.246727
[150]   train's rmse: 0.000388703   train's RMSPE: 0.204433 valid's rmse: 0.000448455   valid's RMSPE: 0.243909
[200]   train's rmse: 0.000380114   train's RMSPE: 0.199916 valid's rmse: 0.000443813   valid's RMSPE: 0.241385
[250]   train's rmse: 0.000373631   train's RMSPE: 0.196507 valid's rmse: 0.000441216   valid's RMSPE: 0.239972
[300]   train's rmse: 0.000368436   train's RMSPE: 0.193774 valid's rmse: 0.000439266   valid's RMSPE: 0.238912
[350]   train's rmse: 0.000363181   train's RMSPE: 0.19101  valid's rmse: 0.000437668   valid's RMSPE: 0.238043
[400]   train's rmse: 0.00035812    train's RMSPE: 0.188349 valid's rmse: 0.000435305   valid's RMSPE: 0.236757
[450]   train's rmse: 0.000353328   train's RMSPE: 0.185828 valid's rmse: 0.000433711   valid's RMSPE: 0.235891
[500]   train's rmse: 0.000348833   train's RMSPE: 0.183464 valid's rmse: 0.000432343   valid's RMSPE: 0.235146
[550]   train's rmse: 0.000345507   train's RMSPE: 0.181715 valid's rmse: 0.000432291   valid's RMSPE: 0.235118
[600]   train's rmse: 0.000341286   train's RMSPE: 0.179495 valid's rmse: 0.000431064   valid's RMSPE: 0.234451
[650]   train's rmse: 0.0003376 train's RMSPE: 0.177557 valid's rmse: 0.000430486   valid's RMSPE: 0.234136
[700]   train's rmse: 0.000334336   train's RMSPE: 0.17584  valid's rmse: 0.000430166   valid's RMSPE: 0.233962
[750]   train's rmse: 0.000330808   train's RMSPE: 0.173984 valid's rmse: 0.000429387   valid's RMSPE: 0.233538
[800]   train's rmse: 0.000327718   train's RMSPE: 0.172359 valid's rmse: 0.000428997   valid's RMSPE: 0.233327
[850]   train's rmse: 0.000324688   train's RMSPE: 0.170766 valid's rmse: 0.000427411   valid's RMSPE: 0.232464
[900]   train's rmse: 0.000321891   train's RMSPE: 0.169295 valid's rmse: 0.000426791   valid's RMSPE: 0.232127
[950]   train's rmse: 0.00031925    train's RMSPE: 0.167906 valid's rmse: 0.000427177   valid's RMSPE: 0.232337
Early stopping, best iteration is:
[917]   train's rmse: 0.000321015   train's RMSPE: 0.168834 valid's rmse: 0.000426489   valid's RMSPE: 0.231962
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000438611   train's RMSPE: 0.231376 valid's rmse: 0.00042281    valid's RMSPE: 0.227356
[100]   train's rmse: 0.000411008   train's RMSPE: 0.216815 valid's rmse: 0.000406819   valid's RMSPE: 0.218757
[150]   train's rmse: 0.000400153   train's RMSPE: 0.211089 valid's rmse: 0.000405418   valid's RMSPE: 0.218003
[200]   train's rmse: 0.000391696   train's RMSPE: 0.206628 valid's rmse: 0.000402529   valid's RMSPE: 0.21645
[250]   train's rmse: 0.000384726   train's RMSPE: 0.202951 valid's rmse: 0.000401444   valid's RMSPE: 0.215867
[300]   train's rmse: 0.000378421   train's RMSPE: 0.199624 valid's rmse: 0.000401839   valid's RMSPE: 0.216079
Early stopping, best iteration is:
[288]   train's rmse: 0.000379659   train's RMSPE: 0.200278 valid's rmse: 0.000400391   valid's RMSPE: 0.2153
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000434816   train's RMSPE: 0.231222 valid's rmse: 0.000421672   valid's RMSPE: 0.219571
[100]   train's rmse: 0.000405794   train's RMSPE: 0.215789 valid's rmse: 0.000402741   valid's RMSPE: 0.209713
[150]   train's rmse: 0.000395559   train's RMSPE: 0.210346 valid's rmse: 0.000399115   valid's RMSPE: 0.207825
[200]   train's rmse: 0.000387121   train's RMSPE: 0.205859 valid's rmse: 0.000397773   valid's RMSPE: 0.207126
[250]   train's rmse: 0.000380664   train's RMSPE: 0.202425 valid's rmse: 0.000396449   valid's RMSPE: 0.206437
[300]   train's rmse: 0.000374477   train's RMSPE: 0.199135 valid's rmse: 0.000394271   valid's RMSPE: 0.205302
Early stopping, best iteration is:
[294]   train's rmse: 0.000374994   train's RMSPE: 0.19941  valid's rmse: 0.00039382    valid's RMSPE: 0.205068
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000432958   train's RMSPE: 0.230737 valid's rmse: 0.000451296   valid's RMSPE: 0.23284
[100]   train's rmse: 0.000404783   train's RMSPE: 0.215722 valid's rmse: 0.000431534   valid's RMSPE: 0.222644
[150]   train's rmse: 0.000393327   train's RMSPE: 0.209616 valid's rmse: 0.000428089   valid's RMSPE: 0.220867
[200]   train's rmse: 0.000384807   train's RMSPE: 0.205076 valid's rmse: 0.000425706   valid's RMSPE: 0.219637
[250]   train's rmse: 0.000377397   train's RMSPE: 0.201127 valid's rmse: 0.000424342   valid's RMSPE: 0.218933
[300]   train's rmse: 0.000371229   train's RMSPE: 0.19784  valid's rmse: 0.00042253    valid's RMSPE: 0.217999
[350]   train's rmse: 0.000365518   train's RMSPE: 0.194796 valid's rmse: 0.000420799   valid's RMSPE: 0.217105
[400]   train's rmse: 0.000360235   train's RMSPE: 0.191981 valid's rmse: 0.000419049   valid's RMSPE: 0.216202
[450]   train's rmse: 0.000355567   train's RMSPE: 0.189493 valid's rmse: 0.000418681   valid's RMSPE: 0.216013
[500]   train's rmse: 0.000351014   train's RMSPE: 0.187067 valid's rmse: 0.000418437   valid's RMSPE: 0.215887
[550]   train's rmse: 0.000347028   train's RMSPE: 0.184942 valid's rmse: 0.000417637   valid's RMSPE: 0.215474
[600]   train's rmse: 0.000343617   train's RMSPE: 0.183125 valid's rmse: 0.000416684   valid's RMSPE: 0.214983
[650]   train's rmse: 0.000340005   train's RMSPE: 0.1812   valid's rmse: 0.000416126   valid's RMSPE: 0.214695
[700]   train's rmse: 0.000336943   train's RMSPE: 0.179568 valid's rmse: 0.000415695   valid's RMSPE: 0.214472
[750]   train's rmse: 0.00033398    train's RMSPE: 0.177989 valid's rmse: 0.000415077   valid's RMSPE: 0.214154
[800]   train's rmse: 0.000330791   train's RMSPE: 0.176289 valid's rmse: 0.000415161   valid's RMSPE: 0.214197
Early stopping, best iteration is:
[781]   train's rmse: 0.000331922   train's RMSPE: 0.176892 valid's rmse: 0.000414799   valid's RMSPE: 0.21401
Our out of folds RMSPE is 0.218, compared to 0.19523224853251037, giving gain 0.022767751467489633
Our cv fold scores are [0.225, 0.232, 0.215, 0.205, 0.214]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000588643   train's RMSPE: 0.214405 valid's rmse: 0.000632458   valid's RMSPE: 0.226375
[100]   train's rmse: 0.000552954   train's RMSPE: 0.201406 valid's rmse: 0.000598578   valid's RMSPE: 0.214248
[150]   train's rmse: 0.000539354   train's RMSPE: 0.196452 valid's rmse: 0.000594393   valid's RMSPE: 0.21275
[200]   train's rmse: 0.000528396   train's RMSPE: 0.192461 valid's rmse: 0.000592854   valid's RMSPE: 0.212199
[250]   train's rmse: 0.000518421   train's RMSPE: 0.188828 valid's rmse: 0.0005899 valid's RMSPE: 0.211142
[300]   train's rmse: 0.000509764   train's RMSPE: 0.185675 valid's rmse: 0.000589761   valid's RMSPE: 0.211092
Early stopping, best iteration is:
[270]   train's rmse: 0.000514584   train's RMSPE: 0.18743  valid's rmse: 0.000588924   valid's RMSPE: 0.210793
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000583894   train's RMSPE: 0.211197 valid's rmse: 0.000630341   valid's RMSPE: 0.232002
[100]   train's rmse: 0.000548864   train's RMSPE: 0.198526 valid's rmse: 0.00061083    valid's RMSPE: 0.224821
[150]   train's rmse: 0.000535658   train's RMSPE: 0.19375  valid's rmse: 0.000608887   valid's RMSPE: 0.224106
Early stopping, best iteration is:
[137]   train's rmse: 0.000538426   train's RMSPE: 0.194751 valid's rmse: 0.000608062   valid's RMSPE: 0.223802
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000590394   train's RMSPE: 0.212985 valid's rmse: 0.000592535   valid's RMSPE: 0.220293
[100]   train's rmse: 0.000555686   train's RMSPE: 0.200464 valid's rmse: 0.000571374   valid's RMSPE: 0.212426
[150]   train's rmse: 0.000541474   train's RMSPE: 0.195337 valid's rmse: 0.000567507   valid's RMSPE: 0.210988
[200]   train's rmse: 0.000530199   train's RMSPE: 0.19127  valid's rmse: 0.000565001   valid's RMSPE: 0.210057
[250]   train's rmse: 0.000520766   train's RMSPE: 0.187867 valid's rmse: 0.000562367   valid's RMSPE: 0.209077
[300]   train's rmse: 0.000513234   train's RMSPE: 0.18515  valid's rmse: 0.000561955   valid's RMSPE: 0.208924
[350]   train's rmse: 0.000506091   train's RMSPE: 0.182573 valid's rmse: 0.000559889   valid's RMSPE: 0.208156
[400]   train's rmse: 0.000500168   train's RMSPE: 0.180436 valid's rmse: 0.000559947   valid's RMSPE: 0.208177
[450]   train's rmse: 0.000494255   train's RMSPE: 0.178303 valid's rmse: 0.000558807   valid's RMSPE: 0.207754
[500]   train's rmse: 0.000488621   train's RMSPE: 0.17627  valid's rmse: 0.000557885   valid's RMSPE: 0.207411
[550]   train's rmse: 0.000482765   train's RMSPE: 0.174158 valid's rmse: 0.000556827   valid's RMSPE: 0.207018
Early stopping, best iteration is:
[545]   train's rmse: 0.000483254   train's RMSPE: 0.174334 valid's rmse: 0.00055655    valid's RMSPE: 0.206914
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000600096   train's RMSPE: 0.21824  valid's rmse: 0.000564049   valid's RMSPE: 0.203173
[100]   train's rmse: 0.000564882   train's RMSPE: 0.205434 valid's rmse: 0.000537837   valid's RMSPE: 0.193731
[150]   train's rmse: 0.000550604   train's RMSPE: 0.200241 valid's rmse: 0.00053228    valid's RMSPE: 0.19173
[200]   train's rmse: 0.000540095   train's RMSPE: 0.196419 valid's rmse: 0.000531757   valid's RMSPE: 0.191541
[250]   train's rmse: 0.000530591   train's RMSPE: 0.192963 valid's rmse: 0.000531029   valid's RMSPE: 0.191279
Early stopping, best iteration is:
[234]   train's rmse: 0.000532835   train's RMSPE: 0.193779 valid's rmse: 0.000530068   valid's RMSPE: 0.190933
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000589744   train's RMSPE: 0.214983 valid's rmse: 0.000608195   valid's RMSPE: 0.216946
[100]   train's rmse: 0.000557254   train's RMSPE: 0.203139 valid's rmse: 0.000591399   valid's RMSPE: 0.210955
[150]   train's rmse: 0.000543523   train's RMSPE: 0.198134 valid's rmse: 0.000587386   valid's RMSPE: 0.209523
[200]   train's rmse: 0.000532058   train's RMSPE: 0.193955 valid's rmse: 0.000583415   valid's RMSPE: 0.208107
[250]   train's rmse: 0.000522554   train's RMSPE: 0.19049  valid's rmse: 0.000582749   valid's RMSPE: 0.207869
[300]   train's rmse: 0.000514292   train's RMSPE: 0.187478 valid's rmse: 0.000580868   valid's RMSPE: 0.207198
[350]   train's rmse: 0.000507252   train's RMSPE: 0.184912 valid's rmse: 0.00058048    valid's RMSPE: 0.20706
[400]   train's rmse: 0.000499927   train's RMSPE: 0.182242 valid's rmse: 0.000579566   valid's RMSPE: 0.206734
Early stopping, best iteration is:
[370]   train's rmse: 0.000503788   train's RMSPE: 0.183649 valid's rmse: 0.000578956   valid's RMSPE: 0.206516
Our out of folds RMSPE is 0.208, compared to 0.1811865306676827, giving gain 0.02681346933231729
Our cv fold scores are [0.211, 0.224, 0.207, 0.191, 0.207]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000828059   train's RMSPE: 0.285577 valid's rmse: 0.000875455   valid's RMSPE: 0.302489
[100]   train's rmse: 0.000785564   train's RMSPE: 0.270922 valid's rmse: 0.000846632   valid's RMSPE: 0.29253
[150]   train's rmse: 0.000759608   train's RMSPE: 0.26197  valid's rmse: 0.000843559   valid's RMSPE: 0.291468
[200]   train's rmse: 0.000737796   train's RMSPE: 0.254448 valid's rmse: 0.000840126   valid's RMSPE: 0.290282
[250]   train's rmse: 0.00071884    train's RMSPE: 0.24791  valid's rmse: 0.000840422   valid's RMSPE: 0.290384
Early stopping, best iteration is:
[224]   train's rmse: 0.000728133   train's RMSPE: 0.251115 valid's rmse: 0.00083791    valid's RMSPE: 0.289516
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000819872   train's RMSPE: 0.280178 valid's rmse: 0.000917117   valid's RMSPE: 0.328137
[100]   train's rmse: 0.000778431   train's RMSPE: 0.266016 valid's rmse: 0.000906833   valid's RMSPE: 0.324457
[150]   train's rmse: 0.000754373   train's RMSPE: 0.257794 valid's rmse: 0.000907306   valid's RMSPE: 0.324626
Early stopping, best iteration is:
[126]   train's rmse: 0.000765034   train's RMSPE: 0.261438 valid's rmse: 0.000904  valid's RMSPE: 0.323444
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000826448   train's RMSPE: 0.285496 valid's rmse: 0.000838225   valid's RMSPE: 0.287698
[100]   train's rmse: 0.000785556   train's RMSPE: 0.27137  valid's rmse: 0.000819586   valid's RMSPE: 0.281301
[150]   train's rmse: 0.000758745   train's RMSPE: 0.262108 valid's rmse: 0.00081078    valid's RMSPE: 0.278278
[200]   train's rmse: 0.000737886   train's RMSPE: 0.254902 valid's rmse: 0.000806716   valid's RMSPE: 0.276883
[250]   train's rmse: 0.000721082   train's RMSPE: 0.249097 valid's rmse: 0.000802443   valid's RMSPE: 0.275417
[300]   train's rmse: 0.000707205   train's RMSPE: 0.244303 valid's rmse: 0.000800247   valid's RMSPE: 0.274663
[350]   train's rmse: 0.000694564   train's RMSPE: 0.239937 valid's rmse: 0.000800199   valid's RMSPE: 0.274647
[400]   train's rmse: 0.000683634   train's RMSPE: 0.236161 valid's rmse: 0.000800307   valid's RMSPE: 0.274684
Early stopping, best iteration is:
[388]   train's rmse: 0.000685841   train's RMSPE: 0.236923 valid's rmse: 0.000799342   valid's RMSPE: 0.274353
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000825374   train's RMSPE: 0.286134 valid's rmse: 0.000830391   valid's RMSPE: 0.280884
[100]   train's rmse: 0.000781957   train's RMSPE: 0.271083 valid's rmse: 0.000828287   valid's RMSPE: 0.280172
Early stopping, best iteration is:
[56]    train's rmse: 0.00081674    train's RMSPE: 0.283141 valid's rmse: 0.000826878   valid's RMSPE: 0.279695
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000831576   train's RMSPE: 0.287952 valid's rmse: 0.000828485   valid's RMSPE: 0.281591
[100]   train's rmse: 0.000786615   train's RMSPE: 0.272383 valid's rmse: 0.000809903   valid's RMSPE: 0.275275
[150]   train's rmse: 0.000762186   train's RMSPE: 0.263924 valid's rmse: 0.00080744    valid's RMSPE: 0.274438
[200]   train's rmse: 0.000741595   train's RMSPE: 0.256794 valid's rmse: 0.000802298   valid's RMSPE: 0.272691
[250]   train's rmse: 0.000724285   train's RMSPE: 0.2508   valid's rmse: 0.000801485   valid's RMSPE: 0.272414
[300]   train's rmse: 0.000708604   train's RMSPE: 0.24537  valid's rmse: 0.000801874   valid's RMSPE: 0.272546
Early stopping, best iteration is:
[283]   train's rmse: 0.00071399    train's RMSPE: 0.247235 valid's rmse: 0.000800565   valid's RMSPE: 0.272101
Our out of folds RMSPE is 0.288, compared to 0.26987327978046927, giving gain 0.018126720219530712
Our cv fold scores are [0.29, 0.323, 0.274, 0.28, 0.272]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000401699   train's RMSPE: 0.238643 valid's rmse: 0.000442377   valid's RMSPE: 0.264108
[100]   train's rmse: 0.000381702   train's RMSPE: 0.226763 valid's rmse: 0.000427399   valid's RMSPE: 0.255166
[150]   train's rmse: 0.000372053   train's RMSPE: 0.221031 valid's rmse: 0.000422513   valid's RMSPE: 0.252249
[200]   train's rmse: 0.000364309   train's RMSPE: 0.21643  valid's rmse: 0.000419789   valid's RMSPE: 0.250623
[250]   train's rmse: 0.000357183   train's RMSPE: 0.212197 valid's rmse: 0.000417128   valid's RMSPE: 0.249035
[300]   train's rmse: 0.000350986   train's RMSPE: 0.208515 valid's rmse: 0.000416276   valid's RMSPE: 0.248525
Early stopping, best iteration is:
[286]   train's rmse: 0.0003523 train's RMSPE: 0.209296 valid's rmse: 0.000415888   valid's RMSPE: 0.248294
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00040107    train's RMSPE: 0.238336 valid's rmse: 0.000441303   valid's RMSPE: 0.263177
[100]   train's rmse: 0.000382854   train's RMSPE: 0.22751  valid's rmse: 0.000428899   valid's RMSPE: 0.25578
[150]   train's rmse: 0.000373179   train's RMSPE: 0.221761 valid's rmse: 0.000422354   valid's RMSPE: 0.251876
[200]   train's rmse: 0.000365913   train's RMSPE: 0.217444 valid's rmse: 0.000419233   valid's RMSPE: 0.250015
[250]   train's rmse: 0.000359086   train's RMSPE: 0.213386 valid's rmse: 0.00041797    valid's RMSPE: 0.249262
[300]   train's rmse: 0.00035322    train's RMSPE: 0.209901 valid's rmse: 0.000415528   valid's RMSPE: 0.247805
[350]   train's rmse: 0.000348003   train's RMSPE: 0.206801 valid's rmse: 0.000414611   valid's RMSPE: 0.247259
[400]   train's rmse: 0.000342748   train's RMSPE: 0.203678 valid's rmse: 0.000414033   valid's RMSPE: 0.246914
[450]   train's rmse: 0.000338547   train's RMSPE: 0.201181 valid's rmse: 0.000413373   valid's RMSPE: 0.24652
[500]   train's rmse: 0.000334521   train's RMSPE: 0.198789 valid's rmse: 0.000412112   valid's RMSPE: 0.245768
[550]   train's rmse: 0.000330751   train's RMSPE: 0.196549 valid's rmse: 0.000412329   valid's RMSPE: 0.245898
Early stopping, best iteration is:
[515]   train's rmse: 0.000333217   train's RMSPE: 0.198014 valid's rmse: 0.000411795   valid's RMSPE: 0.245579
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000409759   train's RMSPE: 0.243841 valid's rmse: 0.000403405   valid's RMSPE: 0.239232
[100]   train's rmse: 0.000389356   train's RMSPE: 0.231699 valid's rmse: 0.000390009   valid's RMSPE: 0.231288
[150]   train's rmse: 0.000379678   train's RMSPE: 0.22594  valid's rmse: 0.000387197   valid's RMSPE: 0.22962
[200]   train's rmse: 0.000372009   train's RMSPE: 0.221376 valid's rmse: 0.000386535   valid's RMSPE: 0.229227
[250]   train's rmse: 0.000365835   train's RMSPE: 0.217702 valid's rmse: 0.000386416   valid's RMSPE: 0.229157
[300]   train's rmse: 0.000359289   train's RMSPE: 0.213806 valid's rmse: 0.000384125   valid's RMSPE: 0.227798
[350]   train's rmse: 0.000354118   train's RMSPE: 0.210729 valid's rmse: 0.000382998   valid's RMSPE: 0.22713
[400]   train's rmse: 0.00034945    train's RMSPE: 0.207952 valid's rmse: 0.000381971   valid's RMSPE: 0.226521
[450]   train's rmse: 0.000345218   train's RMSPE: 0.205433 valid's rmse: 0.000381386   valid's RMSPE: 0.226174
Early stopping, best iteration is:
[439]   train's rmse: 0.000346155   train's RMSPE: 0.205991 valid's rmse: 0.000380787   valid's RMSPE: 0.225819
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00041328    train's RMSPE: 0.244716 valid's rmse: 0.000403752   valid's RMSPE: 0.244161
[100]   train's rmse: 0.000390543   train's RMSPE: 0.231253 valid's rmse: 0.000392447   valid's RMSPE: 0.237324
[150]   train's rmse: 0.000380581   train's RMSPE: 0.225354 valid's rmse: 0.000389765   valid's RMSPE: 0.235703
[200]   train's rmse: 0.000372851   train's RMSPE: 0.220777 valid's rmse: 0.000390688   valid's RMSPE: 0.236261
Early stopping, best iteration is:
[162]   train's rmse: 0.000378702   train's RMSPE: 0.224241 valid's rmse: 0.000389193   valid's RMSPE: 0.235357
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000412835   train's RMSPE: 0.246794 valid's rmse: 0.000392099   valid's RMSPE: 0.228197
[100]   train's rmse: 0.00039232    train's RMSPE: 0.234529 valid's rmse: 0.000382783   valid's RMSPE: 0.222775
[150]   train's rmse: 0.000382831   train's RMSPE: 0.228857 valid's rmse: 0.00038023    valid's RMSPE: 0.221289
[200]   train's rmse: 0.000375149   train's RMSPE: 0.224265 valid's rmse: 0.000378333   valid's RMSPE: 0.220185
[250]   train's rmse: 0.000368589   train's RMSPE: 0.220343 valid's rmse: 0.00037679    valid's RMSPE: 0.219287
[300]   train's rmse: 0.000362429   train's RMSPE: 0.21666  valid's rmse: 0.000375708   valid's RMSPE: 0.218658
[350]   train's rmse: 0.000357127   train's RMSPE: 0.213491 valid's rmse: 0.000375145   valid's RMSPE: 0.21833
[400]   train's rmse: 0.000352025   train's RMSPE: 0.210441 valid's rmse: 0.00037431    valid's RMSPE: 0.217844
[450]   train's rmse: 0.000347068   train's RMSPE: 0.207478 valid's rmse: 0.00037353    valid's RMSPE: 0.21739
Early stopping, best iteration is:
[442]   train's rmse: 0.000347861   train's RMSPE: 0.207952 valid's rmse: 0.000373179   valid's RMSPE: 0.217186

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.235, compared to 0.19908949320829297, giving gain 0.035910506791707014
Our cv fold scores are [0.248, 0.246, 0.226, 0.235, 0.217]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000966531   train's RMSPE: 0.275754 valid's rmse: 0.00105617    valid's RMSPE: 0.296353
[100]   train's rmse: 0.000926933   train's RMSPE: 0.264456 valid's rmse: 0.00104278    valid's RMSPE: 0.292595
[150]   train's rmse: 0.000905197   train's RMSPE: 0.258255 valid's rmse: 0.0010355 valid's RMSPE: 0.290553
[200]   train's rmse: 0.000887828   train's RMSPE: 0.2533   valid's rmse: 0.0010311 valid's RMSPE: 0.289318
Early stopping, best iteration is:
[181]   train's rmse: 0.000894182   train's RMSPE: 0.255112 valid's rmse: 0.00103097    valid's RMSPE: 0.289281
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000970586   train's RMSPE: 0.274907 valid's rmse: 0.00102836    valid's RMSPE: 0.297029
[100]   train's rmse: 0.000927383   train's RMSPE: 0.26267  valid's rmse: 0.00100859    valid's RMSPE: 0.291317
[150]   train's rmse: 0.000905226   train's RMSPE: 0.256394 valid's rmse: 0.00100616    valid's RMSPE: 0.290617
Early stopping, best iteration is:
[135]   train's rmse: 0.000910893   train's RMSPE: 0.258    valid's rmse: 0.00100293    valid's RMSPE: 0.289684
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000974903   train's RMSPE: 0.275911 valid's rmse: 0.000992712   valid's RMSPE: 0.287602
[100]   train's rmse: 0.000930237   train's RMSPE: 0.26327  valid's rmse: 0.000978206   valid's RMSPE: 0.283399
Early stopping, best iteration is:
[85]    train's rmse: 0.000939181   train's RMSPE: 0.265802 valid's rmse: 0.000975098   valid's RMSPE: 0.282499
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000977923   train's RMSPE: 0.279738 valid's rmse: 0.000984575   valid's RMSPE: 0.273238
[100]   train's rmse: 0.00093269    train's RMSPE: 0.266799 valid's rmse: 0.000979423   valid's RMSPE: 0.271808
Early stopping, best iteration is:
[67]    train's rmse: 0.000956192   train's RMSPE: 0.273522 valid's rmse: 0.000977365   valid's RMSPE: 0.271237
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000985914   train's RMSPE: 0.28021  valid's rmse: 0.000958301   valid's RMSPE: 0.273095
[100]   train's rmse: 0.000945632   train's RMSPE: 0.268761 valid's rmse: 0.000924579   valid's RMSPE: 0.263485
[150]   train's rmse: 0.000923084   train's RMSPE: 0.262353 valid's rmse: 0.000920747   valid's RMSPE: 0.262393
[200]   train's rmse: 0.000903248   train's RMSPE: 0.256715 valid's rmse: 0.000914998   valid's RMSPE: 0.260755
[250]   train's rmse: 0.000886836   train's RMSPE: 0.252051 valid's rmse: 0.000913484   valid's RMSPE: 0.260324
Early stopping, best iteration is:
[237]   train's rmse: 0.000890544   train's RMSPE: 0.253104 valid's rmse: 0.000911979   valid's RMSPE: 0.259895

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.279, compared to 0.2556622422314529, giving gain 0.02333775776854713
Our cv fold scores are [0.289, 0.29, 0.282, 0.271, 0.26]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000671503   train's RMSPE: 0.220092 valid's rmse: 0.000719438   valid's RMSPE: 0.231943
[100]   train's rmse: 0.000636213   train's RMSPE: 0.208525 valid's rmse: 0.000697306   valid's RMSPE: 0.224807
[150]   train's rmse: 0.000621263   train's RMSPE: 0.203625 valid's rmse: 0.000690653   valid's RMSPE: 0.222663
[200]   train's rmse: 0.000608352   train's RMSPE: 0.199393 valid's rmse: 0.000687252   valid's RMSPE: 0.221566
[250]   train's rmse: 0.000597091   train's RMSPE: 0.195702 valid's rmse: 0.000684716   valid's RMSPE: 0.220748
[300]   train's rmse: 0.000587965   train's RMSPE: 0.192711 valid's rmse: 0.000684184   valid's RMSPE: 0.220577
Early stopping, best iteration is:
[285]   train's rmse: 0.000590451   train's RMSPE: 0.193526 valid's rmse: 0.000682847   valid's RMSPE: 0.220146
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.00066924    train's RMSPE: 0.21754  valid's rmse: 0.000722351   valid's RMSPE: 0.240661
[100]   train's rmse: 0.000634675   train's RMSPE: 0.206305 valid's rmse: 0.000704468   valid's RMSPE: 0.234703
[150]   train's rmse: 0.00061944    train's RMSPE: 0.201353 valid's rmse: 0.000701847   valid's RMSPE: 0.23383
[200]   train's rmse: 0.000606967   train's RMSPE: 0.197298 valid's rmse: 0.00069715    valid's RMSPE: 0.232265
[250]   train's rmse: 0.000595439   train's RMSPE: 0.193551 valid's rmse: 0.000694832   valid's RMSPE: 0.231493
[300]   train's rmse: 0.000585114   train's RMSPE: 0.190195 valid's rmse: 0.000691334   valid's RMSPE: 0.230328
[350]   train's rmse: 0.000575865   train's RMSPE: 0.187188 valid's rmse: 0.000692137   valid's RMSPE: 0.230595
Early stopping, best iteration is:
[315]   train's rmse: 0.000581916   train's RMSPE: 0.189155 valid's rmse: 0.000690336   valid's RMSPE: 0.229995
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00067624    train's RMSPE: 0.219908 valid's rmse: 0.00067761    valid's RMSPE: 0.225395
[100]   train's rmse: 0.000641301   train's RMSPE: 0.208546 valid's rmse: 0.000654753   valid's RMSPE: 0.217792
[150]   train's rmse: 0.000625732   train's RMSPE: 0.203483 valid's rmse: 0.000654127   valid's RMSPE: 0.217584
Early stopping, best iteration is:
[109]   train's rmse: 0.000638302   train's RMSPE: 0.20757  valid's rmse: 0.000653182   valid's RMSPE: 0.21727
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000682478   train's RMSPE: 0.223354 valid's rmse: 0.000669191   valid's RMSPE: 0.217072
[100]   train's rmse: 0.000647679   train's RMSPE: 0.211966 valid's rmse: 0.000652084   valid's RMSPE: 0.211523
[150]   train's rmse: 0.000632179   train's RMSPE: 0.206893 valid's rmse: 0.000650607   valid's RMSPE: 0.211044
[200]   train's rmse: 0.000619913   train's RMSPE: 0.202878 valid's rmse: 0.000647949   valid's RMSPE: 0.210182
[250]   train's rmse: 0.000608501   train's RMSPE: 0.199144 valid's rmse: 0.000649252   valid's RMSPE: 0.210604
[300]   train's rmse: 0.000599441   train's RMSPE: 0.196179 valid's rmse: 0.000648619   valid's RMSPE: 0.210399
Early stopping, best iteration is:
[265]   train's rmse: 0.000605666   train's RMSPE: 0.198216 valid's rmse: 0.00064644    valid's RMSPE: 0.209692
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000676269   train's RMSPE: 0.221936 valid's rmse: 0.00067738    valid's RMSPE: 0.217227
[100]   train's rmse: 0.00064433    train's RMSPE: 0.211455 valid's rmse: 0.000664445   valid's RMSPE: 0.213079
[150]   train's rmse: 0.000629079   train's RMSPE: 0.20645  valid's rmse: 0.000661125   valid's RMSPE: 0.212014
[200]   train's rmse: 0.000617246   train's RMSPE: 0.202567 valid's rmse: 0.00065925    valid's RMSPE: 0.211413
[250]   train's rmse: 0.000606492   train's RMSPE: 0.199037 valid's rmse: 0.000657244   valid's RMSPE: 0.210769
[300]   train's rmse: 0.000597002   train's RMSPE: 0.195923 valid's rmse: 0.000654204   valid's RMSPE: 0.209795
[350]   train's rmse: 0.000588455   train's RMSPE: 0.193118 valid's rmse: 0.000653909   valid's RMSPE: 0.2097
[400]   train's rmse: 0.000580104   train's RMSPE: 0.190377 valid's rmse: 0.000651924   valid's RMSPE: 0.209063
[450]   train's rmse: 0.000573211   train's RMSPE: 0.188115 valid's rmse: 0.00065125    valid's RMSPE: 0.208847
[500]   train's rmse: 0.000566745   train's RMSPE: 0.185993 valid's rmse: 0.000651571   valid's RMSPE: 0.20895
Early stopping, best iteration is:
[453]   train's rmse: 0.000572761   train's RMSPE: 0.187967 valid's rmse: 0.000650945   valid's RMSPE: 0.20875
Our out of folds RMSPE is 0.217, compared to 0.18802345650080163, giving gain 0.02897654349919837
Our cv fold scores are [0.22, 0.23, 0.217, 0.21, 0.209]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000720035   train's RMSPE: 0.246997 valid's rmse: 0.000785814   valid's RMSPE: 0.265101
[100]   train's rmse: 0.00068521    train's RMSPE: 0.235051 valid's rmse: 0.000762224   valid's RMSPE: 0.257142
[150]   train's rmse: 0.000670944   train's RMSPE: 0.230157 valid's rmse: 0.000757917   valid's RMSPE: 0.255689
[200]   train's rmse: 0.000658389   train's RMSPE: 0.225851 valid's rmse: 0.000754953   valid's RMSPE: 0.254689
[250]   train's rmse: 0.000647975   train's RMSPE: 0.222278 valid's rmse: 0.000753617   valid's RMSPE: 0.254239
[300]   train's rmse: 0.000638913   train's RMSPE: 0.21917  valid's rmse: 0.000751547   valid's RMSPE: 0.25354
[350]   train's rmse: 0.000630726   train's RMSPE: 0.216361 valid's rmse: 0.000751759   valid's RMSPE: 0.253612
Early stopping, best iteration is:
[327]   train's rmse: 0.000634364   train's RMSPE: 0.217609 valid's rmse: 0.000751162   valid's RMSPE: 0.25341
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000719354   train's RMSPE: 0.244942 valid's rmse: 0.000784767   valid's RMSPE: 0.272682
[100]   train's rmse: 0.000686664   train's RMSPE: 0.233811 valid's rmse: 0.000758885   valid's RMSPE: 0.263689
[150]   train's rmse: 0.000671218   train's RMSPE: 0.228551 valid's rmse: 0.000751434   valid's RMSPE: 0.2611
[200]   train's rmse: 0.000657596   train's RMSPE: 0.223913 valid's rmse: 0.000745935   valid's RMSPE: 0.25919
[250]   train's rmse: 0.00064678    train's RMSPE: 0.22023  valid's rmse: 0.000745549   valid's RMSPE: 0.259056
[300]   train's rmse: 0.000638471   train's RMSPE: 0.217401 valid's rmse: 0.000743057   valid's RMSPE: 0.258189
[350]   train's rmse: 0.000630213   train's RMSPE: 0.214589 valid's rmse: 0.000741062   valid's RMSPE: 0.257496
[400]   train's rmse: 0.000621855   train's RMSPE: 0.211743 valid's rmse: 0.000742353   valid's RMSPE: 0.257945
Early stopping, best iteration is:
[362]   train's rmse: 0.000627535   train's RMSPE: 0.213677 valid's rmse: 0.000740873   valid's RMSPE: 0.257431
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000733308   train's RMSPE: 0.249725 valid's rmse: 0.000705627   valid's RMSPE: 0.245063
[100]   train's rmse: 0.000699119   train's RMSPE: 0.238082 valid's rmse: 0.000687647   valid's RMSPE: 0.238818
[150]   train's rmse: 0.000683053   train's RMSPE: 0.232611 valid's rmse: 0.000684786   valid's RMSPE: 0.237825
[200]   train's rmse: 0.000670071   train's RMSPE: 0.22819  valid's rmse: 0.000681267   valid's RMSPE: 0.236603
Early stopping, best iteration is:
[185]   train's rmse: 0.000672819   train's RMSPE: 0.229126 valid's rmse: 0.000680356   valid's RMSPE: 0.236286
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000733082   train's RMSPE: 0.251635 valid's rmse: 0.000717983   valid's RMSPE: 0.241571
[100]   train's rmse: 0.000699778   train's RMSPE: 0.240203 valid's rmse: 0.000705037   valid's RMSPE: 0.237216
[150]   train's rmse: 0.000683459   train's RMSPE: 0.234601 valid's rmse: 0.000703246   valid's RMSPE: 0.236613
Early stopping, best iteration is:
[147]   train's rmse: 0.00068447    train's RMSPE: 0.234949 valid's rmse: 0.000703004   valid's RMSPE: 0.236532
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000729936   train's RMSPE: 0.249774 valid's rmse: 0.000723213   valid's RMSPE: 0.246463
[100]   train's rmse: 0.000696083   train's RMSPE: 0.23819  valid's rmse: 0.000704774   valid's RMSPE: 0.240179
[150]   train's rmse: 0.000678294   train's RMSPE: 0.232103 valid's rmse: 0.000698949   valid's RMSPE: 0.238194
[200]   train's rmse: 0.000666239   train's RMSPE: 0.227978 valid's rmse: 0.00069628    valid's RMSPE: 0.237285
[250]   train's rmse: 0.000654334   train's RMSPE: 0.223904 valid's rmse: 0.000695086   valid's RMSPE: 0.236878
[300]   train's rmse: 0.000643867   train's RMSPE: 0.220322 valid's rmse: 0.000693432   valid's RMSPE: 0.236314
[350]   train's rmse: 0.000635356   train's RMSPE: 0.21741  valid's rmse: 0.000693551   valid's RMSPE: 0.236355
Early stopping, best iteration is:
[308]   train's rmse: 0.000642364   train's RMSPE: 0.219808 valid's rmse: 0.00069275    valid's RMSPE: 0.236082
Our out of folds RMSPE is 0.244, compared to 0.20085218354438011, giving gain 0.04314781645561988
Our cv fold scores are [0.253, 0.257, 0.236, 0.237, 0.236]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000483198   train's RMSPE: 0.247469 valid's rmse: 0.000514765   valid's RMSPE: 0.260316
[100]   train's rmse: 0.000459823   train's RMSPE: 0.235497 valid's rmse: 0.000501464   valid's RMSPE: 0.253589
[150]   train's rmse: 0.000449864   train's RMSPE: 0.230397 valid's rmse: 0.000498144   valid's RMSPE: 0.25191
[200]   train's rmse: 0.000441349   train's RMSPE: 0.226036 valid's rmse: 0.000495083   valid's RMSPE: 0.250362
[250]   train's rmse: 0.000433618   train's RMSPE: 0.222077 valid's rmse: 0.000494468   valid's RMSPE: 0.250051
[300]   train's rmse: 0.000426188   train's RMSPE: 0.218271 valid's rmse: 0.000492902   valid's RMSPE: 0.249259
[350]   train's rmse: 0.000419314   train's RMSPE: 0.214751 valid's rmse: 0.000490036   valid's RMSPE: 0.24781
[400]   train's rmse: 0.000413728   train's RMSPE: 0.21189  valid's rmse: 0.000488779   valid's RMSPE: 0.247175
[450]   train's rmse: 0.000408851   train's RMSPE: 0.209392 valid's rmse: 0.000488751   valid's RMSPE: 0.24716
[500]   train's rmse: 0.000403891   train's RMSPE: 0.206852 valid's rmse: 0.000487112   valid's RMSPE: 0.246332
Early stopping, best iteration is:
[484]   train's rmse: 0.000405522   train's RMSPE: 0.207687 valid's rmse: 0.000485987   valid's RMSPE: 0.245762
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000477994   train's RMSPE: 0.243677 valid's rmse: 0.000535865   valid's RMSPE: 0.276043
[100]   train's rmse: 0.000455431   train's RMSPE: 0.232175 valid's rmse: 0.000519367   valid's RMSPE: 0.267544
[150]   train's rmse: 0.00044389    train's RMSPE: 0.226291 valid's rmse: 0.000515775   valid's RMSPE: 0.265694
[200]   train's rmse: 0.000434962   train's RMSPE: 0.22174  valid's rmse: 0.000512244   valid's RMSPE: 0.263875
[250]   train's rmse: 0.000427247   train's RMSPE: 0.217807 valid's rmse: 0.00050977    valid's RMSPE: 0.262601
[300]   train's rmse: 0.000420445   train's RMSPE: 0.214339 valid's rmse: 0.000508816   valid's RMSPE: 0.262109
[350]   train's rmse: 0.000414658   train's RMSPE: 0.211389 valid's rmse: 0.000507887   valid's RMSPE: 0.26163
[400]   train's rmse: 0.000409255   train's RMSPE: 0.208635 valid's rmse: 0.00050697    valid's RMSPE: 0.261158
Early stopping, best iteration is:
[382]   train's rmse: 0.000411007   train's RMSPE: 0.209528 valid's rmse: 0.000506615   valid's RMSPE: 0.260975
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000489821   train's RMSPE: 0.25016  valid's rmse: 0.000478955   valid's RMSPE: 0.244962
[100]   train's rmse: 0.000465543   train's RMSPE: 0.237761 valid's rmse: 0.000469296   valid's RMSPE: 0.240022
[150]   train's rmse: 0.000454328   train's RMSPE: 0.232034 valid's rmse: 0.000465908   valid's RMSPE: 0.238289
[200]   train's rmse: 0.000444609   train's RMSPE: 0.22707  valid's rmse: 0.000464113   valid's RMSPE: 0.237371
[250]   train's rmse: 0.00043672    train's RMSPE: 0.223041 valid's rmse: 0.00046237    valid's RMSPE: 0.236479
[300]   train's rmse: 0.000429808   train's RMSPE: 0.21951  valid's rmse: 0.000460927   valid's RMSPE: 0.235741
Early stopping, best iteration is:
[284]   train's rmse: 0.000431668   train's RMSPE: 0.220461 valid's rmse: 0.000460801   valid's RMSPE: 0.235677
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000492297   train's RMSPE: 0.25141  valid's rmse: 0.000482719   valid's RMSPE: 0.246946
[100]   train's rmse: 0.000467923   train's RMSPE: 0.238962 valid's rmse: 0.000465912   valid's RMSPE: 0.238348
[150]   train's rmse: 0.000456589   train's RMSPE: 0.233174 valid's rmse: 0.000462464   valid's RMSPE: 0.236584
[200]   train's rmse: 0.000445713   train's RMSPE: 0.22762  valid's rmse: 0.000459206   valid's RMSPE: 0.234918
[250]   train's rmse: 0.000438319   train's RMSPE: 0.223844 valid's rmse: 0.000459761   valid's RMSPE: 0.235201
Early stopping, best iteration is:
[216]   train's rmse: 0.000442836   train's RMSPE: 0.226151 valid's rmse: 0.000458796   valid's RMSPE: 0.234708
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00049029    train's RMSPE: 0.250526 valid's rmse: 0.000487921   valid's RMSPE: 0.249045
[100]   train's rmse: 0.000464893   train's RMSPE: 0.237549 valid's rmse: 0.000478099   valid's RMSPE: 0.244032
[150]   train's rmse: 0.000452375   train's RMSPE: 0.231152 valid's rmse: 0.000476123   valid's RMSPE: 0.243023
[200]   train's rmse: 0.000442941   train's RMSPE: 0.226332 valid's rmse: 0.000476674   valid's RMSPE: 0.243305
[250]   train's rmse: 0.000434371   train's RMSPE: 0.221953 valid's rmse: 0.000476557   valid's RMSPE: 0.243245
Early stopping, best iteration is:
[221]   train's rmse: 0.000438784   train's RMSPE: 0.224208 valid's rmse: 0.000475763   valid's RMSPE: 0.242839
Our out of folds RMSPE is 0.244, compared to 0.21907995999798716, giving gain 0.02492004000201284
Our cv fold scores are [0.246, 0.261, 0.236, 0.235, 0.243]

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000766016   train's RMSPE: 0.247381 valid's rmse: 0.000810082   valid's RMSPE: 0.261403
[100]   train's rmse: 0.000727891   train's RMSPE: 0.235069 valid's rmse: 0.000788124   valid's RMSPE: 0.254318
[150]   train's rmse: 0.00070864    train's RMSPE: 0.228852 valid's rmse: 0.000787296   valid's RMSPE: 0.25405
Early stopping, best iteration is:
[105]   train's rmse: 0.000725561   train's RMSPE: 0.234316 valid's rmse: 0.000786912   valid's RMSPE: 0.253927
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000767556   train's RMSPE: 0.247397 valid's rmse: 0.000803508   valid's RMSPE: 0.261287
[100]   train's rmse: 0.000728942   train's RMSPE: 0.234951 valid's rmse: 0.000783814   valid's RMSPE: 0.254883
[150]   train's rmse: 0.000709525   train's RMSPE: 0.228693 valid's rmse: 0.000779286   valid's RMSPE: 0.25341
Early stopping, best iteration is:
[149]   train's rmse: 0.000709877   train's RMSPE: 0.228807 valid's rmse: 0.000779134   valid's RMSPE: 0.253361
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000772148   train's RMSPE: 0.248495 valid's rmse: 0.000775178   valid's RMSPE: 0.253591
[100]   train's rmse: 0.000735768   train's RMSPE: 0.236787 valid's rmse: 0.000757326   valid's RMSPE: 0.247751
[150]   train's rmse: 0.00071703    train's RMSPE: 0.230757 valid's rmse: 0.000754548   valid's RMSPE: 0.246842
[200]   train's rmse: 0.00069991    train's RMSPE: 0.225247 valid's rmse: 0.000754343   valid's RMSPE: 0.246776
Early stopping, best iteration is:
[172]   train's rmse: 0.000708751   train's RMSPE: 0.228092 valid's rmse: 0.000752804   valid's RMSPE: 0.246272
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000767649   train's RMSPE: 0.248021 valid's rmse: 0.000815939   valid's RMSPE: 0.262813
[100]   train's rmse: 0.000730703   train's RMSPE: 0.236084 valid's rmse: 0.000809155   valid's RMSPE: 0.260628
Early stopping, best iteration is:
[82]    train's rmse: 0.000738596   train's RMSPE: 0.238634 valid's rmse: 0.000805585   valid's RMSPE: 0.259478
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000775758   train's RMSPE: 0.251565 valid's rmse: 0.000763853   valid's RMSPE: 0.242349
[100]   train's rmse: 0.000741239   train's RMSPE: 0.240372 valid's rmse: 0.000746848   valid's RMSPE: 0.236954
[150]   train's rmse: 0.000722732   train's RMSPE: 0.23437  valid's rmse: 0.000746581   valid's RMSPE: 0.236869
[200]   train's rmse: 0.000706244   train's RMSPE: 0.229023 valid's rmse: 0.00074795    valid's RMSPE: 0.237303
Early stopping, best iteration is:
[158]   train's rmse: 0.000720052   train's RMSPE: 0.233501 valid's rmse: 0.000745528   valid's RMSPE: 0.236535
Our out of folds RMSPE is 0.25, compared to 0.22483761681905104, giving gain 0.02516238318094896
Our cv fold scores are [0.254, 0.253, 0.246, 0.259, 0.237]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000796405   train's RMSPE: 0.241336 valid's rmse: 0.000850369   valid's RMSPE: 0.254966
[100]   train's rmse: 0.000762222   train's RMSPE: 0.230978 valid's rmse: 0.000831883   valid's RMSPE: 0.249424
[150]   train's rmse: 0.000743648   train's RMSPE: 0.225349 valid's rmse: 0.00083068    valid's RMSPE: 0.249063
Early stopping, best iteration is:
[145]   train's rmse: 0.000745126   train's RMSPE: 0.225797 valid's rmse: 0.000830512   valid's RMSPE: 0.249012
Training fold 1
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000794044   train's RMSPE: 0.239732 valid's rmse: 0.000860875   valid's RMSPE: 0.261976
[100]   train's rmse: 0.000761981   train's RMSPE: 0.230052 valid's rmse: 0.000838938   valid's RMSPE: 0.2553
[150]   train's rmse: 0.00074641    train's RMSPE: 0.225351 valid's rmse: 0.000836098   valid's RMSPE: 0.254436
[200]   train's rmse: 0.000732617   train's RMSPE: 0.221187 valid's rmse: 0.000832905   valid's RMSPE: 0.253464
[250]   train's rmse: 0.000720847   train's RMSPE: 0.217633 valid's rmse: 0.000828586   valid's RMSPE: 0.25215
[300]   train's rmse: 0.000710347   train's RMSPE: 0.214463 valid's rmse: 0.000825012   valid's RMSPE: 0.251062
[350]   train's rmse: 0.000700525   train's RMSPE: 0.211498 valid's rmse: 0.000821121   valid's RMSPE: 0.249878
[400]   train's rmse: 0.000690936   train's RMSPE: 0.208602 valid's rmse: 0.000817889   valid's RMSPE: 0.248894
[450]   train's rmse: 0.000683951   train's RMSPE: 0.206494 valid's rmse: 0.000816371   valid's RMSPE: 0.248433
[500]   train's rmse: 0.000676005   train's RMSPE: 0.204095 valid's rmse: 0.000815849   valid's RMSPE: 0.248274
[550]   train's rmse: 0.000669009   train's RMSPE: 0.201982 valid's rmse: 0.000816165   valid's RMSPE: 0.24837
Early stopping, best iteration is:
[505]   train's rmse: 0.00067529    train's RMSPE: 0.203879 valid's rmse: 0.00081555    valid's RMSPE: 0.248183
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000803125   train's RMSPE: 0.242739 valid's rmse: 0.000807417   valid's RMSPE: 0.244647
[100]   train's rmse: 0.00076812    train's RMSPE: 0.232159 valid's rmse: 0.000795677   valid's RMSPE: 0.24109
[150]   train's rmse: 0.000751633   train's RMSPE: 0.227176 valid's rmse: 0.000793798   valid's RMSPE: 0.240521
Early stopping, best iteration is:
[120]   train's rmse: 0.000760523   train's RMSPE: 0.229863 valid's rmse: 0.000792885   valid's RMSPE: 0.240244
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000811262   train's RMSPE: 0.244334 valid's rmse: 0.00079528    valid's RMSPE: 0.24432
[100]   train's rmse: 0.00077797    train's RMSPE: 0.234307 valid's rmse: 0.000768355   valid's RMSPE: 0.236048
[150]   train's rmse: 0.000760992   train's RMSPE: 0.229194 valid's rmse: 0.000763801   valid's RMSPE: 0.234649
[200]   train's rmse: 0.000746667   train's RMSPE: 0.22488  valid's rmse: 0.000761645   valid's RMSPE: 0.233986
[250]   train's rmse: 0.000734409   train's RMSPE: 0.221188 valid's rmse: 0.000759407   valid's RMSPE: 0.233299
[300]   train's rmse: 0.000723793   train's RMSPE: 0.21799  valid's rmse: 0.000758377   valid's RMSPE: 0.232983
Early stopping, best iteration is:
[264]   train's rmse: 0.000730825   train's RMSPE: 0.220108 valid's rmse: 0.000757745   valid's RMSPE: 0.232789
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000807005   train's RMSPE: 0.245007 valid's rmse: 0.000794392   valid's RMSPE: 0.236349
[100]   train's rmse: 0.000775519   train's RMSPE: 0.235448 valid's rmse: 0.000778175   valid's RMSPE: 0.231524
[150]   train's rmse: 0.000759005   train's RMSPE: 0.230434 valid's rmse: 0.000776665   valid's RMSPE: 0.231074
[200]   train's rmse: 0.000745231   train's RMSPE: 0.226253 valid's rmse: 0.000772044   valid's RMSPE: 0.2297
[250]   train's rmse: 0.000732808   train's RMSPE: 0.222481 valid's rmse: 0.000769865   valid's RMSPE: 0.229051
[300]   train's rmse: 0.000721846   train's RMSPE: 0.219153 valid's rmse: 0.000767522   valid's RMSPE: 0.228354
[350]   train's rmse: 0.000712418   train's RMSPE: 0.21629  valid's rmse: 0.00076531    valid's RMSPE: 0.227696
[400]   train's rmse: 0.000703604   train's RMSPE: 0.213614 valid's rmse: 0.00076582    valid's RMSPE: 0.227848
Early stopping, best iteration is:
[359]   train's rmse: 0.000710835   train's RMSPE: 0.21581  valid's rmse: 0.000764387   valid's RMSPE: 0.227422
Our out of folds RMSPE is 0.24, compared to 0.2094257055987473, giving gain 0.0305742944012527
Our cv fold scores are [0.249, 0.248, 0.24, 0.233, 0.227]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000458948   train's RMSPE: 0.230019 valid's rmse: 0.00048789    valid's RMSPE: 0.244217
[100]   train's rmse: 0.000431406   train's RMSPE: 0.216216 valid's rmse: 0.000468433   valid's RMSPE: 0.234478
[150]   train's rmse: 0.000420635   train's RMSPE: 0.210817 valid's rmse: 0.000462832   valid's RMSPE: 0.231674
[200]   train's rmse: 0.000412432   train's RMSPE: 0.206706 valid's rmse: 0.000458076   valid's RMSPE: 0.229293
[250]   train's rmse: 0.000404932   train's RMSPE: 0.202947 valid's rmse: 0.000454365   valid's RMSPE: 0.227436
[300]   train's rmse: 0.000399198   train's RMSPE: 0.200073 valid's rmse: 0.000451701   valid's RMSPE: 0.226102
[350]   train's rmse: 0.000394551   train's RMSPE: 0.197744 valid's rmse: 0.000450489   valid's RMSPE: 0.225496
[400]   train's rmse: 0.000389522   train's RMSPE: 0.195224 valid's rmse: 0.000449206   valid's RMSPE: 0.224853
[450]   train's rmse: 0.000385143   train's RMSPE: 0.193029 valid's rmse: 0.000448749   valid's RMSPE: 0.224625
[500]   train's rmse: 0.000381215   train's RMSPE: 0.19106  valid's rmse: 0.000447344   valid's RMSPE: 0.223921
[550]   train's rmse: 0.000377503   train's RMSPE: 0.1892   valid's rmse: 0.000446852   valid's RMSPE: 0.223675
[600]   train's rmse: 0.000373994   train's RMSPE: 0.187441 valid's rmse: 0.000446834   valid's RMSPE: 0.223666
[650]   train's rmse: 0.000370141   train's RMSPE: 0.18551  valid's rmse: 0.000445677   valid's RMSPE: 0.223087
[700]   train's rmse: 0.000366456   train's RMSPE: 0.183663 valid's rmse: 0.000445475   valid's RMSPE: 0.222986
[750]   train's rmse: 0.000363409   train's RMSPE: 0.182136 valid's rmse: 0.000444937   valid's RMSPE: 0.222717
Early stopping, best iteration is:
[742]   train's rmse: 0.000363756   train's RMSPE: 0.18231  valid's rmse: 0.000444528   valid's RMSPE: 0.222512
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000460732   train's RMSPE: 0.229628 valid's rmse: 0.000489259   valid's RMSPE: 0.250292
[100]   train's rmse: 0.000431777   train's RMSPE: 0.215197 valid's rmse: 0.000462107   valid's RMSPE: 0.236402
[150]   train's rmse: 0.00042156    train's RMSPE: 0.210105 valid's rmse: 0.000456917   valid's RMSPE: 0.233747
[200]   train's rmse: 0.00041283    train's RMSPE: 0.205754 valid's rmse: 0.000451039   valid's RMSPE: 0.230739
[250]   train's rmse: 0.000405891   train's RMSPE: 0.202296 valid's rmse: 0.000448419   valid's RMSPE: 0.229399
[300]   train's rmse: 0.000399858   train's RMSPE: 0.199289 valid's rmse: 0.000446214   valid's RMSPE: 0.228271
[350]   train's rmse: 0.000394912   train's RMSPE: 0.196824 valid's rmse: 0.000444931   valid's RMSPE: 0.227615
[400]   train's rmse: 0.000391038   train's RMSPE: 0.194893 valid's rmse: 0.000444232   valid's RMSPE: 0.227257
[450]   train's rmse: 0.000386436   train's RMSPE: 0.192599 valid's rmse: 0.000442176   valid's RMSPE: 0.226205
[500]   train's rmse: 0.000382609   train's RMSPE: 0.190692 valid's rmse: 0.000441828   valid's RMSPE: 0.226028
[550]   train's rmse: 0.000378775   train's RMSPE: 0.188781 valid's rmse: 0.000440307   valid's RMSPE: 0.225249
[600]   train's rmse: 0.000374893   train's RMSPE: 0.186846 valid's rmse: 0.000439491   valid's RMSPE: 0.224832
[650]   train's rmse: 0.000371501   train's RMSPE: 0.185156 valid's rmse: 0.000438783   valid's RMSPE: 0.224469
[700]   train's rmse: 0.000368305   train's RMSPE: 0.183563 valid's rmse: 0.000437734   valid's RMSPE: 0.223933
[750]   train's rmse: 0.000364967   train's RMSPE: 0.181899 valid's rmse: 0.000436804   valid's RMSPE: 0.223457
[800]   train's rmse: 0.000362051   train's RMSPE: 0.180446 valid's rmse: 0.000436188   valid's RMSPE: 0.223142
[850]   train's rmse: 0.000358787   train's RMSPE: 0.178819 valid's rmse: 0.000434968   valid's RMSPE: 0.222518
[900]   train's rmse: 0.00035597    train's RMSPE: 0.177415 valid's rmse: 0.000434842   valid's RMSPE: 0.222454
Early stopping, best iteration is:
[876]   train's rmse: 0.000357382   train's RMSPE: 0.178119 valid's rmse: 0.000434037   valid's RMSPE: 0.222042
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000464912   train's RMSPE: 0.232244 valid's rmse: 0.000457476   valid's RMSPE: 0.231979
[100]   train's rmse: 0.000437984   train's RMSPE: 0.218793 valid's rmse: 0.000438594   valid's RMSPE: 0.222405
[150]   train's rmse: 0.000426986   train's RMSPE: 0.213298 valid's rmse: 0.000434383   valid's RMSPE: 0.22027
[200]   train's rmse: 0.000418668   train's RMSPE: 0.209143 valid's rmse: 0.000431671   valid's RMSPE: 0.218894
[250]   train's rmse: 0.000411784   train's RMSPE: 0.205704 valid's rmse: 0.000430031   valid's RMSPE: 0.218062
[300]   train's rmse: 0.000406248   train's RMSPE: 0.202939 valid's rmse: 0.000429457   valid's RMSPE: 0.217772
[350]   train's rmse: 0.000400867   train's RMSPE: 0.200251 valid's rmse: 0.000428238   valid's RMSPE: 0.217153
[400]   train's rmse: 0.000396268   train's RMSPE: 0.197954 valid's rmse: 0.000426699   valid's RMSPE: 0.216373
[450]   train's rmse: 0.000391712   train's RMSPE: 0.195677 valid's rmse: 0.00042486    valid's RMSPE: 0.21544
[500]   train's rmse: 0.000387547   train's RMSPE: 0.193597 valid's rmse: 0.000423944   valid's RMSPE: 0.214976
[550]   train's rmse: 0.000383392   train's RMSPE: 0.191521 valid's rmse: 0.00042314    valid's RMSPE: 0.214568
[600]   train's rmse: 0.00037973    train's RMSPE: 0.189692 valid's rmse: 0.000423075   valid's RMSPE: 0.214535
Early stopping, best iteration is:
[555]   train's rmse: 0.000382964   train's RMSPE: 0.191308 valid's rmse: 0.000422707   valid's RMSPE: 0.214349
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000466979   train's RMSPE: 0.23441  valid's rmse: 0.000448095   valid's RMSPE: 0.222884
[100]   train's rmse: 0.000439887   train's RMSPE: 0.220811 valid's rmse: 0.000428072   valid's RMSPE: 0.212925
[150]   train's rmse: 0.000428291   train's RMSPE: 0.21499  valid's rmse: 0.000422895   valid's RMSPE: 0.21035
[200]   train's rmse: 0.000419393   train's RMSPE: 0.210524 valid's rmse: 0.000419164   valid's RMSPE: 0.208494
[250]   train's rmse: 0.000412458   train's RMSPE: 0.207043 valid's rmse: 0.000417152   valid's RMSPE: 0.207493
[300]   train's rmse: 0.000406802   train's RMSPE: 0.204203 valid's rmse: 0.000415652   valid's RMSPE: 0.206747
[350]   train's rmse: 0.000401842   train's RMSPE: 0.201714 valid's rmse: 0.000415133   valid's RMSPE: 0.206489
[400]   train's rmse: 0.000397496   train's RMSPE: 0.199532 valid's rmse: 0.000414845   valid's RMSPE: 0.206345
[450]   train's rmse: 0.00039319    train's RMSPE: 0.197371 valid's rmse: 0.000414187   valid's RMSPE: 0.206018
[500]   train's rmse: 0.000388517   train's RMSPE: 0.195025 valid's rmse: 0.00041363    valid's RMSPE: 0.205741
[550]   train's rmse: 0.00038379    train's RMSPE: 0.192652 valid's rmse: 0.000413074   valid's RMSPE: 0.205464
Early stopping, best iteration is:
[537]   train's rmse: 0.000384791   train's RMSPE: 0.193154 valid's rmse: 0.000412737   valid's RMSPE: 0.205297
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000461845   train's RMSPE: 0.232855 valid's rmse: 0.00047745    valid's RMSPE: 0.233171
[100]   train's rmse: 0.000436428   train's RMSPE: 0.220041 valid's rmse: 0.000460432   valid's RMSPE: 0.22486
[150]   train's rmse: 0.000425217   train's RMSPE: 0.214388 valid's rmse: 0.000453468   valid's RMSPE: 0.221459
[200]   train's rmse: 0.000417153   train's RMSPE: 0.210323 valid's rmse: 0.000448987   valid's RMSPE: 0.219271
[250]   train's rmse: 0.00040999    train's RMSPE: 0.206711 valid's rmse: 0.000446508   valid's RMSPE: 0.21806
[300]   train's rmse: 0.00040316    train's RMSPE: 0.203267 valid's rmse: 0.000444308   valid's RMSPE: 0.216986
[350]   train's rmse: 0.000397923   train's RMSPE: 0.200627 valid's rmse: 0.000442411   valid's RMSPE: 0.216059
[400]   train's rmse: 0.000393433   train's RMSPE: 0.198363 valid's rmse: 0.000440745   valid's RMSPE: 0.215246
[450]   train's rmse: 0.000389203   train's RMSPE: 0.196231 valid's rmse: 0.000439736   valid's RMSPE: 0.214753
[500]   train's rmse: 0.000385277   train's RMSPE: 0.194251 valid's rmse: 0.000439198   valid's RMSPE: 0.21449
[550]   train's rmse: 0.000381759   train's RMSPE: 0.192477 valid's rmse: 0.000438258   valid's RMSPE: 0.214031
[600]   train's rmse: 0.000378143   train's RMSPE: 0.190654 valid's rmse: 0.000436786   valid's RMSPE: 0.213312
[650]   train's rmse: 0.00037491    train's RMSPE: 0.189024 valid's rmse: 0.000436589   valid's RMSPE: 0.213216
[700]   train's rmse: 0.000372011   train's RMSPE: 0.187562 valid's rmse: 0.000435893   valid's RMSPE: 0.212876
[750]   train's rmse: 0.00036834    train's RMSPE: 0.185711 valid's rmse: 0.00043454    valid's RMSPE: 0.212215
[800]   train's rmse: 0.000365265   train's RMSPE: 0.184161 valid's rmse: 0.000434059   valid's RMSPE: 0.21198
[850]   train's rmse: 0.000362675   train's RMSPE: 0.182855 valid's rmse: 0.000433454   valid's RMSPE: 0.211685
[900]   train's rmse: 0.000359833   train's RMSPE: 0.181423 valid's rmse: 0.000432465   valid's RMSPE: 0.211202
[950]   train's rmse: 0.000357202   train's RMSPE: 0.180096 valid's rmse: 0.000431875   valid's RMSPE: 0.210914
Early stopping, best iteration is:
[931]   train's rmse: 0.000358074   train's RMSPE: 0.180536 valid's rmse: 0.000431609   valid's RMSPE: 0.210784
Our out of folds RMSPE is 0.215, compared to 0.17836495410073763, giving gain 0.036635045899262364
Our cv fold scores are [0.223, 0.222, 0.214, 0.205, 0.211]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000425698   train's RMSPE: 0.212161 valid's rmse: 0.00044951    valid's RMSPE: 0.225104
[100]   train's rmse: 0.000402518   train's RMSPE: 0.200608 valid's rmse: 0.000435586   valid's RMSPE: 0.218131
[150]   train's rmse: 0.000392035   train's RMSPE: 0.195383 valid's rmse: 0.000432456   valid's RMSPE: 0.216564
[200]   train's rmse: 0.000383172   train's RMSPE: 0.190966 valid's rmse: 0.000430429   valid's RMSPE: 0.215549
[250]   train's rmse: 0.000376149   train's RMSPE: 0.187466 valid's rmse: 0.000427592   valid's RMSPE: 0.214128
[300]   train's rmse: 0.000369913   train's RMSPE: 0.184358 valid's rmse: 0.000425435   valid's RMSPE: 0.213048
[350]   train's rmse: 0.000364599   train's RMSPE: 0.18171  valid's rmse: 0.000424726   valid's RMSPE: 0.212693
[400]   train's rmse: 0.000359984   train's RMSPE: 0.17941  valid's rmse: 0.000423711   valid's RMSPE: 0.212185
[450]   train's rmse: 0.000356014   train's RMSPE: 0.177431 valid's rmse: 0.000422512   valid's RMSPE: 0.211584
Early stopping, best iteration is:
[429]   train's rmse: 0.000357509   train's RMSPE: 0.178176 valid's rmse: 0.000422056   valid's RMSPE: 0.211356
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00042333    train's RMSPE: 0.210311 valid's rmse: 0.00045798    valid's RMSPE: 0.232205
[100]   train's rmse: 0.000400238   train's RMSPE: 0.198839 valid's rmse: 0.000438376   valid's RMSPE: 0.222266
[150]   train's rmse: 0.000388968   train's RMSPE: 0.19324  valid's rmse: 0.000432133   valid's RMSPE: 0.2191
[200]   train's rmse: 0.000380245   train's RMSPE: 0.188907 valid's rmse: 0.000428446   valid's RMSPE: 0.217231
[250]   train's rmse: 0.000372894   train's RMSPE: 0.185255 valid's rmse: 0.000426699   valid's RMSPE: 0.216345
[300]   train's rmse: 0.000366363   train's RMSPE: 0.18201  valid's rmse: 0.000425987   valid's RMSPE: 0.215984
[350]   train's rmse: 0.000360786   train's RMSPE: 0.179239 valid's rmse: 0.000424322   valid's RMSPE: 0.21514
[400]   train's rmse: 0.000355928   train's RMSPE: 0.176826 valid's rmse: 0.000423568   valid's RMSPE: 0.214757
Early stopping, best iteration is:
[394]   train's rmse: 0.000356616   train's RMSPE: 0.177168 valid's rmse: 0.000423105   valid's RMSPE: 0.214523
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000431486   train's RMSPE: 0.21453  valid's rmse: 0.000412045   valid's RMSPE: 0.208287
[100]   train's rmse: 0.00040775    train's RMSPE: 0.202729 valid's rmse: 0.000398324   valid's RMSPE: 0.201352
[150]   train's rmse: 0.000395927   train's RMSPE: 0.196851 valid's rmse: 0.000394094   valid's RMSPE: 0.199213
[200]   train's rmse: 0.000387182   train's RMSPE: 0.192503 valid's rmse: 0.000391926   valid's RMSPE: 0.198117
[250]   train's rmse: 0.00037997    train's RMSPE: 0.188917 valid's rmse: 0.000391152   valid's RMSPE: 0.197726
Early stopping, best iteration is:
[240]   train's rmse: 0.000381381   train's RMSPE: 0.189619 valid's rmse: 0.000390613   valid's RMSPE: 0.197454
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000429147   train's RMSPE: 0.214654 valid's rmse: 0.000427842   valid's RMSPE: 0.21115
[100]   train's rmse: 0.000407087   train's RMSPE: 0.20362  valid's rmse: 0.000412421   valid's RMSPE: 0.20354
[150]   train's rmse: 0.000396668   train's RMSPE: 0.198408 valid's rmse: 0.00041064    valid's RMSPE: 0.202661
[200]   train's rmse: 0.000388957   train's RMSPE: 0.194552 valid's rmse: 0.000407537   valid's RMSPE: 0.201129
[250]   train's rmse: 0.000382261   train's RMSPE: 0.191202 valid's rmse: 0.000404594   valid's RMSPE: 0.199677
[300]   train's rmse: 0.000376024   train's RMSPE: 0.188083 valid's rmse: 0.000402415   valid's RMSPE: 0.198601
[350]   train's rmse: 0.00037076    train's RMSPE: 0.18545  valid's rmse: 0.0004007 valid's RMSPE: 0.197755
[400]   train's rmse: 0.000365346   train's RMSPE: 0.182742 valid's rmse: 0.000399799   valid's RMSPE: 0.19731
[450]   train's rmse: 0.000361336   train's RMSPE: 0.180736 valid's rmse: 0.000399111   valid's RMSPE: 0.196971
[500]   train's rmse: 0.000357424   train's RMSPE: 0.178779 valid's rmse: 0.000397771   valid's RMSPE: 0.19631
Early stopping, best iteration is:
[489]   train's rmse: 0.000358126   train's RMSPE: 0.17913  valid's rmse: 0.000397605   valid's RMSPE: 0.196228
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000427609   train's RMSPE: 0.214545 valid's rmse: 0.000435969   valid's RMSPE: 0.212411
[100]   train's rmse: 0.00040316    train's RMSPE: 0.202278 valid's rmse: 0.00042628    valid's RMSPE: 0.20769
[150]   train's rmse: 0.000391086   train's RMSPE: 0.19622  valid's rmse: 0.000423548   valid's RMSPE: 0.206359
[200]   train's rmse: 0.000382602   train's RMSPE: 0.191964 valid's rmse: 0.000422333   valid's RMSPE: 0.205767
Early stopping, best iteration is:
[197]   train's rmse: 0.000383061   train's RMSPE: 0.192194 valid's rmse: 0.000422251   valid's RMSPE: 0.205727
Our out of folds RMSPE is 0.205, compared to 0.17936019432433678, giving gain 0.025639805675663208
Our cv fold scores are [0.211, 0.215, 0.197, 0.196, 0.206]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000565279   train's RMSPE: 0.221688 valid's rmse: 0.000623958   valid's RMSPE: 0.239671
[100]   train's rmse: 0.000536768   train's RMSPE: 0.210506 valid's rmse: 0.000603674   valid's RMSPE: 0.23188
[150]   train's rmse: 0.000523463   train's RMSPE: 0.205288 valid's rmse: 0.000598626   valid's RMSPE: 0.229941
[200]   train's rmse: 0.000513237   train's RMSPE: 0.201278 valid's rmse: 0.000597222   valid's RMSPE: 0.229402
[250]   train's rmse: 0.000504849   train's RMSPE: 0.197988 valid's rmse: 0.000595947   valid's RMSPE: 0.228912
[300]   train's rmse: 0.00049754    train's RMSPE: 0.195122 valid's rmse: 0.000596103   valid's RMSPE: 0.228972
Early stopping, best iteration is:
[269]   train's rmse: 0.000501778   train's RMSPE: 0.196784 valid's rmse: 0.000595349   valid's RMSPE: 0.228682
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000570647   train's RMSPE: 0.222356 valid's rmse: 0.000603039   valid's RMSPE: 0.237736
[100]   train's rmse: 0.000541848   train's RMSPE: 0.211134 valid's rmse: 0.000580953   valid's RMSPE: 0.229029
[150]   train's rmse: 0.000528188   train's RMSPE: 0.205812 valid's rmse: 0.000577793   valid's RMSPE: 0.227783
[200]   train's rmse: 0.00051792    train's RMSPE: 0.201811 valid's rmse: 0.000578545   valid's RMSPE: 0.228079
Early stopping, best iteration is:
[184]   train's rmse: 0.000520872   train's RMSPE: 0.202961 valid's rmse: 0.000576241   valid's RMSPE: 0.227171
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000576895   train's RMSPE: 0.223829 valid's rmse: 0.000567851   valid's RMSPE: 0.227566
[100]   train's rmse: 0.000548175   train's RMSPE: 0.212686 valid's rmse: 0.000555432   valid's RMSPE: 0.222589
[150]   train's rmse: 0.000535586   train's RMSPE: 0.207802 valid's rmse: 0.000553209   valid's RMSPE: 0.221699
[200]   train's rmse: 0.00052694    train's RMSPE: 0.204447 valid's rmse: 0.000550549   valid's RMSPE: 0.220633
[250]   train's rmse: 0.000518169   train's RMSPE: 0.201044 valid's rmse: 0.000550821   valid's RMSPE: 0.220742
[300]   train's rmse: 0.000511024   train's RMSPE: 0.198272 valid's rmse: 0.000548777   valid's RMSPE: 0.219923
Early stopping, best iteration is:
[293]   train's rmse: 0.000511911   train's RMSPE: 0.198616 valid's rmse: 0.000548192   valid's RMSPE: 0.219688
Training fold 3
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000585283   train's RMSPE: 0.228002 valid's rmse: 0.000549641   valid's RMSPE: 0.216896
[100]   train's rmse: 0.000556658   train's RMSPE: 0.216851 valid's rmse: 0.000530187   valid's RMSPE: 0.209219
[150]   train's rmse: 0.000544229   train's RMSPE: 0.212009 valid's rmse: 0.000530729   valid's RMSPE: 0.209433
Early stopping, best iteration is:
[118]   train's rmse: 0.000551869   train's RMSPE: 0.214985 valid's rmse: 0.0005292 valid's RMSPE: 0.20883
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000570208   train's RMSPE: 0.224361 valid's rmse: 0.000593845   valid's RMSPE: 0.224928
[100]   train's rmse: 0.000541764   train's RMSPE: 0.213169 valid's rmse: 0.000582938   valid's RMSPE: 0.220797
[150]   train's rmse: 0.000529018   train's RMSPE: 0.208154 valid's rmse: 0.00058164    valid's RMSPE: 0.220305
[200]   train's rmse: 0.000518782   train's RMSPE: 0.204127 valid's rmse: 0.000584028   valid's RMSPE: 0.22121
Early stopping, best iteration is:
[153]   train's rmse: 0.000528166   train's RMSPE: 0.207819 valid's rmse: 0.000581532   valid's RMSPE: 0.220264
Our out of folds RMSPE is 0.221, compared to 0.19366846745759744, giving gain 0.02733153254240256
Our cv fold scores are [0.229, 0.227, 0.22, 0.209, 0.22]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000381075   train's RMSPE: 0.236859 valid's rmse: 0.000415942   valid's RMSPE: 0.261251
[100]   train's rmse: 0.000359987   train's RMSPE: 0.223751 valid's rmse: 0.000395288   valid's RMSPE: 0.248278
[150]   train's rmse: 0.000350704   train's RMSPE: 0.217982 valid's rmse: 0.000390159   valid's RMSPE: 0.245057
[200]   train's rmse: 0.000342711   train's RMSPE: 0.213013 valid's rmse: 0.000385669   valid's RMSPE: 0.242236
[250]   train's rmse: 0.000336488   train's RMSPE: 0.209146 valid's rmse: 0.000382371   valid's RMSPE: 0.240165
[300]   train's rmse: 0.000331506   train's RMSPE: 0.206049 valid's rmse: 0.000380519   valid's RMSPE: 0.239001
[350]   train's rmse: 0.00032704    train's RMSPE: 0.203273 valid's rmse: 0.000378413   valid's RMSPE: 0.237679
[400]   train's rmse: 0.000322585   train's RMSPE: 0.200504 valid's rmse: 0.000376018   valid's RMSPE: 0.236175
[450]   train's rmse: 0.000318917   train's RMSPE: 0.198224 valid's rmse: 0.000375343   valid's RMSPE: 0.235751
[500]   train's rmse: 0.000314798   train's RMSPE: 0.195664 valid's rmse: 0.000373198   valid's RMSPE: 0.234403
[550]   train's rmse: 0.000310857   train's RMSPE: 0.193214 valid's rmse: 0.00037238    valid's RMSPE: 0.233889
[600]   train's rmse: 0.000308026   train's RMSPE: 0.191455 valid's rmse: 0.000372201   valid's RMSPE: 0.233777
[650]   train's rmse: 0.000305131   train's RMSPE: 0.189655 valid's rmse: 0.000371863   valid's RMSPE: 0.233565
[700]   train's rmse: 0.000302108   train's RMSPE: 0.187776 valid's rmse: 0.000371373   valid's RMSPE: 0.233257
[750]   train's rmse: 0.000299397   train's RMSPE: 0.186092 valid's rmse: 0.000370679   valid's RMSPE: 0.232821
[800]   train's rmse: 0.000296282   train's RMSPE: 0.184155 valid's rmse: 0.00036924    valid's RMSPE: 0.231917
[850]   train's rmse: 0.000294155   train's RMSPE: 0.182833 valid's rmse: 0.000368611   valid's RMSPE: 0.231522
[900]   train's rmse: 0.0002919 train's RMSPE: 0.181432 valid's rmse: 0.000367623   valid's RMSPE: 0.230902
[950]   train's rmse: 0.000289732   train's RMSPE: 0.180084 valid's rmse: 0.000367233   valid's RMSPE: 0.230657
[1000]  train's rmse: 0.000287602   train's RMSPE: 0.17876  valid's rmse: 0.000366531   valid's RMSPE: 0.230216
[1050]  train's rmse: 0.000285629   train's RMSPE: 0.177534 valid's rmse: 0.000366009   valid's RMSPE: 0.229888
[1100]  train's rmse: 0.000283753   train's RMSPE: 0.176368 valid's rmse: 0.00036534    valid's RMSPE: 0.229468
[1150]  train's rmse: 0.000281874   train's RMSPE: 0.1752   valid's rmse: 0.000365042   valid's RMSPE: 0.22928
[1200]  train's rmse: 0.000279959   train's RMSPE: 0.17401  valid's rmse: 0.000364145   valid's RMSPE: 0.228717
[1250]  train's rmse: 0.000278258   train's RMSPE: 0.172952 valid's rmse: 0.000363887   valid's RMSPE: 0.228555
Early stopping, best iteration is:
[1235]  train's rmse: 0.000278778   train's RMSPE: 0.173276 valid's rmse: 0.000363515   valid's RMSPE: 0.228321
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000380856   train's RMSPE: 0.237084 valid's rmse: 0.00040508    valid's RMSPE: 0.252901
[100]   train's rmse: 0.000359631   train's RMSPE: 0.223872 valid's rmse: 0.000391625   valid's RMSPE: 0.244501
[150]   train's rmse: 0.000350293   train's RMSPE: 0.218059 valid's rmse: 0.000385817   valid's RMSPE: 0.240875
[200]   train's rmse: 0.000343199   train's RMSPE: 0.213642 valid's rmse: 0.000382159   valid's RMSPE: 0.238591
[250]   train's rmse: 0.000336852   train's RMSPE: 0.209692 valid's rmse: 0.000378903   valid's RMSPE: 0.236558
[300]   train's rmse: 0.000331006   train's RMSPE: 0.206052 valid's rmse: 0.00037723    valid's RMSPE: 0.235514
[350]   train's rmse: 0.000325989   train's RMSPE: 0.202929 valid's rmse: 0.000376107   valid's RMSPE: 0.234812
[400]   train's rmse: 0.000321769   train's RMSPE: 0.200302 valid's rmse: 0.000374968   valid's RMSPE: 0.234102
[450]   train's rmse: 0.000318272   train's RMSPE: 0.198125 valid's rmse: 0.000374429   valid's RMSPE: 0.233765
[500]   train's rmse: 0.000315014   train's RMSPE: 0.196097 valid's rmse: 0.000373579   valid's RMSPE: 0.233234
[550]   train's rmse: 0.000311346   train's RMSPE: 0.193814 valid's rmse: 0.00037323    valid's RMSPE: 0.233017
[600]   train's rmse: 0.000308232   train's RMSPE: 0.191875 valid's rmse: 0.000372883   valid's RMSPE: 0.2328
[650]   train's rmse: 0.00030551    train's RMSPE: 0.190181 valid's rmse: 0.000372253   valid's RMSPE: 0.232406
[700]   train's rmse: 0.000302765   train's RMSPE: 0.188472 valid's rmse: 0.000372068   valid's RMSPE: 0.232291
Early stopping, best iteration is:
[678]   train's rmse: 0.000303871   train's RMSPE: 0.18916  valid's rmse: 0.000371776   valid's RMSPE: 0.232109
Training fold 2
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000387423   train's RMSPE: 0.240157 valid's rmse: 0.000377594   valid's RMSPE: 0.239647
[100]   train's rmse: 0.000364307   train's RMSPE: 0.225827 valid's rmse: 0.00035794    valid's RMSPE: 0.227173
[150]   train's rmse: 0.000355338   train's RMSPE: 0.220268 valid's rmse: 0.000354571   valid's RMSPE: 0.225035
[200]   train's rmse: 0.00034753    train's RMSPE: 0.215428 valid's rmse: 0.000351801   valid's RMSPE: 0.223277
[250]   train's rmse: 0.000341687   train's RMSPE: 0.211805 valid's rmse: 0.000350342   valid's RMSPE: 0.222351
[300]   train's rmse: 0.000336688   train's RMSPE: 0.208707 valid's rmse: 0.000349739   valid's RMSPE: 0.221969
[350]   train's rmse: 0.000332339   train's RMSPE: 0.206011 valid's rmse: 0.000348505   valid's RMSPE: 0.221185
Early stopping, best iteration is:
[346]   train's rmse: 0.000332664   train's RMSPE: 0.206212 valid's rmse: 0.000348392   valid's RMSPE: 0.221114
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000386171   train's RMSPE: 0.24211  valid's rmse: 0.000373601   valid's RMSPE: 0.226501
[100]   train's rmse: 0.000364855   train's RMSPE: 0.228746 valid's rmse: 0.000365624   valid's RMSPE: 0.221665
[150]   train's rmse: 0.000355822   train's RMSPE: 0.223083 valid's rmse: 0.000362963   valid's RMSPE: 0.220051
[200]   train's rmse: 0.000348128   train's RMSPE: 0.218259 valid's rmse: 0.000360705   valid's RMSPE: 0.218683
[250]   train's rmse: 0.000342674   train's RMSPE: 0.214839 valid's rmse: 0.000359969   valid's RMSPE: 0.218236
[300]   train's rmse: 0.000336763   train's RMSPE: 0.211134 valid's rmse: 0.000358218   valid's RMSPE: 0.217175
Early stopping, best iteration is:
[298]   train's rmse: 0.000336941   train's RMSPE: 0.211245 valid's rmse: 0.000357917   valid's RMSPE: 0.216992
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000385108   train's RMSPE: 0.240087 valid's rmse: 0.000387591   valid's RMSPE: 0.240549
[100]   train's rmse: 0.000363521   train's RMSPE: 0.226629 valid's rmse: 0.000372545   valid's RMSPE: 0.231211
[150]   train's rmse: 0.000353098   train's RMSPE: 0.220131 valid's rmse: 0.000367746   valid's RMSPE: 0.228233
[200]   train's rmse: 0.000345252   train's RMSPE: 0.215239 valid's rmse: 0.000365988   valid's RMSPE: 0.227142
[250]   train's rmse: 0.00033936    train's RMSPE: 0.211566 valid's rmse: 0.000364648   valid's RMSPE: 0.22631
[300]   train's rmse: 0.00033486    train's RMSPE: 0.208761 valid's rmse: 0.000363786   valid's RMSPE: 0.225775
[350]   train's rmse: 0.000330257   train's RMSPE: 0.205891 valid's rmse: 0.000363302   valid's RMSPE: 0.225475
[400]   train's rmse: 0.000325837   train's RMSPE: 0.203135 valid's rmse: 0.000362523   valid's RMSPE: 0.224991
[450]   train's rmse: 0.000321805   train's RMSPE: 0.200622 valid's rmse: 0.000361421   valid's RMSPE: 0.224307
[500]   train's rmse: 0.000318194   train's RMSPE: 0.19837  valid's rmse: 0.000361578   valid's RMSPE: 0.224405
Early stopping, best iteration is:
[484]   train's rmse: 0.000319316   train's RMSPE: 0.19907  valid's rmse: 0.000361262   valid's RMSPE: 0.224208
Our out of folds RMSPE is 0.225, compared to 0.18671098219925594, giving gain 0.03828901780074406
Our cv fold scores are [0.228, 0.232, 0.221, 0.217, 0.224]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000511642   train's RMSPE: 0.184573 valid's rmse: 0.000553572   valid's RMSPE: 0.201239
[100]   train's rmse: 0.000484061   train's RMSPE: 0.174623 valid's rmse: 0.000529498   valid's RMSPE: 0.192488
[150]   train's rmse: 0.00047364    train's RMSPE: 0.170864 valid's rmse: 0.000524291   valid's RMSPE: 0.190595
[200]   train's rmse: 0.000465031   train's RMSPE: 0.167758 valid's rmse: 0.000522378   valid's RMSPE: 0.189899
[250]   train's rmse: 0.000457455   train's RMSPE: 0.165025 valid's rmse: 0.000521001   valid's RMSPE: 0.189398
[300]   train's rmse: 0.000451133   train's RMSPE: 0.162744 valid's rmse: 0.000519391   valid's RMSPE: 0.188813
[350]   train's rmse: 0.000445738   train's RMSPE: 0.160798 valid's rmse: 0.000518497   valid's RMSPE: 0.188489
[400]   train's rmse: 0.000440333   train's RMSPE: 0.158848 valid's rmse: 0.000518373   valid's RMSPE: 0.188443
Early stopping, best iteration is:
[364]   train's rmse: 0.000444089   train's RMSPE: 0.160203 valid's rmse: 0.000517661   valid's RMSPE: 0.188184
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000509874   train's RMSPE: 0.183675 valid's rmse: 0.000540538   valid's RMSPE: 0.197591
[100]   train's rmse: 0.000482726   train's RMSPE: 0.173895 valid's rmse: 0.000525894   valid's RMSPE: 0.192238
[150]   train's rmse: 0.000472184   train's RMSPE: 0.170098 valid's rmse: 0.000521816   valid's RMSPE: 0.190748
[200]   train's rmse: 0.000463845   train's RMSPE: 0.167094 valid's rmse: 0.000520068   valid's RMSPE: 0.190109
[250]   train's rmse: 0.00045634    train's RMSPE: 0.16439  valid's rmse: 0.000518215   valid's RMSPE: 0.189431
[300]   train's rmse: 0.000449783   train's RMSPE: 0.162028 valid's rmse: 0.00051802    valid's RMSPE: 0.18936
[350]   train's rmse: 0.000443688   train's RMSPE: 0.159832 valid's rmse: 0.000517258   valid's RMSPE: 0.189081
[400]   train's rmse: 0.000438641   train's RMSPE: 0.158014 valid's rmse: 0.000515974   valid's RMSPE: 0.188612
[450]   train's rmse: 0.000433539   train's RMSPE: 0.156176 valid's rmse: 0.000514765   valid's RMSPE: 0.18817
[500]   train's rmse: 0.000428846   train's RMSPE: 0.154486 valid's rmse: 0.000514641   valid's RMSPE: 0.188125
Early stopping, best iteration is:
[452]   train's rmse: 0.000433172   train's RMSPE: 0.156044 valid's rmse: 0.000514432   valid's RMSPE: 0.188048
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000520889   train's RMSPE: 0.188324 valid's rmse: 0.000494535   valid's RMSPE: 0.178203
[100]   train's rmse: 0.000492932   train's RMSPE: 0.178217 valid's rmse: 0.000478912   valid's RMSPE: 0.172573
[150]   train's rmse: 0.000482762   train's RMSPE: 0.17454  valid's rmse: 0.00047617    valid's RMSPE: 0.171585
[200]   train's rmse: 0.000472985   train's RMSPE: 0.171005 valid's rmse: 0.000475029   valid's RMSPE: 0.171174
Early stopping, best iteration is:
[168]   train's rmse: 0.000479138   train's RMSPE: 0.17323  valid's rmse: 0.000474649   valid's RMSPE: 0.171037
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000512173   train's RMSPE: 0.185126 valid's rmse: 0.000541414   valid's RMSPE: 0.195295
[100]   train's rmse: 0.000486104   train's RMSPE: 0.175703 valid's rmse: 0.000528984   valid's RMSPE: 0.190811
[150]   train's rmse: 0.000476131   train's RMSPE: 0.172099 valid's rmse: 0.000526319   valid's RMSPE: 0.18985
[200]   train's rmse: 0.000468502   train's RMSPE: 0.169341 valid's rmse: 0.000523257   valid's RMSPE: 0.188745
[250]   train's rmse: 0.000461742   train's RMSPE: 0.166898 valid's rmse: 0.000522848   valid's RMSPE: 0.188597
[300]   train's rmse: 0.000454922   train's RMSPE: 0.164433 valid's rmse: 0.000522479   valid's RMSPE: 0.188464
Early stopping, best iteration is:
[265]   train's rmse: 0.000459662   train's RMSPE: 0.166146 valid's rmse: 0.000522033   valid's RMSPE: 0.188303
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.00051996    train's RMSPE: 0.188506 valid's rmse: 0.000511448   valid's RMSPE: 0.182241
[100]   train's rmse: 0.000494047   train's RMSPE: 0.179111 valid's rmse: 0.000490213   valid's RMSPE: 0.174675
[150]   train's rmse: 0.000483406   train's RMSPE: 0.175253 valid's rmse: 0.000486409   valid's RMSPE: 0.173319
[200]   train's rmse: 0.00047443    train's RMSPE: 0.171999 valid's rmse: 0.000483315   valid's RMSPE: 0.172217
[250]   train's rmse: 0.000467255   train's RMSPE: 0.169398 valid's rmse: 0.000483706   valid's RMSPE: 0.172356
Early stopping, best iteration is:
[211]   train's rmse: 0.000472733   train's RMSPE: 0.171384 valid's rmse: 0.000482921   valid's RMSPE: 0.172076

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Our out of folds RMSPE is 0.182, compared to 0.16288573188551736, giving gain 0.019114268114482635
Our cv fold scores are [0.188, 0.188, 0.171, 0.188, 0.172]
Training fold 0
Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000326766   train's RMSPE: 0.251728 valid's rmse: 0.000349054   valid's RMSPE: 0.26695
[100]   train's rmse: 0.000310544   train's RMSPE: 0.239231 valid's rmse: 0.00033553    valid's RMSPE: 0.256607
[150]   train's rmse: 0.000303129   train's RMSPE: 0.233519 valid's rmse: 0.000333639   valid's RMSPE: 0.255161
[200]   train's rmse: 0.000297066   train's RMSPE: 0.228849 valid's rmse: 0.000332756   valid's RMSPE: 0.254486
[250]   train's rmse: 0.0002915 train's RMSPE: 0.22456  valid's rmse: 0.000332581   valid's RMSPE: 0.254352
Early stopping, best iteration is:
[207]   train's rmse: 0.000296081   train's RMSPE: 0.22809  valid's rmse: 0.000332096   valid's RMSPE: 0.253981
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000322818   train's RMSPE: 0.247082 valid's rmse: 0.000363422   valid's RMSPE: 0.285101
[100]   train's rmse: 0.000306719   train's RMSPE: 0.23476  valid's rmse: 0.000351669   valid's RMSPE: 0.275881
[150]   train's rmse: 0.000299174   train's RMSPE: 0.228985 valid's rmse: 0.000348354   valid's RMSPE: 0.273281
[200]   train's rmse: 0.000293276   train's RMSPE: 0.224471 valid's rmse: 0.000346203   valid's RMSPE: 0.271593
[250]   train's rmse: 0.000288144   train's RMSPE: 0.220543 valid's rmse: 0.00034496    valid's RMSPE: 0.270618
[300]   train's rmse: 0.000283819   train's RMSPE: 0.217233 valid's rmse: 0.000344151   valid's RMSPE: 0.269983
[350]   train's rmse: 0.00027957    train's RMSPE: 0.213981 valid's rmse: 0.000343253   valid's RMSPE: 0.269279
[400]   train's rmse: 0.000275946   train's RMSPE: 0.211207 valid's rmse: 0.000342919   valid's RMSPE: 0.269017
Early stopping, best iteration is:
[375]   train's rmse: 0.000277822   train's RMSPE: 0.212643 valid's rmse: 0.000342865   valid's RMSPE: 0.268974
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000330568   train's RMSPE: 0.255365 valid's rmse: 0.000320833   valid's RMSPE: 0.242579
[100]   train's rmse: 0.000313727   train's RMSPE: 0.242356 valid's rmse: 0.000311263   valid's RMSPE: 0.235343
[150]   train's rmse: 0.000305853   train's RMSPE: 0.236273 valid's rmse: 0.0003083 valid's RMSPE: 0.233103
[200]   train's rmse: 0.000300339   train's RMSPE: 0.232013 valid's rmse: 0.000306995   valid's RMSPE: 0.232116
[250]   train's rmse: 0.000295162   train's RMSPE: 0.228014 valid's rmse: 0.000306369   valid's RMSPE: 0.231642
[300]   train's rmse: 0.000290442   train's RMSPE: 0.224368 valid's rmse: 0.000305353   valid's RMSPE: 0.230874
[350]   train's rmse: 0.000286873   train's RMSPE: 0.221611 valid's rmse: 0.000305374   valid's RMSPE: 0.230891
Early stopping, best iteration is:
[324]   train's rmse: 0.000288564   train's RMSPE: 0.222917 valid's rmse: 0.000304914   valid's RMSPE: 0.230543
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000334363   train's RMSPE: 0.257079 valid's rmse: 0.000314801   valid's RMSPE: 0.242648
[100]   train's rmse: 0.000316923   train's RMSPE: 0.24367  valid's rmse: 0.000304585   valid's RMSPE: 0.234773
[150]   train's rmse: 0.000309206   train's RMSPE: 0.237736 valid's rmse: 0.000301997   valid's RMSPE: 0.232778
[200]   train's rmse: 0.000303265   train's RMSPE: 0.233169 valid's rmse: 0.000301008   valid's RMSPE: 0.232016
[250]   train's rmse: 0.000298113   train's RMSPE: 0.229208 valid's rmse: 0.000300169   valid's RMSPE: 0.23137
Early stopping, best iteration is:
[239]   train's rmse: 0.000299269   train's RMSPE: 0.230097 valid's rmse: 0.000299802   valid's RMSPE: 0.231087
Training fold 4
Training until validation scores don't improve for 50 rounds

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

[50]    train's rmse: 0.000330887   train's RMSPE: 0.254488 valid's rmse: 0.000333794   valid's RMSPE: 0.256957
[100]   train's rmse: 0.000314275   train's RMSPE: 0.241712 valid's rmse: 0.000324174   valid's RMSPE: 0.249552
[150]   train's rmse: 0.000306024   train's RMSPE: 0.235366 valid's rmse: 0.000322307   valid's RMSPE: 0.248115
[200]   train's rmse: 0.000300087   train's RMSPE: 0.2308   valid's rmse: 0.000322746   valid's RMSPE: 0.248452
Early stopping, best iteration is:
[194]   train's rmse: 0.000300767   train's RMSPE: 0.231323 valid's rmse: 0.00032156    valid's RMSPE: 0.24754
Our out of folds RMSPE is 0.247, compared to 0.20502529231135952, giving gain 0.04197470768864048
Our cv fold scores are [0.254, 0.269, 0.231, 0.231, 0.248]
Training fold 0

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000850416   train's RMSPE: 0.234921 valid's rmse: 0.000873041   valid's RMSPE: 0.244797
[100]   train's rmse: 0.000810529   train's RMSPE: 0.223903 valid's rmse: 0.000846253   valid's RMSPE: 0.237286
[150]   train's rmse: 0.000790967   train's RMSPE: 0.218499 valid's rmse: 0.000840086   valid's RMSPE: 0.235557
[200]   train's rmse: 0.000775797   train's RMSPE: 0.214308 valid's rmse: 0.000837966   valid's RMSPE: 0.234962
[250]   train's rmse: 0.000762035   train's RMSPE: 0.210506 valid's rmse: 0.000838408   valid's RMSPE: 0.235086
[300]   train's rmse: 0.000749732   train's RMSPE: 0.207108 valid's rmse: 0.000835817   valid's RMSPE: 0.23436
[350]   train's rmse: 0.000739317   train's RMSPE: 0.204231 valid's rmse: 0.000832894   valid's RMSPE: 0.23354
[400]   train's rmse: 0.000729864   train's RMSPE: 0.201619 valid's rmse: 0.000835161   valid's RMSPE: 0.234175
Early stopping, best iteration is:
[352]   train's rmse: 0.000739039   train's RMSPE: 0.204154 valid's rmse: 0.000832249   valid's RMSPE: 0.233359
Training fold 1

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000846047   train's RMSPE: 0.234839 valid's rmse: 0.000881822   valid's RMSPE: 0.242583
[100]   train's rmse: 0.00080229    train's RMSPE: 0.222694 valid's rmse: 0.000861711   valid's RMSPE: 0.23705
[150]   train's rmse: 0.000783302   train's RMSPE: 0.217423 valid's rmse: 0.000859685   valid's RMSPE: 0.236493
[200]   train's rmse: 0.000767025   train's RMSPE: 0.212905 valid's rmse: 0.000856834   valid's RMSPE: 0.235709
[250]   train's rmse: 0.000753188   train's RMSPE: 0.209064 valid's rmse: 0.000855508   valid's RMSPE: 0.235344
[300]   train's rmse: 0.000741655   train's RMSPE: 0.205863 valid's rmse: 0.000854622   valid's RMSPE: 0.2351
[350]   train's rmse: 0.000732225   train's RMSPE: 0.203245 valid's rmse: 0.000855011   valid's RMSPE: 0.235207
Early stopping, best iteration is:
[322]   train's rmse: 0.000737335   train's RMSPE: 0.204664 valid's rmse: 0.000853501   valid's RMSPE: 0.234792
Training fold 2

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000847726   train's RMSPE: 0.235111 valid's rmse: 0.000846945   valid's RMSPE: 0.23377
[100]   train's rmse: 0.000808669   train's RMSPE: 0.224279 valid's rmse: 0.000828069   valid's RMSPE: 0.22856
[150]   train's rmse: 0.00078976    train's RMSPE: 0.219034 valid's rmse: 0.000826191   valid's RMSPE: 0.228042
[200]   train's rmse: 0.000774858   train's RMSPE: 0.214901 valid's rmse: 0.000824001   valid's RMSPE: 0.227437
Early stopping, best iteration is:
[189]   train's rmse: 0.000778547   train's RMSPE: 0.215925 valid's rmse: 0.000823702   valid's RMSPE: 0.227355
Training fold 3

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000840108   train's RMSPE: 0.232462 valid's rmse: 0.000889966   valid's RMSPE: 0.247916
[100]   train's rmse: 0.000800433   train's RMSPE: 0.221483 valid's rmse: 0.000867968   valid's RMSPE: 0.241788
[150]   train's rmse: 0.000780858   train's RMSPE: 0.216067 valid's rmse: 0.000867771   valid's RMSPE: 0.241733
[200]   train's rmse: 0.000765963   train's RMSPE: 0.211945 valid's rmse: 0.000865082   valid's RMSPE: 0.240984
Early stopping, best iteration is:
[188]   train's rmse: 0.00076895    train's RMSPE: 0.212772 valid's rmse: 0.000863735   valid's RMSPE: 0.240609
Training fold 4

/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))
/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:153: UserWarning: Found `early_stopping_rounds` in params. Will use it instead of argument
  _log_warning("Found `{}` in params. Will use it instead of argument".format(alias))

Training until validation scores don't improve for 50 rounds
[50]    train's rmse: 0.000850583   train's RMSPE: 0.236059 valid's rmse: 0.000853338   valid's RMSPE: 0.234907
[100]   train's rmse: 0.000811834   train's RMSPE: 0.225305 valid's rmse: 0.000829466   valid's RMSPE: 0.228335
[150]   train's rmse: 0.00079083    train's RMSPE: 0.219476 valid's rmse: 0.000832288   valid's RMSPE: 0.229112
Early stopping, best iteration is:
[100]   train's rmse: 0.000811834   train's RMSPE: 0.225305 valid's rmse: 0.000829466   valid's RMSPE: 0.228335
Our out of folds RMSPE is 0.233, compared to 0.2129300098765868, giving gain 0.020069990123413206
Our cv fold scores are [0.233, 0.235, 0.227, 0.241, 0.228]

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)

rdf(all_preds)

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  

0.2449659904286853

sdf['diff'] = sdf['new'] - sdf['old']
sdf[sdf['diff'] < 0]

	old	new	diff
31	0.55558	0.506	-0.04958

rdf(old_preds[old_preds.stock_id != 31])

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  

0.21287723151184676

rdf(old_preds)

/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  

0.21833054495346182

tmp = old_preds.copy()
tmp.loc[tmp.stock_id == 31, 'pred'] = all_preds.loc[all_preds.stock_id == 31, 'pred']

sdf.loc[[31, 37]]

	old	new	diff
31	0.555580	0.506	-0.049580
37	0.299569	0.311	0.011431

sdf.sort_values('diff')

	old	new	diff
31	0.555580	0.506	-0.049580
81	0.257210	0.260	0.002790
18	0.288923	0.295	0.006077
6	0.203179	0.214	0.010821
37	0.299569	0.311	0.011431
...	...	...	...
51	0.175530	0.221	0.045470
68	0.207333	0.255	0.047667
39	0.196862	0.245	0.048138
34	0.186580	0.235	0.048420
2	0.189522	0.246	0.056478

112 rows × 3 columns

sdf.loc[80]

old     0.25266
new     0.27000
diff    0.01734
Name: 80, dtype: float64