분류 - 산탄데르 고객 만족 예측

Preprocessing

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings

plt.rcParams['font.family'] = 'Noto Sans KR'
warnings.filterwarnings('ignore')

df = pd.read_csv('_data/santander/train.csv', encoding='latin-1')
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 76020 entries, 0 to 76019
Columns: 371 entries, ID to TARGET
dtypes: float64(111), int64(260)
memory usage: 215.2 MB

df.describe()

	ID	var3	var15	imp_ent_var16_ult1	imp_op_var39_comer_ult1	imp_op_var39_comer_ult3	imp_op_var40_comer_ult1	imp_op_var40_comer_ult3	imp_op_var40_efect_ult1	imp_op_var40_efect_ult3	...	saldo_medio_var33_hace2	saldo_medio_var33_hace3	saldo_medio_var33_ult1	saldo_medio_var33_ult3	saldo_medio_var44_hace2	saldo_medio_var44_hace3	saldo_medio_var44_ult1	saldo_medio_var44_ult3	var38	TARGET
count	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	...	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	76020.000000	7.602000e+04	76020.000000
mean	75964.050723	-1523.199277	33.212865	86.208265	72.363067	119.529632	3.559130	6.472698	0.412946	0.567352	...	7.935824	1.365146	12.215580	8.784074	31.505324	1.858575	76.026165	56.614351	1.172358e+05	0.039569
std	43781.947379	39033.462364	12.956486	1614.757313	339.315831	546.266294	93.155749	153.737066	30.604864	36.513513	...	455.887218	113.959637	783.207399	538.439211	2013.125393	147.786584	4040.337842	2852.579397	1.826646e+05	0.194945
min	1.000000	-999999.000000	5.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.163750e+03	0.000000
25%	38104.750000	2.000000	23.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.787061e+04	0.000000
50%	76043.000000	2.000000	28.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.064092e+05	0.000000
75%	113748.750000	2.000000	40.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.187563e+05	0.000000
max	151838.000000	238.000000	105.000000	210000.000000	12888.030000	21024.810000	8237.820000	11073.570000	6600.000000	6600.000000	...	50003.880000	20385.720000	138831.630000	91778.730000	438329.220000	24650.010000	681462.900000	397884.300000	2.203474e+07	1.000000

8 rows × 371 columns

df['var3'].replace(-999999, 2, inplace=True)
df.drop('ID', axis=1, inplace=True)

X_features = df.iloc[:, :-1]
labels = df.iloc[:, -1]

test_df = pd.read_csv('_data/santander/test.csv', encoding='latin-1')
test_df['var3'].replace(-999999, 2, inplace=True)
test_df.drop('ID', axis=1, inplace=True)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_features, labels, test_size=0.2)

• train, test의 label의 비율이 동일한게 좋은걸까

XGBoost

X_tr, X_val, y_tr, y_val = train_test_split(X_train, y_train, test_size=0.3)

from xgboost import XGBClassifier
from sklearn.metrics import roc_auc_score

evals = [(X_tr, y_tr), (X_val, y_val)]
xgb_clf = XGBClassifier(n_estimators=400, 
                    learning_rate=0.05, 
                    early_stopping_rounds=100,
                    eval_metric=['auc'])
xgb_clf.fit(X_tr, y_tr, eval_set=evals, verbose=False)
xgb_roc_score = roc_auc_score(y_test, xgb_clf.predict_proba(X_test)[:, 1])
print(f'{xgb_roc_score:.3f}')

베이지안 최적화

from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score

def objective_func(search_space):
    xgb_clf = XGBClassifier(n_estimators=100, 
                            early_stopping_rounds=30,
                            eval_metric='auc',
                            max_depth=int(search_space['max_depth']),
                            min_child_weight=int(search_space['min_child_weight']),
                            colsample_bytree=search_space['colsample_bytree'],
                            learning_rate=search_space['learning_rate'])
    roc_auc_list = []
    kf = KFold(n_splits=3)
    for tr_index, val_index in kf.split(X_train):
        X_tr, y_tr = X_train.iloc[tr_index], y_train.iloc[tr_index]
        X_val, y_val =  X_train.iloc[val_index], y_train.iloc[val_index]

        xgb_clf.fit(X_tr, y_tr, eval_set=[(X_tr, y_tr), (X_val, y_val)])
        score = roc_auc_score(y_val, xgb_clf.predict_proba(X_val)[:, 1])
        roc_auc_list.append(score)

    return -1 * np.mean(roc_auc_list)

from hyperopt import hp, fmin, tpe, Trials

xgb_search_space = {
  'max_depth': hp.quniform('max_depth', 5, 15, 1),
  'min_child_weight': hp.quniform('min_child_weight', 1, 6, 1),
  'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 0.95),
  'learning_rate': hp.uniform('learning_rate', 0.01, 0.2)
}

trials = Trials()
best = fmin(fn=objective_func,
            space=xgb_search_space,
            algo=tpe.suggest,
            max_evals=50,
            trials=trials)
print(best)

재 학습

from xgboost import XGBClassifier
from sklearn.metrics import roc_auc_score

evals = [(X_tr, y_tr), (X_val, y_val)]
xgb_clf = XGBClassifier(n_estimators=500, 
                    learning_rate=round(best['learning_rate'], 5),
                    max_depth=int(best['max_depth']),
                    min_child_weight=int(best['min_child_weight']),
                    colsample_bytree=round(best['colsample_bytree'], 5),
                    early_stopping_rounds=100,
                    eval_metric=['auc'])
xgb_clf.fit(X_tr, y_tr, eval_set=evals, verbose=False)
xgb_roc_score = roc_auc_score(y_test, xgb_clf.predict_proba(X_test)[:, 1])
print(f'{xgb_roc_score:.3f}')

plot importance

from xgboost import plot_importance

plot_importance(xgb_clf, max_num_features=20, height=0.4)

LightGBM

from sklearn.metrics import roc_auc_score
from lightgbm import LGBMClassifier

lgbm_clf = LGBMClassifier(n_estimators=500, early_stopping_rounds=100, eval_metric='auc')

eval_set = [(X_tr, y_tr), (X_val, y_val)]
lgbm_clf.fit(X_tr, y_tr, eval_set=eval_set)

lgbm_roc_score = roc_auc_score(y_test, lgbm_clf.predict_proba(X_test)[:, 1])
print(f'{lgbm_roc_score:.3f}')

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] early_stopping_round is set=100, early_stopping_rounds=100 will be ignored. Current value: early_stopping_round=100
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 1653, number of negative: 40918
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007850 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 13447
[LightGBM] [Info] Number of data points in the train set: 42571, number of used features: 251
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] early_stopping_round is set=100, early_stopping_rounds=100 will be ignored. Current value: early_stopping_round=100
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038829 -> initscore=-3.208978
[LightGBM] [Info] Start training from score -3.208978
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[28]    training's binary_logloss: 0.117279 valid_1's binary_logloss: 0.137813
[LightGBM] [Warning] Unknown parameter: eval_metric
0.834

베이지안 최적화

from sklearn.model_selection import KFold

def objective_func(search_space):
    lgbm_clf = LGBMClassifier(n_estimators=100, 
                            early_stopping_rounds=30,
                            eval_metric='auc',
                            num_leaves=int(search_space['num_leaves']),
                            max_depth=int(search_space['max_depth']),
                            min_child_samples=int(search_space['min_child_samples']),
                            subsample=search_space['subsample'],
                            learning_rate=search_space['learning_rate'])
    roc_auc_list = []
    kf = KFold(n_splits=3)
    for tr_index, val_index in kf.split(X_train):
        X_tr, y_tr = X_train.iloc[tr_index], y_train.iloc[tr_index]
        X_val, y_val =  X_train.iloc[val_index], y_train.iloc[val_index]

        lgbm_clf.fit(X_tr, y_tr, eval_set=[(X_tr, y_tr), (X_val, y_val)])
        score = roc_auc_score(y_val, lgbm_clf.predict_proba(X_val)[:, 1])
        roc_auc_list.append(score)

    return -1 * np.mean(roc_auc_list)

from hyperopt import hp, fmin, tpe, Trials

lgbm_search_space = {
  'num_leaves': hp.quniform('num_leaves', 32, 64, 1),
  'max_depth': hp.quniform('max_depth', 100, 160, 1),
  'min_child_samples': hp.quniform('min_child_samples', 60, 100, 1),
  'subsample': hp.uniform('subsample', 0.7, 1),
  'learning_rate': hp.uniform('learning_rate', 0.01, 0.2)
}

trials = Trials()
best = fmin(fn=objective_func,
            space=lgbm_search_space,
            algo=tpe.suggest,
            max_evals=50,
            trials=trials)
print(best)

  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009804 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Total Bins 12809
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.168309
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      Training until validation scores don't improve for 30 rounds
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      Early stopping, best iteration is:
[36]    training's binary_logloss: 0.121676 valid_1's binary_logloss: 0.127049
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011714 seconds.
You can set `force_col_wise=true` to remove the overhead.
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Total Bins 12874
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.194075
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      Training until validation scores don't improve for 30 rounds
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      Early stopping, best iteration is:
[44]    training's binary_logloss: 0.115084 valid_1's binary_logloss: 0.135595
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007309 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Total Bins 12874
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.233233
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      Training until validation scores don't improve for 30 rounds
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      Early stopping, best iteration is:
[50]    training's binary_logloss: 0.110571 valid_1's binary_logloss: 0.140209
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006776 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Total Bins 12809
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Start training from score -3.168309
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 Early stopping, best iteration is:
[68]    training's binary_logloss: 0.119949 valid_1's binary_logloss: 0.127337
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:02<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008185 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Total Bins 12874
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Start training from score -3.194075
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 Did not meet early stopping. Best iteration is:
[74]    training's binary_logloss: 0.114945 valid_1's binary_logloss: 0.135003
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005666 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Total Bins 12865
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Info] Start training from score -3.233233
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:03<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 Did not meet early stopping. Best iteration is:
[75]    training's binary_logloss: 0.111732 valid_1's binary_logloss: 0.140191
  2%|▏         | 1/50 [00:04<01:41,  2.08s/trial, best loss: -0.8354243542379886]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<01:41,  2.08s/trial, best loss: -0.8354243542379886]  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006637 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12907
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.168309
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[21]    training's binary_logloss: 0.121998 valid_1's binary_logloss: 0.127349
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:04<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006521 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12970
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.194075
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[26]    training's binary_logloss: 0.115056 valid_1's binary_logloss: 0.136143
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008614 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 13049
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 208
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.233233
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[30]    training's binary_logloss: 0.110308 valid_1's binary_logloss: 0.140967
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:05<01:41,  2.12s/trial, best loss: -0.8361046999787884]  6%|▌         | 3/50 [00:05<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006389 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12809
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.168309
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[20]    training's binary_logloss: 0.119702 valid_1's binary_logloss: 0.127682
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005965 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12874
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.194075
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[21]    training's binary_logloss: 0.11491  valid_1's binary_logloss: 0.13632
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:06<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006447 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12865
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.233233
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[19]    training's binary_logloss: 0.113764 valid_1's binary_logloss: 0.141398
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:07<01:31,  1.95s/trial, best loss: -0.8361046999787884]  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006690 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12809
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.168309
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:07<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Did not meet early stopping. Best iteration is:
[71]    training's binary_logloss: 0.114179 valid_1's binary_logloss: 0.127237
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006128 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12874
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.194075
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:08<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[57]    training's binary_logloss: 0.113751 valid_1's binary_logloss: 0.136174
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007084 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12865
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.233233
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[58]    training's binary_logloss: 0.11113  valid_1's binary_logloss: 0.140897
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:09<01:24,  1.83s/trial, best loss: -0.8361046999787884] 10%|█         | 5/50 [00:09<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:09<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:09<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006562 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12809
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.168309
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[23]    training's binary_logloss: 0.120721 valid_1's binary_logloss: 0.127623
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005976 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12874
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.194075
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[21]    training's binary_logloss: 0.117914 valid_1's binary_logloss: 0.135692
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:10<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006221 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12865
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.233233
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[18]    training's binary_logloss: 0.117142 valid_1's binary_logloss: 0.141073
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:11<01:28,  1.98s/trial, best loss: -0.8361046999787884] 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005795 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12809
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.168309
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[23]    training's binary_logloss: 0.122492 valid_1's binary_logloss: 0.127389
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:11<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005857 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12882
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.194075
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[20]    training's binary_logloss: 0.119931 valid_1's binary_logloss: 0.13599
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007378 seconds.
You can set `force_col_wise=true` to remove the overhead.
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12883
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.233233
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[21]    training's binary_logloss: 0.116742 valid_1's binary_logloss: 0.14122
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:12<01:20,  1.83s/trial, best loss: -0.8361046999787884] 14%|█▍        | 7/50 [00:12<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:12<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:12<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:12<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 14%|█▍        | 7/50 [00:12<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006486 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12809
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.168309
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[37]    training's binary_logloss: 0.118076 valid_1's binary_logloss: 0.12711
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008708 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12874
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.194075
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:13<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[27]    training's binary_logloss: 0.118244 valid_1's binary_logloss: 0.135768
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007417 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12865
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.233233
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[34]    training's binary_logloss: 0.11261  valid_1's binary_logloss: 0.140798
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:14<01:12,  1.69s/trial, best loss: -0.8361046999787884] 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007634 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12907
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:14<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.168309
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 Did not meet early stopping. Best iteration is:
[93]    training's binary_logloss: 0.113871 valid_1's binary_logloss: 0.127108
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007168 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12934
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.194075
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:15<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 Did not meet early stopping. Best iteration is:
[75]    training's binary_logloss: 0.113106 valid_1's binary_logloss: 0.135792
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008788 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12989
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:16<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 16%|█▌        | 8/50 [00:17<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.233233
 16%|█▌        | 8/50 [00:17<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:17<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 Did not meet early stopping. Best iteration is:
[82]    training's binary_logloss: 0.109277 valid_1's binary_logloss: 0.140921
 16%|█▌        | 8/50 [00:17<01:14,  1.76s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:17<01:14,  1.76s/trial, best loss: -0.8361046999787884] 18%|█▊        | 9/50 [00:17<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:17<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:17<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:17<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 18%|█▊        | 9/50 [00:17<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007124 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 18%|█▊        | 9/50 [00:17<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12809
 18%|█▊        | 9/50 [00:17<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:17<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.168309
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[57]    training's binary_logloss: 0.120677 valid_1's binary_logloss: 0.127111
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009349 seconds.
You can set `force_col_wise=true` to remove the overhead.
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12874
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.194075
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:18<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[50]    training's binary_logloss: 0.118347 valid_1's binary_logloss: 0.135488
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.013450 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Total Bins 12865
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Info] Start training from score -3.233233
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:19<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 Early stopping, best iteration is:
[54]    training's binary_logloss: 0.114424 valid_1's binary_logloss: 0.140196
 18%|█▊        | 9/50 [00:20<01:27,  2.14s/trial, best loss: -0.8361046999787884]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:20<01:27,  2.14s/trial, best loss: -0.8361046999787884] 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006974 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12907
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:20<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[78]    training's binary_logloss: 0.111459 valid_1's binary_logloss: 0.12715
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006741 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12943
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[58]    training's binary_logloss: 0.112371 valid_1's binary_logloss: 0.13579
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:21<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007176 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 13017
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:22<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 20%|██        | 10/50 [00:25<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 20%|██        | 10/50 [00:25<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:25<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[76]    training's binary_logloss: 0.105423 valid_1's binary_logloss: 0.141018
 20%|██        | 10/50 [00:25<01:30,  2.26s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:25<01:30,  2.26s/trial, best loss: -0.8361046999787884] 22%|██▏       | 11/50 [00:25<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006851 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[19]    training's binary_logloss: 0.123258 valid_1's binary_logloss: 0.127671
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008232 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:26<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[19]    training's binary_logloss: 0.118847 valid_1's binary_logloss: 0.135735
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006765 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12865
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[20]    training's binary_logloss: 0.115967 valid_1's binary_logloss: 0.140884
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:27<02:09,  3.33s/trial, best loss: -0.8361046999787884] 24%|██▍       | 12/50 [00:27<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:27<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:27<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007459 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[17]    training's binary_logloss: 0.118279 valid_1's binary_logloss: 0.128419
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010830 seconds.
You can set `force_col_wise=true` to remove the overhead.
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12882
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:28<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[16]    training's binary_logloss: 0.114963 valid_1's binary_logloss: 0.136964
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008271 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12935
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[18]    training's binary_logloss: 0.111041 valid_1's binary_logloss: 0.141811
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:29<01:49,  2.88s/trial, best loss: -0.8361046999787884] 26%|██▌       | 13/50 [00:29<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:29<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:29<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:29<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006716 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12911
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.117911 valid_1's binary_logloss: 0.127609
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007818 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12970
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:30<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[26]    training's binary_logloss: 0.112846 valid_1's binary_logloss: 0.135945
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007760 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 13049
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 208
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[21]    training's binary_logloss: 0.11335  valid_1's binary_logloss: 0.141758
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:31<01:36,  2.60s/trial, best loss: -0.8361046999787884] 28%|██▊       | 14/50 [00:31<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011169 seconds.
You can set `force_col_wise=true` to remove the overhead.
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[15]    training's binary_logloss: 0.123793 valid_1's binary_logloss: 0.127794
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006417 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:32<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[17]    training's binary_logloss: 0.117509 valid_1's binary_logloss: 0.136341
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012417 seconds.
You can set `force_col_wise=true` to remove the overhead.
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[14]    training's binary_logloss: 0.118131 valid_1's binary_logloss: 0.141827
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:33<01:29,  2.48s/trial, best loss: -0.8361046999787884] 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006221 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:33<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.116669 valid_1's binary_logloss: 0.127316
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008685 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:34<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.112195 valid_1's binary_logloss: 0.13634
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008114 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12865
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.110627 valid_1's binary_logloss: 0.141491
 30%|███       | 15/50 [00:35<01:18,  2.23s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:36<01:18,  2.23s/trial, best loss: -0.8361046999787884] 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006910 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[44]    training's binary_logloss: 0.11468  valid_1's binary_logloss: 0.127009
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:36<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008679 seconds.
You can set `force_col_wise=true` to remove the overhead.
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.116699 valid_1's binary_logloss: 0.136132
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009082 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12865
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 32%|███▏      | 16/50 [00:37<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:38<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:38<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 32%|███▏      | 16/50 [00:38<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 32%|███▏      | 16/50 [00:38<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:38<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[37]    training's binary_logloss: 0.110851 valid_1's binary_logloss: 0.140914
 32%|███▏      | 16/50 [00:38<01:17,  2.28s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:38<01:17,  2.28s/trial, best loss: -0.8361046999787884] 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009004 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:38<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.133099 valid_1's binary_logloss: 0.130118
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010889 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:39<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.128591 valid_1's binary_logloss: 0.138373
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006967 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:40<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.125837 valid_1's binary_logloss: 0.144181
 34%|███▍      | 17/50 [00:41<01:17,  2.35s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:41<01:17,  2.35s/trial, best loss: -0.8361046999787884] 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007196 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:41<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[35]    training's binary_logloss: 0.117838 valid_1's binary_logloss: 0.127509
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009471 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12882
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[34]    training's binary_logloss: 0.114266 valid_1's binary_logloss: 0.136132
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:42<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007509 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12935
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[44]    training's binary_logloss: 0.107097 valid_1's binary_logloss: 0.141644
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:43<01:18,  2.47s/trial, best loss: -0.8361046999787884] 38%|███▊      | 19/50 [00:43<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:43<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:43<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006872 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12911
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[32]    training's binary_logloss: 0.120651 valid_1's binary_logloss: 0.12748
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007690 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12970
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:44<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.117806 valid_1's binary_logloss: 0.135748
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007569 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 13049
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 208
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  Early stopping, best iteration is:
[39]    training's binary_logloss: 0.111183 valid_1's binary_logloss: 0.140593
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:45<01:17,  2.49s/trial, best loss: -0.8361046999787884] 40%|████      | 20/50 [00:45<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:45<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:45<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010567 seconds.
You can set `force_col_wise=true` to remove the overhead.
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.127621 valid_1's binary_logloss: 0.127975
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:46<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.028233 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123117 valid_1's binary_logloss: 0.136484
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:47<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007220 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12865
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.120407 valid_1's binary_logloss: 0.141888
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:48<01:10,  2.36s/trial, best loss: -0.8361046999787884] 42%|████▏     | 21/50 [00:48<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:48<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:48<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008396 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12809
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[94]    training's binary_logloss: 0.120321 valid_1's binary_logloss: 0.12706
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007023 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12874
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 42%|████▏     | 21/50 [00:49<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[95]    training's binary_logloss: 0.116042 valid_1's binary_logloss: 0.135298
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007237 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Total Bins 12865
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:50<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.112382 valid_1's binary_logloss: 0.140103
 42%|████▏     | 21/50 [00:51<01:13,  2.53s/trial, best loss: -0.8361046999787884]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:51<01:13,  2.53s/trial, best loss: -0.8361046999787884] 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007562 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [00:51<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.120691 valid_1's binary_logloss: 0.127296
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009653 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [00:52<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[99]    training's binary_logloss: 0.116561 valid_1's binary_logloss: 0.135521
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007650 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [00:53<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.113883 valid_1's binary_logloss: 0.140482
 44%|████▍     | 22/50 [00:54<01:11,  2.56s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:54<01:11,  2.56s/trial, best loss: -0.8361206531552328] 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009103 seconds.
You can set `force_col_wise=true` to remove the overhead.
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [00:54<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.12506  valid_1's binary_logloss: 0.127517
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007298 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [00:55<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.120784 valid_1's binary_logloss: 0.135909
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006853 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [00:56<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.11795  valid_1's binary_logloss: 0.141026
 46%|████▌     | 23/50 [00:57<01:11,  2.65s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [00:57<01:11,  2.65s/trial, best loss: -0.8361206531552328] 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006418 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [00:57<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 48%|████▊     | 24/50 [01:00<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 48%|████▊     | 24/50 [01:00<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:00<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[49]    training's binary_logloss: 0.120728 valid_1's binary_logloss: 0.12726
 48%|████▊     | 24/50 [01:00<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:00<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:00<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:00<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007255 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[51]    training's binary_logloss: 0.116085 valid_1's binary_logloss: 0.13534
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006430 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:01<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[60]    training's binary_logloss: 0.11095  valid_1's binary_logloss: 0.140369
 48%|████▊     | 24/50 [01:02<01:10,  2.71s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:02<01:10,  2.71s/trial, best loss: -0.8361206531552328] 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007186 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:02<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[97]    training's binary_logloss: 0.118162 valid_1's binary_logloss: 0.127362
 50%|█████     | 25/50 [01:03<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:03<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006679 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[93]    training's binary_logloss: 0.114804 valid_1's binary_logloss: 0.135408
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:04<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007134 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[98]    training's binary_logloss: 0.111333 valid_1's binary_logloss: 0.140214
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:05<01:28,  3.53s/trial, best loss: -0.8361206531552328] 52%|█████▏    | 26/50 [01:05<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006927 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[35]    training's binary_logloss: 0.11927  valid_1's binary_logloss: 0.127628
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007967 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:06<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[33]    training's binary_logloss: 0.11608  valid_1's binary_logloss: 0.136215
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007403 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:07<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[37]    training's binary_logloss: 0.111732 valid_1's binary_logloss: 0.141059
 52%|█████▏    | 26/50 [01:08<01:23,  3.49s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:08<01:23,  3.49s/trial, best loss: -0.8361206531552328] 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011520 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:08<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[65]    training's binary_logloss: 0.11962  valid_1's binary_logloss: 0.127063
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007475 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:09<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[72]    training's binary_logloss: 0.114002 valid_1's binary_logloss: 0.135602
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008925 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:10<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[71]    training's binary_logloss: 0.111386 valid_1's binary_logloss: 0.140329
 54%|█████▍    | 27/50 [01:11<01:11,  3.11s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:11<01:11,  3.11s/trial, best loss: -0.8361206531552328] 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006519 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[26]    training's binary_logloss: 0.123961 valid_1's binary_logloss: 0.127545
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010127 seconds.
You can set `force_col_wise=true` to remove the overhead.
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:11<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[32]    training's binary_logloss: 0.117126 valid_1's binary_logloss: 0.13534
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007890 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:12<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[41]    training's binary_logloss: 0.111145 valid_1's binary_logloss: 0.140511
 56%|█████▌    | 28/50 [01:13<01:06,  3.03s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:13<01:06,  3.03s/trial, best loss: -0.8361206531552328] 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010351 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:13<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.135256 valid_1's binary_logloss: 0.131344
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007594 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.130846 valid_1's binary_logloss: 0.139185
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:14<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006775 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.127867 valid_1's binary_logloss: 0.14514
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:15<00:57,  2.75s/trial, best loss: -0.8361206531552328] 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006594 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:15<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.127239 valid_1's binary_logloss: 0.127384
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006879 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:16<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.12288  valid_1's binary_logloss: 0.135571
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007380 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.120284 valid_1's binary_logloss: 0.140863
 60%|██████    | 30/50 [01:17<00:54,  2.70s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:18<00:54,  2.70s/trial, best loss: -0.8361206531552328] 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008255 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:18<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[76]    training's binary_logloss: 0.119732 valid_1's binary_logloss: 0.127276
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007609 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[70]    training's binary_logloss: 0.116807 valid_1's binary_logloss: 0.135585
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:19<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008248 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[80]    training's binary_logloss: 0.112368 valid_1's binary_logloss: 0.14032
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:20<00:49,  2.59s/trial, best loss: -0.8361206531552328] 64%|██████▍   | 32/50 [01:20<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:20<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:20<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007186 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[50]    training's binary_logloss: 0.120657 valid_1's binary_logloss: 0.126949
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006888 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:21<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[42]    training's binary_logloss: 0.118801 valid_1's binary_logloss: 0.135645
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006469 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[51]    training's binary_logloss: 0.113559 valid_1's binary_logloss: 0.140513
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:22<00:47,  2.66s/trial, best loss: -0.8361206531552328] 66%|██████▌   | 33/50 [01:22<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012104 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[36]    training's binary_logloss: 0.121629 valid_1's binary_logloss: 0.127166
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008693 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:23<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[34]    training's binary_logloss: 0.117903 valid_1's binary_logloss: 0.13587
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011244 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[34]    training's binary_logloss: 0.115553 valid_1's binary_logloss: 0.140845
 66%|██████▌   | 33/50 [01:24<00:42,  2.48s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:25<00:42,  2.48s/trial, best loss: -0.8361206531552328] 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007149 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[16]    training's binary_logloss: 0.118882 valid_1's binary_logloss: 0.128522
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008471 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 68%|██████▊   | 34/50 [01:25<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[11]    training's binary_logloss: 0.12009  valid_1's binary_logloss: 0.136809
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012520 seconds.
You can set `force_col_wise=true` to remove the overhead.
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:26<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[14]    training's binary_logloss: 0.114296 valid_1's binary_logloss: 0.141912
 68%|██████▊   | 34/50 [01:27<00:37,  2.36s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:27<00:37,  2.36s/trial, best loss: -0.8361206531552328] 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009374 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12870
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[67]    training's binary_logloss: 0.117834 valid_1's binary_logloss: 0.127248
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:27<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008813 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12934
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[55]    training's binary_logloss: 0.116506 valid_1's binary_logloss: 0.135743
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008382 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12939
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 70%|███████   | 35/50 [01:28<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:29<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:29<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 70%|███████   | 35/50 [01:29<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 70%|███████   | 35/50 [01:29<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:29<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[62]    training's binary_logloss: 0.112207 valid_1's binary_logloss: 0.140686
 70%|███████   | 35/50 [01:29<00:33,  2.26s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:29<00:33,  2.26s/trial, best loss: -0.8361206531552328] 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008790 seconds.
You can set `force_col_wise=true` to remove the overhead.
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:29<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[72]    training's binary_logloss: 0.116081 valid_1's binary_logloss: 0.12713
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008642 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:30<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[49]    training's binary_logloss: 0.117446 valid_1's binary_logloss: 0.135845
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006554 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:31<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  Early stopping, best iteration is:
[54]    training's binary_logloss: 0.113495 valid_1's binary_logloss: 0.140635
 72%|███████▏  | 36/50 [01:32<00:32,  2.33s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:32<00:32,  2.33s/trial, best loss: -0.8361206531552328] 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008911 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12809
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:32<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[97]    training's binary_logloss: 0.11803  valid_1's binary_logloss: 0.126746
 74%|███████▍  | 37/50 [01:35<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:35<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008127 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12874
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[85]    training's binary_logloss: 0.115626 valid_1's binary_logloss: 0.135332
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:36<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.013095 seconds.
You can set `force_col_wise=true` to remove the overhead.
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Total Bins 12865
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  Did not meet early stopping. Best iteration is:
[89]    training's binary_logloss: 0.112365 valid_1's binary_logloss: 0.140135
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:37<00:31,  2.40s/trial, best loss: -0.8361206531552328] 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007640 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12870
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:37<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[98]    training's binary_logloss: 0.123399 valid_1's binary_logloss: 0.126912
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007213 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12934
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:38<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.119208 valid_1's binary_logloss: 0.135189
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011345 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12939
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:39<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.116397 valid_1's binary_logloss: 0.140343
 76%|███████▌  | 38/50 [01:40<00:40,  3.35s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:40<00:40,  3.35s/trial, best loss: -0.8368093643173017] 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006904 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12870
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:40<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.13496  valid_1's binary_logloss: 0.13089
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006719 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12934
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.130487 valid_1's binary_logloss: 0.138913
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:41<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009924 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12939
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.12765  valid_1's binary_logloss: 0.144942
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:42<00:34,  3.12s/trial, best loss: -0.8368093643173017] 80%|████████  | 40/50 [01:42<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:42<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:42<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008446 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12907
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[91]    training's binary_logloss: 0.119162 valid_1's binary_logloss: 0.126781
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010092 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12943
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:43<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[76]    training's binary_logloss: 0.117526 valid_1's binary_logloss: 0.135504
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018761 seconds.
You can set `force_col_wise=true` to remove the overhead.
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 13017
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:44<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[84]    training's binary_logloss: 0.113084 valid_1's binary_logloss: 0.140427
 80%|████████  | 40/50 [01:45<00:29,  2.92s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:45<00:29,  2.92s/trial, best loss: -0.8368093643173017] 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007948 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12870
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:45<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.124667 valid_1's binary_logloss: 0.127059
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006578 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12934
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:46<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.120428 valid_1's binary_logloss: 0.135621
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006144 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12935
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.117733 valid_1's binary_logloss: 0.140661
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:47<00:25,  2.86s/trial, best loss: -0.8368093643173017] 84%|████████▍ | 42/50 [01:47<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008411 seconds.
You can set `force_col_wise=true` to remove the overhead.
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12809
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[28]    training's binary_logloss: 0.120438 valid_1's binary_logloss: 0.127484
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006109 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12874
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:48<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.118015 valid_1's binary_logloss: 0.13605
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006744 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12865
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.115064 valid_1's binary_logloss: 0.14127
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:49<00:22,  2.76s/trial, best loss: -0.8368093643173017] 86%|████████▌ | 43/50 [01:49<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008998 seconds.
You can set `force_col_wise=true` to remove the overhead.
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12907
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[16]    training's binary_logloss: 0.117532 valid_1's binary_logloss: 0.128445
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009310 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12970
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:50<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[13]    training's binary_logloss: 0.116506 valid_1's binary_logloss: 0.136088
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007065 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 13049
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 208
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[18]    training's binary_logloss: 0.10854  valid_1's binary_logloss: 0.14215
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:51<00:17,  2.51s/trial, best loss: -0.8368093643173017] 88%|████████▊ | 44/50 [01:51<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009960 seconds.
You can set `force_col_wise=true` to remove the overhead.
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12907
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[31]    training's binary_logloss: 0.120736 valid_1's binary_logloss: 0.127726
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007563 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12934
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:52<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[28]    training's binary_logloss: 0.117682 valid_1's binary_logloss: 0.135689
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010431 seconds.
You can set `force_col_wise=true` to remove the overhead.
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12989
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[35]    training's binary_logloss: 0.112434 valid_1's binary_logloss: 0.141034
 88%|████████▊ | 44/50 [01:53<00:14,  2.37s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:54<00:14,  2.37s/trial, best loss: -0.8368093643173017] 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007370 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12907
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.114951 valid_1's binary_logloss: 0.127139
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:54<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007464 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12970
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[20]    training's binary_logloss: 0.114337 valid_1's binary_logloss: 0.136683
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007329 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 13017
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [01:55<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[22]    training's binary_logloss: 0.110257 valid_1's binary_logloss: 0.141881
 90%|█████████ | 45/50 [01:56<00:11,  2.29s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:56<00:11,  2.29s/trial, best loss: -0.8368093643173017] 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011444 seconds.
You can set `force_col_wise=true` to remove the overhead.
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12809
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[45]    training's binary_logloss: 0.120473 valid_1's binary_logloss: 0.127224
 92%|█████████▏| 46/50 [01:56<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007092 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12874
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[47]    training's binary_logloss: 0.115765 valid_1's binary_logloss: 0.135738
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 92%|█████████▏| 46/50 [01:57<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006695 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12865
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[43]    training's binary_logloss: 0.114087 valid_1's binary_logloss: 0.140748
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [01:58<00:09,  2.26s/trial, best loss: -0.8368093643173017] 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007464 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12870
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [01:58<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[50]    training's binary_logloss: 0.117042 valid_1's binary_logloss: 0.127461
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006729 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12934
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [01:59<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[58]    training's binary_logloss: 0.110513 valid_1's binary_logloss: 0.135839
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011866 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12989
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [02:00<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[61]    training's binary_logloss: 0.107023 valid_1's binary_logloss: 0.140678
 94%|█████████▍| 47/50 [02:01<00:06,  2.26s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:01<00:06,  2.26s/trial, best loss: -0.8368093643173017] 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008940 seconds.
You can set `force_col_wise=true` to remove the overhead.
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12818
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:01<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[50]    training's binary_logloss: 0.116856 valid_1's binary_logloss: 0.127122
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009554 seconds.
You can set `force_col_wise=true` to remove the overhead.
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12934
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:02<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[36]    training's binary_logloss: 0.117447 valid_1's binary_logloss: 0.13599
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006844 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12935
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[48]    training's binary_logloss: 0.110674 valid_1's binary_logloss: 0.140834
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:03<00:04,  2.44s/trial, best loss: -0.8368093643173017] 98%|█████████▊| 49/50 [02:03<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1637, number of negative: 38907
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007160 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12809
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040376 -> initscore=-3.168309
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.168309
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[28]    training's binary_logloss: 0.117901 valid_1's binary_logloss: 0.128002
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:04<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1597, number of negative: 38947
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008421 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12874
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039389 -> initscore=-3.194075
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.194075
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.11285  valid_1's binary_logloss: 0.135927
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of positive: 1538, number of negative: 39006
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007163 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Total Bins 12874
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.037934 -> initscore=-3.233233
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Info] Start training from score -3.233233
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:05<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.110102 valid_1's binary_logloss: 0.141424
 98%|█████████▊| 49/50 [02:06<00:02,  2.50s/trial, best loss: -0.8368093643173017]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:06<00:02,  2.50s/trial, best loss: -0.8368093643173017]100%|██████████| 50/50 [02:06<00:00,  2.48s/trial, best loss: -0.8368093643173017]100%|██████████| 50/50 [02:06<00:00,  2.53s/trial, best loss: -0.8368093643173017]
{'learning_rate': 0.043324531254078945, 'max_depth': 133.0, 'min_child_samples': 85.0, 'num_leaves': 36.0, 'subsample': 0.7305792288105732}

재학습

lgbm_clf = LGBMClassifier(n_estimators=500, 
                          num_leaves=int(best['num_leaves']),
                          max_depth=int(best['max_depth']),
                          min_child_samples=int(best['min_child_samples']),
                          subsample=round(best['subsample'], 5),
                          learning_rate=round(best['learning_rate'], 5),
                          early_stopping_rounds=100, 
                          eval_metric='auc')

eval_set = [(X_tr, y_tr), (X_val, y_val)]
lgbm_clf.fit(X_tr, y_tr, eval_set=eval_set)

lgbm_roc_score = roc_auc_score(y_test, lgbm_clf.predict_proba(X_test)[:, 1])
print(f'{lgbm_roc_score:.3f}')

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] early_stopping_round is set=100, early_stopping_rounds=100 will be ignored. Current value: early_stopping_round=100
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 1653, number of negative: 40918
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009941 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 12969
[LightGBM] [Info] Number of data points in the train set: 42571, number of used features: 192
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] early_stopping_round is set=100, early_stopping_rounds=100 will be ignored. Current value: early_stopping_round=100
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038829 -> initscore=-3.208978
[LightGBM] [Info] Start training from score -3.208978
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[62]    training's binary_logloss: 0.119449 valid_1's binary_logloss: 0.137449
[LightGBM] [Warning] Unknown parameter: eval_metric
0.838

제출

target = lgbm_clf.predict(test_df)

submit = pd.read_csv('_data/santander/sample_submission.csv', encoding='latin-1')
submit['TARGET'] = target
submit.to_csv('_data/santander/submission.csv', encoding='latin-1', index=False)

[LightGBM] [Warning] Unknown parameter: eval_metric