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: 1694, number of negative: 40877
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 13592
[LightGBM] [Info] Number of data points in the train set: 42571, number of used features: 248
[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=0.039792 -> initscore=-3.183475
[LightGBM] [Info] Start training from score -3.183475
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[33]    training's binary_logloss: 0.117693 valid_1's binary_logloss: 0.137269
[LightGBM] [Warning] Unknown parameter: eval_metric
0.836

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: 1647, number of negative: 38897
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007286 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 12947
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
  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=0.040623 -> initscore=-3.161962
  0%|          | 0/50 [00:00<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.161962
  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=?]                                                      Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.119239 valid_1's binary_logloss: 0.131547
  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: 1572, number of negative: 38972
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009173 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 13055
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
  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=0.038773 -> initscore=-3.210495
  0%|          | 0/50 [00:01<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.210495
  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=?]                                                      Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.11513  valid_1's binary_logloss: 0.139265
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:02<?, ?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 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008461 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 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Total Bins 12996
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:02<?, ?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 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      [LightGBM] [Info] Start training from score -3.179828
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      Training until validation scores don't improve for 30 rounds
  0%|          | 0/50 [00:02<?, ?trial/s, best loss=?]                                                      Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.116828 valid_1's binary_logloss: 0.136952
  0%|          | 0/50 [00:03<?, ?trial/s, best loss=?]                                                      [LightGBM] [Warning] Unknown parameter: eval_metric
  0%|          | 0/50 [00:03<?, ?trial/s, best loss=?]  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [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<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008509 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<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12835
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [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<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.161962
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:03<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.134361 valid_1's binary_logloss: 0.134539
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008662 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 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12988
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.210495
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:04<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.129831 valid_1's binary_logloss: 0.142347
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008359 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 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12898
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.179828
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  2%|▏         | 1/50 [00:05<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.131634 valid_1's binary_logloss: 0.139054
  2%|▏         | 1/50 [00:06<02:57,  3.63s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  2%|▏         | 1/50 [00:06<02:57,  3.63s/trial, best loss: -0.8341540202815528]  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008437 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 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12902
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.161962
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:06<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 Did not meet early stopping. Best iteration is:
[88]    training's binary_logloss: 0.113936 valid_1's binary_logloss: 0.131766
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008792 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 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12988
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.210495
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:07<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 Did not meet early stopping. Best iteration is:
[71]    training's binary_logloss: 0.11326  valid_1's binary_logloss: 0.139317
  4%|▍         | 2/50 [00:08<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:08<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009763 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 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12898
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.179828
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  4%|▍         | 2/50 [00:09<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 Did not meet early stopping. Best iteration is:
[77]    training's binary_logloss: 0.113657 valid_1's binary_logloss: 0.136864
  4%|▍         | 2/50 [00:10<02:30,  3.13s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  4%|▍         | 2/50 [00:10<02:30,  3.13s/trial, best loss: -0.8341540202815528]  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009179 seconds.
You can set `force_col_wise=true` to remove the overhead.
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12835
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.161962
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 Early stopping, best iteration is:
[39]    training's binary_logloss: 0.12109  valid_1's binary_logloss: 0.131246
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:10<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008369 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 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12988
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.210495
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 Early stopping, best iteration is:
[39]    training's binary_logloss: 0.116743 valid_1's binary_logloss: 0.139211
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008111 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 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Total Bins 12898
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Info] Start training from score -3.179828
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 Training until validation scores don't improve for 30 rounds
  6%|▌         | 3/50 [00:11<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 Early stopping, best iteration is:
[35]    training's binary_logloss: 0.120149 valid_1's binary_logloss: 0.136702
  6%|▌         | 3/50 [00:12<02:38,  3.38s/trial, best loss: -0.8341540202815528]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  6%|▌         | 3/50 [00:12<02:38,  3.38s/trial, best loss: -0.8341540202815528]  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008782 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 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12947
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.161962
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:12<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 Early stopping, best iteration is:
[30]    training's binary_logloss: 0.111064 valid_1's binary_logloss: 0.131895
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010004 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 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 13055
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.210495
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:13<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 Early stopping, best iteration is:
[27]    training's binary_logloss: 0.108994 valid_1's binary_logloss: 0.139854
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009049 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 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12996
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.179828
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 Early stopping, best iteration is:
[20]    training's binary_logloss: 0.116146 valid_1's binary_logloss: 0.13756
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
  8%|▊         | 4/50 [00:14<02:16,  2.96s/trial, best loss: -0.8346097688713522] 10%|█         | 5/50 [00:14<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008251 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 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12947
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.161962
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 Early stopping, best iteration is:
[25]    training's binary_logloss: 0.120067 valid_1's binary_logloss: 0.131511
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007845 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 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 13055
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.210495
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:15<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 Early stopping, best iteration is:
[31]    training's binary_logloss: 0.112434 valid_1's binary_logloss: 0.139423
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009165 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 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12996
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.179828
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 Early stopping, best iteration is:
[28]    training's binary_logloss: 0.115651 valid_1's binary_logloss: 0.136891
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 10%|█         | 5/50 [00:16<02:06,  2.80s/trial, best loss: -0.8346097688713522] 12%|█▏        | 6/50 [00:16<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010475 seconds.
You can set `force_col_wise=true` to remove the overhead.
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12902
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.161962
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:17<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.128605 valid_1's binary_logloss: 0.133093
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008203 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 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12988
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.210495
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:18<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.124147 valid_1's binary_logloss: 0.141061
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007711 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 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12898
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.179828
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 12%|█▏        | 6/50 [00:19<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.125878 valid_1's binary_logloss: 0.13813
 12%|█▏        | 6/50 [00:20<01:52,  2.55s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 12%|█▏        | 6/50 [00:20<01:52,  2.55s/trial, best loss: -0.8346097688713522] 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008418 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 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12947
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.161962
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 Did not meet early stopping. Best iteration is:
[73]    training's binary_logloss: 0.119266 valid_1's binary_logloss: 0.131216
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:20<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007783 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 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12998
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.210495
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 Early stopping, best iteration is:
[63]    training's binary_logloss: 0.116719 valid_1's binary_logloss: 0.139009
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:21<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008308 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 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Total Bins 12968
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Info] Start training from score -3.179828
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 Training until validation scores don't improve for 30 rounds
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 Early stopping, best iteration is:
[56]    training's binary_logloss: 0.120087 valid_1's binary_logloss: 0.136444
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 14%|█▍        | 7/50 [00:22<01:58,  2.76s/trial, best loss: -0.8346097688713522] 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009077 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 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Total Bins 12835
 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:22<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Start training from score -3.161962
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 Early stopping, best iteration is:
[57]    training's binary_logloss: 0.120993 valid_1's binary_logloss: 0.131385
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007612 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 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Total Bins 12988
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Start training from score -3.210495
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:23<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 Early stopping, best iteration is:
[62]    training's binary_logloss: 0.115325 valid_1's binary_logloss: 0.13881
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007536 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 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Total Bins 12898
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Start training from score -3.179828
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 Training until validation scores don't improve for 30 rounds
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 Early stopping, best iteration is:
[50]    training's binary_logloss: 0.120231 valid_1's binary_logloss: 0.136346
 16%|█▌        | 8/50 [00:24<01:51,  2.65s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 16%|█▌        | 8/50 [00:25<01:51,  2.65s/trial, best loss: -0.8354478683012264] 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008124 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 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Total Bins 12835
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Start training from score -3.161962
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 Early stopping, best iteration is:
[23]    training's binary_logloss: 0.116031 valid_1's binary_logloss: 0.132494
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:25<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007737 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 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Total Bins 12988
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Start training from score -3.210495
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 Early stopping, best iteration is:
[22]    training's binary_logloss: 0.112419 valid_1's binary_logloss: 0.140329
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007861 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 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Total Bins 12898
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Info] Start training from score -3.179828
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 Training until validation scores don't improve for 30 rounds
 18%|█▊        | 9/50 [00:26<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 Early stopping, best iteration is:
[20]    training's binary_logloss: 0.115687 valid_1's binary_logloss: 0.137694
 18%|█▊        | 9/50 [00:27<01:46,  2.59s/trial, best loss: -0.8354478683012264]                                                                                 [LightGBM] [Warning] Unknown parameter: eval_metric
 18%|█▊        | 9/50 [00:27<01:46,  2.59s/trial, best loss: -0.8354478683012264] 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007751 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 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12835
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:27<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[45]    training's binary_logloss: 0.117033 valid_1's binary_logloss: 0.131893
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007925 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 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[30]    training's binary_logloss: 0.11876  valid_1's binary_logloss: 0.139543
 20%|██        | 10/50 [00:28<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008278 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 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12898
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[38]    training's binary_logloss: 0.117423 valid_1's binary_logloss: 0.136738
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 20%|██        | 10/50 [00:29<01:40,  2.52s/trial, best loss: -0.8354478683012264] 22%|██▏       | 11/50 [00:29<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:29<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:29<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008637 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 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12902
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  Did not meet early stopping. Best iteration is:
[78]    training's binary_logloss: 0.115732 valid_1's binary_logloss: 0.13138
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007739 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 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:30<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  Did not meet early stopping. Best iteration is:
[74]    training's binary_logloss: 0.112624 valid_1's binary_logloss: 0.139339
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008653 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 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12898
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 22%|██▏       | 11/50 [00:31<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  Did not meet early stopping. Best iteration is:
[74]    training's binary_logloss: 0.114351 valid_1's binary_logloss: 0.136737
 22%|██▏       | 11/50 [00:32<01:37,  2.49s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 22%|██▏       | 11/50 [00:32<01:37,  2.49s/trial, best loss: -0.8354478683012264] 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010321 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 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12947
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:32<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[46]    training's binary_logloss: 0.11276  valid_1's binary_logloss: 0.13165
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009865 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 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 13055
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 24%|██▍       | 12/50 [00:33<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[40]    training's binary_logloss: 0.111011 valid_1's binary_logloss: 0.139831
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008053 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 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12996
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 24%|██▍       | 12/50 [00:34<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:35<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 24%|██▍       | 12/50 [00:35<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 24%|██▍       | 12/50 [00:35<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 24%|██▍       | 12/50 [00:35<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 24%|██▍       | 12/50 [00:35<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[43]    training's binary_logloss: 0.111276 valid_1's binary_logloss: 0.137335
 24%|██▍       | 12/50 [00:35<01:35,  2.51s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 24%|██▍       | 12/50 [00:35<01:35,  2.51s/trial, best loss: -0.8354478683012264] 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008260 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 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12844
 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:35<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 26%|██▌       | 13/50 [00:36<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 26%|██▌       | 13/50 [00:36<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:36<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.12681  valid_1's binary_logloss: 0.13222
 26%|██▌       | 13/50 [00:36<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:36<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:36<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:36<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008850 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 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.122208 valid_1's binary_logloss: 0.139981
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:37<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009314 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 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12898
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 26%|██▌       | 13/50 [00:38<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.124131 valid_1's binary_logloss: 0.137316
 26%|██▌       | 13/50 [00:39<01:41,  2.74s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 26%|██▌       | 13/50 [00:39<01:41,  2.74s/trial, best loss: -0.8354478683012264] 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010560 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 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12943
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:39<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[19]    training's binary_logloss: 0.119948 valid_1's binary_logloss: 0.132615
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009233 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 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[17]    training's binary_logloss: 0.116812 valid_1's binary_logloss: 0.140251
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:40<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009001 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 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12958
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[19]    training's binary_logloss: 0.117331 valid_1's binary_logloss: 0.137237
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 28%|██▊       | 14/50 [00:41<01:49,  3.04s/trial, best loss: -0.8354478683012264] 30%|███       | 15/50 [00:41<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:41<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:41<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010186 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 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12943
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[17]    training's binary_logloss: 0.120445 valid_1's binary_logloss: 0.132691
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 30%|███       | 15/50 [00:42<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010950 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 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[16]    training's binary_logloss: 0.117054 valid_1's binary_logloss: 0.139941
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008302 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 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12906
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 30%|███       | 15/50 [00:43<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[20]    training's binary_logloss: 0.115401 valid_1's binary_logloss: 0.137413
 30%|███       | 15/50 [00:44<01:38,  2.83s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 30%|███       | 15/50 [00:44<01:38,  2.83s/trial, best loss: -0.8354478683012264] 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009370 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 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12835
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[18]    training's binary_logloss: 0.11605  valid_1's binary_logloss: 0.133209
 32%|███▏      | 16/50 [00:44<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008886 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 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[15]    training's binary_logloss: 0.114923 valid_1's binary_logloss: 0.140959
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:45<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008924 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 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12898
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[14]    training's binary_logloss: 0.117846 valid_1's binary_logloss: 0.13746
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 32%|███▏      | 16/50 [00:46<01:32,  2.72s/trial, best loss: -0.8354478683012264] 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009462 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 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12835
 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:46<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[49]    training's binary_logloss: 0.11538  valid_1's binary_logloss: 0.131723
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010342 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 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:47<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[31]    training's binary_logloss: 0.117853 valid_1's binary_logloss: 0.139219
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007465 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 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12898
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.120676 valid_1's binary_logloss: 0.136931
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 34%|███▍      | 17/50 [00:48<01:26,  2.63s/trial, best loss: -0.8354478683012264] 36%|███▌      | 18/50 [00:48<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007036 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 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12835
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.119523 valid_1's binary_logloss: 0.131926
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008304 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 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:49<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[27]    training's binary_logloss: 0.115902 valid_1's binary_logloss: 0.139583
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010535 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 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12898
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[24]    training's binary_logloss: 0.11906  valid_1's binary_logloss: 0.137256
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 36%|███▌      | 18/50 [00:50<01:20,  2.53s/trial, best loss: -0.8354478683012264] 38%|███▊      | 19/50 [00:50<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014995 seconds.
You can set `force_col_wise=true` to remove the overhead.
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12835
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[28]    training's binary_logloss: 0.117616 valid_1's binary_logloss: 0.132237
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 38%|███▊      | 19/50 [00:51<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007897 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 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12988
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[23]    training's binary_logloss: 0.115822 valid_1's binary_logloss: 0.140243
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.020219 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 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 12898
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 38%|███▊      | 19/50 [00:52<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.116668 valid_1's binary_logloss: 0.137218
 38%|███▊      | 19/50 [00:53<01:13,  2.38s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 38%|███▊      | 19/50 [00:53<01:13,  2.38s/trial, best loss: -0.8354478683012264] 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009889 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 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 13057
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 211
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:53<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  Did not meet early stopping. Best iteration is:
[71]    training's binary_logloss: 0.118472 valid_1's binary_logloss: 0.130909
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008639 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 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 13161
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 208
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:54<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[60]    training's binary_logloss: 0.116535 valid_1's binary_logloss: 0.138826
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009875 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 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Total Bins 13044
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  Training until validation scores don't improve for 30 rounds
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  Early stopping, best iteration is:
[58]    training's binary_logloss: 0.118597 valid_1's binary_logloss: 0.136638
 40%|████      | 20/50 [00:55<01:12,  2.41s/trial, best loss: -0.8354478683012264]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 40%|████      | 20/50 [00:56<01:12,  2.41s/trial, best loss: -0.8354478683012264] 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008395 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 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 13057
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 211
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  Did not meet early stopping. Best iteration is:
[96]    training's binary_logloss: 0.116806 valid_1's binary_logloss: 0.131693
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:56<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008654 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 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 13161
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 208
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  Did not meet early stopping. Best iteration is:
[75]    training's binary_logloss: 0.116396 valid_1's binary_logloss: 0.138474
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:57<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009684 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 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 13044
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  Early stopping, best iteration is:
[65]    training's binary_logloss: 0.119662 valid_1's binary_logloss: 0.136275
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 42%|████▏     | 21/50 [00:58<01:11,  2.46s/trial, best loss: -0.8361261980967356] 44%|████▍     | 22/50 [00:58<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [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`.
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 13057
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 211
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  Early stopping, best iteration is:
[63]    training's binary_logloss: 0.117775 valid_1's binary_logloss: 0.131201
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [00:59<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009518 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 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 13161
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 208
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  Early stopping, best iteration is:
[55]    training's binary_logloss: 0.11576  valid_1's binary_logloss: 0.138797
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [01:00<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009738 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 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 13044
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  Early stopping, best iteration is:
[48]    training's binary_logloss: 0.119114 valid_1's binary_logloss: 0.136592
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 44%|████▍     | 22/50 [01:01<01:12,  2.58s/trial, best loss: -0.8361261980967356] 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009989 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 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 12993
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.121674 valid_1's binary_logloss: 0.131107
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [01:01<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009480 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 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 13086
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.11765  valid_1's binary_logloss: 0.138596
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [01:02<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009169 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 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Total Bins 12996
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  Training until validation scores don't improve for 30 rounds
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  Did not meet early stopping. Best iteration is:
[95]    training's binary_logloss: 0.119781 valid_1's binary_logloss: 0.136145
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 46%|████▌     | 23/50 [01:03<01:11,  2.65s/trial, best loss: -0.8361261980967356] 48%|████▊     | 24/50 [01:03<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:03<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:03<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008895 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 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Total Bins 12993
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123945 valid_1's binary_logloss: 0.131312
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:04<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009337 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 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Total Bins 13086
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.119785 valid_1's binary_logloss: 0.138758
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:05<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009450 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 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Total Bins 12996
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  Training until validation scores don't improve for 30 rounds
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.121345 valid_1's binary_logloss: 0.136253
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 48%|████▊     | 24/50 [01:06<01:04,  2.46s/trial, best loss: -0.8362934408440913] 50%|█████     | 25/50 [01:06<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:06<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:06<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010296 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 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12993
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:07<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[99]    training's binary_logloss: 0.115076 valid_1's binary_logloss: 0.131544
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011525 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 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13086
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:08<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[99]    training's binary_logloss: 0.110704 valid_1's binary_logloss: 0.139171
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009677 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 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12996
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 50%|█████     | 25/50 [01:09<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[74]    training's binary_logloss: 0.117386 valid_1's binary_logloss: 0.137077
 50%|█████     | 25/50 [01:10<01:06,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 50%|█████     | 25/50 [01:10<01:06,  2.65s/trial, best loss: -0.8365225708987197] 52%|█████▏    | 26/50 [01:10<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:10<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:10<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009136 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 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12993
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[40]    training's binary_logloss: 0.118745 valid_1's binary_logloss: 0.13174
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009702 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 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13086
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 52%|█████▏    | 26/50 [01:11<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[37]    training's binary_logloss: 0.115548 valid_1's binary_logloss: 0.138995
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010037 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 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12996
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 52%|█████▏    | 26/50 [01:12<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[32]    training's binary_logloss: 0.119523 valid_1's binary_logloss: 0.136814
 52%|█████▏    | 26/50 [01:13<01:13,  3.05s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 52%|█████▏    | 26/50 [01:13<01:13,  3.05s/trial, best loss: -0.8365225708987197] 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009837 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 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12993
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:13<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[97]    training's binary_logloss: 0.119337 valid_1's binary_logloss: 0.131417
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010306 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 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13086
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:14<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.114456 valid_1's binary_logloss: 0.139157
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008807 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 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12996
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 54%|█████▍    | 27/50 [01:15<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[97]    training's binary_logloss: 0.11659  valid_1's binary_logloss: 0.136713
 54%|█████▍    | 27/50 [01:16<01:07,  2.92s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 54%|█████▍    | 27/50 [01:16<01:07,  2.92s/trial, best loss: -0.8365225708987197] 56%|█████▌    | 28/50 [01:16<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:16<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:16<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:16<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 56%|█████▌    | 28/50 [01:16<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008596 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 [01:16<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12943
 56%|█████▌    | 28/50 [01:16<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 56%|█████▌    | 28/50 [01:16<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[22]    training's binary_logloss: 0.121698 valid_1's binary_logloss: 0.132138
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008173 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 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12998
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:17<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[25]    training's binary_logloss: 0.11611  valid_1's binary_logloss: 0.139307
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008090 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 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12958
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[17]    training's binary_logloss: 0.122317 valid_1's binary_logloss: 0.136889
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 56%|█████▌    | 28/50 [01:18<01:06,  3.03s/trial, best loss: -0.8365225708987197] 58%|█████▊    | 29/50 [01:18<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:18<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:18<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:18<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 58%|█████▊    | 29/50 [01:18<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009576 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 [01:18<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12943
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.117385 valid_1's binary_logloss: 0.131388
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 58%|█████▊    | 29/50 [01:19<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008772 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 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12998
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[98]    training's binary_logloss: 0.113488 valid_1's binary_logloss: 0.139105
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:20<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008569 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 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12958
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.114829 valid_1's binary_logloss: 0.136714
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 58%|█████▊    | 29/50 [01:21<00:57,  2.74s/trial, best loss: -0.8365225708987197] 60%|██████    | 30/50 [01:21<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:21<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:21<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008604 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 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12993
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.136137 valid_1's binary_logloss: 0.135864
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007794 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 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13059
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:22<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.131758 valid_1's binary_logloss: 0.143909
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008173 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 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12996
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 60%|██████    | 30/50 [01:23<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.133319 valid_1's binary_logloss: 0.140365
 60%|██████    | 30/50 [01:24<00:56,  2.84s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 60%|██████    | 30/50 [01:24<00:56,  2.84s/trial, best loss: -0.8365225708987197] 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007810 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 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12902
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:24<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[52]    training's binary_logloss: 0.120557 valid_1's binary_logloss: 0.131463
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008104 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 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12988
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[42]    training's binary_logloss: 0.119216 valid_1's binary_logloss: 0.138844
 62%|██████▏   | 31/50 [01:25<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008458 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 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12898
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[43]    training's binary_logloss: 0.120672 valid_1's binary_logloss: 0.136077
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 62%|██████▏   | 31/50 [01:26<00:52,  2.78s/trial, best loss: -0.8365225708987197] 64%|██████▍   | 32/50 [01:26<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:26<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:26<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008051 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 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12943
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.116782 valid_1's binary_logloss: 0.132291
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008398 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 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12998
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:27<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.112525 valid_1's binary_logloss: 0.139834
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008790 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 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12958
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 64%|██████▍   | 32/50 [01:28<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[26]    training's binary_logloss: 0.116376 valid_1's binary_logloss: 0.13759
 64%|██████▍   | 32/50 [01:29<00:47,  2.65s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 64%|██████▍   | 32/50 [01:29<00:47,  2.65s/trial, best loss: -0.8365225708987197] 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007899 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 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13047
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 210
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.121235 valid_1's binary_logloss: 0.131795
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:29<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010711 seconds.
You can set `force_col_wise=true` to remove the overhead.
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13161
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 208
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:30<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.117131 valid_1's binary_logloss: 0.139355
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009935 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 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13044
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 66%|██████▌   | 33/50 [01:31<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.11886  valid_1's binary_logloss: 0.136817
 66%|██████▌   | 33/50 [01:32<00:43,  2.55s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 66%|██████▌   | 33/50 [01:32<00:43,  2.55s/trial, best loss: -0.8365225708987197] 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009479 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 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13047
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 210
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[87]    training's binary_logloss: 0.119217 valid_1's binary_logloss: 0.131162
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009390 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 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13130
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:32<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[95]    training's binary_logloss: 0.11377  valid_1's binary_logloss: 0.138774
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008385 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 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13000
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 68%|██████▊   | 34/50 [01:33<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[71]    training's binary_logloss: 0.119355 valid_1's binary_logloss: 0.136516
 68%|██████▊   | 34/50 [01:34<00:44,  2.76s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 68%|██████▊   | 34/50 [01:34<00:44,  2.76s/trial, best loss: -0.8365225708987197] 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008260 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 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12993
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[20]    training's binary_logloss: 0.117227 valid_1's binary_logloss: 0.132479
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:34<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007966 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 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13059
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[22]    training's binary_logloss: 0.110745 valid_1's binary_logloss: 0.140016
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007917 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 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12996
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 70%|███████   | 35/50 [01:35<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[18]    training's binary_logloss: 0.116325 valid_1's binary_logloss: 0.136868
 70%|███████   | 35/50 [01:36<00:37,  2.51s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 70%|███████   | 35/50 [01:36<00:37,  2.51s/trial, best loss: -0.8365225708987197] 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007671 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 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12902
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:36<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.133676 valid_1's binary_logloss: 0.135443
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007838 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 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12988
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:37<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.129111 valid_1's binary_logloss: 0.143846
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008862 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 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12898
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 72%|███████▏  | 36/50 [01:38<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.131021 valid_1's binary_logloss: 0.140157
 72%|███████▏  | 36/50 [01:39<00:32,  2.33s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 72%|███████▏  | 36/50 [01:39<00:32,  2.33s/trial, best loss: -0.8365225708987197] 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007790 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 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12943
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:39<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[65]    training's binary_logloss: 0.118118 valid_1's binary_logloss: 0.131584
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007983 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 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12988
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:40<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[65]    training's binary_logloss: 0.113718 valid_1's binary_logloss: 0.139017
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008166 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 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12906
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 74%|███████▍  | 37/50 [01:41<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[59]    training's binary_logloss: 0.116916 valid_1's binary_logloss: 0.136382
 74%|███████▍  | 37/50 [01:42<00:34,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 74%|███████▍  | 37/50 [01:42<00:34,  2.62s/trial, best loss: -0.8365225708987197] 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008362 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 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12943
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:42<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[43]    training's binary_logloss: 0.120211 valid_1's binary_logloss: 0.131444
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011524 seconds.
You can set `force_col_wise=true` to remove the overhead.
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12998
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[47]    training's binary_logloss: 0.114602 valid_1's binary_logloss: 0.139106
 76%|███████▌  | 38/50 [01:43<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008487 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 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12968
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[40]    training's binary_logloss: 0.118651 valid_1's binary_logloss: 0.136544
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 76%|███████▌  | 38/50 [01:44<00:32,  2.68s/trial, best loss: -0.8365225708987197] 78%|███████▊  | 39/50 [01:44<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:44<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:44<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007906 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 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12947
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[40]    training's binary_logloss: 0.119116 valid_1's binary_logloss: 0.131438
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010484 seconds.
You can set `force_col_wise=true` to remove the overhead.
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13059
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:45<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[37]    training's binary_logloss: 0.116085 valid_1's binary_logloss: 0.138771
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008602 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 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12996
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[34]    training's binary_logloss: 0.11889  valid_1's binary_logloss: 0.136703
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 78%|███████▊  | 39/50 [01:46<00:28,  2.62s/trial, best loss: -0.8365225708987197] 80%|████████  | 40/50 [01:46<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009081 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 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12947
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.119904 valid_1's binary_logloss: 0.131539
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:47<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011067 seconds.
You can set `force_col_wise=true` to remove the overhead.
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13055
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[28]    training's binary_logloss: 0.115561 valid_1's binary_logloss: 0.139612
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007772 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 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12996
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 80%|████████  | 40/50 [01:48<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[26]    training's binary_logloss: 0.118624 valid_1's binary_logloss: 0.136879
 80%|████████  | 40/50 [01:49<00:24,  2.49s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 80%|████████  | 40/50 [01:49<00:24,  2.49s/trial, best loss: -0.8365225708987197] 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012833 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 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12943
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[39]    training's binary_logloss: 0.121601 valid_1's binary_logloss: 0.131732
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:49<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012323 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 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12998
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[34]    training's binary_logloss: 0.118977 valid_1's binary_logloss: 0.13905
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008469 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 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12958
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 82%|████████▏ | 41/50 [01:50<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 82%|████████▏ | 41/50 [01:51<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 82%|████████▏ | 41/50 [01:51<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 82%|████████▏ | 41/50 [01:51<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[34]    training's binary_logloss: 0.120814 valid_1's binary_logloss: 0.136438
 82%|████████▏ | 41/50 [01:51<00:21,  2.40s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 82%|████████▏ | 41/50 [01:51<00:21,  2.40s/trial, best loss: -0.8365225708987197] 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008567 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 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12947
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:51<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.119106 valid_1's binary_logloss: 0.131122
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011181 seconds.
You can set `force_col_wise=true` to remove the overhead.
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12998
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 194
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:52<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[90]    training's binary_logloss: 0.116567 valid_1's binary_logloss: 0.138873
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007625 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 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12968
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 199
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 84%|████████▍ | 42/50 [01:53<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[85]    training's binary_logloss: 0.118801 valid_1's binary_logloss: 0.136111
 84%|████████▍ | 42/50 [01:54<00:18,  2.35s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 84%|████████▍ | 42/50 [01:54<00:18,  2.35s/trial, best loss: -0.8365225708987197] 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008375 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 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13047
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 210
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:54<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.12624  valid_1's binary_logloss: 0.132688
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008360 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 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13130
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 205
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:55<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.121959 valid_1's binary_logloss: 0.140097
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012750 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 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13000
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 86%|████████▌ | 43/50 [01:56<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123583 valid_1's binary_logloss: 0.137169
 86%|████████▌ | 43/50 [01:57<00:17,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 86%|████████▌ | 43/50 [01:57<00:17,  2.52s/trial, best loss: -0.8365225708987197] 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009549 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 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12902
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:57<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[23]    training's binary_logloss: 0.114805 valid_1's binary_logloss: 0.132779
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007723 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 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12988
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[18]    training's binary_logloss: 0.11472  valid_1's binary_logloss: 0.140404
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:58<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012523 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 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12898
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[19]    training's binary_logloss: 0.115511 valid_1's binary_logloss: 0.137588
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 88%|████████▊ | 44/50 [01:59<00:16,  2.70s/trial, best loss: -0.8365225708987197] 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008368 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 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12835
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 90%|█████████ | 45/50 [01:59<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[70]    training's binary_logloss: 0.115612 valid_1's binary_logloss: 0.131625
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007622 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 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12988
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 90%|█████████ | 45/50 [02:00<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[57]    training's binary_logloss: 0.114417 valid_1's binary_logloss: 0.139373
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007511 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 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12898
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 90%|█████████ | 45/50 [02:01<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:02<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 90%|█████████ | 45/50 [02:02<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 90%|█████████ | 45/50 [02:02<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 90%|█████████ | 45/50 [02:02<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 90%|█████████ | 45/50 [02:02<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[62]    training's binary_logloss: 0.114805 valid_1's binary_logloss: 0.136936
 90%|█████████ | 45/50 [02:02<00:12,  2.56s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 90%|█████████ | 45/50 [02:02<00:12,  2.56s/trial, best loss: -0.8365225708987197] 92%|█████████▏| 46/50 [02:02<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008690 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 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12943
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[14]    training's binary_logloss: 0.123339 valid_1's binary_logloss: 0.132372
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009379 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 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12988
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [02:03<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[13]    training's binary_logloss: 0.119633 valid_1's binary_logloss: 0.141193
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [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`.
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12906
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 195
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[13]    training's binary_logloss: 0.12138  valid_1's binary_logloss: 0.137428
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 92%|█████████▏| 46/50 [02:04<00:10,  2.75s/trial, best loss: -0.8365225708987197] 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008990 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 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12835
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[52]    training's binary_logloss: 0.119292 valid_1's binary_logloss: 0.131268
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:04<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007926 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 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12988
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[51]    training's binary_logloss: 0.11486  valid_1's binary_logloss: 0.139012
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007646 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 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12898
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 94%|█████████▍| 47/50 [02:05<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 94%|█████████▍| 47/50 [02:06<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 94%|█████████▍| 47/50 [02:06<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 94%|█████████▍| 47/50 [02:06<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[45]    training's binary_logloss: 0.118593 valid_1's binary_logloss: 0.13685
 94%|█████████▍| 47/50 [02:06<00:07,  2.52s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 94%|█████████▍| 47/50 [02:06<00:07,  2.52s/trial, best loss: -0.8365225708987197] 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009040 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 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12902
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 197
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:06<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.123923 valid_1's binary_logloss: 0.132366
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009230 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 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12988
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:07<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.119581 valid_1's binary_logloss: 0.140569
 96%|█████████▌| 48/50 [02:08<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:08<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:08<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:08<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010048 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 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12898
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 192
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 96%|█████████▌| 48/50 [02:09<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  Did not meet early stopping. Best iteration is:
[100]   training's binary_logloss: 0.121237 valid_1's binary_logloss: 0.137601
 96%|█████████▌| 48/50 [02:10<00:04,  2.25s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 96%|█████████▌| 48/50 [02:10<00:04,  2.25s/trial, best loss: -0.8365225708987197] 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1647, number of negative: 38897
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010404 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 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12947
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 203
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.040623 -> initscore=-3.161962
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.161962
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[26]    training's binary_logloss: 0.118459 valid_1's binary_logloss: 0.131829
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:10<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1572, number of negative: 38972
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010339 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 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 13059
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 200
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.038773 -> initscore=-3.210495
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.210495
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[29]    training's binary_logloss: 0.11189  valid_1's binary_logloss: 0.139652
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of positive: 1619, number of negative: 38925
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010263 seconds.
You can set `force_col_wise=true` to remove the overhead.
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Total Bins 12996
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Number of data points in the train set: 40544, number of used features: 202
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] early_stopping_round is set=30, early_stopping_rounds=30 will be ignored. Current value: early_stopping_round=30
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.039932 -> initscore=-3.179828
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Info] Start training from score -3.179828
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  Training until validation scores don't improve for 30 rounds
 98%|█████████▊| 49/50 [02:11<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  Early stopping, best iteration is:
[26]    training's binary_logloss: 0.115607 valid_1's binary_logloss: 0.137612
 98%|█████████▊| 49/50 [02:12<00:02,  2.66s/trial, best loss: -0.8365225708987197]                                                                                  [LightGBM] [Warning] Unknown parameter: eval_metric
 98%|█████████▊| 49/50 [02:12<00:02,  2.66s/trial, best loss: -0.8365225708987197]100%|██████████| 50/50 [02:12<00:00,  2.55s/trial, best loss: -0.8365225708987197]100%|██████████| 50/50 [02:12<00:00,  2.65s/trial, best loss: -0.8365225708987197]
{'learning_rate': 0.028291797782733982, 'max_depth': 154.0, 'min_child_samples': 64.0, 'num_leaves': 32.0, 'subsample': 0.9145203867432408}

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: 1694, number of negative: 40877
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.012601 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 13334
[LightGBM] [Info] Number of data points in the train set: 42571, number of used features: 209
[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=0.039792 -> initscore=-3.183475
[LightGBM] [Info] Start training from score -3.183475
Training until validation scores don't improve for 100 rounds
Early stopping, best iteration is:
[131]   training's binary_logloss: 0.118645 valid_1's binary_logloss: 0.137022
[LightGBM] [Warning] Unknown parameter: eval_metric
0.839

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